ProFutures Blog

The APF Profutures blog features posts by the Emerging Fellows and other APF futurists. We will be sharing intriguing futures ideas and information about professional futurists and the practice of strategic foresight.

You can more about the Emerging Fellowship program and the inaugural class on the Emerging Fellows page. Please direct your questions to Terry Collins

Your comments are welcome, so long as they are courteous, brief, and on topic. 
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  • 15 Dec 2014 2:06 AM | Anonymous member (Administrator)

    Written by: Alireza Hejazi, APF Emerging Fellow

    Talking to an architecture company CEO recently, I was confronted with this question: “How can corporate foresight create value in my company?” I wanted to offer a “business-as-usual” response, but I changed my mind by remembering Rohrbeck and Schwarz’s (2013) clear-cut response identifying four faces of value creation through corporate foresight. Basing my response on their view, I told my CEO friend that corporate foresight may create an enhanced capacity to perceive, interpret and respond to change, an enhanced capacity for organizational learning, and more impacts on other actors.

    In fact, the philosophy of applying corporate foresight is to reduce the uncertainty by scanning the unknown in the environment. If this is the least and perhaps the most value it can create, then employing corporate foresight is worthy enough to be considered by managers and leaders. I also suggested my CEO pal to form a multi-disciplinary team who might lower the risk of disregarding and misunderstanding the change factors. In this way, his company wouldn’t fall into the traps that might be made by personal biased assumptions about future.

    My suggestion for shaping a multi-disciplinary team originated from Gracht and Stillings’ (2013) observation maintaining that interdisciplinary cooperation not only could solve the problem of biases, but also satisfies the future needs of the target customer. In this sense, techniques like scenario planning may sound useful as far as they depict the picture of the future market and introduces new product concepts that might provide new opportunities and development routes for the market and the technology. Corporation decision makers can enrich their short-, medium- and long- term decisions significantly through alternative scenarios or by technology road-mapping.

    However, as Rohrbeck and Schwarz admit, the implementation of corporate foresight activities is still limited due to uncertainty in getting desirable outcomes and return on investment and the degree of their value creation for strategic planning. On the other hand, too much focus on current conditions and activities makes the organizations inattentive to small changes that are taking place in the wider environment but impactful in the future.

    Rohrbeck and Schwarz’s review of foresight research in the European context reveals that foresight can create value for innovation and strategic management through utilizing appropriate methods in the process of decision-making and strategic planning. Companies who practice foresight in different sectors gradually find out that foresight is a tool of value-creation. It contributes to their survival in the competitive business environment, especially in time of discontinuous change. More importantly, the application of corporate foresight methods can lead to the improvement of organizational responses and thereby improving values in innovation management. This shapes Rohrbeck and Schwarz’s (2013) paradigm that links knowledge creation to value generation.

    In my view, if the value of foresight is to influence decision, then foresight practitioners should extend their efforts beyond conventional business decision making to discover alternative methods and analyses that might enrich businesses, organizations and policy makers with new solutions. The simple world of Shell Company and its well-known six scenarios in oil crisis is evolved into a complex world of STEEPV interactions and interpersonal relations where the survival of values is tested every day. Today, value networks are drenched in intangible value exchanges that create their strategic advantage in the market.

    Corporate foresight is able to aid companies which create value by connecting clients and customers that prefer to depend on each other. These companies create and distribute tangible and intangible values through networks that are webs of dynamic relationships and exchanges between two or more individuals, groups or organizations. In my view, the success of corporate foresight in the future depends on the contributions that it would make to the development and management of these networks. For such success to happen, effective interpersonal networks must be built on a foundation of expertise, trust and shared understanding. I think that APF is exactly established to build that foundation now and in the future.

    References

    Rohrbeck, R. & J. O. Schwarz. (2013). The value contribution of strategic foresight: Insights from an empirical study on large European companies. Technological Forecasting and Social Change, 80(8), 1593-1606.

    Von der Gracht, H. A., & Stillings, C. (2013). An innovation-focused scenario process: A case from the materials producing industry. Technological Forecasting & Social Change, 80, 599-610.

    About the author

    Alireza Hejazi is a PhD candidate in Organizational Leadership at Regent University and a member of APF Emerging Fellows. His works are available at: http://regent.academia.edu/AlirezaHejazi

  • 08 Dec 2014 5:22 PM | Daniel Bonin (Administrator)

    The Theory of Inventive Problem Solving (TRIZ)

    Some weeks ago I learned about the basics of TRIZ (Theory of Inventive Problem Solving). I find the method itself and also the history of its development fascinating. The development of TRIZ started during the mid 1940s in Russia. Round about 40.000 patents were analyzed to reveal patterns, similarities, differences and laws in order to formulate methods that help to standardize the problem solving processes*. One of the inventors TRIZ, Genrich Altshuller had to endure years in the gulag after he criticized the ignorance of the leadership regarding innovation and invention (Mishra 2006). During this time, he continued to develop TRIZ and made friends with other prisoners by telling them science fiction stories he analyzed as well. The TRIZ toolkit finally made its way to Europe and the U.S. after the end of the cold war.

    The theory TRIZ assumes that typical solutions can be found for recurring problems and that psychological barriers like inertia hinder problem solving. Thus algorithmic problem solving methods and creativity techniques were developed to overcome such problems. One can say that in contrast to brainstorming or trail and error, TRIZ relies on solutions that have proven to be useful in the past. Famous methods of the TIRZ toolkit include the 40 TRIZ Principles (described later on) or the Algorithm of Inventive Problem Solving (ARIZ).

    Clearly, TRIZ aims to find solutions to technical problems and does not intend to describe possible futures. But the inventors of TRIZ believed that creativity techniques are helpful to over overcome psychological inertia and can increase the degree of inventiveness of ideas. For instance the Size-Time-Cost-Operator method assumes that material, space, time and money/costs are (a) unlimited or (b) limited/ nonexistent to find new solutions to problems (Hentschel et al. 2010, Savransky). I believe that approaches like the Size-Time-Cost-Operator could be used to imagine or invent unusual and extreme futures. And what I find particularly interesting is the idea to use some of the TRIZ creativity techniques to create a “warming up and stretching program” for workshops in order to familiarize participants with outside of the box thinking.


    Using TRIZ to facilitate creativity and encourage out of the box thinking in workshops

    Imagine you have to carry out a workshop with participants that have never thought about the future. To make the topic easily understandable, a simplified perspective might be presented. Reading a book of Savransky (2002) on TRIZ, I came across some methods and games that might be used to create such a “warm up and stretching program”.


    The Value Changing Method confronts participants with the question of what if an object (e.g. technology or societal values and norms) with an extraordinary value is rendered useless. One could then possibly use the Good Bad Game, a game that requests to find something good in a bad situation (or the other way around) to direct the focus toward positive implications and thus further facilitate creativity. The Snow Ball Method could then finally be used as a warming up activity to introduce the basics of system dynamics. Here you think about interrelationships and ask questions like: what happens to X if Y is changed and how does this affect Z.


    Other application fields of TRIZ

    Furthermore the more technical parts like the 40 TRIZ Principles might be used to simplify foresight methods. The 40 TRIZ Principles are usually applied to reduce complexity and increase effectiveness of systems. Foresight methods can be undoubtedly considered complex. The 40 TRIZ principles (e.g. “Taking out”, “Merging of Objects”,  “Periodic Action” (replace continuous action with a periodic one), Skipping”, “Cheap Short-Lived Objects”) consist of reoccurring solutions that were used in the patents analyzed to solve problems and cut through complexity**. As foresights processes are labor and time intensive small and medium sized companies might struggle to deploy the necessary resources. A simplification of foresight methods might be desirable when educating or establishing foresight processes for such clients. Bannert and Warschat (2007) used the principles to modify management methods like the scenario analysis (click here for a illustration of their simplified method and a brief overview on some TRIZ principles).


    The methods described in this blog post aim to create novel ideas by changing an existing object or its function. I am wondering if the TRIZ toolkit could be used to invent Wild Cards based on the present by using tools such as the 40 TRIZ Principles or the so called Fantogram. The Fantogram describes two dimensions: (a) the way an object is changed and (b) the methods used (see figure below; click to enlarge). The advantage of this method is that you create more creative ideas. Normally you would tend to come up with a new based on only one dimension (Zhuravleva 2005). The invention of Wild Cards will be a covered in another blog post.

    Click to enlarge

    Fantogram: Savransky (2002) and Frenklach (1998)



    *Please see Souchkov (2005) for more information on the history and development of  TRIZ

    **A complete list and precise description of all 40 TRIZ principles can be found here.


    References

    Altshuller, G. (1996). And suddenly the inventor appeared: TRIZ, the theory of inventive problem solving. Technical Innovation Center, Inc. (translated and edited by Lev Shulyak and Steven Rodman)

    Bannert, M., & Warschat, J. (2007). Vereinfachung von Managementmethoden durch TRIZ. TRIZ: Anwendung und Weiterentwicklung in nicht-technischen Bereichen (Rietsch, P., Ed.), 61-89.

    Frenklach, G. (1998). Creative Imagination Development. TRIZ Journal, October

    Hentschel, C., Gundlach, C., & Nähler, H. T. (2010). TRIZ Innovation mit System. München.

    Mishra, U. (2006). The Father of TRIZ-As we know him-A short biography of Genrich Altshuller. TRIZsite Journal.

    Savransky, S. D. (2002). Engineering of creativity: Introduction to TRIZ methodology of inventive problem solving. CRC Press.

    Souchkov, V. (2005). Accelerate innovation with TRIZ. ICG T&C.

    Zhuravleva, V. (2005). Ballad of the Stars: Stories of Science Fiction, Ultraimagination, and TRIZ. Technical Innovation Center, Inc..

  • 01 Dec 2014 4:18 PM | Anonymous member (Administrator)

    A Shrinking Cone of Plausibility?

    Jason Swanson, APF Emerging Fellow


    Photo by r2hox CC by 

    In my colleague Julian Valkieser’s latest blog post, Julian wrote about the start-up Mapegy, the programming language “R”, and Big Data analysis as they relate to creating systems models and possible applications in foresight.  It was a fascinating post and I look forward to reading more of his analysis as I am excited about the uses for Big Data in the foresight.  The potential for Big Data to be disruptive is massive. One of the potential disruptions could to the foresight field.

    With the development of “R” and start-ups like Mapegy, along with the generation and capture of more and more data, and new tools for analysis, our ability to analyze massive data sets is growing in leaps and bounds. Analysis of complex data sets combined with predictive analytics is allowing us to create increasingly accurate models and predict outcomes and behaviors. By now most people are familiar with the story of Target  using data analysis to correctly predict that one of their customers was pregnant. A more recent example could be found with HealthMap , a project of Harvard Medical School and Boston Children’s Hospital, which predicted an Ebola outbreak 9 days before the World Health Organization began reporting irregular spikes in cases.

    While neither of these are long range predictions, as we capture and analyze larger and larger data sets the ability to predict outcomes and behaviors with accuracy, at least in the near term, goes up. Even though Futurists are not in the prediction business, will being able to accurately assess the near term cancel out the need for long range thinking in multiple narratives? Furthermore, would an increasing reliance on Big Data analysis and prediction affect not only the business side of foresight, but also the the study or practice of foresight itself? Would the cone of plausibility shrink as we develop the ability to analyze larger data sets with increasing sophisticated tools? Would we see a rise in a rise in wild cards?

    While I can only speculate on these questions, there is a possible implication that as we gain the ability to use data analysis and models to predict outcomes with greater accuracy there is the potential for the cone of plausibility to shrink. The highest probability in terms of outcome or behavior might become a major piece, or the piece, in terms of a baseline future, with variability from the models in terms of outcomes or behaviors as your alternative futures, or greatly influencing alternative futures. Those probabilities could create or influence the bounds of the cone of plausibly. The greater the degree of accuracy, even in the near term, could potentially act to focus or tighten the cone, in effect shrinking the bounds of plausibility.

    As the cone of plausibility shrinks, there might also be a potential rise in wild cards, specifically Type 2 wild cards.  Introduced by Dr. Oliver Markley in his article, “A New Methodology for Anticipating STEEP Surprises” , Dr. Markley defines type 2 wilds cards as “having high probability and high impact as seen by experts if present trends continue, but low credibility for non-expert stakeholders of importance”. If the bounds of plausibility were to tighten, even some alternative futures which in the past might have been considered plausible alternate futures might fall out of the bounds of plausibility. By falling out of the bounds of plausibility, those same alternative futures have the potential to fall out of creditably for non-expert stakeholders of importance and as a result could be classified as type 2 wild cards if the impact were thought to be enough. In the event that the potential impact is possibly too low to be considered a wild card, a new term may be needed for the alternative futures that do not fit inside of the bounds of the predictive models.

    It will be interesting to see the effect that Big Data will have on the foresight field. Will clients shy away from long term thinking in favor of near or short term predication? Will increasingly accurate models add to or possibly alter our foresight toolboxes? How is the futures community currently utilizing big data and predictive analytics?

    --------------------------------------------------------------------------


    http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

    http://www.uschamberfoundation.org/blog/post/can-big-data-predict-future/41983

    Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from http://www.imaginalvisioning.com/wp-content/uploads/2010/08/Anticipating_STEEP_Surprises-TFSC2.pdf



    Jason Swanson, APF Emerging Fellow

    Photo by r2hox CC by

    In my colleague Julian Valkieser’s latest blog post, Julian wrote about the start-up Mapegy, the programing language “R”, and Big Data analysis as they relate to creating systems models and possible applications in foresight.  It was a fascinating post and I look forward to reading more of his analysis as I am excited about the uses for Big Data in the foresight.  The potential for Big Data to be disruptive is massive. One of the potential disruptions could to the foresight field.

    With the development of “R” and startups like Mapegy, along with the generation and capture of more and more data, and new tools for analysis, our ability to analyze massive data sets is growing in leaps and bounds. Analysis of complex data sets combined with predictive analytics is allowing us to create increasingly accurate models and predict outcomes and behaviors. By now most people are familiar with the story of Target using data analysis to correctly predict that one of their customers was pregnant. A more recent example could be found with HealthMap , a project of Harvard Medical School and Boston Children’s Hospital, which predicted an Ebola outbreak 9 days before the World Health Organization began reporting irregular spikes in cases.

    While neither of these are long range predications, as we capture and analyze larger and larger data sets, the ability to predict outcomes and behaviors with accuracy, at least in the near term goes up. Even though Futurists are not in the prediction business, will being able to accurately assess the near term cancel out the need for long range thinking in multiple narratives? Furthermore, would an increasing reliance on Big Data analysis and prediction affect not only the business side of foresight, but also the the study or practice of foresight itself? Would the cone of plausibility shrink as we develop the ability to analyze larger data sets with increasing sophisticated tools? Would we see a rise in a rise in wild cards?

    While I can only speculate on these questions, there is a possible implication that as we gain the ability to use data analysis and models to predict outcomes with greater accuracy there is the potential for the cone of plausibility to shrink. The highest probability in terms of outcome or behavior might become a major piece, or the piece, in terms of a baseline future, with variability from the models in terms of outcomes or behaviors as your alterative futures, or greatly influencing alternative futures. Those probabilities could create or influence the bounds of the cone of plausibly. The greater the degree of accuracy, even in the near term, could potentially act to focus or tighten the cone, in effect shrinking the bounds of plausibility.

    As the cone of plausibility shrinks, there might also be a potential rise in wild cards, specifically Type 2 wild cards.  Introduced by Dr. Olivier Markley in his article, “A New Methodology for Anticipating STEEP Surprises, Dr. Markley defines type 2 wilds cards as “having high probability and high impact as seen by experts if present trends continue, but low credibility for non-expert stakeholders of importance”. If the bounds of plausibility were to tighten, even some alternative futures which in the past might have been considered plausible alternate futures might fall out of the bounds of plausibility. By falling out of the bounds of plausibility, those same alternative futures have the potential to fall out of creditably for non-expert stakeholders of importance and as a result could be classified as type 2 wild cards if the impact were thought to be enough. In the event that the potential impact is possibly too low to be considered a wild card, a new term may be needed for the alternative futures that do not fit inside of the bounds of the predictive models.

    It will be interesting to see the effect that Big Data will have on the foresight field. Will clients shy away from long term thinking in favor of near or short term predication? Will increasingly accurate models add to or possibly alter our foresight toolboxes? How is the futures community currently utilizing big data and predictive analytics?

    Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from http://www.imaginalvisioning.com/wp-content/uploads/2010/08/Anticipating_STEEP_Surprises-TFSC2.pdf

    Jason Swanson, APF Emerging Fellow

    Photo by r2hox CC by

    In my colleague Julian Valkieser’s latest blog post, Julian wrote about the start-up Mapegy, the programing language “R”, and Big Data analysis as they relate to creating systems models and possible applications in foresight.  It was a fascinating post and I look forward to reading more of his analysis as I am excited about the uses for Big Data in the foresight.  The potential for Big Data to be disruptive is massive. One of the potential disruptions could to the foresight field.

    With the development of “R” and startups like Mapegy, along with the generation and capture of more and more data, and new tools for analysis, our ability to analyze massive data sets is growing in leaps and bounds. Analysis of complex data sets combined with predictive analytics is allowing us to create increasingly accurate models and predict outcomes and behaviors. By now most people are familiar with the story of Target using data analysis to correctly predict that one of their customers was pregnant. A more recent example could be found with HealthMap , a project of Harvard Medical School and Boston Children’s Hospital, which predicted an Ebola outbreak 9 days before the World Health Organization began reporting irregular spikes in cases.

    While neither of these are long range predications, as we capture and analyze larger and larger data sets, the ability to predict outcomes and behaviors with accuracy, at least in the near term goes up. Even though Futurists are not in the prediction business, will being able to accurately assess the near term cancel out the need for long range thinking in multiple narratives? Furthermore, would an increasing reliance on Big Data analysis and prediction affect not only the business side of foresight, but also the the study or practice of foresight itself? Would the cone of plausibility shrink as we develop the ability to analyze larger data sets with increasing sophisticated tools? Would we see a rise in a rise in wild cards?

    While I can only speculate on these questions, there is a possible implication that as we gain the ability to use data analysis and models to predict outcomes with greater accuracy there is the potential for the cone of plausibility to shrink. The highest probability in terms of outcome or behavior might become a major piece, or the piece, in terms of a baseline future, with variability from the models in terms of outcomes or behaviors as your alterative futures, or greatly influencing alternative futures. Those probabilities could create or influence the bounds of the cone of plausibly. The greater the degree of accuracy, even in the near term, could potentially act to focus or tighten the cone, in effect shrinking the bounds of plausibility.

    As the cone of plausibility shrinks, there might also be a potential rise in wild cards, specifically Type 2 wild cards.  Introduced by Dr. Olivier Markley in his article, “A New Methodology for Anticipating STEEP Surprises, Dr. Markley defines type 2 wilds cards as “having high probability and high impact as seen by experts if present trends continue, but low credibility for non-expert stakeholders of importance”. If the bounds of plausibility were to tighten, even some alternative futures which in the past might have been considered plausible alternate futures might fall out of the bounds of plausibility. By falling out of the bounds of plausibility, those same alternative futures have the potential to fall out of creditably for non-expert stakeholders of importance and as a result could be classified as type 2 wild cards if the impact were thought to be enough. In the event that the potential impact is possibly too low to be considered a wild card, a new term may be needed for the alternative futures that do not fit inside of the bounds of the predictive models.

    It will be interesting to see the effect that Big Data will have on the foresight field. Will clients shy away from long term thinking in favor of near or short term predication? Will increasingly accurate models add to or possibly alter our foresight toolboxes? How is the futures community currently utilizing big data and predictive analytics?

    Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from http://www.imaginalvisioning.com/wp-content/uploads/2010/08/Anticipating_STEEP_Surprises-TFSC2.pdf

    http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

    http://www.uschamberfoundation.org/blog/post/can-big-data-predict-future/41983

    Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from http://www.imaginalvisioning.com/wp-content/uploads/2010/08/Anticipating_STEEP_Surprises-TFSC2.pdf

  • 24 Nov 2014 3:42 AM | Julian Valkieser (Administrator)

    Of course, the topic "Big Data" was already mentioned a few times in the Profuturist blog. Of course, we all know what it involves and consists of. We now move to a higher and higher activity on the Internet. We produce data – massive data. Worldwide, already 3 billion people are online. We spend much of our time online. The amount of data that is created, rise to a stunning 107,958 petabytes per month by 2018. For example, these are over 100 mio. hard drives with a capacity of 1 Terabyte – a drive with capacity the most of us would never use.

    Companies like Google act and work with this data. Of course, they are not focused solely on this one business model. So Google is spreading in different directions. But a focus can be seen. Google is also spreading more and more offline. Why?

    The data created online, are relatively negligible in comparison to the data you can still receive from the physical world. Behavior patterns online are certainly interesting, e.g. for the field of e-commerce – but behavior and properties offline are much more interesting. The greatest benefit would be to analyze all information that can be obtained and secondly to be able to deduce something. Exciting!

    Here I want to present an example specifically for research-intensive areas. The start-up "Mapegy" from Berlin in Germany.

    Mapegy is the compass for the high-tech world, referring to their own definition. One possible application would be the following. Let’s imagine.

    I am interested in a specific topic and I would like to evaluate. Now Big Data comes into the game. Let’s take the example of a patent analysis. With tools like Mapegy I could figure out easily, who is an important stakeholder of a particular technology development, as he is related to another and what influence he has. A method of representation is about maps. Stakeholders and technological developments are illustrated via a kind of map. The larger the island, the more stakeholders gather around a particular development. The higher the mountain, the more patents were applied by a stakeholder. The closer the islands are arranged to each other, the stronger is the reference to one another. With this kind of Visual Analytic it is quite easy to illustrate how a certain subject area is connected to others.

    And that is the sticking point. A lot of data is already available. But finally the correct processing and representation make this data useful.

    At this point I want to mention "R". 

    "R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls and surveys of data miners are showing R's popularity has increased substantially in recent years." (Wikipedia)

    Someone who can program in "R" is well paid. Even at the upper end of the scale. And not for no reason. To be able to understand a context and deduce recommendations for action, not only in the economy, but also in science and research, such as in biotechnology and of course the pharmacy, is a higher aim in business and decision processes.

    If you already understand some small connections, you can use it to create a network and may even explain the behavior of systems. In this specific example, it would be human behavior. Of course, the influencing factors are still too complex to be able to make reliable predictions from available data collections. But the more powerful computational resources, the closer is the opportunity to analyze all factors.

    Mapegy is an example of visualizing relationships and influencing factors via big data analysis. For example, the cost of genetic testing is an indicator of how quickly data analysis will change in the next years. The costs decreased in recent years more as the price of computer chips in relation to Moore's Law. In my next article I go further to the development in big data analysis with "R".


  • 17 Nov 2014 2:17 AM | Bridgette Engeler Newbury (Administrator)

    This isn’t some existential analysis of foresight and futures work, but a simpler question about value, purpose, intention and utility. Some pretty basic research (there is a methodology if you want it) suggests most blogs are written to raise profile, to drive traffic to a website, to build an email list, to share information, an opinion or thoughts on a subject, and/or to sell books. So leaving aside that last point, I ask myself after just over one year as an Emerging Fellow, are we doing any or all of that? And does it matter?

    Two decades or so in, blogging (still) has its challenges. People have been writing about themselves and things they find interesting but it’s easier for some than others. Sharing opinions and thoughts isn’t for everyone. And we dont have to blog. Just because we can, doesn’t mean we do.

    Maybe it’s because not everyone has something to write about and share. Maybe we do, but can’t write about it. Maybe what we write isn’t getting us the sign-ups/comments/views/likes/website hits… Then later, maybe we run out of topics, ideas and clever headlines. And maybe we wonder if it’s worth all the time we spend on it.

    Why would a futurist blog? Why would we share what we have to say? Would it make futures work interesting and digestible? Are we doing it to get noticed? To be read and understood? And why would we assume that others value what we have to say?

    To connect with people. If our long-term goal is to build a community involved or interested in futures thinking, a blog might kick off two-way communication with people who will spread the word.

    To be better communicators. Writing and honing a blog and consistently delivering (good) content is a great way to practice craft, discipline, voice and style. It’s almost inevitable that your writing will improve over time. And your ability to distil complex ideas into small sound bites.

    To form relationships. There’s a community out there who want to read, learn from and challenge our ideas. People who can help us find our way. Let's find them and have a conversation.

    To find our feet. A blog can be fertile ground for idea exploration and expression.

    To get noticed. Apparently a goal (or two) of every blog is to generate content that becomes a book that you then sell. Maybe not exactly true for APF, but we could suggest that our blog generates content good enough to prompt visitors to come back regularly, subscribe to our other social media outlets and perhaps other futures blogs and media. Our blog can get readers, colleagues and peers, and anyone else who may be able to offer support, discussion and/or opportunity.

    So are we doing any of this? And how well are we doing it? Are we creating interesting, useful and challenging content that has value, purpose and utility? Blogs are not intrusive. No one has to respond. Reading is voluntary, and done when convenient. So who decides if we are making it worthwhile?

  • 10 Nov 2014 9:03 AM | Sandra Geitz (Administrator)





    Who and what would you bring to your desert island?


    Imagine for a second, that you’re planning your own island retreat… a self-imposed, indefinite island retreat. Who would you take on your journey? Whose skills are most useful? What seems essential to bring along?



    Now, is this scenario really so far-fetched? Let’s consider emerging social dynamics. Both the pace and volume of social media streams and vast hidden forces like globalisation and digitisation promote increased competitive and attention-seeking behaviours. How do we tend to respond to all this? By withdrawing to the familiar, comfortable and well-known? Are we retreating into closed worlds, hostages within reassuring personalisation algorithms, Eli Pariser’s filter bubbles, with a world outside hostile to our comforting ideas and worldviews, filled with those shouting, trolling and blocking any chance of real debate and learning?


    “Both Whatsapp and Secret represent the ascendency of the phone book over the friend graph. It’s back to the future,”  tweeted Yammer CEO/ Founder, David Sacks (Meeker 2014).


    Ever more sophisticated filtering will reduce external noise in our social media feeds, and the potential for proliferating private desert islands of our close friends and genuine interests, according to Steven Rosenbaum, content curation author and promoter (Decugis 2014). Naturally, he advises business to curate quality content or face extinction via irrelevance. Seth Godin’s concept of permission marketing on steroids.




    So what, you may ask?


    Although, it appears an attractive solution in the current carcophany of noise, attention-seeking and celebrity trivia, there are significant downsides to this future of private retreat. Antony Funnell’s (2014) recent Future Tense program on ABC Radio National, examined this in perspectives on the power of provocation.


    Funnell’s (2014) first guest, Graeme Turner, Emeritus Professor of Cultural Studies at the University of Queensland explained that the purpose of provocation used to be about challenging and debating ideas. Now, modern provocation has become a competition for attention, rather than ideas. It is about promotion and entertainment, requiring greater shock value and/or engagement over time to be noticed by provocation- immune audiences and/or participants. Turner believes the future of public debate and innovative ideas seems quite bleak (in Australia, at least). There are enormous competitive media pressures to entertain, whilst countering public dis-engagement with more complex or sophisticated issues.


    Another perspective was offered by Scott Stephens, Religion and Ethics program editor for ABC Online (Funnell 2014). In his studies of the spread of philosophy, provocation and innovation were the product of dialogue and debate within historical constraints. Stephens suggests a future of greater discernment and discrimination is possible, if we are able to overcome cultural relativism or permissiveness for anything goes. Potential awaits for futures of value, integrating judgement with broad social acceptance.


    Very similar conclusions to those of Alex Pentland’s (2014) Social Physics, were reviewed in a prior post. Pentland designed experiments that measued the productive output of different groups and the patterns of groups interactions. He found that innovation was optimised with iterative patterns of exploration for novelty interspersed with the socialisation of these ideas for acceptance. Pentland believes a diversity of shared experiences and history builds a stores of both trust and experiences to associate with for future application.


    “Feedstock for innovation is insight - an imaginative understanding of an internal or external opportunity that can be tapped to improve efficiency, generate revenue, or boost engagement,” states the recent HBR article of Mohanbir Sawhney and Sanjay Khosla (2014). Similarly, foresight can be thought of as the imaginative understanding of potential impacts of internal and/or external factors in the future. The purpose of foresight is to help make decisions, solve problems, identify and adapt to changes by thinking about what could happen and how to influence and enable what should happen.



    Future implications?


    Both foresight and innovation introduce novel ideas for social acceptance to organisations and/or the public. They involve challenge existing ways of thinking, provocation of current thinking to generate alternative ideas, perspectives and spark imagination.



    In current social dynamics, can foresight practitioners and the field expect a desert island welcome?


    How might we further socialise foresight?




    References


    Decugis G 2014, The Desert Island: the future is the curated Web for Steve Rosenbaum in Curate This!, Scoop.it!, viewed 7Nov 2014, http://blog.scoop.it/2014/11/07/the-desert-island-the-future-of-the-curated-web-according-to-steve-rosenbaums-curate-this/


    Funnell A 2014, Perspectives on the power of provocation, Future Tense, ABC Radio National program audio and transcript, viewed 3Nov 2014, http://www.abc.net.au/radionational/programs/futuretense/june-29th-segment/5548814


    Meeker, M 2014, Internet Trends 2014: Code Conference, Kleiner Perkins Caulfield & Byers, slideshare, pp. 35-37, viewed on 9Nov 2014, http://www.slideshare.net/kleinerperkins/internet-trends-2014-05-28-14-pdf


    Pariser E 2011, Beware online “filter bubbles”, TED Talks, viewed 9Nov 2014,

    http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles?language=en


    Pentland A 2014, Social Physics: How Good Ideas Spread - the lessons from a new science, Scribe Publications Pty Ltd, Brunswick, Australia and London, United Kingdom.


    Sawhaney M and Khosla S 2014, Managing Yourself: Where to Look for Insight, Harvard Business Review, November 2014, pp.126-129, viewed 5Nov 2014, https://hbr.org/2014/11/where-to-look-for-insight/

  • 03 Nov 2014 3:08 AM | Anonymous member (Administrator)
    Written by: Alireza Hejazi, APF Emerging Fellow

    Teaching foresight is both enjoyable and challenging. New and experienced teachers alike are constantly faced with making foresight theory and practice meaningful for their students. Developing and running a foresight course is a challenging job, but evaluating it can be more thought-provoking. Looking at a foresight course from different points of view, foresight instructors may find this question meaningful: “how should we evaluate a foresight course to ensure the credibility of learning outcomes?” This blog post reviews three stages of evaluation and deserves foresight coaches’ care and appropriate action.

    1. Pre-evaluation
    Many observers believe that an evaluation agenda can be developed only after running an educational program. However, if foresight instructors inspect these three points in their syllabi with the support of an expert, they will save much energy, time and fund for future reviews and corrections: (1) Establishing instructional objectives, (2) Planning instructional strategies, and (3) Assessing learning outcomes. Without enough care for these three items, every educational initiative is doomed to failure.

    Instructional objectives are “statements describing what the student will be able to do after completing a unit of instruction” (Kibler, Cegala, Barker & Miles, 1974, p. 2). Instructional objectives are typically articulated on the course syllabus, and many teachers provide detailed instructional objectives for specific units covered in a course. They help students know what to expect. In using instructional objectives, teachers are better able to articulate what they teach, and can better help students meet those objectives. For example, we can tell our students that they will be able to lead a scenario learning process for a leadership team that tests their strategy against a range of possible future developments.

    Instructional strategies that are usually used in foresight courses include futurist lectures, discussions, group activities, reflection papers, and presentations. The choice of instructional strategy depends on the particular goals of a specific lesson or unit. In the domain of strategic foresight, common education base indicates that instructional strategies should be developed so that students become skillful at learning and practicing foresight knowledge, engaging in both written and oral academic discourse, working fluently with foresight data, building environmental scanning systems, developing scenarios and problem solving effectively. All these require providing students with particular opportunities, models, and guidance needed to develop each of those sets of skills.

    Learning outcomes are more determined by the motivation, skills and behaviors of the student and less by differences among instructional strategies. In other words, any single instructional strategy is inherently more effective than all other strategies. Lerner et al. (1985) found that there must be a “goodness of fit” between the instructional situation and the student. Not surprisingly, some students are in situations where they “fit well” with their instructional situation and those students excel academically; other students have a poor fit with the instructional environment and are at risk academically.

    Bringing that observation into the foresight field an instructor may find certain instructional strategies effective in advancing specific learning outcomes. For example, while discussions reflect learners’ understanding and analysis of futures concepts, reflection papers and presentations show how competent they are in producing foresight outputs. A foresight teacher can facilitate assessing learning outcomes by creating a table of authorities that identifies the objectives covered by the assessment tool as well as questions corresponding to each objective. Using a flexible variety of questions in the assessment tool (to be changed occasionally) and talking friendly to the students about the test are also good techniques that can be applied.

    New foresight coaches can always check the practicality of their educational programs by conducting a pilot course project and may enjoy experienced foresight teachers and gurus’ ideas and views about their project.

    2. Evaluation
    A foresight course can be monitored effectively by asking a number of questions like these: Is the specific need of learners in learning foresight being addressed? Are the general and special teaching methods are applied effectively? Is the instructor confident about the data presented to the students? What is running right and what is being practiced wrongly by both the teacher and the students? What major conclusions do the students make in their discussions? Are their conclusions supported by the teaching and learning materials? How are educational data being used by the students? Are there other possible explanations for students’ understandings and reflections? What are they?

    At the basic level, foresight instructors might be able to answer some of the above questions, but at the expert level, they and their students need to be monitored by expert observers. A good way to do this is inviting some expert foresight teachers to inspect our courses and receive their ideas. Their appraisal would be a wealth of knowledge that can advance our teaching effort in constructive ways. Being open to critiques and welcoming necessary reforms and improvements that should be made in the course will enrich our educational experience and will satisfy our students’ expectations. The following table summarizes stages of evaluation, involved parties and sources of evaluation clearly.

    Table 1. Stages of evaluating a foresight course


    In addition to involved parties and sources of evaluation mentioned above, a foresight course should be also evaluated and compared against courses conducted in similar areas such as strategic planning and management. Foresight teachers may be entitled to a wide range of knowledge and experience shared by many teachers online on strategic matters around the world. The best source of evaluation that is always available to an instructor is the students’ feedbacks. If they report cases like following items, the instructor requires a serious revision of the course material or teaching system: “You’ve left me behind. I can’t follow. The level of jargon in this course is beyond my understanding. I cannot use the LMS (Learning Managing System) easily. I don’t enjoy reading this.” Down the road, everything should be tuned according to students’ needs and level of understanding.

    3. Post-evaluation
    An eagle knows when a storm is approaching long before it breaks. It flies to some high spot and wait for the winds to come. When the storm hits, it sets its wings so that the wind will pick it up and lift it above the storm. While the storm rages below, the eagle is soaring above it. The eagle does not escape the storm. It simply uses the storm to lift it higher. It rises on the winds that bring the storm.

    Managing a foresight course can appear as a storm and a foresight coach should be as clever as an eagle. When the course is completed and the students are graduated, it’s a good time to look back and find weak and strong points in our foresight educational program. Problems that students reported during the course period such as working with LMS (Learning Managing System), using foresight methods and tools, using and applying foresight data and preparing assigned outputs along with other unpredicted difficulties that appeared during the course all may come upon us like a storm. We can rise above them by setting our course up to higher levels of learning and teaching foresight. The storms do not have to overcome us. We can let our checking do the balancing work for us and lift us above them. Instructor’s experience coupled with students and experts’ feedbacks that had monitored our course make a compound that can enrich our educational effort.

    Revisiting and post-evaluating a foresight course can be done in long middle and short runs. In long term, we should consider where our course fits into the curricular goals and course sequences. Perhaps the broad goals of our foresight course should be redefined, and a rearrangement of textbooks and study materials is necessary. For example, setting a goal such as leading a departmental team to develop strategic plans should consider developing mission, vision, and goals, appropriately matched to the near-term competitive, customer and industry environment. In middle term, learning objectives should be articulated for course and appropriate readings; videos, slides, websites, etc. need re-identification. The nature of assignments and activities should be also determined according to objectives, assessments, and instructional activities. And finally in short term, the calendar of activities, syllabus, LMS should be checked and updated.

    References
    Kibler, R., Cegala, D., Barker, L., & Miles, D. (1974). Objectives for instruction and evaluation. Boston: Allyn and Bacon, Inc.
    Lerner, J. V., Lerner, R. M., & Zabski, S. (1985). Temperament and elementary school children’s actual and rated academic performance: A test of a “goodness-of-fit” model. Journal of Child Psychology and Psychiatry and Allied Disciplines, 26, 125-126.

    About the author
    Alireza Hejazi is a PhD candidate in Organizational Leadership at Regent University and a member of APF Emerging Fellows. His works are available at: http://regent.academia.edu/AlirezaHejazi
  • 27 Oct 2014 1:45 PM | Daniel Bonin (Administrator)


    A month ago I took up a second job at an innovation consultancy. I was familiarized with all the knowledge I needed and everything went smoother than originally expected. I learned about new methods and got to know new workflows. That motivated me to rethink my own knowledge management and workflow.

    First of all, I personally have to admit that I have internalised the problem solving approach of management consultancies. At least here in Germany, there is no job interview or recruiting workshop without such a question as: “What do you think is the market size for ski-rental services in Austria?” You are not expected to come up with a single number out of the blue, but you have to present a well-structured and efficient problem-solving approach. Only then you are allowed to go on and enrich this structure with information. Due to this influence I tend to not only structure information, but also processes. For instance, before starting desk research I create a list with buzzwords that are helpful for the research. In the next step I use this list to search for synonyms. Then finally, I work off this list step by step.  


    Photography and Future Studies


    Most photographers develop routines to cope with large amounts of photos. These routines are called workflows. Workflows usually consist of the same steps (capturing, sorting & organizing, processing, saving final pictures to a library, sharing). Plugins and presets improve the efficiency and also the effectiveness even further. These templates can be customised to meet individual needs and tastes. Today the number of photos we take increase steadily. In a similar fashion, futurists have to cope with an ever increasing “supply of raw material” – information and new impressions. Sooner than we expect, we might be annoyed about all the information we did not archive or process properly.

    “The Evolution of My Workflow”

    Back when I started to become interested in future studies, I mainly used bookmarks and folders to sort and organize information. Then, whenever needed I had to “excavate” my knowledge for different projects. But recently, I switched to programs like Evernote, Citavi (esp. useful for academic work) and XMind to organize my knowledge. I also started carrying around a paper notepad to write down interesting information on the go. At a first glance it might be strange to take down notes like "Brazil: the cattle stock will double till 2018 – Le Monde diplomatique 08/14" and store them digital. But from my experience I can say, that sometimes those "pointless facts" turn out to be the most important ones. For the future I am planning to turn one wall at my flat into a huge pinboard so that I can create oversized mind maps. Moreover I started to visualize the structure of my thought-processes (i.e. create my own templates). Currently I am working on a template (click for more information) to assess the attitude of consumers towards future products or technologies. My ultimate goal is to develop a workflow that (a) incorporates established methods/ templates (e.g. STEEP) and my own templates that reflect my own line of reasoning and (b) concludes with an insight rather than a bookmark.


    How can futurists manage their explicit (and tacit) knowledge?

    Imagine you are sitting at the breakfast table reading a newspaper. You came across an interesting article. How do you save and organize new information, if at all? What does my workflow look like?

    Does your workflow end after you saved and stored the information? This might save time and effort in the short run, but in the longer term you have to search for the information in your (possibly messy) knowledge database. The other option would be to go through the whole workflow process (e.g. add a new factor to your exhausting list of  STEEP factors). And when you think about it, another question arises: do you save, organize and process information in a way that allows you to share information with your colleagues? How can organisational structures be designed to enable and facilitate knowledge exchange?

  • 20 Oct 2014 10:29 AM | Anonymous member (Administrator)

    Samples and Sample Screeners.

    By Jason Swanson, APF Emerging Fellow.


    Photo by The Bees. CC by 2.0

    Samples and sample screeners. These two things have been top of mind for me as of late. As we progress in slowly building a survey instrument for our “state of the futures field” survey the challenge and importance of a correct sample has really come front and center. Obviously we want any survey to be an accurate sample, however a survey attempting to figure out what the world thinks of the futures field, specifically those that might hire us, certainly has its challenges when constructing a sample frame.

    One such challenge that we came across was cost. For quantitative research projects it is not uncommon to go to a sample house to purchases respondents that fit your sample frame. Due to the nature of this survey, the cost we were quoted was a bit more than what we had initially estimated. This is where sample screeners entered into the picture.

    A sample screener is just what it sounds; questions to help weed out respondents who we may not need and to zero in on the proper folks who would make an accurate sample, in the process hopefully lowering the price point for the sample frame. This is very challenging in that we are really seeking to become more granular in our sample yet stay as representative as we can.

    As we consider what screeners we might use, I am curious to know a few things from the members of our community and those that may read this blog. In my last post I pondered about whether or not we could figure out a client archetype. Along the same lines of thinking, I want to know who it is you might work with inside a company or organization whenever someone reaches out for foresight services. Is it someone at a “director level” of an organization? Does the company or the department typically look at the future? Do they have any experience at all with hiring futurists? Who is that person that makes the call to hire you?

    Answers to any of these questions will help us build better screeners which in turn will help to build a better sample. It is exciting to watch this come together. From a personal level, I am very interested in how we can expand our market to clients that have never used a futurist. I am also excited to see if in the data a client archetype might appear, and what that might mean for our field in the future…..


  • 13 Oct 2014 11:12 AM | Julian Valkieser (Administrator)

    In my previous article I went out on a limb. I argued that professional futurists need to support their conclusions, even take a bet on their statement.

    What does a futurist do? 

    His scenarios serve as guidance for future decisions. Often he also gives a direct recommendation. He bases this on scientific methods and tools. This is quite legitimate. Finally, analysis techniques are mostly scientifically established.

    What I miss at this point is the own bet for a recommendation. The Futurist Advisor has to ask himself: Would I invest a part of my money or my capabilities in this recommendation? Actually, every futurist has to do exactly this on every completed job.

    At this point we need to differentiate. On the one hand, I argue in my previous article that futurists have to act more like entrepreneurs, on the other hand futurists should not neglect entrepreneurs in their analysis as all other factors in the typical environmental analysis. 

    What is the most promising technological possibility good for, if there is no entrepreneur who can convince his supporters and the market? So would Hype Cycles, Trend Radars and Technology Scenarios hardly worth anything, if they don’t analyze the creators of these trends and achievements.

    Pinchot and Pellman wrote:

    “Bet On People, Not Just Ideas – Many traditional management practices are based on making sure subordinates get the results specified in the plan. However, since innovation never goes according to plan, betting on plans for innovation is foolish. When making investments in innovation, bet instead on a team of people who can fix things fast when they don’t work as expected.” (Pinchot/Pellman, 1999)

    However that may be – it is easier said than done. There are a few foresight methods that look at personalities and characters. One example is the agent-based model analysis. Wherein this tends to focus more on macro level and behavior of systems.

    Looking at the list of methods of analysis, e.g. of Magruk or Gordon & Glenn, you can hardly find methods that dive deeper into the more micro level, to characterize a few relevant individuals for future development according to their influence.

    Zhu et al. classified characteristics of participants in a corporate crowdsourcing competition. They identified two main characteristics to distinguish: Creativity and proactivity. In a matrix this characteristics could be clustered as mentioned in my previous article: Intrapreneur, Creative Innovator, Proactive Promoter and Follower. The Intrapreneur is referred to be highly creative and proactive. (Zhu et al., 2014) Pinchot characterized it similarly. (Pinchot, 1986)

    Pinchot lists a few more characters that are necessary for a successful establishment of a future project or innovation: Sponsor, Protector and Promoter. So as they are related to the Intrapreneur in a company, these characters can also be found in the external environment of a representing Entrepreneur.

    In my opinion, a prospective analysis should rather refer to personalities and entrepreneurial characters, than on bare circumstances. In the next article I will go deeper into it.


    References:

    Gordon, T. J.; Glenn J. C. (2004): Paper7: Integration, Comparisons, and Frontierof Future Research Methods. For: EU-US Seminar: New Technology Foresight, Forecasting& Assessment Methods, Seville, 13-14 May 2004

    Magruk, A. (2011): Innovative Classification of Technology Foresight Methods. In:Technological and Economic Development of Economy, Vol. 17, No. 4, S. 700-715

    Pinchot, G. (1986): Intrapreneuring

    Pinchot, G. & Pellman, R. (1999): Intrapreneuring in Action

    Zhu et al. (2014): Innovative behavior types and their influence on individual crowdsourcing performances


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