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Part One
The Game of Technology Transfer
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CHAPTER 1
The Pieces
INTRODUCTION
In this part we introduce you to the cognitive framework for understanding and doing technology transfer. We use a game metaphor, as that is the easiest way to understand the model. As with other games, there are pieces and there is a board. In this chapter we introduce some of the key pieces. In the next chapter we explain the board. In Chapter 3, we discuss strategy.
Technology is simply an aid for conducting an activity which is repeated time and time again. It may be a tool, a technique, a material, etc. Because humans engage in activities that are repeated over and over again, it makes sense to build tools and other useful aids so we can do this activity more effectively and efficiently.
Consider a game in which the object is to move a technology out of the hands of one player into the hands of another in such a way as each player is better off after the technology has moved than before. In plain English: You win when you do a good deal. You lose if you do a bad one or do not get one at all. Since you have two ways to lose and only one to win, all other things being equal, simply relying on luck should lead to a loss.
Now, what makes technology-based aids different from those' developed on the basis of experience or Eureka bursts of inspiration is that we can explain why we built the tool the way we did. Technology occurs where thought precedes action and is applied to the improvement of that action. In modern times, this thought is usually a scientific or engineering finding that explains why if you do X, you will get Y with some degree of confidence.
It is these aids we are trying to move from one player to another. Our game board is a geophysical-temporal space on which are laid out a series of channels. Players move messages, goods (including technology), and themselves through these channels. The channels run between nodes or arenas where the players live and work. If a channel does not exist, the players are allowed to construct one.
Players, messages, and goods can only be moved where relationships exist. Relationships exist where the players develop predictable patterns of behavior that is patterns that have some probability of occurring. These patterns involve interactions between two or more players.
Rules govern how you can bring players, messages, and goods into relationships by defining what constitutes coherence between attributes of those entities. By defining what constitutes coherence, that is, a permissible relationship, the rules also de facto define what is impermissible. The rules can change over time. By changing a coherence between attributes into an incoherence, players can block the movement of their opponents’ players, messages, and goods.
Relationships can be described via equations. These equations use terms like “constrains” (→), “equals” (=), or “approximately equals” (≈) to describe how an attribute of one entity coheres with the attributes of another entity. For example, an equation can express the equivalence in value between a technology that is being offered to other parties and what other parties are seeking to exchange for technologies.
When players interested in a deal agree the values are equal (or close enough to equal), technologies can be moved from one party to another. This part of the book explains the game. The rest of the book is about how to win this game.
THE PROBLEM WITH MODELS ABOUT HUMAN BEHAVIOR
Like Monopoly™, this game purports to reflect certain aspects of reality. However, social science requires abstracting essential features out of the flux of everyday life. Just what is essential depends on what is being studied. Here we are studying human behavior.
Social scientists will tell you building models about human behavior is fraught with problems because the object of study is active, dynamic, and intelligent. There is a famous debate concerning the anthropologist Margaret Mead, who studied the differences between adolescent sexual behavior in South Pacific and Western cultures. The debate centers on whether Mead was subject to a hoax pulled by the Samoans she interviewed.1
According to Derek Freeman, two of the people Mead relied upon, Fa’apua’a and Fofoa, were kidding when they said they spent their nights with boys. Freeman said Fa’apua’a told him that she never thought Mead would have believed them because it is a Samoan custom to joke and exaggerate about sexual behavior. For our purposes it does not really matter what was the truth. We just need to be aware that asking people about what they are doing or thinking does not necessarily lead us to the truth.
Unfortunately watching people may not be any better. Observation does allow us to develop statistical probabilities for behavior. But without an understanding of what motivates people, we have no way of knowing with any certainty if the behavior will continue. For example, in a study of workers at the Western Electric Company’s Hawthorne plant in Chicago, various factors were changed to see if they had an impact on productivity. The factors were things like pay, light levels, and rest breaks. Curiously, every change brought productivity increases. Then, over time, in each instance the productivity increase dissipated. Finally the researchers came to the conclusion that it was not the factors being manipulated that led to the increase in productivity. Rather, it was the workers’ awareness that they were being studied. As the studies wound down, so did the productivity gains.2
A third path is called participant observation. In this method, the scientist uses a carefully structured research protocol to analyze a situation in which the researcher is also a participant. The idea is that by participating, you share in the intersubjectivity of human experience and thereby are able to combine both the “ask them” and the “watch them” approaches. The problem is the tendency to “go native” and lose objectivity. Even if this problem can be avoided, by becoming a participant, the researcher can never be sure his or her presence has not skewed behavior and views from what they would be in the researcher’s absence. It is the social scientific equivalent of the Heisenberg uncertainty principle.3
What this brief digression demonstrates is that any scientific method for collecting data on which to build a model has problems. So, I hope the reader will be sympathetic when I acknowledge this model is based on none of these approaches. Instead my approach is philosophic in the Platonic sense. This model is based on contemplation: reflection on my experiences, reflection on what I have read, and thinking about how to systematize the data.
CONSTRUCTS
Following Max Weber, I have created constructs or ideal types, which are then explored to create the model.4 Constructs are objects (entities, model elements) that carry attributes and can be placed into relationships.5 The attributes define (when instantiated) entities. The relationships use these attributes to link one contract or entity to other constructs. The constructs have no intrinsic merit. They merely are more or less useful, depending on how well they help us understand the phenomenon being modeled.
Science is premised on the assumption that with the right knowledge, we can form predictions of the form “if X then Y” with a reasonable level of confidence. If we can do that, then we can combine this knowledge of X and Y with other knowledge and know-how and end up with technologies of the form “do X and Z will result” with some level of confidence.
Assuming we want Z, then the ability to use X to get Z is useful. For example, I supported initial commercialization of a barnacle protein-based technology for Tufts University, based on a breakthrough by David Kaplan. The university’s invention disclosure states:
As the above summary highlights, if we know specific proteins are involved in barnacle adhesion, (our “if X then Y”) then we can use that knowledge to invent a set of technologies (our if “X then Z” where X is our knowledge of the protein, and Z is some desired end, such as making glue, making a coating, or making antifouling paint). To make glues, we combine our knowledge of the amino acid sequence (X) with tools for synthesizing sequences. To make antifouling additives we combine our knowledge of those same sequences with knowledge of how to cut them or inhibit their formation and with tools for making those enzymes and chemicals. Assuming we want either under-water curing glues or antifouling coatings, knowing the amino acid sequences is useful. In other words, we can design “how-to’s” if we have a reliable and replicable understanding of “what-is.”
Carrying this instrumental orientation back to our model, if we want to build a technology for technology transfer, one beauty of constructs is that they can be sustained or falsified empirically. You can go out and test to see if the attributes and relationships actually exist in the phenomenon being modeled, to see if they accurately reflect “what-is.” A sustained construct is called valid—that is, to the extent we have tested, it is a fair abstraction of “what-is.” If we create valid constructs, we should be able to improve the “how-to” involved in technology transfer.
PORTRAYING CONSTRUCTS
Before continuing, I need to take care of a housekeeping chore. I am going to use graphics to portray constructs. The graphic in Exhibit 1.1 is the legend for understanding the portrayals.
Note that to be included in a construct, an attribute must be capable of being measured. At least a yes/no, 0/1 scale must be conceivable. For us, technology transfer is a quantitative interdisciplinary social scientific field.
Also note that defining a relationship is never enough. There must be a special-temporal path, which, following the marketing and communication literatures, we call a channel through which the relationship can be formed and endure. While the ideas behind inventions and cre...
