Complexity Approach to New Media Market
Complexity Approach to New Media Market
Yong Ho Chang
SogangUniversity
New Media as Evolving Complex System and Complexity Approach
New media is a complex system[1]. It consists of elements. Basically the elements are production, distribution and consumption of media industry. The elements could be hierarchical from low state to higher state. At the lower level,, cable and satellite industry consist of PP (Program Provider) and SO (System Operator). Online service industry is formed by PCP (Proprietary Content Provider) and ISP (Internet Service Provider). At the higher level, broadcasting industry consists of broadcasting related system networks. At the highest level, broadcasting industry, telecommunication industry, and newspaper industry consist of a huge inter-industrial networks. This is a static definition of media system. As time passes, media industry shows the aggregate pattern such as clustering, de-clustering, segregating, and decaying. Around the aggregate pattern, the elements adapt and respond to the neighboring elements’ state. For example, in cable industry SO adapts and react to the clustering pattern the SO and PP create. As time passes, SO becomes MSO (Multiple System Operator). PP adapts and reacts to the clustering pattern and it becomes MPP (Multiple Program Provider). Overtime the MPP and MSO react and adapt to the aggregate pattern (clustering) they co-create(장용호 2002B). The same pattern can be applied to other media industries such as online service industry, broadcasting, newspaper, telecommunication, and other newly emerged digital media industries. Media could be a typical evolving complex system. As the case shows, the media elements act and respond to the aggregate pattern the elements create. Overtime, the elements are responding and adapting to the neighboring elements’ state. They show endogenously changing process[2].
The clustering pattern of theatres shows another interesting adaptive evolving system(장용호, 2000). At the initial stage, theaters scattered randomly across different areas. A chance event happened and as a consequence theaters clustered to a certain location. As the numbers of theaters increased beyond a critical point, the clustering speed accelerated overtime. Most interestingly, the clustering became segregated into local blocs. Whereas commercial theaters clustered around backstreets,art theaters got clustered around front streets. The clustering blocs bifurcated. The bifurcated clustering blocks evolved again.As lots of theaters agglomerated in the same area, the rent went up. As a consequence, theaters started to disperse. The theatre case showed a typical mechanism of dynamic evolving system. It did not show any asymptotic state or uniqueequilibrium. It was a system in process, systems that wereconstantly evolving and unfolding over time.
What is complexity approach? The complexity approach focuses on emergent structures that come as unexpected phenomena. Asthe theater clustering pattern shows, the complexity approach focuses on the bifurcation processwhich occured in these systems. In a word, the complexity approach takes seriously the complexity and self-organizing[3] nature of the systems and then analyzes the systems along the historical path. It takes process seriously and tries to find out the aggregate pattern of the evolving complex system.
Why do we use complexity approach to media? First of all, the media firm has been evolved to hub and multi product firm continuously. Its production process has become multi-production system. The market of media industry also has evolved to complex one. There is no boundaries between media industries. New product has been emerged and old product location has been changed. Consumers have been changed also. Subscribers and users have been evolved. Their tastes and preferences have been changed. Subscriber loyalty and addictionprocess of consumption became dominant phenomena. Pricing strategies are also dynamic. There is no optimal and unique price. Pricing shows a preferable process adapting to the changes of market share. These phenomena challenge the classical questions of classical economics. Classical media economics only causes puzzles or anomalies regarding theses questions. That is why we need a new approach or complexityapproach to find out the process or aggregate pattern of evolving complex systems. As the cases show, the media has been evolved to complex systemscontinuously.
Second, complexity approach is very useful in that it broadens the classical approach by turning into the question of how actions, strategies or expectations might react in general to - might endogenously change with- the aggregate patterns these create. As theabove cases show, the complexity approach could be applied to broad areas such as dynamic pricing strategies, multi-product form, multi-production process, product location, agglomeration,bifurcation, convergence, endogenous growth of quality and other dynamic process. The approach is broadly surpassing and complimenting the classical economics.
Third, the complexity approach is realistic in that it tackles a media situation in an inductive way, not a deduction of classical economics. It portrays the media economy not as deterministic, predictable, and mechanistic, but as process-dependent, organic, and always evolving. It is dynamic. As a consequence of changes and adjustment, newly emerged phenomena, or theaggregate pattern appears. Let me illustrate two basic mechanisms of adaptive complex media system.
Positive Feedbacks
How does a positive feedback[4] work? Let’s consider the Korean online service market in which three major companies have competed severely: Serom Tech., Daum, and NHN. A few years ago Serom Tech. was the leading company using an Internet phone as major business model. On the other hand, Daum has established the biggest online community by making a internet café and e-mail service ran by ordinary internet users. For NHN, it started its businesswith a search engine and online games. The three companies atarted with different business models. As time has passed, Café based Daum has adopted direct news production system to expand its menu. In order to expand media business, it has recruited 31 professional reporters. On the other hand, search and game based NHN has used strategic alliances to expand and secure its contents. In case of NHN, it madenewssupplycontracts with Cable News Channels, YTN, and Digital Chosun. NHN made news hub as its business strategy. In present, the rank between these three companies has been reversed; NHN has become the biggest company among the three, followed by Daum and Serom.
Such example shows us the feedback system of complexity economics. First, these three companies showed multiple business solutions. (multiplicity of potential solutions). Second, the present result couldn’t be predicted in advance. Third, the sequential business process was based on the initial business base (lock-in). Forth, business strategies of these companies couldn’tbe the best one. Fifth, they took adoption path (path dependency). Sixth, the rank between these companies was and could be reversed over a period of time. These are pre-conditions for positive feedbacks which complexity have suggested: multiple equilibria, non-predictability, lock in, inefficiency, historical path dependence[5], and asymmetry.
Positive feedback or increasing rate of returns is also observable in Newspaper market(장용호, 1995 &1996). Subscribers practice repetitive consumption. As a result, a switching cost of subscribershiphas been occurred. Then, switching cost causes consumption path dependence (Lock-in[6]). In the newspaper industries, there was increasing returns to market share. As a market share increases, the quality of newspaper endogenously improves as well. In case of cable industry, there is a dynamic relationship between price and market share. The pricing strategy is related with a natural positive feedback. It is a basic fact that more profit is gained as the number of cable users increases. Thus, a cable company sets an initial price as low or even zero, and then increases it later as an efficientprice strategy. Due to the evolving characteristics of the cable market, there is no equilibrium right price that markets are to find. Instead, there is a preferable process which finds a price. Any price that is found out by the preferable process may be assumed to be as close to right price as can be found.
Digital media technology shows another typical aspects of positive feedbacks. It could be also adaptive nonlinear system and the effect of it accelerates positive feedbacks on new market structure, firm structure, new industrial boundary formation, creation of new product, and new product location. To sum up, digital media technology caused drastic revision of basic assumptions of classical economics. NC Soft sets online game technology as their basis to supply computer games. It continuously improves game technology by using its initial technology. In the present, the life cycle of NC Soft’ game technology belongs to second generation. It is now evolving to the third generation. In the beginning stage of new media technology, there was a severe competition. The selection of technology for NC Soft was determined by chance event; it was entirely chosen by the owner. In the next stage, NC Soft’ technology was improved by keeping its technology the owner had chosen. Therefore in the middle stage, upgrade is the main progression. The problems faced by NC Soft at this point, a point where it is now confronting third generation, is technological discontinuity. NC Soft worries new technology will substitute their initial technology. The most recommending strategy NC Soft can take is a self-cannibalization. In case when it confronts a discontinuity of new technology, there is a substantial cost as a burden when it chooses to change its path. Therefore, due to high cost of changing, an initial technology has become locked in. For a creative destruction to occur, a company must pay a great amount of money. Nowadays, NC Soft has invested mostly on development of new technology. In addition, the game technology of NC Soft is a gigantic system. Game technology is a combination of content technology, archive technology, and search engine technology. Each technology possesses multiple potential solutiona and there are various combination patternsamongthose technologies. NC Soft demonstrates an ideal positive feedbacks.
Sixproperties areapplied to digital media technologies:Multiple equilibria, non-predictability, lock in, inefficiency, historical path dependence, and asymmetry. The effect of digital media technology accelerates positive feedbacks on whole new media systems. CRM (Costumer Relationship Management) in newspaper business, DMB (Digital Multimedia Broadcasting), IMT – 2000 are the major examples. The digital media technologyhas been reducing transaction cost, and changedthe asset specificity of media. Media technologyhas decreased search cost of customers. In addition, implementation cost occurs when there is a transaction. Media technology smoothes the interaction and decreases the implementation cost. Monitoring cost takes a big part of transaction cost. This part manages risk of transaction. New media technology decreases transaction cost by decreasing the monitoring cost. It leads to dramatic changes in transaction. Digital media technology also has changed asset specificity of media. Products using an analogue system had high asset specificity. But due to digital technology, there no longer exists specified application of technology to certain information. Digital mediatechnologyfreely combines, produces and delivers information. Frictionless economy hasfinally been arrived.
Thus, the advance of digital media technology has brought new market system (infinite horizon), new firm organization (multi-product firm), and new product location. Digital medial technology also has changed “individual” concept. New media technology sets its focus on individual life. The concept of heterogeneous consumer has begun to appear, and new media provided customized service for individuals. Therefore, the range of services for an individual has become wider.Different services for every individual has become possible. People are not identical anymore. Moreover, media researchers focus on the preference formation process. After researching about repetitive consumption pattern on game or other information, customer loyalty increased and moreover, incidence of addiction process of consumption could be observed. As a customer enjoys a game, he or she becomes deeply involved by forming a community or becoming as a union member in the game. Customers evolve by becoming more and more involvedin the game.
Bounded Rationality
Complex approach is based on bounded rationality[7]. Realistic economic agent is not complete, predictive, and optimal. Every human can not fully comprehend what kind of situation they are put in. They are not able to choose optimal choice or behavior either. Human is bounded by habit, institution, and culture. These factors filter and screen out one’s choice. Therefore, when humans are faced with complex situations, they can not take the optimal choice. Bounded rationality is similar to what had been discovered in sociology and socio-psychology. Humans are restrictedby predispositions and trained incapacity. Complexity approach proposes that prediction capacity is bounded. Humans’expectation and behavior is path dependent. That is why complete rationality is impossible to achieve. There are lots of cases of incompleterationality due to path dependence. Newspaper subscribers are path dependent. Social norms of media industry are path dependent.
Bounded rationality could be also naturally applied to new media industry. New media industry is chaotic. Administrators and board of directors wish to choose the optimal business model or business direction. But their problem is they can not estimateperfectly how market will turn out in the future. Each CEO of high technology companies is like a fortune teller. Business models they selected are extremely fluctuating. In addition, in the high tech business, there are unstopping imitations between one another. Due to application of new media technology,media venture market is formed. In the media venture market, price anomalies, unexpected price bubbles, price crashes, and random periods of high and low volatility are formed. Investors can not assume or deduce expectations but they should do random walk. Market participants create and use multiple hypothesis of what moves the market price. If the rate of updating of hypothesis is turned up, the market undergoes a bubble phase. The venture capital market develops a rich psychology of divergent beliefs that do not converge over time. If subpopulations of market participants are expecting that market is going down, it becomes a crash. As the mutually reinforcing expectation arises, market volatility is high. We conjecture that venture capital market lie in this complex regime.
There is a similar phenomenon in movie investment. There is no definite rule for optimal investment. A movie investor faces multiplicity of investment options. Investors take a random walk. If the first investor succeeds by investing certain amount of money, the following investors imitate the first investor. When the following investors imitate the first investor, optimal size of investment seems to be happened. But in this case, investors consider this investment level as optimal size expectation. In a case of sequential investment situation, the distribution of investors becomes as investment guidance. When many choose a certain investment, that investment size is conceived as the optimal one. Many investors are investing exactly the same each other. In the Movie investment, mutually reinforcing expectations take place and this phenomenon causes occasional bubbles and crushes. Movie investment market also shows periods of high volatility in amounts followed randomly by periods of low volatility. This is because if some investors discover new profitable hypothesis they change their expectationsslightly which cause other investors to also change their expectations.
Conclusion
Facing with evolving dynamic system, media economists have been studying the general emergence of structures and unfolding patterns of media economy. Media economic theory has always recognized that in the actual media economy, agents continuously adjust their behaviors to the aggregate pattern these behaviors create. The complexity approach is expanding and far-reaching in every areas of media economics: dynamic evolution of media organization, evolution of media market, multi-formity of product and process system, evolution of product space, learning of the consumer behavior, preference and taste formation, customization process, investment cascade, dynamic world trade of film market, evolution of online community, dynamic evolution of online service industry, and etc(조은기, 1995&1999&2002, 김영주, 2002, 자장용호 1999, 2000B, 장용호·김영주, 2001). The complexity approach helps us understand that small chance events can ‘tip’ the system towards a path of development from approximately equal market shares to a long-run state of highly unequal market shares. And it makes us aware that policies succeed better by influencing the natural processes of formation of economic structures than by forcing static outcomes.
This paper is not to declare a break with classical economics. It rather tries to suggest that there are more fruitful areas if we cross-fertilize between classical economics and complexity economics. We could consider both dynamic process and static statesby introducing classical economics and complexity approach.For example, in explaining media diversification, the forward looking models (repetitive use of public goods’character of asset in classical economics) can be supplemented by a complexity approach which suggeststhat a small chance event can change the whole system in a dynamic way. Moreover, the reality is very complex and complexity approach is complex, too.