Professor, Velammal Engineering College, Chennai. International ... Learning Model to answer questions relating to growth of software industry. The analysis ...
Social Forecasting –Tool for Corporate Planning and Application to Information Technology Industry
Dr.K.Prabhakar, Professor, Velammal Engineering College, Chennai.
International Conference on Business Research (ICBR) School of Management, SRM University, Kattankulathur 603 203, Tamilnadu, India
Electronic copy available at: http://ssrn.com/abstract=1781470
Abstract Corporate planning largely relied on technological and economic forecasting. However, failure to predict emergence of new business models, economic down turns led to search for a tool. Social forecasting takes into account different variables that are ignored for lack of precision. Its genesis and methodological premises such as model procedure, validation process, and past studies are explored. Social forecasting is applied for information technology industry with help Generational Learning Model to answer questions relating to growth of software industry. The analysis offered promising results and predicted the worldwide recession. Key words: Social forecasting, Economic forecasting, Technological forecasting, Kwaves, structural cycles, Generational Learning Model, predictive capability, explanative capability. Can future be forecasted? The answer is not a strong affirmative. If there is something known or defined as ‘the future’ then it can be attempted. The word future is a relational term (Bell, 1973) it can be discussed as the future of something. Is it possible to forecast results? The answer is more not affirmative. Forecasts can specify the constraints or limits within which decisions can be effective. Forecasting has different modes. Social forecasting differs from other modes in its scope and techniques. The most important distinction is study of sociological variables that are independent or exogenous and least precise, which affect the behaviour of other variables in the areas of economic, technological, demographic, or ecological forecasts. In this article the fundamentals of social forecasting is documented with application to information technology industry to answer the question of how innovations are likely to take shape in that industry. Bertrand de Jouvenel (1967), the French philosopher-economist defined social forecasting as the prediction of big, slow changes in society. This definition indicates that the entity in question is nothing less than whole society. Social forecasting is thus concerned with the sweeping and ineluctable features of socio-cultural change. It describes the larger context within which volition may be exerted and alternatives effected, if desired. For de Jouvenel, there is no single tomorrow—the future consists of fan like array of possibilities, alternative futures that man can shape. The term is not a recent addition to business vocabulary.
Electronic copy available at: http://ssrn.com/abstract=1781470
Objectives of study Present study is undertaken to, 1. To provide a working definition of social forecasting for corporate planning . 2. Apply social forecasting to information technology industry with the help of K-waves and Generational Learning Model. Introduction The use of social forecasting stems from recognition that social pressures are becoming an increasing determinant for the success of any organization (James, 1978). Various indicators point that the society will be experiencing a total change in next few years (Morris, 1975). Some of these changes have to be anticipated and incorporated in any long-range plans. The changes that are happening in the environment are fundamental. They are not evolutionary. The greatest challenge is the discontinuities that are happening in the environment (Jain and Singhvi, 1977). Examination of history indicates that there are a series of significant shifts in the conditions of human society - the renaissance, the agricultural revolution and the industrial Revolution. Human society is entering a period of rapid change that is more dramatic in its consequences compared to earlier revolutions. Globalization, network economy, and democratization of polity lead modern societies to transform themselves at more and more accelerated cadences. The inevitability of internet and convergence of technologies is not only due to revolution in microelectronics, but also due to exhaustion of material resources essential for maintaining industrial economy. Similarly, what kind of explanation is provided for investment in social networking websites such as ORKUT, TWITTER, and FACEBOOK? Does Information Technology promote it because of individualism that has become stronger and at the same time the primitive need to live with tribe? Purpose of social forecasting is to provide an analytical framework for helping the corporate decision-maker to make his or her own judgment based on analysis. Social forecasting provides better understanding of the forces shaping the environment and provides confidence to manager that his decisions reflect assessment of these issues. Social forecasting includes all those other factors that are not
considered by
economic or technological forecasting. Primarily it involves individual as customer,
supplier, manager, or employee. It concerns people in-groups both inside as well as outside organizations. It further unfolds to government, society in general and to transnational organizations such as World Trade Organization. Therefore, Social Forecasting is a term, which includes political, legal, and ecological factors in addition to social. Social Forecasting in the Context of Corporate Planning Social forecasting is to enlarge the scope of traditional business forecasting to include relevant domain of social-psychological-economic-political-ecologicaltechnological environment. Through social forecasting, the inclusion of sociopolitical-ecological dimensions in strategic planning and policy formulation brings social issues into the mainstream of an organization’s operations. By providing an open-system perspective of the organization, social forecasting helps relate social responsiveness to organizational efficiency by providing a longer time horizon of social issues relevant to the organization. Social forecasting thus, encourages the organization to perceive itself in mutual interaction with its external environment, enables management’s application of appropriate period for the effective planning, analyzing, and implementing of its social involvement in a complex and dynamic environment. Social forecasting is defined as “a systematic process for identifying social trends and their underlying attitudes, analyzing these social changes for their relevance to the organization, and integrating these findings with other forecasts”.
Economic-Political-Demographic-Technological-Ecological Factors Systematic Process
Identification of social trends
Underlying Attitudes
Incident-possibilistic, social-volitional, and structureprobabilistic
Social Forecasts
Social Changes
Integrating into Organizational Forecasts
Figure 1.1 Representation of Social forecasting definition
Social Forecasting relationship with economic forecasting and technological forecasting Economic forecasting is essentially concerned with modeling how people behave using utility criteria as a means for maximizing welfare. It is dependent on certain assumptions of people behaviour. If the behaviour changes the forecast is likely to change. Therefore, one role of social forecast is to find the underlying relationships used by economic forecasters and to modify them as necessary. In the case of technological forecasts, it has been assumed that past data can be extrapolated into future. However, observations indicate that past relationships are unable to predict future. In the case of pharmaceutical organizations, the new molecule development is more dependent on the R & D expenditure allocated. The advances cannot be attributable to serendipity. In fact, they result from managerial investment decisions (James, 1978). In case of pharmaceutical industry, these resources have been raising due to society’s growing concern for health. The liberalization of health insurance in India combined with accepting patent regime, has totally changed the role of Indian pharmaceutical organizations. The example of pharmaceutical industry in India illustrates the complexity consisting of technological, economic potential, economic support based on legislation, governmental action, and globalization of business. 1. Economists and technologists developed the forecasting techniques. It is dissatisfaction with the methods and tools have led to development of social forecasting. 2. Active involvement of sociologists and business organizations are needed to further the objectives of social forecasting. 3. The disadvantage of social forecasting is for phenomenon of interest, these are no clearly defined measures compared to technological and economic forecasting. Though objectivity is desired, subjectively is inevitable. 4. The forecasts cannot be ‘ends’, they are only ‘means’ through which better view can be obtained about future.
Technological Forecasting with Focus on Social Variables With the basic understanding of social forecasting, its theory is applied to information technology industry. The phenomenon of long waves theory is examined to understand the trend. The purpose is to find; will there be more innovations in the next five years? Alternatively, have a plateau is reached with respect to advances in innovations in computers? How the growth is likely to come; is it from new research in different areas or from the consolidation of existing business? An attempt is made to find answers. Economists analyze the business cycles that happened during different times for the past three centuries. However, Kondratieff curves, known as long wave phenomenon, gives one of the most important theories. The Kondratieff phenomenon will be discussed with respect to technological forecasting rather than with economic forecasting as the evidence proved that it deals with more physical phenomenon than economic phenomenon (Marchetti, 1988). In the introduction, the basic dimensions of K-wave theory are examined followed by discussion on its validation. Finally, K-wave theory is used to discern the likely changes in technological environment. For the purpose of study extensive references is obtained from Devezas, Linstone & Santos (2005). Kondratieff Curves – Social Phenomena Russian economist Nikolai Kondartieff was the first to bring observations about long waves also called super cycles, surges, long waves or K-waves-are described as regular cycles in a capitalist world economy. These cycles consist of alternating periods between high growth and periods of negative growth. The cycle is more relevant to world economy rather than individual national economies. It affects all the sectors of an economy, and concerns mainly output rather than prices (although Kondratieff had made observations focusing more on prices, inflation, and interest rates). According to Kondratieff, the ascendant phase is characterized by an increase in prices and low interest rates, while the other phase consists of a decrease in prices and high interest rates.
(http://en.wikipedia.org/wiki/Kondratiev_wave) The curves are described to be economic waves. However, the explanation offered by Kondratieff, includes social shifts and public mood. Similarly, it also changes the attitude towards work. However, researcher presume the 5th wave started by 1970. Kondratieff pointed out the first two cycles and the discussion of social phenomena as follows; •
1790 – 1849 with a turning point in 1815.
•
1850 – 1896 with a turning point in 1873.
Kondratieff supposed that in 1896, a new cycle had started. The phases of Kondratieff's waves also carry with them social shifts and changes in the public mood. The first stage of expansion and growth, the spring stage, encompasses a social shift in which the wealth, accumulation, and innovation that are present in this first period of the cycle create upheavals and displacements in society. The economic changes result in redefining work and the role of participants in society. In the next phase, the summer stagflation, there is a mood of affluence from the previous growth stage that changes the attitude towards work in society, creating inefficiencies. The next stage is season of deflationary growth, or the plateau period. The popular mood changes during this period. It shifts toward stability, normalcy, and isolationism after the policies and economics during unpopular excesses of war. Finally, the winter stage, which of severe depression, includes the integration of previous social shifts, and changes into the social fabric of society, supported by the shifts in innovation and technology. There are four schools of thought and one of the most important schools of thought is innovation school propounded by Joseph Schumpeter. He suggested that these waves arise from the bunching of basic
innovations that launch technological revolutions that in turn create leading industrial or commercial sectors. The theory hypothesized the existence of very long-run macroeconomic and price cycles, estimated to last 50–54 years. Validity of K-wave phenomenon Economists and economic historians have divided opinion regarding long wave phenomenon. Two areas are for continued debate. First, on the facts, are long waves a real phenomenon? If it is true, what is the nature of long wave movement? The reason for economists not accepting long wave phenomenon is that econometric research from 1980’s onwards does not give total support to the long wave phenomenon (Metz, 2005). Regarding empirical evidence Marchetti (http://cesaremarchetti.org) in several publications have proved that the real evidence of long waves is not in time series data of economic parameters, but in the observation of physical entities associated economic domain, such as innovations, energy consumption, infrastructure etc. Berry (1991, 2000) using chaos theory and spectral analysis has found sound and robust evidence of the existence of K-waves. Moreover, Berry has observed that K-waves are not growth cycles, but instead structural cycles. That explained the regularity for every 55 years found by Marchetti (1988) in his extensive analysis of physical parameters. This explanation will answer the first question that it is a real phenomenon. The second question is the essence of discussion on long waves: their nature in relation to structural cycles and clusters of innovations. Metz (2005) found evidence of clusters of innovation activity. He used database of (15,000 innovations from the period of 1750 to 1991 collected by researchers of the Institute of Employment Research in Nuremberg) and found evidence for clusters having a peak at 1840, 1890, 1935, and 1986. His research shows that innovation activity followed by an upswing in growth of economic activity with a lag of about 18 years. These studies provide support for Generational-Learning Model that will help understanding the phenomenon better. Among theories that explained K-waves, the plausible evidence is from cluster of radical innovations that peak during the ‘downswing’, phases of each Kwave. This cluster of innovations originates a completely new technological new
environment, which is called Technosphere according to Devezas and Corredine (2001). The 50-60 year K-waves are usually measured from trough to trough, for the purpose of study, the cycles are calculated from peak to peak. Thus, Technosphere commences with a downswing of the K-wave, the period of knowledge innovation or Schumpter’s “creative destruction,” and proceeds through the trough to the knowledge consolidation in the K-wave upswing culminating at its peak.
Figure1.3 The Generational Learning Model of Long Waves. The overall growth curve of a new technoeconomic system (technosphere) encompasses two successive logistic structural cycles: an innovation structural cycle with characteristic duration tGI triggered during the “disintensity down slope” of the previous technosphere, and a consolidating structural cycle, with characteristic duration tG, which marks the definitive entrenching of the new technosphere and the vigorous “intensity upslope” of the long wave. SOURCE: Adopted from Devezas T.C, Linstone H.A, and Santos H.J.S. (2005). The Growth Dynamics of the Internet and Long Wave Theory, Technological Forecasting and Social Change, 72, p. 916.
Each such period from peak to peak has associated with it, an overarching technology that has a dominant impact. The mechanization of textile industry galvanized the first K-wave upswing before 1800. Steam powered transportation was the dominant technology of the period encompassing the subsequent first wave down swing and second K-wave upswing (about 1800-1856). Steel and electricity are important in the second downswing and third upswing (1856-1915), while oil was important technology in the era of the third downswing and fourth upswing (1916-1969). The overreaching technology is now, in the cycle of fourth down swing and fifth upswing (1970-2025). The cyclical patterns are given in the following table for reference. Table 1.1 Cyclical Patterns of Innovation Domain Innovation
2nd st nd 1 to 2 wave of wave 1800-1855
to
3rd 3rdto4th wave
1856-1915
1916-1969
4th to 5th wave 1970-2025
Overarching technology
Steam power
Steel/electricity
Oil
Information Technology
Transportation
Rail roads
Automobiles
Air craft
Space craft
Communication Periodicals
Telegraph, Telephone
Radio, TV
Internet, WWW
Primary Global Wood Energy
Coal
Oil
Natural Nuclear
Manufacturing Process
Factory
Scientific Management assembly line
Mass production, in-house R&D
Minimal inventory, CAD
Corporate organization
Hierarchy
Division
Matrix
Network, Virtual company
Gas,
SOURCE: Adopted from Devezas T.C, Linstone H.A, and Santos H.J.S. (2005). The Growth Dynamics of the Internet and Long Wave Theory, Technological Forecasting and Social Change, 72, page 917. It has been widely observed that the K-wave rhythm is observed not only in economic sphere, where Kondratieff focused his attention, but also in global
reliance on energy, in disruptive innovations in communication, transportation modes, infrastructure, manufacturing, business organization. However, these domains are related. Marchetti (1988) stated, “With increasing mechanical transport and speed, the personal territory increases, and so rises the opportunity to set up communication poles farther and farther away. Movement generates communication (not vice-versa).” The nature of this pattern made him to think that it is not an economic phenomenon, but an expression of deeper physical one relating to the basic working of the society: especially society as a learning system (Marchetti, 1980). The prime mover of any evolutionary process is the information transfer, which can be named as learning process. The rate of information transfer is initially low, then overcoming the inertia of system, grows, reaches a maximum rate of growth, slows down, and reaches a ceiling, following then a typical logistic growth pattern. In the evolutionary process, a system self-organizes and learns configuring and reconfiguring itself towards greater and greater efficiency and with successive iteration improving performance. Each stage corresponds to a given structure that corresponds previous selforganization, learning and current limitations. Thus, it may be said that, “selforganization and learning are embodied in the system’s structure, and the learning rate is an overall system property.” Social Forecasting for an Information Technology Firm
Figure 1.4 Generational Learning Model Interpretations
The fifth upswing is supposed to start from circa 1970 and likely to end by 2030 culminating 60years of cycle as indicated by the arrow. There is swarm of innovations approximately from 1983 to1995 and this period is characterized by advent of internet. Period starting from 1995-2005 exhibited diffusion phase with synergism and disintensity of convergence of different technologies. Thus, the information technology industry attained its peak in innovations from 2005-2010. This also corresponds to touching of the lowest point of economic growth as indicated by arrow. This study remarkably corresponds to the recession (2005-2009) that is not expected by many but could be inferred from the generational learning model.
Thus,
from 2010 onwards growth will happen not due to new technological innovations and due to further consolidation of the present technologies. It indicates that economies will have a recovery at a slower phase in the next fifteen years. What are the implications for a information technology firm from this analysis? The manpower requirements are not for high end research, but to implement the innovations that have already taken place. Thus the firms will be requiring mostly human resources will skills to implement the innovations and researcher’s hypothesis is post recession requirements will be in large numbers. These predictions of Generational Learning Model are being validated by upswing of hiring pattern of information technology industry for the past six months in India. Conclusion Social forecasting encompasses variables that are generally ignored by economic and technological forecasting. With availability of data and robust methodology, social forecasting offers promise of predicting trends better than economic and technological forecasting. Present study with respect to Generational Learning Model offered better explanation of past and could predict phenomena relating to present advent of hiring of information technology industry. However, the results are not to be viewed as a starting point for more rigorous research.
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