Advertisement

Is Networking of People, Attitudes and Ideas Exploitable for Marketing of New Energy Solutions?

Chapter

Abstract

Strategic decisions regarding future energy production and the resulting energy consumption patterns at the general societal level, also selecting the respective regional or national patterns, are both essential and highly controversial. Alternative technological paths which may be taken into account are surprisingly diverse and sometimes interrelated. However, the spectrum of alternatives ranges from “predominantly conceptual” to “well grounded in practical terms”, and hence, they are associated with many types of uncertainties stemming from technological to societal risks including for instance acceptance barriers. Perceptual, psychological and the socio-economic factors which may not be universal across different economic regions are involved in a stable adoption processes of new energy solutions: think, for example, of a country like Germany which “owns access” to virtually all energy alternatives and may consider abandoning the most controversial technologies like nuclear. An emerging country like Romania, however, which is not actively pursuing but a few of the available energy technologies may consider diversifying its future energy portfolio. The present contribution argues in favour of positively considering marketing-type recommendation as a complementary factor to “purely political” decision finding in the complex domain of future energy production and consumption, in that these marketing-related mechanisms must not “just manipulate” but can genuinely aid the adaptation processes of “ubiquitous social computation” which is unfolding anyway in modern present-day societies. Such adoption processes are based on collective information-processing mechanisms like those involved in the functioning of markets and by using networking of persons, attitudes and ideas. We also discuss whether such processes may be used in order to “predict” mutually acceptable new regional energy solutions. The objective is to propose a problem-oriented recommendation mechanism but not that of naming the most preferable future energy solutions.

Keywords

Green energy EU energy policy Romania energy EU energy standards Alternative energy 

Bibliography

  1. Abernethy J, Bach FR, Evgeniou T, Vert J-P (2009) A new approach to collaborative filtering: operation estimation with spectral regularization. J Mach Learn Res 10:803–826Google Scholar
  2. Ahn Y-Y, Bargow J, Lehmann S (2009) Communities and hierarchical organization of links in complex networks. Online at arXiv:0903.3178v1. Posted 18 Mar 2009Google Scholar
  3. Amran M, Kulatilaka N (1999) Real options. Harvard Business School Press, BostonGoogle Scholar
  4. Andergassen R, Nardini F, Ricottilli M (2006) Innovation waves, self-organized criticality and technological convergence. J Econ Behav Organ 61:710–728CrossRefGoogle Scholar
  5. Anderson SP, De Palma A, Thisse JF (1992) Discrete choice theory of product differentiation. MIT-Press, Cambridge and LondonGoogle Scholar
  6. Axelrod R, Mitchell W, Thomas RE, Bennett DS, Bruderer E (1995) Coalition formation in standard-setting alliances. Manag Sci 41(9):1493–1508CrossRefGoogle Scholar
  7. Barbu A, Lay N (2011) An introduction to artificial prediction markets, at arXiv:1102.1465v2. [stat.ML] 9 Feb 2011, pp. 25Google Scholar
  8. Baronchelli A, Dall’Asta L, Barrat A, Loreto V (2007) Non-equilibrium phase transition in negotiation dynamics. Online at arXiv:0611.717v2. Posted 23 July 2007Google Scholar
  9. Bidder B, Schepp M, Traufetter G (2012) Arktisches Roulette, Der Spiegel 34/2012Google Scholar
  10. Blondel VD, Hendrickx JM, Tsitsiklis JN (2008) On Krause’s consensus model with state-dependent connectivity. Online at arXiv:0807.2028v1. Posted 13 July 2008Google Scholar
  11. Brondizio ES, Ostrom E, Young OR (2009) Connectivity and the governance of multilevel social-ecological systems: the role of social capital. Ann Rev Environ Resour 34:253–278CrossRefGoogle Scholar
  12. Bolton GE, Katok E, Ockenfels A (2004) How effective are electronic reputation systems? An experimental Investigation. Manag Sci 50(11):1587–1602CrossRefGoogle Scholar
  13. Candes E, Recht B (2009) Exact matrix completion via convex optimization. Found Comput Math 9(2009):717–772CrossRefGoogle Scholar
  14. Collins JM, Ruefli TW (1996) Strategic risk: a state defined approach. In: Frey BJ, Dueck D (eds) Clustering by passing messages between data points, Science. Kluwer Academic Publishers, Dordrecht. 315(5814, 2007):972–976Google Scholar
  15. Gassmann O (2010) Crowdsourcing: Innovationsmanagement mit Schwarmintelligenz, Hanser, MünchenGoogle Scholar
  16. Hoeffler S (2003) Measuring preferences for really new products. J Mark Res 40:406–420 NovemberCrossRefGoogle Scholar
  17. Hull JC (2009) Options, futures and other derivatives, 7th edn. Pearson Prentice Hall, New JerseyGoogle Scholar
  18. Krause U (2000) A discrete nonlinear and non-autonomous model of consensus formation. Commun Diff Equ 227–236Google Scholar
  19. Martins ACR, Carlos de B. Pereira (2008) An opinion dynamics model for the diffusion of innovations, at arXiv:0809.5114 [physics.soc-ph] 30 Sep 2008, pp. 1–7Google Scholar
  20. Natter M, Mild A (2003) DELI: an interactive new product development tool for the analysis and evaluation of market research data. J Target, Measur, Anal Mark 12(1):43–52CrossRefGoogle Scholar
  21. McNerney J, Farmer DJ, Redner S, Trancic JE (2009) The role of design complexity in technology improvement. http://www.pnas.org/cgi/doi/10.1073/pnas.0709640104
  22. Newman MEJ, Barabasi A-L, Watts DJ (2006) The structure and dynamics of networks. Princeton University Press, PrincetonGoogle Scholar
  23. Ostrom E (2009) Beyond markets and states: polycentric governance of complex economic systems. Economics Nobel Prize Lecture, Dec 8Google Scholar
  24. Packing some power, the economist technology quarterly, 3 Mar 2012Google Scholar
  25. Pfister H-R, Böhm G (2012) Emotionen und Moral bei Risikowahrnehmung, Spektrum der Wissenschaft, 1/12:67–73Google Scholar
  26. Die Strompreislüge, Die Zeit Nr. 35, 23 Aug 2012Google Scholar
  27. Schebesch KB, Pop NAl, Pelău C (2010) A new paradigm in contemporary marketing: computational marketing. Rom J Mark 1:36–74Google Scholar
  28. Schebesch KB (2011) IC and knowledge formation by hidden structures: long term costs of new technology and participative design. Electron J Knowl Manag 9(3):221–235Google Scholar
  29. Schebesch KB (2012) Enabling IC formation in composite organizations: the action of trust and reputation mechanisms. In: Surakka J (ed) Proceedings of the ECIC 2012, Academic Publishing International 2012: 427–436Google Scholar
  30. Simonson I, Tversky A (1992) Choice in context and extremes A version. J Mark Res 29:231–295CrossRefGoogle Scholar
  31. Terwiesch C, Xu Y (2008) Innovation contests, open innovation, and multiagent problem solving. Manag Sci 54(8):1529–1543CrossRefGoogle Scholar
  32. Tödtling F, Lehner P, Kaufmann A (2009) Do different types of innovation rely on specific kinds of knowledge interaction? Technovation 29:59–71CrossRefGoogle Scholar
  33. Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6:1453–1484Google Scholar
  34. Tversky A (1972) Choice by elimination. J Math Psychol 9:341–367CrossRefGoogle Scholar
  35. Urban GL, Weinberg BD, Hauser GR (1996) Prelaunch forecasting of really new products. J Mark 60:47–60CrossRefGoogle Scholar
  36. Vembu S (2009) Learning to predict combinatorial structures. Accessed at http://arxiv.org/abs/0912.4473v1. [cs.LG] 22 Dec 2009
  37. Wang W, Chen Y, Huang J (2009) Heterogeneous preferences, decision making capacity, and phase transitions in a complex adaptive system. PNAS 106(21):8423–8428CrossRefGoogle Scholar
  38. Westra MT, Kuyvenhoven S (2002) Energy powering your world, produced for EFDA by Institute of Plasma Physics, FOM Rijinhuizen, The NetherlandsGoogle Scholar
  39. Wu F, Huberman BA (2007) Novelty and collective attention. PNAS 105(45):17599–17603CrossRefGoogle Scholar
  40. Xiao B, Benbasat I (2007) eCommerce product recommendation agents: use, characteristics, and impact. MIS Quarterly 31(1):137–209Google Scholar
  41. Fusion-fission hybrid system (2009) Gaithersburg 2009 report of the US Department of Energy (DOE), p. 184. Online at http://fire.pppl.gov/Hybrid_Report_Final.pdf

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Faculty of EconomicsVasile Goldiş Western University AradAradRomania

Personalised recommendations