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Eco-innovation diffusion

From EverybodyWiki Bios & Wiki



Eco-innovation diffusion is a research subfield of innovation diffusion that seeks to explain how, why, and at what rate new "ecological" ideas and technology spread. Eco-innovations refer to a wide range of innovations such as efficient energy use, renewable energy technologies, pollution prevention schemes, waste management equipment, green technology and sustainable agriculture. Although the eco-innovation literature typically focuses on policy, regulations, technology, market and firm specific factors rather than diffusion, understanding of eco-innovation diffusion has recently gained importance.

What drives eco-innovation?[edit]

Macro (or company) level[edit]

There is no consensus as to what factors drive eco-innovation at the company level. Studies leaning towards the field of innovation indicate that demand factors play an important role for the generation of eco-innovations.[1] On the other hand, insights from the management literature on corporate social responsibility (CSR) suggest that the societal pressure and demand for environmentally friendly products and processes may affect the decision of the firm to undertake eco-innovations, but have little or no impact upon the level of investments in eco-innovations.[2][3] This may suggest that firms initiate eco-innovations in order to satisfy the minimum customer and societal requirements, but increased investments in eco-innovations are stimulated by other factors such as cost savings, firms' organizational capabilities and stricter regulations. Stringency of environmental regulations affects eco-innovation as firms respond to stricter environmental regulations with higher levels of eco-innovations,[4] but only the least and most innovative firms are driven by regulatory requirements.[3]

Micro (or consumer) level[edit]

Survey research shows that customers generally hold positive attitudes towards various types of eco-innovations, yet adoption rates of solutions such as green or dynamic electricity tariffs remain unsatisfactorily low.[5] The "Not In My Back Yard" (NIMBY) concept is often used to describe what at first seems to be a confusing intention-behavior gap between high levels of public support for eco-innovations and frequent non-engagement or even local hostility towards specific projects, like onshore wind farms.[6] As Gyamfi et al.[7] and Byrka et al.[8] argue, social psychology and economic behavior models should be used to overcome these challenges.

A well explored factor that fosters diffusion of eco-innovations is conformity to others.[9] Innovations naturally involve uncertainty triggered by insufficient knowledge. In such situations of uncertainty, opinions and behaviors of friends and neighbors often serve people as a guideline for their own behaviors.[10] In empirical research peer effects were found to increase adoption of solar panels or reduce residential energy usage at households.[8] Conformity has been also considered in eco-innovation diffusion models, in particular in the context of adoption of dynamic electricity tariffs,[5] efficient energy usage,[11] electric vehicles and sustainable transport[12] or residential photovoltaic systems.[13]

A less studied factor is the cost related to adoption that may effectively hinder this process. If external barriers are modeled in the literature at all, they usually refer to price regulations and economic burden set on customers.[5][11] However, Byrka et al.[8] argue that adoption of sustainable products and practices bears costs that go well beyond financial expenses. They introduce the notion of difficulty of engagement (see below) to the eco-innovation diffusion literature and argue that it possesses three desired properties:

  • parsimony - it is represented by a single value,
  • interpretability - it can be regarded as an estimator of the otherwise complex notion of behavioral cost, and
  • applicability - it can be easily measured through market surveys.

Modeling eco-innovation diffusion[edit]

Understanding of eco-innovation diffusion has recently gained importance since some eco-innovations, like photovoltaic (PV) panels, have reached a mature stage at which they can compete with conventional alternatives.[14] Two main classes of models are considered in the literature: aggregate innovation diffusion models and agent-based models.

It were the seminal works of Fourt and Woodlock,[15] Rogers[16] and Bass[17] in the 1960s that have triggered studies on innovation diffusion.[18] Particularly the Bass model, which is defined by a simple differential equation that characterizes the diffusion as a contagious process initiated by mass media and propelled by word-of-mouth communication, has seen countless applications. In the context of eco-innovations, variants of the Bass model have been used among other to study the diffusion of wind power technology, green electricity tariffs, stationary fuel cells, photovoltaic-system support schemes and consumer demand for smart metering tariffs.[8]

The downside of aggregate innovation diffusion models, such as the Bass model, is that they possess very limited predictive and explanatory power. They are not designed for analyzing what-if scenarios, they do not explicitly consider consumer heterogeneity nor the complex dynamics and interpersonal relationships encountered in real-world social systems. On the other hand, agent-based models (ABMs) provide the required flexibility and over the last two decades have become a widely used approach in the innovation diffusion literature[18] and the social sciences in general.[19]

What makes a good eco-innovation diffusion (agent-based) model?[edit]

It is not easy to define what makes a good model for the diffusion of eco-innovations. Simplicity and usefulness are the key factors, however, the challenge resides in simplifying the often complex theories of social science and the complex reality into simple sets of rules.[20] Byrka et al.[8] discuss the most important features of a good agent-based model (ABM) for modeling the diffusion of green products and practices. Although focused on eco-innovation diffusion, in some aspects their guidelines reach far beyond this modeling context.

Binary, multi-state or continuous opinions[edit]

The simplest and probably the most popular choice is to define an agent (also referred to as actor, consumer, customer, individual or spinson) by a single binary variable that represents an adopted (+1) or an unadopted (0 or -1) state.[5][21][22] The idea of binary states is natural from the social point of view, since dichotomous response format with 1 for "yes", "true" or "agree" and 0 for "no", "false" or "disagree" as response options is one of the most common in social experiments.[23] Agents with more than two states[24] or continuous opinions[25] have been also considered in the literature, but Byrka et al.[8] argue that in most cases binary opinions are realistic enough.

Types of social response[edit]

In innovation diffusion models the state of an agent is typically affected by external (advertising, mass media) and internal influence (interactions between the agents, e.g. word-of-mouth).[18] Many implemented rules, including epidemic processes and threshold models, rely on imitating the behavior of others, i.e. they describe only one type of social response - conformity.[21][26] However, conformity is not the only type of social response and the other three main types (independence, anti-conformity and congruence) should be also taken into account.[27][28] Recently these types of social response have been formalized within the class of "spinson models", i.e. models of interacting dichotomous agents.[29]

Difficulty of engagement[edit]

Difficulty is considered a key predictor of behavior in psychology and is included in the most recognized models of behavior change, such as the Theory of Planned Behavior. Typically, behavioral difficulty is considered to be subjective and person-dependent (relying on people's perceptions). However, a more objective, person-independent difficulty seems more relevant for environmental or energy policy, because people often fail to recognize how challenging the barriers that they have to overcome really are and their perceptions may depend on mood or current circumstances.[8]

Kaiser et al.[30] proposed a model of behavior change with person-independent difficulty and called it the Campbell Paradigm. Their model treats the likelihood of individual behavior as a function of a person's attitude and of the difficulty of engaging in this behavior. The more demanding these barriers are, the more favorable attitude towards a general goal, such as environment protection, a person needs to have to overcome them. The relation between difficulty of behaviors, attitude and behaviors can be computed using a one-parameter logistic Rasch model and the proportion of persons that engage in a given behavior. Since it can be easily measured through market surveys, the difficulty of engagement provides a much needed practical link between the model and the real world.

The size of the influence group[edit]

The optimal group size for discussions, collaboration, etc., has been the issue of interest for decades. In the management context, Hackman and Vidmar[31] concluded from a cross-sectional study that the optimal team size was 4.6 members, while Wheelan[32] found that groups containing 3 to 6 members were significantly more productive and more developmentally advanced than larger groups. From the point of view of social influence, the size of the group cannot be too small (it has to be of a sufficient size to invoke the social pressure) nor too large (people tend to discuss in small groups); the "optimal" group size has been found to vary between 3 and 5, depending on the experiment.[33] For these reasons, instead of using threshold models,[34] so-called q-voter models[29][35] in which each individual interacts with a set of q neighbors instead of all of its neighbors (in a social network context: all nodes with a link to the target node) may be used.

Unanimity or majority[edit]

Conformity can be modeled in different ways, but the most common is to assume that a predefined ratio of adopted neighbors is needed to influence an agent.[28][34] The threshold may be data-driven. For instance, income-based thresholds of perceived photovoltaic market share over which agents are willing to consider adopting the new technology[12] or thresholds estimated from survey data reflecting households' ability to adopt.[21] However, it has been observed in number of social experiments that a small unanimous group may be more efficient in convincing others than a much larger group with a non-unanimous majority, see e.g. the Asch experiments on conformity. Hence, for modeling eco-innovation diffusion, which involves a high level of uncertainty, the unanimity rule (i.e. ) seems more appropriate.[8]

Person or situation-driven independent behavior[edit]

In real societies agent independence is probably described by a bell-shaped curve, as many other quantitative traits in nature, but it might instead be better described by a skewed distribution, as wealth, or a multimodal distribution, as political beliefs often are. In a simplistic modeling framework, we may consider two extreme cases:[36]

  • In the person approach there is a fraction of individuals that are permanently immune and a fraction of agents who are susceptible to the social influence. The distribution of independence consists of two peaks - one of height at and the second of height at , and can be treated as an approximation of a bimodal distribution.
  • In the situation approach all agents have the same level of independence , i.e. every individual behaves independently with probability and conforms to group pressure with probability . The distribution of independence consists of only one peak of height 1 at and can be treated as an approximation of a uni-modal bell-shaped curve.

Such two extreme scenarios introduced on the agent level can yield very different results on the aggregate or system level.[36] As there is no consensus on the prevailing type of influence (→ person-situation debate) caution should be taken when making the decision on how to model independence.

Homogeneous or heterogeneous agents[edit]

Aggregate diffusion models, like the Bass model, generally assume agent homogeneity. In reality, however, potential adopters are heterogeneous in terms of income, individual preferences or the information they possess. In an attempt to make agent-based models more like real social systems, heterogeneous agents have been considered also in the eco-innovation literature.[12][13] However, if model parsimony is of primary concern, homogeneous agents may be used. Such an approach allows for concentrating on societal factors, without the need to eliminate or correct for the influence of individualistic traits.

Social network structure[edit]

In aggregate diffusion models, the network of agents is assumed to be fully connected and homogeneous. On the other hand, agent-based models in a natural way admit different social network structures. However, the choice of a particular network structure (or topology) is not obvious as its role on innovation diffusion is not well understood.[37][38] As there is no single, optimal structure, different specifications have been considered in the literature:

  • Complete graphs, in which every agent is connected with every other agent. Although this is unrealistic for large social networks, it is suitable for describing small groups (or cliques) within larger structures. This topology allows for analytical treatment.
  • Regular networks, like the chessboard, which are commonly used for spatial modeling.
  • Random graphs, in which any two agents are connected with some probability, independently of the other links. This topology also allows for analytical treatment, but is unrealistic as it does not admit high clustering. Yet, it often serves as a benchmark for comparisons with other network structures.[18]
  • Small-world networks, which is a much more realistic topology that possesses two wanted features - short path length and high clustering. Communities exhibit the small-world topology with strong clustering.
  • Scale-free or Barabasi-Albert networks, which are characterized by a power-law tail in the degree distribution. Structures like the Internet are regarded as scale-free networks.
  • Real-world networks, like "friends lists" from Facebook or "circles" from Google+.

References[edit]

  1. ↑ Horbach, Jens (2008). "Determinants of environmental innovation—New evidence from German panel data sources". Research Policy. 37 (1): 163–173. doi:10.1016/j.respol.2007.08.006.
  2. ↑ Bansal, Pratima; Hunter, Trevor (2003). "Strategic Explanations for the Early Adoption of ISO 14001". Journal of Business Ethics. 46 (3): 289–299. doi:10.1023/A:1025536731830. ISSN 0167-4544.
  3. ↑ 3.0 3.1 Kesidou, Effie; Demirel, Pelin (2012). "On the drivers of eco-innovations: Empirical evidence from the UK". Research Policy. 41 (5): 862–870. doi:10.1016/j.respol.2012.01.005.
  4. ↑ Frondel, Manuel; Horbach, Jens; Rennings, Klaus (2008). "What triggers environmental management and innovation? Empirical evidence for Germany". Ecological Economics. Special Section: Integrated Hydro-Economic Modelling for Effective and Sustainable Water Management. 66 (1): 153–160. doi:10.1016/j.ecolecon.2007.08.016.
  5. ↑ 5.0 5.1 5.2 5.3 Kowalska-Pyzalska, Anna; Maciejowska, Katarzyna; Suszczyński, Karol; Sznajd-Weron, Katarzyna; Weron, Rafał (2014). "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs". Energy Policy. 72: 164–174. doi:10.1016/j.enpol.2014.04.021.
  6. ↑ Devine-Wright, Patrick, ed. (2011). Renewable energy and the public: from NIMBY to participation. Taylor & Francis. Search this book on
  7. ↑ Gyamfi, Samuel; Krumdieck, Susan; Urmee, Tania (2013). "Residential peak electricity demand response—Highlights of some behavioural issues". Renewable and Sustainable Energy Reviews. 25: 71–77. doi:10.1016/j.rser.2013.04.006.
  8. ↑ 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 Byrka, Katarzyna; Jȩdrzejewski, Arkadiusz; Sznajd-Weron, Katarzyna; Weron, Rafał (2016). "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices". Renewable and Sustainable Energy Reviews. 62: 723–735. doi:10.1016/j.rser.2016.04.063.
  9. ↑ Young, H. Peyton (2011). "The dynamics of social innovation". Proceedings of the National Academy of Sciences. 108 (Supplement 4): 21285–21291. doi:10.1073/pnas.1100973108. ISSN 0027-8424. PMC 3271568. PMID 22198762.
  10. ↑ Nolan, Jessica M.; Schultz, P. Wesley; Cialdini, Robert B.; Goldstein, Noah J.; Griskevicius, Vladas (2008). "Normative Social Influence is Underdetected". Personality and Social Psychology Bulletin. 34 (7): 913–923. doi:10.1177/0146167208316691. ISSN 0146-1672. PMID 18550863.
  11. ↑ 11.0 11.1 Bravo, Giangiacomo; Vallino, Elena; Cerutti, Alessandro K.; Pairotti, Maria Beatrice (2013). "Alternative scenarios of green consumption in Italy: An empirically grounded model". Environmental Modelling & Software. 47: 225–234. doi:10.1016/j.envsoft.2013.05.015.
  12. ↑ 12.0 12.1 12.2 Eppstein, Margaret J.; Grover, David K.; Marshall, Jeffrey S.; Rizzo, Donna M. (2011). "An agent-based model to study market penetration of plug-in hybrid electric vehicles". Energy Policy. 39 (6): 3789–3802. doi:10.1016/j.enpol.2011.04.007.
  13. ↑ 13.0 13.1 Palmer, J.; Sorda, G.; Madlener, R. (2015). "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation". Technological Forecasting and Social Change. 99: 106–131. doi:10.1016/j.techfore.2015.06.011.
  14. ↑ Karakaya, Emrah; Hidalgo, Antonio; Nuur, Cali (2014). "Diffusion of eco-innovations: A review". Renewable and Sustainable Energy Reviews. 33: 392–399. doi:10.1016/j.rser.2014.01.083.
  15. ↑ Fourt, Louis A.; Woodlock, Joseph W. (1960). "Early Prediction of Market Success for New Grocery Products". Journal of Marketing. 25 (2): 31–38. doi:10.2307/1248608.
  16. ↑ Rogers, E. (1962). Diffusion of innovations. New York: The Free Press. Search this book on
  17. ↑ Bass, F. (1969). "A New Product Growth for Model Consumer Durables". Management Science. 15 (5): 215–227. doi:10.1287/mnsc.15.5.215. ISSN 0025-1909.
  18. ↑ 18.0 18.1 18.2 18.3 Kiesling, Elmar; Günther, Markus; Stummer, Christian; Wakolbinger, Lea M. (2012). "Agent-based simulation of innovation diffusion: a review". Central European Journal of Operations Research. 20 (2): 183–230. doi:10.1007/s10100-011-0210-y. ISSN 1435-246X.
  19. ↑ Wilensky, U.; Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press. Search this book on
  20. ↑ Jager, Wander; Mosler, Hans Joachim (2007). "Simulating Human Behavior for Understanding and Managing Environmental Resource Use". Journal of Social Issues. 63 (1): 97–116. doi:10.1111/j.1540-4560.2007.00498.x. ISSN 1540-4560.
  21. ↑ 21.0 21.1 21.2 Bale, Catherine S. E.; McCullen, Nicholas J.; Foxon, Timothy J.; Rucklidge, Alastair M.; Gale, William F. (2014). "Modeling diffusion of energy innovations on a heterogeneous social network and approaches to integration of real-world data". Complexity. 19 (6): 83–94. doi:10.1002/cplx.21523. ISSN 1099-0526.
  22. ↑ Diaz-Rainey, Ivan; Ashton, John K. (2015). "Investment inefficiency and the adoption of eco-innovations: The case of household energy efficiency technologies". Energy Policy. 82: 105–117. doi:10.1016/j.enpol.2015.03.003.
  23. ↑ Robins, Richard W.; Fraley, R. Chris; Krueger, Robert F., eds. (2007). Handbook of Research Methods in Personality Psychology. Guilford Press. Search this book on
  24. ↑ Deffuant, Guillaume; Huet, Sylvie; Amblard, Frédéric (2005). "An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit". American Journal of Sociology. 110 (4): 1041–1069. doi:10.1086/430220. ISSN 0002-9602.
  25. ↑ Janssen, Marco A; Jager, Wander (2001). "Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics". Journal of Economic Psychology. 22 (6): 745–772. doi:10.1016/S0167-4870(01)00063-0.
  26. ↑ Cantono, Simona; Silverberg, Gerald (2009). "A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies". Technological Forecasting and Social Change. Evolutionary Methodologies for Analyzing Environmental Innovations and the Implications for Environmental Policy. 76 (4): 487–496. doi:10.1016/j.techfore.2008.04.010.
  27. ↑ Nail, Paul R.; Di Domenico, Stefano I.; MacDonald, Geoff (2013). "Proposal of a double diamond model of social response". Review of General Psychology. 17 (1): 1–19. doi:10.1037/a0030997.
  28. ↑ 28.0 28.1 Nyczka, Piotr; Sznajd-Weron, Katarzyna (2013). "Anticonformity or Independence?—Insights from Statistical Physics". Journal of Statistical Physics. 151 (1–2): 174–202. doi:10.1007/s10955-013-0701-4. ISSN 0022-4715.
  29. ↑ 29.0 29.1 Nail, P.R.; Sznajd-Weron, K. (2016). "The Diamond Model of Social Response within an Agent-Based Approach" (PDF). Acta Physica Polonica A. 129 (5): 1050–1054. doi:10.12693/aphyspola.129.1050.
  30. ↑ Kaiser, Florian G.; Byrka, Katarzyna; Hartig, Terry (2010). "Reviving Campbell's Paradigm for Attitude Research". Personality and Social Psychology Review. 14 (4): 351–367. doi:10.1177/1088868310366452. ISSN 1088-8683. PMID 20435803.
  31. ↑ Hackman, J. Richard; Vidmar, Neil (1970). "Effects of Size and Task Type on Group Performance and Member Reactions". Sociometry. 33 (1): 37–54. doi:10.2307/2786271.
  32. ↑ Wheelan, Susan A. (2009). "Group Size, Group Development, and Group Productivity". Small Group Research. 40 (2): 247–262. doi:10.1177/1046496408328703. ISSN 1046-4964.
  33. ↑ Bond, Rod (2005). "Group Size and Conformity". Group Processes & Intergroup Relations. 8 (4): 331–354. doi:10.1177/1368430205056464. ISSN 1368-4302.
  34. ↑ 34.0 34.1 Granovetter, Mark (1978). "Threshold Models of Collective Behavior". American Journal of Sociology. 83 (6): 1420–1443. doi:10.1086/226707. ISSN 0002-9602.
  35. ↑ Castellano, Claudio (2009). "Nonlinear". Physical Review E. 80 (4). doi:10.1103/PhysRevE.80.041129.
  36. ↑ 36.0 36.1 Sznajd-Weron, Katarzyna; Szwabiński, Janusz; Weron, Rafał (2014). "Is the Person-Situation Debate Important for Agent-Based Modeling and Vice-Versa?". PLOS ONE. 9 (11): e112203. doi:10.1371/journal.pone.0112203. ISSN 1932-6203. PMC 4219838. PMID 25369531.
  37. ↑ Peres, Renana; Muller, Eitan; Mahajan, Vijay (2010). "Innovation diffusion and new product growth models: A critical review and research directions". International Journal of Research in Marketing. 27 (2): 91–106. doi:10.1016/j.ijresmar.2009.12.012.
  38. ↑ Sznajd-Weron, Katarzyna; Szwabiński, Janusz; Weron, Rafał; Weron, Tomasz (2014). "Rewiring the network. What helps an innovation to diffuse?". Journal of Statistical Mechanics: Theory and Experiment (P03007). doi:10.1088/1742-5468/2014/03/p03007.


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