The Role of Social Networks in the Diffusion of Bio-Waste Products: The Case of Mulching Films Derived from Organic Waste in Province of Foggia

  • Angela Barbuto
  • Antonio Lopolito
  • Myriam Anna Scaringelli
  • Giacomo Giannoccaro


In this work we consider the biodegradable mulching film containing soluble bio-based substances (SBOs), as a new Sustainable Agricultural Practice (SAP) potentially useful in both broadening the spectrum of Organic Fraction of Municipal Solid Wastes (OFMSW) management, and improving agricultural sustainability. Of course, the exploitation of such advantages depends on the actual adoption of the novelty from a critical mass of users. Among the various factors influencing this process, we stress the importance of interpersonal channels involving a face-to-face exchange. This implies the fact that people adopt an innovation when sufficient information has reached them, and shows the relevance of the role of social networks in the diffusion of innovations. Specifically, the network position of an actor affects the power and influence he can exert on its immediate neighbors as well as on the collective behavior of the members. This influence can be viewed as a strategic resource for innovation diffusion purpose in a marketing or policy context. The success as injection points, namely, the actors where the novelty is first inoculated, is typically measured as the proportion of actors who adopts the innovation at the end of the process. Following this line of reasoning, the aim of this work is to identify the network characteristics associated with effective injection points. In order to capture the network characteristics of the actors we used typical Social Network Analysis (SNA) measures. From an operative perspective, our purpose is to find the SNA measures associated with high adoption rates. However, being the innovation process new in nature, there are not available experimental data to conduct this kind of analysis. Therefore, we chose to simulate the diffusion process among agents by means of an Agent Based Model (ABM) depicting a population of farmers. The model was calibrated on real world data gathered from the case of a network of specialist vegetables producers in the Province of Foggia. Both SNA measures and rate of adoption are simulated data. The results achieved represent the basis for the breaking down of a tailored SAP diffusion strategy within an environmental and sustainability oriented development policy in a rural context, like the one studied. In particular, this study offers valuable hints on the kind of spreaders that should be enrolled, indicating, at the same time, the path for further research. This includes a more in depth analysis on various structure of networks (e.g. very dense and very sparse, very randomized and very regular, with high and low medium degree) and the investigation on the effects of the number of exposures of the agents to the promotional strategy.


Organic Fraction of Municipal Solid Wastes Diffusion of Innovation Sustainable Agricultural Practice Social Network Analysis Agent Based Model 


  1. Alkemade F, Castaldi C (2005) Strategies for the diffusion of innovations on social networks. Comput Econ 25(1–2):3–23CrossRefMATHGoogle Scholar
  2. Assmuth T, Kalevi K (1992) Concentrations and toxicological significance of trace organic compounds in municipal solid waste landfill gas. Chemosphere 24(9):1207–1216CrossRefGoogle Scholar
  3. Banerjee AV, Duflo E, Glennerster R, Kinnan C (2013) The miracle of microfinance? Evidence from a randomized evaluation. Am Econ J: Appl Econ 7(1):22–53Google Scholar
  4. Bass FM (1969) A new product growth model for consumer durables. Manage Sci 15(5):215–227Google Scholar
  5. Deroï̈an F (2002) Formation of social networks and diffusion of innovations. Res Policy 31(5):835–846CrossRefGoogle Scholar
  6. Berelson B, Steiner GA (1964) Human behavior: an inventory of scientific findings. Harcourt, Brace & WorldGoogle Scholar
  7. Blau PM, Beeker C, Fitzpatrick KM (1984) Intersecting social affiliations and intermarriage. Soc Forces 62(3):585–606CrossRefGoogle Scholar
  8. Centola D, Gonzalez-Avella JC, Eguiluz VM, San Miguel M (2007) Homophily, cultural drift, and the co-evolution of cultural groups. J Conflict Resolut 51(6):905–929CrossRefGoogle Scholar
  9. Chatterjee RA, Eliashberg J (1990) The innovation diffusion process in a heterogeneous population: a micromodeling approach. Manage Sci 36(9):1057–1079CrossRefGoogle Scholar
  10. Coleman JS, Katz E, Menzel H (1966) Medical innovation: a diffusion study, Bobbs-Merrill Co, IndianapolisGoogle Scholar
  11. Copp JH, Sill ML, Brown EJ (1958) The function of information-sources in the farm practice adoption process. Rural Sociology 23(2):146–157Google Scholar
  12. Czepiel JA (1974) Word-of-mouth processes in the diffusion of a major technological innovation. J Mark Res pp 172–180Google Scholar
  13. Delre SA, Jager W, Bijmolt TH, Janssen MA (2007) Targeting and timing promotional activities: an agent-based model for the takeoff of new products. J Bus Res 60(8):826–835CrossRefGoogle Scholar
  14. Delre SA, Jager W, Bijmolt TH, Janssen MA (2010) Will it spread or not? The effects of social influences and network topology on innovation diffusion. J Prod Innov Manage 27(2):267–282CrossRefGoogle Scholar
  15. Deroïan F (2002) Formation of social networks and diffusion of innovations. Res Policy 31(5):835–846CrossRefGoogle Scholar
  16. De Tarde, G. (1903). The laws of imitation. H. HoltGoogle Scholar
  17. Deutsch M, Gerard HB (1955) A study of normative and informational social influences upon individual judgment. J Abnorm Soc Psychol 51(3):629CrossRefGoogle Scholar
  18. Edwards GP, Zeng B, Saalfeld WK, Vaarzon-Morel P (2010) Evaluation of the impacts of feral camels. Rangeland J 32(1):43–54CrossRefGoogle Scholar
  19. Freeman LC, Freeman SC, Michaelson AG (1988) On human social intelligence. J Soc Biol Struct 11(4):415–425Google Scholar
  20. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark lett 12(3):211–223CrossRefGoogle Scholar
  21. Goldenberg Jacob, Libai Barak, Moldovan Sarit, Muller Eitan (2007) The NPV of Bad News. Int J Res Mark 24(3):186–200CrossRefGoogle Scholar
  22. Granovetter MS (1973) The strength of weak ties. Am J Sociol pp 1360–1380Google Scholar
  23. Granovetter M (1978) Threshold Models of Collective Behavior. Am J Sociol 83:1420–1443CrossRefGoogle Scholar
  24. Grewal R, Mehta R, Kardes FR (2000) The role of the social-identity function of attitudes in consumer innovativeness and opinion leadership. J Econ Psychol 21(3):233–252CrossRefGoogle Scholar
  25. ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) (2013) Italian greenhouse gas inventory 1990–2011. National inventory report, available at
  26. ISTAT (Italian National Institute of Statistics) (2010) 6th General census of agricultureGoogle Scholar
  27. Kasirajan S, Ngouajio M (2012) Polyethylene and biodegradable mulches for agricultural applications: a review. Agron Sustain Dev 32(2):501–529CrossRefGoogle Scholar
  28. Katz E, Lazarsfeld PF (1966) Personal Influence The part played by people in the flow of mass communications. Transaction Publishers, USAGoogle Scholar
  29. Katz E (1961) The social itinerary of technical change: two studies on the diffusion of innovation. Human Organ 20(2):70–82CrossRefGoogle Scholar
  30. Kiesling E, Günther M, Stummer C, Wakolbinger LM (2012) Agent-based simulation of innovation diffusion: a review. CEJOR 20(2):183–230CrossRefGoogle Scholar
  31. Lazarsfeld PF (1944) The controversy over detailed interviews—an offer for negotiation. Public Opin Q 8(1):38–60CrossRefGoogle Scholar
  32. Lazarsfeld PF, Merton RK (1954) Friendship as a social process: A substantive and methodological analysis. Freedom Control Mod Soc 18(1):18–66Google Scholar
  33. Lazarsfeld PF, Menzel H (1963) Mass media and personal influence. In: The science of human communication. Wilbur Schramm, pp 94–115Google Scholar
  34. Lopolito A, Morone P, Taylor R (2013) Emerging innovation niches: an agent based model. Res Policy 42(6):1225–1238CrossRefGoogle Scholar
  35. Markus ML (1987) Toward a ‘critical mass’ theory of interactive media universal access, interdependence and diffusion. Commun Res 14(5):491–511CrossRefGoogle Scholar
  36. Martins ACR, Pereira CdB, Vicente R (2009) An opinion dynamics model for the diffusion of innovations. Phys A Stat Mech Appl 388(15–16):3225–3232CrossRefGoogle Scholar
  37. McCraw D, Motes JE (1991) Use of plastic mulch and row covers in vegetable production. Coop Extension Ser Oklahoma State University. OSU Extension Facts F-6034Google Scholar
  38. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annual Rev Sociol pp 415–444Google Scholar
  39. Moldovan S, Goldenberg J (2004) Cellular automata modeling of resistance to innovations: Effects and solutions. Technol Forecast Soc Chang 71(5):425–442CrossRefGoogle Scholar
  40. Montoneri E, Boffa V, Savarino P, Perrone D, Ghezzo M, Montoneri C, Mendichi R (2011) Acid soluble bio-organic substances isolated from urban bio-waste. Chemical composition and properties of products. Waste Manag 31(1):10–17CrossRefGoogle Scholar
  41. Morone P, Tartiu VE, Falcone P (2015) Assessing the potential of biowaste for bioplastics production through social network analysis. J Clean Prod 90:43–54CrossRefGoogle Scholar
  42. Reimer AP, Weinkauf DK, Prokopy LS (2012) The influence of perceptions of practice characteristics: an examination of agricultural best management practice adoption in two Indiana watersheds. J Rural Stud 28:118–128CrossRefGoogle Scholar
  43. Rogers EM, Kincaid DL (1981) Communication networks: toward a new paradigm for research. Free Press, New YorkGoogle Scholar
  44. Rogers EM (2003) Diffusion of innovations. Free Press, New YorkGoogle Scholar
  45. Scaringelli MA, Giannoccaro G, Prosperi M, Lopolito A (2016) Adoption of biodegradable mulching films in agriculture: is there a negative prejudice towards matherials derived from organic wastes? Ital J Agron 11:90–97Google Scholar
  46. Tey YS, Li E, Bruwer J, Abdullah AM, Brindal M, Radam A, Darham S (2014) The relative importance of factors influencing the adoption of sustainable agricultural practices: a factor approach for Malaysian vegetable farmers. Sustain Sci 9(1):17–29CrossRefGoogle Scholar
  47. Valente TW (1996) Social network thresholds in the diffusion of innovations. Soc Networks 18(1):69–89MathSciNetCrossRefGoogle Scholar
  48. Valente TW, Davis RL (1999) Accelerating the diffusion of innovations using opinion leaders. Annals Am Acad Polit Soc Sci 566(1):55–67CrossRefGoogle Scholar
  49. Wasserman S Faust K (1994) Social network analysis: methods and applications. Cambridge University PressGoogle Scholar
  50. Weimann G, Tustin DH, Van Vuuren D, Joubert JPR (2007) Looking for opinion leaders: Traditional vs. modern measures in traditional societies. Int J Public Opini Res 19(2):173–190CrossRefGoogle Scholar
  51. Wilensky U (1999) {NetLogo}Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Angela Barbuto
    • 1
  • Antonio Lopolito
    • 1
  • Myriam Anna Scaringelli
    • 1
  • Giacomo Giannoccaro
    • 2
  1. 1.Department of Agricultural, Food and Environmental SciencesUniversity of FoggiaFoggiaItaly
  2. 2.Department of Agricultural and Environmental ScienceUniversity of Bari “Aldo Moro”BariItaly

Personalised recommendations