Cámaras de maduración

from the Swiss fruit industry

3.1 Introduction

Innovation arises from different sources and is measured differently depending on specific features of the domains in which it has been diffused. In domains dominated by un-codified and informal knowledge transfer processes, observation and measurement of innovation become difficult. Hence, the usual metrics adopted by economists like patents do not work and therefore different approaches should be applied. In this chapter, the ‘productive interactions’ approach has been chosen. This type of interaction was defined and described in the Social Impact Assessment Methods Through Productive Interactions (SIAMPI) project (Molas-Gallart & Tang, 2011). Few studies have investigated the structure of interactions in a minor crop frame. Hence, the present study focuses on (productive) interactions, as a marker of interactions that might lead to innovation.

This study has two main objectives: testing the SIAMPI approach and exploring the field conditions and innovation impact to understand how the productive interactions are efficient for innovation. This is done by matching the analysis of interactions with an analysis of the market and the actor structure in the agricultural sector. Thus, this research contributes to the understanding of what makes interactions productive in terms of innovation under domain-specific conditions in which the interactions take place.

The chapter is organized as follows. The first section sheds light on previous works related to the interaction structure in agriculture. Characteristics of the sector are then presented, followed by the methods used. The results section introduces the structure of the interactions occurring in the network and the observed innovations. An analysis of the collaborations occurring in the network is conducted. Finally, in the last sections findings are discussed and conclusions presented.

3.2 Measurement and observation of innovation

3.2.1 Technological change in agriculture

Technological change has been studied via two models - ‘technology-push’ and ‘demand-pull’ (Dosi, 1993; Ruttan, 1997). The former relates to technology as a factor of technical change, starting from science and technology through the economic sector, while the latter relates to changes in market demand. However, these theories do not emphasise economic and structural sectoral characteristics as factors of change. Furthermore, technological change in agriculture has greatly evolved. In the post World War II period, the main goal was productivity increase to satisfy growing population needs (Dosi, 1993; Ruttan, 1997). Therefore, biological technology and mechanisation targeted land productivity and labour productivity respectively (Ruttan, 2002), depending on the characteristics of the countries, demographic pressure, soil and climate features and capacities to adopt technology (Giampietro et al., 1999; Hayami & Ruttan, 1970b; Ruttan, 1997; Wright, 2012). As agricultural technological change is endogenous, the choice of resources to increase productivity on either the land or labour level will be made in favour of scarce resources in order to sustain them. Productivity growth can be explained by factors like endowment of resources, technological capital, human capital and investment in private and public research (Hayami and Ruttan, 1970a). The latter factor is highlighted in this study and is explored through collaborations between agricultural sector and research on innovation generation. Hence, innovations are at the core of the sector dynamism.

3.2.2 Innovation with an interaction perspective

Economists have stressed the importance of codified indicators like patents, co-publications, licenses and spinoffs as proxy for innovation have been extensively studied (Foray and Lissoni, 2010; Norn et al., 2014; Rossi and Rosli, 2013). Nevertheless, these approaches to innovation are inadequate in sectors where informal interactions prevail. Many domains do not use research and development (R&D) as a driver of economic growth, do not use patents, or do not even innovate. Taxonomy of Pavitt (1984) resulted in a shift of how innovation is perceived, according to patterns like technology sources, users’ requirements and appropriation possibilities. Four categories were created. Agricultural sector belongs to the supplier-dominated class. Technology originates from researchers, suppliers of inputs (e.g. chemistry, machines) and partly from the downstream side of the chain (e.g. retailers). However, users’ needs became important in terms of innovation objectives (Rossi & Rosli, 2013). Users are an important source of innovation, especially farmers. The “locus of almost the entire innovation process is centred on the user”, however the commercialisation is carried out by the manufacturer (von Hippel, 1988c). Firms may be involved earlier in the innovation process by looking for users who innovate or lead users. These experienced users can provide accurate data on ‘future’ needs.

‘the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations […]. Innovation activities are all scientific, technological, organisational, financial and commercial steps which actually, or are intended to, lead to the implementation of innovations.’

Innovation is divided into four types: product, process, marketing and organisational. In farming, product innovation is the most implemented one (Oreszczyn, Lane, & Carr, 2010). Generation of innovation arise through steps like generation of ideas, screening of ideas, testing of concepts, development and launch (H. Chesbrough, 2010; Roy, Sivakumar, & Wilkinson, 2004).

Interactions between various actors of the value chain during the innovation process are critical for the innovation itself like interactions between buyer and seller in supply chain framework (Roy, 2004). Interactions between customer and supplier differ between sectors and stage of the innovation life cycle, relationships with customers being increasingly seen as critical for innovation performance in the introduction stage while less important in maturity stage (Codini, 2015; Johnsen, Phillips, Caldwell, & Lewis, 2006). Furthermore, internal factors (e.g. information technology adoption, commitment and trust) and external factors (e.g. network connections stability within and across industries, and implied knowledge related to technology) of these interactions have moderating impact on innovation generation (Roy et al., 2004). The interactions occurring between different stages of the innovation process are profitable for the innovation itself (Kaufmann & Tödtling, 2001). Innovation is a by-product of network collaboration activities (Knickel, Brunori, Rand, & Proost, 2009; Teece, 2000). Thus, interactions are one of the driving forces for innovation generation. This study is focussing on that interaction aspect of a network where innovations have been introduced.

3.2.3 Knowledge and information transfer through

interactions

Firms use different channels to access knowledge like patents and publications. In the frame of university-industry collaboration, D’Este and Patel (2005) defined interactions as “creation of new physical facilities, consultancy and contract research, joint research, training, and meetings and conferences.” This definition is adequate to the agricultural setting. Furthermore, interactions are mostly established on a long-term basis and target knowledge exchange (Wood et al., 2014). Nonetheless, knowledge and information transfer is costly and depends on knowledge patterns like codification degree (codified versus tacit) and technology embeddedness (Schartinger et al., 2002). Von Hippel (1994) used the term ‘stickiness’ to define information that can be transferrable to different environments, depending on the nature of the information, its amount and the characteristics of the future user. This stickiness is related to the tacit nature of knowledge which is difficult to diffuse due to its un-codified nature (Cowan et al., 1999). Moreover, knowledge is embedded in local contexts, hindering its transfer to other settings (Ruttan, 2002a). Local

knowledge is transmitted via ‘social processes’ because rooted within a social framework (Ingram, 1985). Thus, informal interactions are a good channel through which knowledge can be diffused (Dahl & Pedersen, 2004).