Social Media for Enhancing Stakeholders' Innovation

Journal of Agricultural & Food Information

ISSN: 1049-6505 (Print) 1540-4722 (Online) Journal homepage: http://www.tandfonline.com/loi/wafi20

Social Media for Enhancing Stakeholders' Innovation Networks in Ontario, Canada Pawandeep Kaushik, Ataharul Chowdhuy, Helen Hambly Odame & Annemarie van Passen To cite this article: Pawandeep Kaushik, Ataharul Chowdhuy, Helen Hambly Odame & Annemarie van Passen (2018): Social Media for Enhancing Stakeholders' Innovation Networks in Ontario, Canada, Journal of Agricultural & Food Information, DOI: 10.1080/10496505.2018.1430579 To link to this article: https://doi.org/10.1080/10496505.2018.1430579

Published online: 20 Mar 2018.

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JOURNAL OF AGRICULTURAL & FOOD INFORMATION , VOL. , NO. , – https://doi.org/./..

Social Media for Enhancing Stakeholders’ Innovation Networks in Ontario, Canada Pawandeep Kaushika , Ataharul Chowdhuyb , Helen Hambly Odamec , and Annemarie van Passend a School of Environmental Design and Rural Development, University of Guelph, Ontario; b Department of Agricultural Economics and Extension, The University of the West Indies, St. Augustine and Adjunct Faculty, School of Environmental Design and Rural Development, University of Guelph, Ontario; c School of Environmental Design and Rural Development, University of Guelph, Ontario; d Knowledge, Technology & Policy, Wageningen University, The Netherlands

ABSTRACT

ARTICLE HISTORY

This case study assessed local food stakeholders’ use of Facebook and Twitter to support interaction and build their networks of innovation in Ontario. Data were collected using Netlytic − an online data mining tool from the social media platforms − and key informant interviews. Findings revealed that stakeholders could be more innovative in their use of social media, but they would be unlikely to do so, without tapping into beneficial interactions of weak ties, as well as fostering strong ties. They also need to utilize the high brokerage role of key facilitating organizations and develop a social media strategy by integrating both ‘online’ and ‘offline’ interactions.

Received  October  Revised  January  Accepted  January  KEYWORDS

Local Food; Ontario; Social Media; Innovation; Networks; Communication

Introduction A local food system aims to ensure socially, culturally, environmentally and economically viable processes of food production and consumption (Feagan, 2007). In Ontario, Canada, the local food system is strategically important for growth and development, as the province comprises one quarter of all Canadian agricultural operations (over 75,000 farms). The Southern Ontario corridor, which links major urban areas within Ontario and across the border between the United States and Canada, is the second largest food production region in North America (Statistics Canada, 2017). The Government of Ontario, realizing the importance of agricultural and food industries in its economy, emphasizes increasing production and consumption of local produce. As such, the Local Food Act was passed in 2013 in order to ensure the supply of local food to meet local demands, improve access to local food, increase awareness and support learning about local food (OMAFRA, 2016). Since 2000, the agri-food industry and the public sector have undertaken CONTACT Ataharul Chowdhuy [email protected]; atahar@yahoo.com Department of Agricultural Economics and Extension, The University of the West Indies, St. Augustine, Trinidad & Tobago and School of Environmental Design and Rural Development, University of Guelph,  Stone Road E, Guelph, NGW, ON, Canada Copyright © Taylor and Francis Group, LLC

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various marketing and promotional activities to inform and engage consumers in local food issues. Developing and sustaining trustworthy networks and relationships among local producers, consumers, and policymakers is essential to any local food system (Feenstra, 1997; Mount et al., 2013; Schneider, Salvate, & Cassol, 2016). In Ontario, there is an emphasis on ‘connecting the dots’ between key stakeholders (e.g., public sector organizations, non-profits, small and medium entrepreneurs, and smallholders) to promote a sustainable local food system (Campsie, 2008; Nelson, Knezevic, & Landman, 2013). However, local food stakeholders in Ontario face barriers in building trustworthy and collaborative relationships with each other (Mount et al., 2013; Nelson et al., 2013). One of the necessary conditions for promoting a local food system is strong relationships and networks among stakeholders of the value chain (OMAFRA, 2013, 2015). Currently, there is only anecdotal evidence about how social media enable local food stakeholders in Ontario to initiate and strengthen ‘online networks,’ which are formed when two or more people meet in virtual spheres with others as strangers or acquaintances using online communication tools and then articulate their relationships by acknowledging shared relationships and conversational exchange (Bristy, 2016; Hall, 2016). Largely due to improved Internet service in rural areas, social media tools gained importance for enhancing online networks and strengthening collaboration among various agricultural and food stakeholders (Andres & Woodard, 2013; Chowdhury & Hambly Odame, 2013b; Saravanan, Suchiradipta, Chowdhury, Hall, & Hambly Odame, 2015; Strong, Dooley, Irby, & Snyder, 2014). Facebook and Twitter are the most popular social media platforms used by agricultural and rural development stakeholders in Ontario (Chowdhury & Hambly Odame, 2013b). Facebook and Twitter are flexible tools for fostering active links and passive connections within personal and professional networks. In addition, both platforms are open to the public for searching and sharing information, which provides opportunities to view and interact with both strong and weak connections (Guner, 2010). Social networking sites allow users to create one or more personal or business profiles and invite friends and colleagues to join them online and share their ideas and information through photos, videos and text messages (Kaplan & Haenlein, 2010). Social networking media strengthens ‘agricultural innovation’-a process of creating opportunities for sharing new ideas, knowledge, learning from each other’s experiences and creating and maintaining networks and relationships offline and online (EU SCAR, 2014; Leeuwis & Aarts, 2011). The important functions of social media for supporting agricultural innovation are peer-to-peer communication, farmer-industry networking, and community engagement. Social media also help to communicate about controversial topics, such as the cultivation of genetically modified crops, potential changes to trade policies and safety regulations within the sector and the ethical and social responsibilities of multinational corporations (Eisee & Hodde, 2017; Saravanan et al., 2015). The benefits accruing from the online networks depend, however, on the underlying intentions of actors who use online tools to achieve their own strategic communication goals, such as

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information broadcasting, public relations, and engagement (James & Gururajan, 2015; Nicholson, Nugroho, & Rangaswamy, 2016). In this study, we focus on use of social media by four key food stakeholders in Ontario, Canada, including cases from the public sector, non-profit sector and private-sector farms and producers. Following scholars (e.g., Leeuwis & Arts, 2011, Spielman, Davis, Negash, & Ayele, 2011) who suggest that agricultural innovation is an outcome of interaction among diverse actors, this research illustrates and compares how these key stakeholders used social media (Facebook and Twitter) to interact and forge online networks with other innovation actors. Innovation is inclusive (Foster & Heeks, 2013) when the use of online communication tools, such as social media, bring benefits to those who have been excluded from or lacked the resources to participate in the mainstream networks of communication and innovation. Theoretical approach This study used a social network lens. Where innovation is concerned, the unit of organization is not individual actors per se (e.g., businesses) but actor networks or networks operating at different levels or social groups. According to Wellman and Hampton (1999), humans continuously interact with members of different groups, such as people from their workplace or community. These groups function in networks in which members of groups have links with multiple groups with different interests, giving rise to the notion of ‘networked societies’, which have very flexible boundaries in terms of transferring information, communicating and generating knowledge, and influencing action or a lack thereof (Acevedo, 2011). When information is forwarded or shared with “friends of friends” that cut across group boundaries, these indirect or weak ties become direct relationships or strong ties. Social networks are structures or a set of individuals or organisations (actors/nodes) and their relationships (Hanneman & Riddle, 2005). Social networking is an implicit dimension of the theoretical construct of agricultural innovation systems, which emphasizes diverse innovation actors interacting with one another and making connections with others both within and outside the systems (Spielman et al., 2011; Tropical Agriculture Platform, 2016). ‘Innovation networks’ are alliances of multiple actors (usually three or more), often related to a territory or a commodity, who voluntarily become part of a learning exercise to pursue innovation opportunities (e.g., value creation for existing products), while realizing the need to acknowledge and adapt to the interests and requirements of other actors and the wider environment (Hartwich & Jansen, 2007; Klerkx & Aarts, 2013). With regard to literature of innovation studies (e.g., Hermans, Sartas, van Schagen, van Asten, & Schut, 2017; Tropical Agriculture Platform, 2016) and studies related to local food networks (e.g., Johnson, Fraser, & Hawkins, 2016; Mount et al., 2013; Nelson et al., 2013), each stakeholder has a specific role to play in supporting local food innovation networks. For instance, the government and non-profits should broker knowledge between producers and other actors, forge networks and support organization of producers, and facilitate

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inclusion of ideas and interests of disadvantaged groups, such as smallholders. On the other hand, small producers and entrepreneurs need to seek out and apply new ideas and knowledge, raise concerns about farming, and articulate demand for new opportunities. Within the local-food sector in Ontario, the need to overcome barriers and create opportunities for strengthening innovation networks will furthermore depend on a convergence of ideas from diverse stakeholders who may be external to the agricultural and food sector (Mount et al., 2013). The revolutionary context of the Internet has provided an opportunity to develop networks at a distance and sustain relationships among actors of the same and different backgrounds. The Internet can further play an important role in transforming networks in physical spheres into virtual realms (Wellman & Hampton, 1999). Online networks also help people to maintain their offline relationships online (O’Murchu, Breslin, & Decker, 2004). According to McLennan (2016, p. 382), “the potential of online networking and social media in development is premised on the idea that these characteristics of networks are particularly characteristics of online networks and social media”. Social media connect people irrespective of time and space, hierarchical level, and organizational subunit. They are particularly revolutionary with respect to linking information to action when they augment reality by converging time and space. In today’s technological climate, social networking sites are essential among individuals and organizations for personal as well as professional use, including launching new business developments and making new contacts. They provide an online space for communication, interaction, and the exchange of information and ideas for a community of certain interests, such as food production, processing, and marketing. As tools for disintermediation or reducing the intermediaries between information production and consumption, social media and online networks can potentially remove linear or top-down communication and eliminate dependency on media corporations, governments and other traditional gatekeepers of knowledge, thereby allowing freedom to create, modify, delete, and share ideas and act on information with individual and collective users (Chowdhury & Hambly Odame, 2013b; McLennan, 2016). Strong and weak ties in innovation networks The social network is comprised of nodes and their relationships (Haythornthwaite, 2005; Scott, 2013). Networks are based on interpersonal or inter-organizational relationships. O’Murchu et al. (2004) employ the ‘concept of six degrees of separation,’ whereby the first degree is close friends, the second degree includes friends of friends, the third degree is for friends of friends of friends, the fourth degree is friends of friends of friends of friends, and the fifth and sixth degrees are one and two steps further. The social media network is usually based on such relationships; for instance, Twitter users have friends or contacts who have their own friends or contacts, and these contacts are further expanded to other contacts, and so forth. The concept of six degrees of separation considers the depth of the relationship of the contacts that they share with others. These degrees are actually relational ratings


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Figure . Forbidden Triad (Adapted from Granovetter, ).

and can be seen as a type of weighting. The degrees of relationships are called ‘ties’ (contacts) that differ in their interpersonal strength (Christensen & O’Sullivan, 2015; O’Murchu et al., 2004). Granovetter (1973, p.1361) initially interpreted the concept of the tie strength as “a combination of the amount of time, the emotional intensity, and intimacy and the reciprocal services which characterize the tie”. Hence, the tie does not only determine the ways, means, and expression of communication, but also influences motivation, needs, and desires for communication (Constant, Sproull, & Kiesler, 1996). Granovetter (1973) further posited that a network of relationships always comprises strong, weak, and absent ties. He states ‘weak ties’ are the connection to others outside the strongly tied network and to the information and resources circulating in other arenas. These weak ties provide the possibility to bridge to other social networks (Granovetter, 1973). He used the concept of a “forbidden triad” (Figure 1) in his early research. All weak and absent ties of the network have the potential to become strong ties. For instance, there is always a possibility of a future tie between two disconnected actors who are strongly connected to a third actor. He asserts that “the triad as shown never occurs; but that the B-C tie is always present, whether weak, strong or absent” (Granovetter, 1973, p. 1363). Analysis of information flows and knowledge exchange depends, therefore, upon the number of strong and weak ties in the network (Friedkin, 1982). Ties are weak when there are no frequent or important interactions and ties are strong when there are multiple interactions (Haythornthwaite, 2005). Following Gilbert, Karahalios and Sandvig (2010), a tie is considered strong if there are 10 or more interactions between an ego and its contact, and a tie is considered weak if the number of interactions is less than 10. Strong ties are more efficient contributors to information flows within an organization and groups, whereas weak ties are more important in terms of information flow outside the groups. Granovetter’s work also shows that pairs who share strong ties may have limited opportunities for additional new knowledge and information gaining because of their similar interests in terms of perspective and knowledge. On the other hand, weak-tied clusters have more opportunities for diverse knowledge and information sharing, because members of the network operate in different social networks and have access to different knowledge and information (Granovetter, 1983). New and complementary resources are therefore accessed only through weak ties. Weak ties, therefore, play a key role in

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bridging structural holes that exist within social networks by connecting individual complementary resources (Burt, 2000). The theoretical work on social networks pertaining to information flows and knowledge exchange informs our analysis of how social media can strengthen local food innovation networks of different stakeholders and support interaction among their contacts to disseminate information and share knowledge. This may include turning the potential of more and new data and information into complementary knowledge, networks, and novel markets. For instance, actors of local food systems need to utilize networks to generate and take advantage of new opportunities for knowledge and resource mobilization and business (Hekkert, Suurs, Negro, Kuhlmann, & Smits, 2007; Schneider et al., 2016). Therefore, where local food innovation networks in Ontario are the focus, we hypothesize that online networks employing social media tools, such as Facebook and Twitter, can bring people with similar interests together and provide them with an opportunity to disseminate information, as well as share and build knowledge for a range of benefits. Social media should enable local food stakeholders to create weak ties through widening the networks, to bridge structural holes and build trust-based relationships intrinsic to alliances that lead to value creation, knowledge management and innovation. We hypothesize that strengthening innovation networks is concerned with finding a balance between exploiting weak ties and fostering strong ties. It is also related to integration of both formal (rules-based) and informal (trust- and emotion-based) interaction (Klerkx & Aarts, 2013) and utilization of appropriate means (strategic use of online and offline services) of communication (Townsend, Wallace, Smart, & Norman, 2016). Methodology Social Network Analysis (SNA) is considered a robust and appropriate approach to search and assess networks of relationships that influence innovations in the local agri-food sector (Klerkx, Aarts & Leeuwis, 2010; Spielman, Ekboir, & Davis, 2009). We focus on the ‘ego,’ or specific stakeholder, and its interaction on Facebook and Twitter with the nodes/contacts (referred to as ‘alters’), as well as the position of the ego in the social media network to support online conversations, knowledge sharing and learning (Haythornthwaite, 1996; Spielman et al., 2011). An egocentric network approach is useful when the population is large or the boundaries of the population are hard to define (Haythornthwaite, 1996). Ontario’s 75,000 farms cover thousands of hectares of land. Therefore, it is not within the scope of this study to conduct a full network analysis that includes all stakeholders involved in the local food sector. A purposive selection of four cases represents the main stakeholder groups of the local food sector: the public, not-for-profit and private sectors (small and medium producers). We used online data mining, a popular method across academic disciplines for studying social media (Sloan & Quan-Haase, 2017). We followed a similar method of exploratory case study adopted by Cui (2014), who purposively analyzed a single


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farmer’s market Facebook page and included data of past interactions. In our study, we included data of real-time interactions on Facebook and Twitter. Furthermore, we used a sequential, mixed-methods approach (Cho & Park, 2011) by collecting online data-mining and face-to-face data using key informant interviews. There is a growing interest in integrating online data-mining and face-to-face data collection methods, such as surveys and key-informant interviews (Guner, 2010; Nightingale, 2011). Face-to-face data helped to supplement and validate observations from online data. Methodological integration was also considered, in terms of using both quantitative and qualitative data collection and analysis: qualitative data provided further explanation to the quantitative findings. Phase 1: Selection of cases and quantitative data collection

The first phase of the research began with the collection of quantitative data from the Facebook page and Twitter account of the four purposively selected cases of local agri-food stakeholders: 1) Foodland Ontario–a brand and local food promotion program established by the Ontario Ministry of Agriculture, Food and Rural Affairs in 1977 for facilitating linkages between consumers, retailers and producers; 2) Sustain Ontario–a non-profit organization that works on issues of local food and facilitates networks of members coming from diverse parts of the food and farming sectors (e.g., producers, retailers, cooperatives, producer associations, etc.); 3) Farm M–a small-scale producer and seller of local food; and 4) Farm B–a medium-sized, local-food-market-oriented producer. We used two criteria to select the cases: (1) an entity working for the last five years or more in one or more areas such as production, distribution, access, capacity building and other dimensions of food grown in Ontario; and (2) active users of social media for communicating issues of local food. Data were collected from the four stakeholders’ social media sites (Facebook & Twitter) using a cloud-based software Netlytic, a text and social network analyzer that can automatically summarize large volumes of text and discover social networks from online conversations on social media sites such as Twitter and Facebook, as well as YouTube, blogs, online forums and chats (Gruzd, 2016). The volume of data in Twitter is largely dependent on the interaction of users. We set up Netlytic to extract 1,000 tweets/records from the Twitter profile accounts of the four cases of local food stakeholders. Starting in October 2014, it took different time intervals (three to five months) to extract 1,000 records from different Twitter feeds. The data mining from Facebook pages continued for three months. The Netlytic datasets were exported into an Excel spreadsheet and saved for further analysis. Phase 2: Qualitative data collection

In the second phase, data were collected using semi-structured interviews with the selected four stakeholders from November to March 2015. The interviews were comprised of open- and closed-ended questions to guide the general flow of conversation. From each of the selected organizations, one key informant was selected

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based on his/her experience in managing social media and work experience (at least a year) in the field. Interview questions were developed to support and complement the online data. Interviews were conducted face-to-face and over the phone. Phase 3: Data analysis

Two spreadsheets were prepared by extracting information from the raw data collected through Netlytic. A general dataset was prepared based on the selected attributes and the second dataset was prepared for the ego networks of each of the stakeholders. For the compilation and analysis of the general dataset, several attributes were considered such as characteristics of the contacts/alters (e.g., type of organization, time period for active use of social media, number and type of audience), topics of discussion (what knowledge and information they share with each other online), and the intimacy of the ties/relationship based on the frequency of contact (i.e., likes, posts, shares, comments). Microsoft SQL Server Express 2012 was used to extract and analyze data from the general dataset. Data were imported to SQL format and then analyzed using SQL interface and SQL queries. Twitter data were used for analyzing the stakeholders’ online networks. Microsoft SQL Server was fairly compatible with Twitter data. On the other hand, this tool did not work with Facebook data because it was not possible to extract one-on-one contact data without doing a manual data transfer. Therefore, only Twitter data was analysed for the ego network analyses. For the ego network analyses, datasets were prepared based on the questions, “Who is talking to whom?” and “Who is connected to whom and how?” (Haythornthwaite & De Laat, 2010; Mapila, Yauney, Thangata, Droppelmann, & Mazunda, 2016). The datasets were analysed with the help of UCINET (Borgatti, Everett, & Freeman, 2002). Standard social network measures, such as the size of the ego network, ties, density of the ego network, degree of betweenness and brokerage of ego within a network were used to understand and compare network structure of the ego (Mapila et al., 2016). NetDraw was used for data visualization (Borgatti et al., 2002). For qualitative data analysis, field notes were taken and interviews were recorded, transcribed and coded manually. The statements and topics were analyzed, based on their relation and relevance to the analytic measures and questions used to interpret the quantitative data. Results Stakeholders’ use of social media

The findings (Table 1) indicate that all four local food system actors had been active on Facebook and Twitter for the last five to six years, with Foodland Ontario using Twitter for substantially longer (i.e., 12 years). As a large public-sector organization, Foodland Ontario developed a strong Twitter audience of 26,200 followers and a Facebook audience of 155,121 Likes. Sustain Ontario is a medium sized non-profit organisation and had relatively fewer followers (14,500) on Twitter and Likes (4,761)

Farm B

Sustain Ontario Farm M

Foodland Ontario

Name of the organisation

Non-profit Private producer & business Private producer and farmers’ market

Government

Organisation type



Facebook    

Twitter   

Duration of actively using social media (years)

Table . Ego attributes based on presence on Facebook and Twitter.

Medium organisation Medium to big producer Small producer

Big organisation

Size of the organization



 



Number of Twitter followers



 



Number of Facebook page likes JOURNAL OF AGRICULTURAL & FOOD INFORMATION 9

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on Facebook. Next to these two organizations, Farm B, a small privately owned producer organization, exhibited its presence in social media with 1,233 Twitter followers and 1,290 Facebook Likes. Farm M had the smallest audience size in their social media channels, with 880 followers on Twitter and 1,050 Likes on Facebook. The audience size of Farm M and Farm B is comparable. As private-sector producers, both local farms had been active for about six years in their social media channels. Foodland Ontario had been actively using social media as part of their overall communication strategy, which included other public communication channels, such as radio, print media and conventional advertising through television. Foodland Ontario uses Facebook and Twitter as broadcasting channels, mainly for disseminating information related to food recipes, products, and events and promoting local agricultural producers. In supporting engagement and interaction among clients, their use of social media was guided by their own organizational interests and rules. As explained by a key informant, ‘(our) key focus is marketing [ …] we facilitate [ …] conversation answering the questions from the consumer, but it is not the key focus, it is not the reason for being’. Foodland Ontario could take the opportunity to use social media for supporting connections and conversations among different stakeholder groups (e.g., producers, farmers markets, processors, retailers, researchers, restaurants involved in local food marketing). However, the organization was not comfortable engaging in conversations, especially regarding controversial topics, such as issues related to Genetically Modified Organisms (GMOs), pesticide hazards and conflicting opinions related to agricultural trade and food policy. The organization’s unwillingness to move beyond their comfort zone might limit their efforts to encourage conversations in their networks. Sustain Ontario has its own website, active blog and communication through media campaigns. Unlike Foodland Ontario, Sustain Ontario is a membershipbased organization which uses social media for facilitating connections and interaction among their members. The organization showed an interest in engaging in a wide range of conversations, including controversial topics. As influenced by its membership-based alliance, Sustain Ontario encouraged sharing of information related to members’ interests such as food products, poultry, feed for cattle, farm innovations, public health, GMOs, pesticide hazards and environmental awareness. Sustain Ontario had been gradually moving from face-to-face interaction to online communication for engaging with its membership through social media. As explained by a key informant, ‘it is a very good platform which allows us to continue meaningful discussions [ …]we get a good reputation with the people that we serve [ …] facilitating ongoing conversations on certain issues and to continue information and knowledge sharing is our main goal’. Farm M had been using Facebook and Twitter for marketing, enhancing popularity and business growth. In comparison to the other three stakeholders, Farm M was not active on Facebook and Twitter. As a medium-sized, wholesale-oriented producer, the Farm was more interested in maintaining direct links for dealing with their contracted partners. According to the key informant, it was difficult for Farm M to find agri-food entrepreneurial opportunities online. They were


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Table . Characteristics of Twitter alters of each ego. Number of Alters in Ego Network Types of Alters Individual farmer or family farms Customers/audience such as student, doctor, teacher, sports person, etc. Health and nutrition expert/fitness Chef and restaurant owner Individuals and activists concerning sustainable agriculture and environment Farmers market or farmer association/organisation Other organizations and groups (e.g., public-sector, non-profits, medium and large entrepreneurs)

Foodland Ontario

Sustain Ontario

Farm M

Farm B

 

 

 

 

  

  

  

  

 

 

 

 

reluctant to discuss general issues of business online, particularly price or potential customer information. As a wholesaler, the focus of their online presence was to gain recognition or connect with other medium and large entrepreneurs (e.g., large supermarket chains). They had no interest in networking with other farmers and local consumers in social media. Therefore, they had been focusing on offline direct contacts with their clients and business partners. In the case of Farm M, efforts to expand and maintain online networks were not evident. Farm B is a producer which used an online farm retail market to sell its own and other local produce directly from farmers to consumers. Farm B used both Facebook and Twitter to inform local consumers and the general public about the seasonal availability of produce, announce due dates for customers to order online and to send and receive delivery confirmations. As explained by a key informant, ‘we do not see the same traffic as we used too [ …] Twitter is a wasteland of one line bits and [ …] Facebook has now choked the news feeds so no one actually sees your posts’. Apparently, the informant was not satisfied with the performance of Facebook and Twitter. Initially the organization successfully used social media to develop its audience online and get responses from their customers. Many of these contacts had prior relationships with the organization either through professional or personal communications. In order to expand their network online, it was necessary to dedicate time and staff resources. Farm B did not recognize the benefit of spending resources (e.g., financial and staff time) on social media. Neither did they perceive the full potential of social media for translating business ideas and increasing their customer base using online tools. As stated by the key informant, ‘[ …] using a Facebook page does not mean it can transform an individual to becoming a customer’. This comment indicates issues related to relationship-building, including trust and confidence between producers and the online audience. A comparison of profile characteristics indicates how online networking focuses on different types of contacts (alters) on Twitter (see Table 2). Organizations were major types of contacts on the Twitter networks of Foodland Ontario and Sustain Ontario. This suggests that their online networks focused on connecting with other organizations. At the next level, customers/audiences, such as students, doctors,

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Table . Social network measures of different stakeholders. Network characteristics Name of Ego/Stakeholder

Size/ Degree (n)

Ties

Density

Between-ness of ego in own network

Pairs

Brokerage/ Structural holes

Foodland Ontario Sustain Ontario Farm M Farm B

   

   

. . . .

. . . .

   

. . . .

teachers, and athletes constituted further contacts. The four organizations had an almost equal number of contacts with farmers markets or associations. Sustain Ontario had a greater number of contacts with individuals and activists concerning sustainable agriculture and environmental awareness, compared to the other three stakeholders. Farmer or family farms featured the least in the Twitter networks of all four stakeholders.

Online social media network structure and network ties of stakeholders

Using Twitter data, the online network structures of the four stakeholders were analyzed. The analysis looked into relationships between egos and alters in their respective network. Foodland Ontario and Sustain Ontario had an almost equal number of nodes in their network (Table 3). The number of nodes determines the size of the network. Network size is the total number of actors/nodes that are directly connected to the ego (Burt, Minor, & Alba, 1983). The size of the networks of Farm B and Farm M was almost half of that of the networks of the other two organizations. The number of ties determines the total connections in the network. Sustain Ontario had the highest number of ties/connections in their network. Foodland Ontario had fewer ties than Sustain Ontario. Farm B and Farm M had the lowest number of ties with, respectively, 122 and 118 active ties in their networks. Ties in each of the egos’ networks indicate a relationship with ego and alters, whereas the number and kind of ties determines the behaviour, opportunities, influence and power of the actors (Hanneman & Riddle, 2005). In terms of ties and size, Sustain Ontario had a relatively high influence in their network, compared to the other three organizations. Betweenness is an important centrality measure that indicates an ego’s position in its network. An ego is ‘between’ two other actors if the ego lies on the shortest directed path from one to the other. The measure of ego betweenness indicates ‘the percentage of all shortest paths from neighbour to neighbour that pass through the ego’ (Hanneman & Riddle, 2005). The findings indicate that Foodland Ontario’s betweenness factor was 95.7 for sending or receiving information in its network (Table 3). On the other hand, Sustain Ontario’s betweenness factor was 89. An actor (ego) with high betweenness can play important roles in making connections or developing relations with others. In terms of betweenness, Foodland Ontario had a strong position in its network, acting as a node for building the connection to


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Figure . Alter-to-alter interaction.

diverse actors in the network. Although the size of the network was small, Farm M had a high betweenness factor (93.6) in its network. Farm B had the lowest betweenness factor (84) in its network. Structural holes and brokerage are important ego network measures which depict a number of pairs not directly connected to each other. Brokerage means there is an absence of a relationship between two alters who are otherwise connected to the ego. In this circumstance, the ego acts as a broker between two alters and that absence of relation is called a structural hole. It can be a group of alters who are connected to an ego but not connected to the other members or groups of the ego network. Foodland Ontario and Sustain Ontario had a similar level of brokerage roles, with Foodland Ontario acting as the broker for 9,715.5 pairs and Sustain Ontario as a broker for 9,739.5 pairs. On the other hand, the brokerage scores of Farm M and Farm B were 1,832 and 1,214 respectively. In order to understand the interaction pattern of alters (see Figure 2), the number of contacts having interaction with each other and the number of contacts not having interactions with anyone were categorized for each ego. Foodland Ontario had 142 contacts (25%) having contact with each other and 423 contacts (75%) who did not interact with each other, but had contact with Foodland Ontario. Sustain Ontario had 143 contacts (32%) who interacted with each other and 306 contacts (68%) who did not interact with anyone other than Sustain Ontario. Farm M had 63 contacts (36.5%) who interacted with each other and 110 (63.5%) who did not. In the network of Farm B, 53 alters (46.5%) had contact with each other and 61 (53.5%) had no connection to anyone other than the ego. Almost half of the contacts of the small and highly dense network of Farm B interacted with each other. These findings indicate that small producer organizations maintain their existing relationships instead of expanding their online networks. This means they have less focus on expanding their networks but maintain their already existing online relationships by facilitating their (regular) interactions. All stakeholders had more weak ties than strong ties (Figures 3, 4, 5 and 6). The results further indicate that the networks of Foodland Ontario (see Figure 3) and Sustain Ontario (see Figure 4) were large but sparse. Foodland Ontario and Sustain Ontario therefore had a large, loose and potentially unstable social network. The

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Figure . Ego network of Foodland Ontario. Note- In ego network visualisation, nodes shown in red color are the alters who share strong relationship/ties with the ego (based on frequent interaction).

Figure . Ego network of Sustain Ontario. Note- In ego network visualisation, nodes shown in red color are the alters who share strong relationship/ties with the ego (based on frequent interaction).

networks of these organizations were comprised of diverse external contacts that had weak degree of attachment. On the other hand, the networks of Farm B and Farm M were denser than the other two egos. Farm B and Farm M had dense and closely-knit ties among a relatively small number of nodes (see Figures 4 and 5). Most of these strong ties had prior contacts or acquaintances with one another either through business partnerships or other direct means of communication.

Figure . Ego network of Farm M.


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Figure . Ego network of Farm B. Note- In ego network visualisation, nodes shown in red color are the alters who share strong relationship/ties with the ego (based on frequent interaction).

A key informant from Farm B observed, ‘we do not see any scope on Facebook to achieve our professional goals’. The organization indicated that only about 25% of its customers are informed about what Farm B is growing, promoting or selling through social media. The majority of their customers are informed through offline means such as face-to-face meetings or phone conversations. As the key informant from Farm M stated, ‘around 370 people (based on farm records) have visited us [ …] and other organizations have direct contacts with us [ …] who are also active on our Facebook and Twitter pages’. These findings indicate that the strength of online relationships among private-sector and public-sector organizations was influenced by their prior or offline acquaintance with the contacts.

Discussion The growing penetration of the Internet and online tools, especially social media, has offered opportunities for rural and agricultural communities to expand and strengthen networks for sharing ideas and mobilizing knowledge for innovative activities. Notwithstanding these major changes, the impact of these tools on the ways local food stakeholders interact and network with their development partners remains relatively undocumented, with only a handful of studies exploring the use of social media within the agricultural and food sectors (e.g., Chowdhury & Hambly Odame, 2013b; Cui, 2014; Strong et al., 2014). The main objective of this exploratory study was to assess how different stakeholders of the local food sector in Ontario used Facebook and Twitter for strengthening innovation networks. In order to fulfill the objective, we discuss the findings with respect to two major questions: How do stakeholders use social media to support interactive functions of innovation? And how do they use social media to strengthen networks of local food innovation actors in Ontario? Our findings indicate that stakeholders differed in their use of social media to interact with their contacts online. Organizational goals and interests played an important role in informing the use of social media by different entities. The main concern of Foodland Ontario is to make its audience, in theory Ontario consumers,

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aware of locally grown produce. In this way, they support local producers (farmers) by connecting them to the market industry (grocery stores and local farmers markets) and consumers. The major content on their Twitter and Facebook pages was seasonal agricultural produce in the market and related recipes. The use of social media was aimed at creating consumer interest in buying and processing locally produced fresh vegetables and fruits. The organization avoided discussing other food issues related to GMOs, sustainability of production/consumption, policies and more controversial topics on social media. As such, their use of social media has been selective; it is not necessarily responsive or inclusive of all local food issues. The online communication behaviour of Foodland Ontario is strongly influenced by its organizational interests and guidelines rather than the emerging interests of its users and wider audience. On the other hand, Sustain Ontario provided information to their members (farmer associations and organizations, activists for sustainable agriculture and environment awareness, etc.) about policy issues, sustainable agriculture practices, and organic agriculture and promoted their members’ local produce among existing or potential customers. The organization demonstrated an open attitude to engaging in discussions about controversial topics, such as environmental impact and health-related food issues. Social media used by organizations, especially public-sector and non-profits, contributed to building networks of other organizations. These organizations comprised the major contacts of Twitter and Facebook of Foodland Ontario and Sustain Ontario. Although there were a substantial number of individuals among their social media contacts, farmers and farm families did not receive important attention. The social media contacts for the two producer organizations (Farm M and Farm B) confirmed this observation. The findings of the study reinforce earlier observations of Chowdhury and Hambly (2013b), who concluded that the use of social media by agricultural and rural development stakeholders did not meet the expectations of communication for innovation using dialogue and engagement through online social interaction. This study of four local food organizations in Ontario offers additional insights into how different types of organizations use social media to support interactive functions of innovation. The public-sector organization used social media as another means to disseminate information to other organizations and the wider local public. The main interest that guided their use of social media was to inform other stakeholders about their presence and activities related to local food. The organization utilized an interaction ‘tone’ that did not invite discussion of controversial topics or encourage regular interaction in which two parties might have the opportunity for an ongoing dialogue. This might be due to the fact that the organizational communication practices and institutional culture (e.g., top-down and hierarchical, rules-based) of public-sector organizations influence their use of online communication tools (Paris, Thomas, & Wan, 2012). Therefore, the tendency was to follow an information ‘push-out’ strategy (De Saulles, 2011; Reddick & Jaramillo, 2014), while communicating issues related to food and agriculture.


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On the other hand, the case of Sustain Ontario’s use of social media balanced the achievement of strategic goals, such as responding to membership needs, with the discussion of changes in policies or investment in local food, rather than merely an open-ended process of information exchange. Sustain Ontario used social media for building an online presence and getting their message out, but their online communication did not necessarily establish a process for getting inputs from outside the organizational networks into offline decision-making processes. Although in comparison to Foodland Ontario, Sustain Ontario used relatively more emotive interactions (e.g., Paris et al., 2012), there might be a tension between instrumental (interaction based on membership/network interests) and dialogical forms of communication (open to interests beyond network) in the use of social media (Greenberg & MacAualy, 2009). In order to strengthen agricultural and food innovation networks, some scholars (Hermans et al., 2017; Klerkx & Aarts, 2013; Nelson et al., 2013; Ohberg, 2012) recommend that facilitating organizations from the public sector and non-profits find a balance between formal (rules-based) and informal (emotion- and trust-based) approaches to interaction. The findings indicate that the Internet and social media and their use by the local food stakeholders may not, in themselves, create spaces for interactive and inclusive communication. More accurately, success is dependent on a strategic use of social media (Chowdhury & Hambly Odame, 2013a; Nicholson et al., 2016) by different stakeholders that help them fulfill and complement their interests. For producer organizations, use of social media does not yet translate into tangible benefits, such as how to receive and send time-sensitive feedback to private inquiries (e.g., price, business plan), including from clients and agencies who serve them. While local food literature often frames itself as alternative discourse, insofar as it rejects industrial agri-food systems that distance producers from consumers (Nelson et al., 2013), where social media are concerned, the use of these tools is not particularly ‘alternative’ to mainstream public and business communication use of social media. In theory, stakeholders of local food can potentially use social media for creating and strengthening networks that allow for inclusive processes of interaction, relationship building, sharing of knowledge and collaboration. From our analysis, we can identify three major issues of social media networks: network formation, network attributes, and role of strong and weak ties in connecting different actors. Our findings indicate that prior offline contacts, ‘place-based’ affiliations and acquaintances played important roles in creating and expanding networks online. The online networks of the two producer organizations were comprised mainly of their prior contacts, developed either through face-to-face or other direct means of communication. Our study did not identify strong interest within these two organizations (Farm M & B) in expanding and diversifying their online networks beyond the existing networks. Both the public-sector (Foodland Ontario) and the nonprofit organization (Sustain Ontario) missed the opportunity to use social media as a means to get connected to small producers. The literature has shown (e.g., Gilbert et al., 2010; Townsend et al., 2016) that trust is a critical factor for rural people in building networks online. Rural people are more comfortable with knowing each

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other offline before moving the relationship online (Gilbert et al., 2010). Although Foodland Ontario and Sustain Ontario had a large network, a high number of ties and maximum brokerage, this does not mean that their networks evolved through mainly online interactions. Both organizations do have a strong presence in the local food sector in Ontario. As a public-sector organization, Foodland Ontario built a strong presence in the local food sector through multiple media channels. Stakeholders, such as farmers, retailers, chefs and restaurant owners know the organization by their name, their logo on food products or from the advertisements and commercials on television, radio and newspapers. Produce marked with the Foodland Ontario logo indicates locally grown produce. Foodland Ontario also participates in and organizes events to promote local food. These offline means of communication might help Foodland Ontario get connected with a larger audience in their social media networks, but the link is not evident. On the other hand, Sustain Ontario is a membership-based organization that has a strong audience built through programs, such as organizing and supporting advocacy events, newsletters, and the sharing of job openings related to local foods. Therefore, social media alone does not suffice in forming and evolving networks of innovation actors. The cases examined here from Ontario’s local food sector suggest that the number of weak ties was significantly higher than that for strong ties on both social media platforms for all four stakeholders. Strong ties are considered more close and reliable, based on the intensity and length of relationship, reciprocity of communication and frequency of interactions. Therefore, strong ties are considerably beneficial for both large and sparse (Foodland Ontario and Sustain Ontario), as well as small and dense, networks (Farm B and Farm M) in certain ways, such as by providing collaborative opportunities and ideas for action and in gathering emotional strength and mobilizing knowledge to create change and innovation. They are also important in terms of circulating the information and resources within the organization; thus, they are conducive to a smooth flow of services within an organization (Friedkin, 1982; Haythornthwaite, 2002). On the other hand, weak ties are major contributors to the novel information flow outside the organizations’ networks. The weak ties become important for innovation by providing information and opportunities to the organisation from outside of the organizational networks (Friedkin, 1982; Granovetter, 1973; Spielman et al., 2011). The weak ties bring diverse actors into the network, whose activities and interactions contribute to circulating information and ideas within the network. From an innovation systems perspective, it is very important to have diverse contacts in a network; many of them start as acquaintances or by meeting in physical or virtual spaces (Spielman et al., 2011). Innovation occurs through strong and cohesive networks when there is limited new information and through weakly knit networks when there is abundant novel information. Since local food systems draw on the collective actions of multiple and emerging actors demanding dynamic knowledge and information (Christensen & O’Sullivan, 2015; Crespo, RéquierDesjardins & Vicente, 2014; Schneider et al., 2016), public-sector and non-profit organizations should pay attention to utilizing weak ties in their network. To do so, online communication should not be considered as a differentiated space from


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offline social relations (Reichelt et al., 2016). Virtual and non-virtual interactions are iterative processes that mutually reinforce one another (Materia, Giarè, & Klerkx, 2015) and bring benefits for diverse stakeholders, especially for small producers and entrepreneurs who otherwise would miss the opportunities to reap the benefits of online innovation networks. According to Borgatti et al. (2002), it is advantageous, in many settings, for the ego to be connected to many alters who are themselves unconnected to the other alters in the ego’s network. An ego receives more significant information from contacts that are not connected directly to others but with the ego (Burt, 2000). However, the ego cannot entirely control the connections amongst alters on social media, nor can it offer exclusive benefits, as anyone can approach the other alters without the direct control of an ego. If this happens on social media platforms, then activities and flow of interaction in the network will slow down or cease. Since Foodland Ontario and Sustain Ontario had high brokerage scores, they might receive diverse information. This suggests that organizations might take advantage of brokering connections and mobilizing resources and opportunities for creativity and innovation in their social media network.

Conclusion This study, although limited in its scope, designed an approach to examine online network building and interactions of local food stakeholders, with a focus on Twitter and Facebook. The findings indicate that social media can be used to build innovation networks which are inclusive and able to bring beneficial goods and services to organizations and individuals in the network, including small-scale actors (farmers, families and small business). This effort can be forged if social media are used as an iterative process through which online interactions serve as a driver of offline and face-to-face interactions and not just produce passive online interactions (e.g., information push and consumption). The local food system in Ontario, Canada could be more innovative in its use of social media, but it is unlikely to do so without tapping into the strength of weak ties, high brokerage of key organizations with well-established online networks (i.e., Foodland Ontario and Sustain Ontario), and the inclusion of small producers who are key stakeholders in the sector and implicated in conversations that may involve controversial topics. In order to strengthen innovation networks, organizations should adopt a social media strategy that focuses on the interactive, emerging, and discursive nature of communication and builds on multidimensional processes of knowledge sharing. The strategic use of social media should focus on dynamic interactions between the virtual and physical realms of communication. The facilitating organization (e.g., public-sector, non-profit, and key private-sector organization) should pay attention to communication needs and services for small and medium producers in social media spheres and strategies to improve engagement and strengthen relationships with them. For instance, capacity-building initiatives (e.g., coaching, mentoring and training) may be undertaken to build confidence and skills of small- and

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medium-size producers and processors about online communication processes (e.g., social media tools, related communication services, time management, etc.). Limitations and future work In this study, we focused on only four cases of social media use in the local food sector in Ontario. Future studies should include more and diverse cases and focus on in-depth content analysis (e.g., Cui, 2014) of social media conversations to further identify and compare motivations, goals, approaches and preferred media (e.g., video, image) among key stakeholders. The interactive functions of diverse actors using social media can also be compared with their overall roles in strengthening the local food system in Ontario. Future research may consider including other stakeholders and more participants and explore how stakeholders currently use social media and more crucial aspects, such as learning and building social capital. A comprehensive survey with a wide range of social media users and non-users is necessary to further delve into these issues and understand local food systems. Acknowledgements This research was conducted within the post-doctoral project of the second author. The Social Science and Humanities Research Council of Canada is acknowledged for support of the postdoctoral research and the Netherlands Fellowship Program is acknowledged for support of the graduate study of the first author.

References Acevedo, M. (2011). Network cooperation: Development cooperation in the network society. In S. Chhabra, & H. Rahman (Eds.), Human development and global advancements through information communication technologies: New initiatives (pp. 1–21). Hershey, PA: IGI Global. Andres, D., & Woodard, J. (2013). Social media handbook for agricultural development practitioners. Retrieved from http://ictforag.org/toolkits/social/SocialMedia4AgHandbook.pdf Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies. Bristy, J. F. (2016). Factors affecting the adoption of social network: A study of Facebook users in Bangladesh. The Journal of Social Media in Society, 5(1), 137–159. Burt, R. S. (2000). The network structure of social capital. Research in organizational behavior. In N. Lin, K. Cook, & R. S. Burt (Eds,), Social capital: Theory and research (pp. 345–423). Hawthorn, NY: Aldine de Gruyter. Burt, R. S., Minor, M. J., & Alba, R. D. (1983). Applied network analysis: A methodological introduction. Beverly Hills, CA: Sage Publications. Campsie, P. (2008). Food connects us all: Sustainable local food in southern Ontario: Toronto, Canada: Metcalf Foundation. Cho, S. E., & Park, H. W. (2011). Government organizations’ innovative use of the Internet: The case of the Twitter activity of South Korea’s Ministry for Food, Agriculture, Forestry and Fisheries. Scientometrics, 90(1), 9–23. Chowdhury, A., & Hambly Odame, H. (2013a). Do social media support ‘social’ change? Insights from a communication for social change perspective in agriculture and rural development. In P. Ross, & J. Shtern (Eds.), TEM 2013: Proceedings of the Technology & Emerging Media Track: Annual Conference of the Canadian Communication


21

Association (Victoria, June 5–7, 2012). Retrieved from http://www.acc-cca.ca/resources/ Documents/TEM_proceedings/TEM_2013/CHOWDHURY-HAMBLY-TEM2013.pdf Chowdhury, A., & Hambly Odame, H. (2013b). Social media for enhancing innovation in agrifood and rural development: Current dynamics in Ontario, Canada. The Journal of Rural and Community Development, 8(2), 97–119. Christensen, L., & O’Sullivan, R. (2015). Using social networking analysis to measure changes in regional food systems collaboration: A methodological framework. Journal of Agriculture, Food Systems, and Community Development, 5(3), 113–129. Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: The usefulness of electronic weak ties for technical advice. Organization Science, 7(2), 119–135. Crespo, J., Réquier-Desjardins, D., & Vicente, J. (2014). Why can collective action fail in local agri-food systems? A social network analysis of cheese producers in Aculco, Mexico. Food Policy, 46, 165–177. Cui, Y. (2014). Examining farmers markets’ usage of social media: An investigation of a farmers market Facebook page. Journal of Agriculture, Food Systems, and Community Development, 5(1), 87–103. De Saulles, M. (2011). Social media and local government in England: Who is doing what? In M. Klun, M. Decman, & T. Jukić (Eds.), Proceedings of the 11th European Conference on eGovernment, 16–17 June, 2011, University of Ljubljana, Ljubljana, Slovenia (pp. 187–192). London: Academic Conferences and Publishing International Ltd. Eise, J., & Hodde, W. (2017). The communication scarcity in agriculture. New York: Routledge. EU SCAR. (2014). Agricultural knowledge and innovation systems towards 2020 –An orientation paper on linking innovation and research. Brussels, Belgium: European Union. Feagan, R. (2007). The place of food: mapping out the ‘local’ in local food systems. Progress in Human Geography, 31(1), 23–42. Feenstra, G. W. (1997). Local food systems and sustainable communities. American Journal of Alternative Agriculture, 12(01), 28–36. Foster, C., & Heeks, R. (2013). Conceptualising inclusive innovation: Modifying systems of innovation frameworks to understand diffusion of new technology to low-income consumers. European Journal of Development Research, 25(3), 333–355. Friedkin, N. E. (1982). Information flow through strong and weak ties in intra organizational social networks. Social Networks, 3(4), 273–285. Gilbert, E., Karahalios, K., & Sandvig, C. (2010). The network in the garden: Designing social media for rural life. American Behavioral Scientist, 53(9), 1367–1388. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360– 1380. Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1(1), 201–233. Greenberg, J., & MacAulay, M. (2009). NPO 2.0? Exploring the Web presence of environmental nonprofit organizations in Canada. Global Media Journal- Canadian Edition, 2(1), 63–88. Gruzd, A. (2016). Netlytic: Software for automated text and social network analysis. Available at http://Netlytic.org Guner, K. (2010). They don’t know their own strength: Weak ties, online social networks and how they affect modern job seekers (Unpublished master’s thesis). School of Professional Studies, Gonzaga University. Hall, J. A. (2016). When is social media use social interaction? Defining mediated social interaction. New Media & Society. doi:10.1177/1461444816660782 Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California. Hartwich, F., & Jansen, H.-G. (2007). The role of government in agricultural innovation: Lessons from Bolivia (Research policy brief No. 8). Washington: International Food Policy Research Institute.

22

P. KAUSHIK ET AL.

Haythornthwaite, C. (1996). Social network analysis: An approach and technique for the study of information exchange. Library & Information Science Research, 18(4), 323–342. Haythornthwaite, C. (2002). Strong, weak, and latent ties and the impact of new media. The Information Society, 18(5), 385–401. Haythornthwaite, C. (2005). Social networks and Internet connectivity effects. Information, Community & Society, 8(2), 125–147. Haythornthwaite, C., & De Laat, M. (2010). Social networks and learning networks: Using social network perspectives to understand social learning. In L. Dirckinck-Holmfeld, V. Hodgson, C. Jones, M. de Laat, D. McConnell, & T. Ryberg (Eds.), Proceedings of the 7th International Conference on Networked Learning, 3–4 May, Aalborg, Denmark (pp. 183–190). Lancaster, UK: Lancaster University. Hekkert, M. P., Suurs, R. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413–432. Hermans, F., Sartas, M., van Schagen, B., van Asten, P., & Schut, M. (2017). Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling. PLoS One, 12(2), e0169634. doi:10.1371/journal.pone.0169634 James, J., & Gururajan, R. (2015). It’s all about the benefits: Why extension professionals adopt Web 2.0 technologies. Rural Extension & Innovation Systems Journal, 11(1), 72–82. Johnson, R., Fraser, E., & Hawkins, R. (2016). Overcoming barriers to scaling up sustainable alternative food systems: A comparative case study of two Ontario-based wholesale produce auctions. Sustainability, 8(4), 328. doi:10.3390/su8040328 Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59–68. Klerkx, L., & Aarts, N. (2013). The interaction of multiple champions in orchestrating innovation networks: Conflicts and complementarities. Technovation, 33(6–7), 193–210. Klerkx, L., Aarts, N., & Leeuwis, C. (2010). Adaptive management in agricultural innovation systems: The interactions between innovation networks and their environment. Agricultural Systems, 103(6), 390–400. Leeuwis, C., & Aarts, N. (2011). Rethinking communication in innovation processes: Creating space for change in complex systems. The Journal of Agricultural Education and Extension, 17(1), 21–36. Mapila, M. A., Yauney, J., Thangata, P., Droppelmann, K., & Mazunda, J. (2016). Who talks to whom in Malawi’s agricultural research information network? The Journal of Agricultural Education and Extension, 22(1), 7–23. Materia, V. C., Giarè, F., & Klerkx, L. (2015). Increasing knowledge flows between the agricultural research and advisory system in Italy: Combining virtual and non-virtual interaction in communities of practice. The Journal of Agricultural Education and Extension, 21(3), 203–218. McLennan, S. J. (2016). Techno-optimism or information imperialism: Paradoxes in online networking, social media and development. Information Technology for Development, 22(3), 380– 399. doi:10.1080/02681102.2015.1044490 Mount, P., Hazen, S., Holmes, S., Fraser, E., Winson, A., Knezevec, I., … Landman, K. (2013). Barriers to the local food movement: Ontario’s community food projects and the capacity for convergence. Local Environment, 18(5), 592–605. Nelson, E., Knezevic, I., & Landman, K. (2013). The uneven geographies of community food initiatives in southwestern Ontario. Local Environment, 18(5), 567–577. Nicholson, B., Nugroho, Y., & Rangaswamy, N. (2016). Social media for development: Outlining debates, theory and praxis. Information Technology for Development, 22(3), 357–363. doi:10.1080/02681102.2016.1192906 Nightingale, V. (2011). The handbook of media audiences (Vol. 5). London: Wiley-Blackwell.


23

Ohberg, L. A. (2012). What’s stopping us? Identifying barriers to the local food movement using Ontario, Canada as a case study (Unpublished master’s thesis). Department of Geography and Planning, University of Toronto, Canada. OMAFRA. (2013). Local food: The local food movement. Retrieved March 15, 2016, from http://www.omafra.gov.on.ca/english/about/consultation_paper.htm OMAFRA. (2015). Ontario’s local food report: 2014–15 edition. Retrieved March 16, 2016, from http://www.omafra.gov.on.ca/english/about/local_food_rpt.htm OMAFRA. (2016). Ontario’s local food strategy. Retrieved March 16, 2016, from http://www.omafra.gov.on.ca/english/about/localfood.htm O’Murchu, I., Breslin, J. G., & Decker, S. (2004). Online social and business networking communities. Paper presented at the ECAI Workshop on Application of Semantic Web Technologies to Web Communities, August 23–27, 2004, Valencia, Spain. Paris, C., Thomas, P., & Wan, S. (2012). Differences in language and style between two social media communities. In ICWSM 2012—Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (pp. 539–542). Palo, Alto, CA: AAAI Press. Reddick, C. G., & Jaramillo, P. A. (2014). New digital media use and preferences for government: A survey of Canadians. Electronic Government, 11(1–2), 39–58. Reichelt, N. T., Wallis, P. J., Ison, R. L., Davies, J., Carberry, P., Sparrow, A., … Maru, Y. (2016). Mediating boundaries between knowledge and knowing: ICT and R4D praxis. Outlook on Agriculture, 45(4), 238–245. Saravanan, R., Suchiradipta, B., Chowdhury, A., Hall, K., & Hambly Odame, H. (2015). Social media for rural advisory services (Note 15. GFRAS Good Practice Notes for Extension and Advisory Services). Lindau, Switzerland: GFRAS. Retrieved from https://www.gfras.org/en/download.html?download=355:ggp-note-15-social-media-for-rural-advisoryservices Schneider, S., Salvate, N., & Cassol, A. (2016). Nested markets, food networks, and new pathways for rural development in Brazil. Agriculture, 6(4), 61. doi:10.3390/agriculture6040061 Scott, J. (2013). Social network analysis. London: Sage Publications. Sloan, L. & Quan-Haase, A. (Eds.) (2017). Handbook of social media research methods: Goals, challenges & innovation. Thousands Oaks, CA: Sage Publishing. Spielman, D. J., Davis, K., Negash, M., & Ayele, G. (2011). Rural innovation systems and networks: Findings from a study of Ethiopian smallholders. Agriculture and Human Values, 28(2), 195– 212. Spielman, D. J., Ekboir, J., & Davis, K. (2009). The art and science of innovation systems inquiry: Applications to Sub-Saharan African agriculture. Technology in Society, 31(4), 399–405. Statistics Canada. (2017). 2016 Census of agriculture. Retrieved on 1 July 2017 from http://www.statcan.gc.ca/daily-quotidien/170510/dq170510a-eng.htm Strong, R., Dooley, L. M., Irby, T. L., & Snyder, L. U. (2014). Mexican banks’ acceptance and use of Twitter to assist in evaluating farm loan applications: Exploring the role of agricultural loans on food security. Journal of International Agriculture and Extension Education, 21(2), 45–57. doi:10.5191/jiaee.2014.21204 Townsend, L., Wallace, C., Smart, A., & Norman, T. (2016). Building virtual bridges: How rural micro-enterprises develop social capital in online and face-to-face settings. Sociologia Ruralis, 56(1), 29–47. Tropical Agriculture Platform. (2016). Capacity for change: Common Framework on Capacity Development for Agricultural Systems: Guidance note on operationalization. Wallingford, UK: CABI International. Retrieved from https://www.cabi.org/Uploads/CABI/aboutus/4.8.5-other-business-policies-and-strategies/tap-guidance-note.pdf Wellman, B., & Hampton, K. (1999). Living networked on and offline. Contemporary Sociology, 28(6), 648–654. doi:10.2307/2655535