Analyses of interlinked actors in determining the potential business beneficiaries of small-scale pig farming systems in West Papua, Indonesia.

Pig production is a key livelihood sector and a source of economic and social beneficiaries, which has many interest and interlinked actors. The inventory which includes all resources and the roles played by actors is utmost important in pig farming system. Some stakeholders are interlinked in function, forming a complex system with multi-disciplinary actors. This research aims to distinctively map and provide clear involvement of actors or stakeholders in relation to their contribution towards pig business. As much as 32 institutions were interviewed based on the roles and resources of individuals working inside the organizations formally and informally. The parameters collected inlude the structure, status of law, and types of organization. As well as stakeholders' role, effect, importance, threat, and turn-back impact. The data obtained include resources sharing, duration, continuity, power, and interventions. Those related to intervention were policy, finance, space, time, access, satisfaction, knowledge, skills, threats, and power. In terms of innovation, the data collected include power, finance, space, time, access, satisfaction, knowledge, skills, threats, and power. And were stored in Microsoft excel worksheet and exported to Social Network Visualizer software version 2.5. The key and strategic stakeholder in pig business beneficiary were identified and determined based on power and interest. The following were identified in the first rank: crop farmers, private credit business, village officer, and local community. In the second rank, the factors identified include government (local and national), student community services, and security.

Introduction

Stakeholder is defined as individuals, groups, and institutions that have both direct and indirect effects in determining a certain developmental process, or changing the entire program (Freeman, 2015). They are officially formed by the law both in the international and national levels, which includes the state and regional government, i.e., governor and regent (Kawuma and Ouma 2015; Devitt et al. 2016; Vasco et al. 2018) However, some stakeholders are not formed by the law, and they play a strategic and prominent role in determining developmental process. In developing livestock farming systems, many parties interlinked by forming complex systems (Barcellos et al. 2013; Leen et al. 2018), in the social, economic, and environmental domain (Baxter and Edwards, 2017), with defined roles and responsibilities. Without understanding the roles of the system, it is difficult to instruct the parties towards the development of the proposed model. Every stage of the development has its process and related parties or stakeholders involved (Günther and Hüske, 2014). Examples are industrial and livestock business, particularly pig farming (Phiri, 2012; Widayati et al. 2018), which is in line with Kenyan dairy (Nyokabi et al. 2018). The industrial and livestock business around the world have been developed due to the involvement of many stake and shareholders’ interest and intervention (Bergstra et al. 2017; Iyai et al. 2018). Each actor cares and desire to increase their business, in conjunction with the objective, which is to provide feeds for the entire globe. Stake and shareholders have prominent roles in creating compact demand of livestock products, particularly pig production (Baršauskas et al. 1996). This is in accordance with the report in France by Petit and van der Werf (2003). The enticing appearance of pig products at food stores attract the interest of consumers to buy the commodity (Brookes et al. 2014).

In many tropical and developed countries, the involvement of stakeholders (Phuong et al. 2014; Leroy et al. 2017) is undoubtedly enormous. Some play a vital role in controlling the powers (Phiri, 2012; Bryson, 2007), resources (Govoeyi et al. 2019), access (Devitt et al. 2016), and even the threat encountered (Leroy et al. 2017). This condition was reported by pig farmers in Uganda (Dione et al. 2014). Also, they play vital roles and share important relationship. Stakeholders involvement should clearly be developed and mapped, in various aspects of pig production (Govoeyi et al. 2019). Presently, no existing good livestock governance is made by the central and local authority, particularly regarding pig farming. This causes the development of livestock production in Indonesia, specifically in west Papua to be without connectivity, undistinguished, unclear conformity, rules, and functions. Scientifically, social networks are important when examining problems, diseases, organization, and the degree to which individuals succeed in achieving their goals.

Software are being developed and applied to explain this phenomena. Social Network Visualizer is a powerful analysis besides SmartPLS (Ringle et al. 2005), Netmap (Schiffer, 2007), and Gephi (Bastian et al. 2009). By mapping the stakeholders and institutions, which have no power and interest, it is easy to identify and promote their roles. Therefore, it is necessary to identify the core function and responsibilities of actors involved in pig business sectors. Also, it is important to map and provide clear involvement of stakeholders in relation to their contribution towards pig business.

Conceptual framework

Interlinked actors are shaped based on the existing mutual relationship (Hendriks et al., 1997; Blok et al., 2015; Laktic et al., 2020), shared resources (Holman 2008; Mandarano 2009), and similar administrative background. Each actor has its own power in determining which groups or institutions to be established. Each also has two significant powers, namely high and low (Stephen P Borgatti et al., 2003; Blok et al., 2015; Blanchet and James, 2015). Similar to this, each actor has its relationship, namely strong, neutral, and weak (Oosting et al., 2017; Foti et al., 2018; Dempwolf and Lyles, 2012). The actors in real world of livestock development shaped complex dynamic network (Dempwolf and Lyles, 2012; Blanchet and James, 2015; Huang et al., 2020). Therefore, it is crutial to simplify the networks and relationships. In shaping decisions for establishing networks, actors possess tight consideration, i.e., benefit, sharing power, and participation.

In pig farming systems as identified by Iyai and Yaku (2015), Iyai et al. (2013a, 2020), Iyai (2011), Iyai et al. (2011), Iyai et al. (2018, 2013b), engagement of actors is either horizontal or vertical (Dempwolf and Lyles 2012; Hauck et al., 2016; Gardner et al., 2019; Mappigau et al., 2015a, b). In Indonesia, vertical actors consisted of central government, while the horizontals comprised of local or grass root stakeholders. Central government and its networks played significant roles in determining better and mutual relationships, therefore, sharing similar innovation and interventions. The Innovation and interventions needed in the pig production of West Papua are poor resources, lack of infrastructure, and less access to market places (Govoeyi et al., 2019; Nyokabi et al., 2018; Widi 2015; Orthner et al., 2004). Added to this is having weak coordination, governance, and policy (Greef et al., 2011; Govoeyi et al., 2019). Therefore, it is important and strategic to invent the core dynamic typology of administration, with the formal customs regarding pig business actors’ relationship.

An analytical organizational and actors tools were used to analyze this relationship, as well as the Social Network analysis (SNA) (Borgatti, 2006). The SNA provide user to quantify the relationship by using numbers, whether it is existing and non-existing actors network. It is necessary to have better understanding in designing a multilayer network of stakeholder for improving actors’ involvement. The West Papuan pig production systems should have better pig governance (GPG) by employing the SNA, in order to have appropriate framework to grasp the roles, function, relationship, and network.

Materials and methods

Location and involved actors

A participatory research was conducted in Manokwari, West Papua. This location was chose due to its complexity as reported by Iyai and Yaku (2015), Pattiselanno et al. (2014), Iyai et al. (2013a), Iyai (2011) and Iyai et al. (2011). In order to grasp information and data, several organizations, groups, and individuals represented the institutions involved. From the 40 different groups, only 32 (Table 1) were eager to share and participate in this study. Relevant data concerning existing pig farming business were collected from them using focus group discussions and desk study from qualitative research (Moleong, 1991). The data collected consisted research reports, policy documents, articles, daily newspapers, and magazines (Papua Barat, 2017; Iyai et al., 2013a; Sagrim et al., 2017; BPS-Papua Barat, 2018; Papua Barat, 2019; Iyai et al., 2013b; Homer et al., 2017; Iyai and Yaku, 2015; Manokwari, 2017; Iyai, 2011; Iyai et al., 2011; 2018; Widayati et al., 2018; Iyai and Runtuboi, 2016; Bambar et al., 2019). Empirical materials were also used as highlighted in references (1–8). This was considered because the available data were easily accessed.

Table 1

Stakeholders and their responsibility and roles under pig development sector.

No.	Identified actors	Responsibility
1	Consumers	Buying and consume the meat products
2	Government	Rule policy and regulation
3	Extensionist	Giving community service
4	Irresponsible men	Annoying community living and activities
5	Local community	Perceiving and determining community perception
6	Banks	Providing loans and savings account
7	Private credit business	Providing loans
8	Butchers	Serving slaughtering process
9	Shipping	Serving transportation
10	Harbor porter	Serving harbor transportation
11	Vehicles	Serving transportation of the goods
12	Crop farmers	Providing feed materials for men and animals
13	Food court/Restaurant	Providing food for consumers
14	Retailer	Provide and distribute sale cut
15	Other farmers	Provide goods, skills and product utilized by other people
16	Fishermen	Provide fishing activities
17	Market officer	Serving market activities
18	University researchers	Provide researches themes based on identified problems
19	Student community services	Provide student for community services
20	National research grant institution	Provide grants for university researchers
21	Quarantine officer	Serving the safety of in- and out- coming animals
22	Harbor inspector	Serving the safety and well managed things in harbor
23	Village officer	Provide community needs
24	Sub district officer	Provide community needs
25	Community security	Provide safety and peace
26	Retribution officer	Serving taxes from community
27	Breeder	Selling breed of pigs
28	Feeder	Selling feed ingredient
29	Labor	Serving men powers
30	Inseminator	Serving animal reproduction
31	Veterinarian	Serving health of animal and farmers needs
32	Police	Provide safety and peace

The roles of stake and shareholders in determining the pattern of pig business development in West Papua, particularly in Manokwari was setup according to Netherlands Agriculture plans in 1960, by local livestock provincial offices of West Papua province, Indonesia. All stakeholders were grouped into local citizens, government, finance institutions (banks), markets, private transportation and university.

Data collection

During the research, the data related to organizational function and characteristics of pig business-related stakeholders were collected, namely structure, status of law, types, roles, effect and importance of organization. The information about threats and turn-back effect towards pig farming development were also obtained. The knowledge of the stakeholders' roles and presence cause the sharing of resources, duration, continuity, power, and interventions.

Method of analyses

Social Network Visualizer (SocNetV) was used in analyzing the power and flows of information among stakeholders. SocNetV (Kalamaras, 2019) is a cross-platform, free of charged social-stakeholder related software in network analyses and visualization. The following were used to visualize the graphs, PCC matrix, similarity matrix (SM), power centrality (PC), Hierarchical clustering (HCA), clique census (CLQs), and information centrality (IC). The adjacency matrix of a social network (Supplement no. 1 & 2.) is a set where each element a(i,j) is equal to the weight of the arc from actor (node) i to j. When the actors are not connected, then a(i,j) = 0. By computing the Cocitation matrix, C = AT ∗ A. C is an n x n symmetric matrix where each element (i,j) is the number of actors that have outbound ties/links to both i and j. The diagonal elements, Cii, of the Cocitation matrix are equal to the number of inbound edges of i (in Degree). A key notion in SNA is a structural equivalence.

The idea is to map the relationships in a graph by creating classes or groups of actors that are equivalent. This is carried out by identifying the groups of actors that are structurally equivalent, examining the relationships between them for similarity patterns. There are many methods for measuring the similarity or dissimilarity of actors in a network. SocNetV supports the following methods: Similarity measure and Pearson Correlation Coefficients. By applying one of these techniques, SocNetV created a pair-wise actor similarity/dissimilarity matrix. By computing a pair-wise actor similarity matrix, where each element (i,j) in the ratio of tie (or distance), matches the actors i and j to others. In the case of Simple Matching, the similarity matrix depicts the ratios of exact pair matches of actors to others. When the element (i,j) = 0.5, this means that actors i and j have the same ties present or absent, which is 50% different from others. These measures of similarity are particularly useful when ties are binary (not valued). Correlation matrix is computed, where the elements are the Pearson correlation coefficients between actor pairs in terms of their tie profiles or distances (in, out, or both). The Pearson product-moment correlation coefficient (PPMCC or PCC or Pearson's r) is a measure of the linear dependence/association between two variables X and Y. This correlation measure the similarity, which is particularly useful when ties are valued/weighted denoting strength, cost, or probability. The Power Centrality (PC) is a generalized degree measure, suggested by Gil and Schmidt (1996). For each node u, this index sums the degree (with weight 1), with the size of the 2nd-order neighborhood (with weight 2), and in general, with the size of the kth order neighborhood (with weight k). Therefore, for each node u the most important are its immediate neighbors, and then in decreasing importance the nodes of the 2nd-order neighborhood, 3rd-order, etc. For each node, the sum obtained is normalized by the total number of nodes in the same component minus 1. This index is calculated in both graphs and digraphs, however, it is usually best suited for undirected graphs. It is also calculated in weighted graphs, although the weight of each edge (u,v) in E is always considered to be 1 (therefore negligible). Hierarchical clustering (or hierarchical cluster analysis, HCA) is a method which builds a hierarchy of clusters, based on elements dissimilarity. In the SNA context, these clusters usually consist of network actors. This method takes the social network distance matrix as input and uses the Agglomerative "bottom up" approach where each actor starts in its own cluster (Level 0). In each subsequent Level, by moving up the clustering hierarchy, a pair of clusters merged into a larger type, until all actors end up in the same cluster. To decide which clusters should be combined at each level, a measure of dissimilarity between sets of observations is required. This measure consists of a metric for the distance between actors (i.e., Manhattan distance) and a linkage criterion (i.e., single-linkage clustering). This linkage criterion (essentially for determining the distance between clusters), distinguishes between the different HCA methods. The Hierarchical Cluster Analysis is the clusters per level and a dendrogram. The concept of a clique makes a group of people to interact with each other more regularly and intensely than with those not belong to the organization. Therefore, a group of people form a clique when they are all connected to each other. It is the largest subgroup of actors in the social network that are all directly connected to each other. In terms of graph theory, this notion is the same as a maximal complete subgraph of the equivalent graph of the social network. The word maximal means that for each clique, the group of its members is expanded to include as many actors as possible. Essentially, a clique in Social Network Analysis consists of several overlapping closed triads.

SocNetV applies the Bron–Kerbosch algorithm to determine all the maximal cliques in an undirected or directed graph. It produces a census of all MAXIMAL cliques in the network and reports some useful statistics. The clique census report includes disaggregation by vertex and co-membership information. The Information Centrality (IC) is an index suggested by Stephenson and Zalen (1989) which focuses on how data flow through many different paths. Compare to SC and BC, the IC metric uses all paths between actors weighted by strength of tie and distance.

The IC′ score is the recognized standardized (IC divided by the sum), and the proportion of total information flow that is controlled by each actor. Note that standard IC′ values sum to unity, compared to most other centrality measures. Since there is no known generalization of Stephenson & Zelen's theory for information centrality to directional relations, the index should be calculated only for undirected graphs and more meaningful in weighted graphs/networks. Note: to compute this index, SocNetV drops all isolated nodes and symmetrizes (when needed) the adjacency matrix even when the graph is directed Algorithm (Wasserman and Khaterine, 1994). In order to calculate the IC index of each actor, a N x N matrix A was created from the (symmetrized) sociomatrix with: Aii = 1 + di, Aij = 1 if (i,j) = 0, and Aij = 1−wij if (i,j) = wij. Next, the inverse matrix of A was computed, for instance C, using the LU decomposition. Note that C is always computed, since A is a diagonally strong matrix, therefore, it is invertible. Finally, IC is computed by the formula: ICi−1Cii + T−2⋅RN, where: T is the trace of matrix C (the sum of diagonal elements) and R is the sum of the elements of any row (since all rows of C have the same sum). IC has a minimum value and not a maximum.

The steps in running this SocNetV version 2.5 was presented in Figure 1. To understand the intervention shared by organization, the stakeholders' roles were determined based on the innovations carried out. All data were collectively entered into a Microsoft Excel worksheet and tabled in a manuscript.

Figure 1

Design of actors on a map relationships using SNA under pig sectors.

Result and discussion

Organizational function and characteristics

The structure, law status, types, roles, effect, importance, threats, and turn-back effect of the organization were discussed. It is indeed interesting, understanding that 32 actors formed the development of pig farming system in Manokwari, West Papua (Table 1). Thirteen individual stakeholders were selected for playing a vital role in sharing dynamic of pig farming systems. Seventeen and two mass groups of stakeholders assured the development of pig production (Table 2). While individuals work and act without using certain role and policy. No regulation was guiding individual stakeholders to work legally according to law, making the face of pig farming business remains insolvency. In the meantime, group stakeholders tended to have legal operational business document and ruled in overnment law. They made business unit that provided services and access to community, therefore, mass stakeholders tend to have no clear structure. This is still causing problems due to no leadership and institutional regulations (Leen et al., 2018). These types of organization have induced the social and cultural constraints (Kawuma and Ouma 2015). They are now having vast number and remaining helpless, and using men power as source of laborers.

Table 2

Characteristic of pig actors.

Actors	Total	Proportion (%)
Shape of organization
Individual	13	40.02
Group	17	53.13
Mass	2	6.25
Status of Law
Law	22	68.75
Unlaw	10	31.25
Roles
Stakeholder	16	50.00
Shareholder	16	50.00
Importance
Important	28	87.50
Unimportant	4	12.50
Threat (effect)
Direct effect	21	65.62
Indirect effect	11	34.38
Effect (Benefit)
Positive	23	71.87
Negative	9	28.18
Neutral	0	0.00
Turnback Effect
Feedback	15	46.88
Unfeedback	17	53.12

The effects of actors in determining pig production in Manokwari have been identified. Several stakeholders generally play positive roles, while few numbers play the negatives. The negative roles were found among irresponsible men (destructive behaviour), banks, private credit business, shipping, harbor porters, food courts, retailers, and grants from national institution. Irresponsible men shown destructive actions during pig production. Therefore, perception is utmost important as experienced in Netherlands and Denmark (Boogaard et al. 2011). Banks have lack of concerns in providing loans for small-scale pig production, as well as private credit business. Lack of loans from financial institutions caused pig farmers to experience difficulties in advancing the business. Unregulated taxes that were freely collected by shipping and harbor porters hampered the business development.

Added to this shipping was the ticket for two folds, namely for the passengers and the pigs. The tickets for the passenger was cheap and expensive for the pigs. Reluctant to order meat product from food courts and retailers create weakness of losing market shares. Also, porters determined the ticket or unrolled taxes on pigs and this burdened the farmers. Food courts in Manokwari specifically have no place. Although majority of the community are Christians, demand and preferences to consume products of pigs were lagging behind the chicken and beef. Retailers often import kilos of meat from outside Manokwari, causing pig production to lag behind. Grants available for implementing research related to pigs in Manokwari, and developing the programs from national institutions in Indonesia are poor compared to those related to cattle and goat developments. Actors involved in pig production are classified into the group of importance and unimportance. Threat is classified into two structures, namely direct and indirect. Most actors played direct effect, while few determined the indirects. Turn back effect ascertains whether the related actors have feedback impact both sides of pig production. None of the actors had similar roles in feedback effects, i.e., the success in developing pig production depends on feedback effects.

Resources, powers and intervened needs

The resources provided and shared by each stakeholder varied (Table 3). The available resources that contributed to the development of pig farming systems were policy, financial, space, time, access, satisfaction, knowledge, skills, threat, power, and feed material. Counting the resources used and shared by stakeholders give the overall picture of the strength and domination of their effect. Policy resources were shared by 8 stakeholders (25%), meaning that the many custodian do not provide or use policy in acting, supporting, and linking towards pig farming. This induces weakness in pig farming business. Unspecification of policy hampered and ruined the business due to un-cleared and illegal payment transaction. Therefore, there is need to manage their existence legally by the government. Similar experience was reported by Dione et al., (2016) in Uganda. The fresh money in terms of aids and loans were shared by five stakeholders (16%), meaning that less custodian do provide and use the money to support and link pig farming. The 10th stakeholders (31.25%) provided spaces in terms of programs by government, land by local community, shipping by ship captain, and market officers. The eight custodian (25%) allocated resources of time in terms of services and guides, while 22 stakeholders (68.75%) shared access.

Table 3

Resources, power and intervention.

Actors	Total	Proportion (%)
Sharing Resources
Policy	8	25
Financial	5	15.63
Space	10	31.25
Time	8	25
Access	22	68.75
Satisfaction	4	12.5
Knowledge	10	31.25
Skills	10	31.25
Threat	10	31.25
Power	9	28.13
Feed materials	4	12.5
Duration of period
Short term	16	50
Long term	16	50
Continuity of resources
Sustain	22	68.75
Unsustain	10	31.25
Power of Resources
Strong	15	46.68
Neutral	11	34.38
Weak	6	18.74
Intervention
Need	17	53.12
No need	15	46.78

This access includes the allowance to obtain services and materials needed to establish pig business. The access is provided regularly and periodically by consumers and government with their related stakeholders. Satisfaction is needed by pig farmers including non-physical resource. Four stakeholders (12.5%) were found to provide values of satisfaction. From this figure, the satisfaction showed the preferences and the perception in promoting the development of pig farming production.

Ten stakeholders (31.25%) provided resource of knowledge, skills (31.25%), and threats (31.25%). This occurs due to irresponsible men and local community perception as experienced in Europe (Hou et al., 2018). Threats hamper and become weakness, such as stealing, destroying, and annoying someone else pig business causing the failure of such organization. Therefore, attention is needed to curtail these threats from related actors to assure the safeness in business. Sources of threats, such as alcoholic drinks and other prohibited materials should have high priority attention. Nine stakeholders (28.12%) provided resource of power.

Power is a legitimation for actor to function based on aspect of legalization. It is provided for actors in the form of rules and policy from local government, ethnic organization, and community (Figure 2). The structure of actor connectivity in Figure 2. showed regular, tidy, simple, distinguishable, clear rules, and functions compared to Figure 1. Four stakeholders (12.5%) provided resources of feed materials, and actors urgently needed to make sure all stages are safe. The duration in measuring resources availability was grouped into short and long term period. It was observed that the resources shared in short-term period were provided by 9 stakeholders, compare with the 32 that provided long-term period of resources. Therefore, pig business has future prospects due to stakeholders’ long-term support.

Figure 2

Stakeholder relationships analyzed based on power centrality (analysis referred to supplement data). Small and big cycles determined the power. Changed red to green and blue colours meant the importance and strategies of actors from high to sub-dominant power.

In terms of continuity, 22 stakeholders (68.75%) were found to sustain the future and prospect of pig business. In line with power of resources, i.e., measuring their availability, it was shown that large number of resources remained and belong to pig business. Eleven (34.37%) stakeholders have the neutral power. From the intervention measurement needed in pig business, sixteen stakeholders (50%) were found to be in need of interventions towards providing resources.

From Figure 2. Pearson Coefficient Correlation (PCC) matrix explained that several actors (1–32) have thigh positive correlation with PCC = 1. The PCC has Max value of 1 and Min of -0.185. Several actors have PCC = 0, meaning that there was no correlation at all. The value of PCC >0 when there is positive correlation, i.e., +1 meant actors with same patterns of ties/distances. PCC <0 when there is negative correlation, i.e., -1 for actors with exactly opposite patterns of ties (Table 4). Some actors have no correlation at all, i.e., irresponsible men (4), banks (6), market officer (17), university researchers (18), sub district officers (24), and inseminator (30). This is in line with the examples drawn by Brookes et al. (2014) in Australia. The remaining were negatively correlated, such as consumers (1), government (2), extensionist (3), and police (32). The positive correlation found in several actors, such as consumers with feeder (1 & 28), and government with village officer (2 & 23) (Table 4).

Table 4

Pearson correlation coefficients matrix.

Actors	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32
1	1.000	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	-0.077	-0.417	-0.032	0.000	-0.032	-0.045
2	-0.068	1.000	-0.122	0.000	-0.098	0.000	-0.068	-0.068	-0.098	-0.068	-0.046	-0.068	-0.143	-0.143	-0.068	-0.058	0.000	0.000	-0.068	-0.098	-0.068	-0.068	0.475	0.000	-0.068	-0.068	0.098	-0.163	-0.068	0.000	-0.068	-0.098
3	-0.058	-0.122	1.000	0.000	-0.083	0.000	-0.058	0.558	0.36	-0.058	-0.058	-0.058	0.203	-0.122	-0.058	0.558	0.000	0.000	-0.058	-0.083	-0.058	-0.058	-0.058	0.000	-0.058	0.558	0.157	0.157	-0.058	0.000	0.558	-0.083
4	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
5	-0.046	-0.098	-0.083	0.000	1.000	0.000	0.696	-0.046	-0.067	-0.045	-0.046	-0.046	0.293	0.293	-0.046	-0.046	0.000	0.000	-0.045	-0.067	-0.046	-0.046	-0.045	0.000	-0.032	-0.046	-0.111	-0.111	-0.046	0.000	-0.046	-0.057
6	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
7	-0.032	-0.068	-0.058	0.000	0.696	0.000	1.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.475	0.475	-0.032	-0.032	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	-0.046
8	-0.032	-0.068	0.338	0.000	-0.046	0.000	-0.032	1.000	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	-0.046
9	-0.046	-0.098	0.36	0.000	-0.067	0.000	-0.046	-0.046	1.000	-0.045	-0.046	-0.046	0.293	0.293	-0.046	0.696	0.000	0.000	-0.046	-0.067	-0.046	-0.046	-0.045	0.000	-0.046	-0.046	0.244	-0.111	-0.046	0.000	0.696	-0.057
10	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	1.000	1.000	-0.032	-0.068	0.475	-0.032	-0.032	0.000	0.000	-0.032	-0.046	1.000	1.000	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	0.696
11	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	1.000	1.000	-0.032	-0.068	0.475	-0.032	-0.032	0.000	0.000	-0.032	-0.046	1.000	1.000	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	0.696
12	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	1.000	-0.068	-0.068	-0.032	-0.032	0.000	0.000	-0.032	-0.046	0.000	-0.032	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	-0.045
13	-0.068	-0.143	0.203	0.000	0.293	0.000	0.475	-0.068	0.293	-0.068	-0.068	-0.068	1.000	0.429	-0.068	-0.068	0.000	0.000	-0.068	-0.098	-0.068	-0.068	-0.058	0.000	-0.068	-0.068	-0.163	0.098	-0.058	0.000	-0.068	-0.098
14	-0.068	-0.143	-0.122	0.000	0.293	0.000	0.475	-0.068	0.293	0.475	0.475	-0.068	0.429	1.000	-0.068	-0.068	0.000	0.000	-0.058	-0.098	0.475	0.475	-0.058	0.000	-0.068	-0.068	-0.163	-0.163	-0.068	0.000	-0.068	0.293
15	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	1.000	-0.032	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	0.417	-0.077	-0.032	0.000	-0.032	-0.045
16	-0.032	-0.068	0.558	0.000	-0.046	0.000	-0.032	-0.032	0.696	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	1.000	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	0.417	-0.077	-0.032	0.000	1.000	-0.045
17	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
18	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
19	-0.032	-0.068	-0.038	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	1.000	-0.045	-0.032	-0.032	-0.032	0.000	1.000	-0.032	-0.077	0.417	-0.032	0.000	-0.032	-0.045
20	-0.046	-0.098	-0.083	0.000	-0.067	0.000	-0.046	-0.046	-0.067	-0.045	-0.046	-0.046	-0.098	-0.098	-0.046	-0.046	0.000	0.000	-0.046	1.000	-0.046	-0.046	-0.046	0.000	-0.046	-0.046	-0.111	-0.111	-0.046	0.000	-0.046	-0.067
21	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	1.000	1.000	-0.032	-0.068	0.475	-0.032	-0.032	0.000	0.000	-0.032	-0.046	1.000	1.000	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	0.696
22	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	1.000	1.000	-0.032	-0.068	0.475	-0.032	-0.032	0.000	0.000	-0.032	-0.045	1.000	1.000	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	0.696
23	-0.032	0.475	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	-0.032	-0.045	-0.032	-0.032	1.000	0.000	-0.032	-0.032	-0.077	-0.077	-0.032	0.000	-0.032	-0.045
24	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
25	-0.032	-0.086	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	1.000	-0.046	-0.032	-0.032	-0.032	0.000	1.000	-0.032	-0.077	0.417	-0.032	0.000	-0.032	0.045
26	-0.032	0.098	0.558	0.000	-0.046	0.000	-0.032	-0.032	0.696	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	1.000	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	1.000	0.417	-0.077	-0.032	0.000	1.000	0.046
27	-0.077	-0.163	0.157	0.000	-0.111	0.000	-0.077	-0.077	0.244	-0.077	-0.077	-0.077	-0.163	-0.163	0.417	0.417	0.000	0.000	-0.077	-0.111	-0.077	-0.077	-0.077	0.000	-0.077	0.417	1.000	-0.185	-0.077	0.000	0.417	-0.111
28	-0.417	-0.163	0.157	0.000	-0.111	0.000	-0.077	-0.077	-0.111	-0.077	-0.077	-0.077	0.098	-0.163	-0.077	-0.077	0.000	0.000	0.417	-0.111	-0.077	-0.077	-0.077	0.000	0.417	-0.077	-0.185	1.000	-0.077	0.000	-0.077	-0.111
29	-0.032	-0.068	-0.058	0.000	-0.046	0.000	-0.032	-0.032	-0.046	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	-0.032	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	-0.032	-0.077	-0.077	1.000	0.000	-0.032	-0.046
30	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
31	-0.032	-0.068	0.558	0.000	-0.046	0.000	-0.032	-0.032	0.696	-0.032	-0.032	-0.032	-0.068	-0.068	-0.032	1.000	0.000	0.000	-0.032	-0.046	-0.032	-0.032	-0.032	0.000	-0.032	1.000	0.417	-0.077	-0.032	0.000	1.000	-0.046
32	-0.046	-0.098	-0.083	0.000	-0.067	0.000	-0.046	-0.046	-0.067	0.696	0.696	-0.046	-0.098	0.293	-0.046	-0.046	0.000	0.000	0.046	-0.067	0.696	0.696	-0.046	0.000	-0.046	-0.046	-0.111	-0.111	-0.046	0.000	-0.046	1.000

Similarity matrix clearly stated that actors have some matches in their ties/distances. The matching coefficient (SMCC) was in the range of minimal value of 0.6875 and maximal of 1. When SMMC is 0, there is no tie profile similarity at all. However, when higher than 0, the actors had some matches in their ties/distance (Table 5.). The tie correlation of r = 1.000 was found on the following actors: irresponsible men (4) to banks (6), market officer (17), university researcher (18), sub district officers/local government (24) (Padmakumar et al. 2017), and inseminator (30) (Brookes et al., 2014). Similarity was also found between the harbor porter (10), vehicle (11), quarantine (21), and harbor inspector (22). Constraint hampered pig farmers when planning to sell their products to outside Manokwari using ships in the harbors.

Table 5

Similarity matrix: Matching coefficients (SMMC).

Actors	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32
1	1.000	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	0.938	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.813	0.875	0.938	0.969	0.938	0.906
2	0.844	1.000	0.781	0.785	0.813	0.875	0.844	0.844	0.813	0.844	0.844	0.844	0.750	0.75	0.844	0.844	0.875	0.875	0.844	0.813	0.844	0.844	0.906	0.875	0.844	0.844	0.781	0.719	0.844	0.875	0.844	0.813
3	0.875	0.781	1.000	0.906	0.844	0.906	0.875	0.938	0.906	0.875	0.875	0.875	0.844	0.781	0.875	0.938	0.906	0.906	0.875	0.844	0.875	0.875	0.875	0.906	0.875	0.938	0.813	0.813	0.875	0.906	0.938	0.844
4	0.969	0.785	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.938	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
5	0.906	0.813	0.844	0.938	1.000	0.938	0.969	0.906	0.875	0.906	0.906	0.906	0.875	0.875	0.906	0.906	0.938	0.938	0.906	0.875	0.906	0.906	0.906	0.938	0.906	0.906	0.781	0.781	0.906	0.938	0.906	0.875
6	0.969	0.875	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.938	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
7	0.938	0.844	0.875	0.969	0.969	0.969	1.000	0.938	0.906	0.938	0.938	0.938	0.906	0.906	0.938	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.906
8	0.938	0.844	0.938	0.969	0.906	0.969	0.938	1.000	0.906	0.938	0.938	0.938	0.844	0.844	0.938	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.906
9	0.906	0.813	0.906	0.938	0.875	0.938	0.906	0.906	1.000	0.906	0.906	0.906	0.875	0.875	0.906	0.969	0.938	0.938	0.906	0.875	0.906	0.906	0.906	0.938	0.906	0.969	0.844	0.781	0.906	0.938	0.969	0.875
10	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	1.000	1.000	0.938	0.844	0.906	0.938	0.938	0.969	0.969	0.938	0.906	1.000	1.000	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.969
11	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	1.000	1.000	0.938	0.844	0.906	0.938	0.938	0.969	0.969	0.938	0.906	1.000	1.000	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.969
12	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	1.000	0.844	0.844	0.938	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.906
13	0.844	0.750	0.844	0.875	0.875	0.875	0.906	0.844	0.875	0.844	0.844	0.844	1.000	0.875	0.844	0.844	0.875	0.875	0.844	0.813	0.844	0.844	0.844	0.875	0.844	0.844	0.719	0.781	0.844	0.875	0.844	0.813
14	0.844	0.750	0.781	0.875	0.875	0.875	0.906	0.844	0.875	0.906	0.906	0.844	0.875	1.000	0.844	0.844	0.875	0.875	0.844	0.813	0.906	0.906	0.844	0.875	0.844	0.844	0.719	0.719	0.844	0.875	0.844	0.875
15	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	1.000	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.875	0.813	0.938	0.969	0.938	0.906
16	0.938	0.844	0.938	0.969	0.906	0.969	0.938	0.938	0.969	0.938	0.938	0.938	0.844	0.844	0.938	1.000	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	1.000	0.875	0.813	0.938	0.969	1.000	0.906
17	0.969	0.875	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.938	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
18	0.969	0.875	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.938	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
19	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	0.938	0.938	0.969	0.969	1.000	0.906	0.938	0.938	0.938	0.969	1.000	0.938	0.813	0.875	0.938	0.969	0.938	0.906
20	0.906	0.813	0.844	0.938	0.875	0.938	0.906	0.906	0.875	0.906	0.906	0.906	0.813	0.813	0.906	0.906	0.938	0.938	0.938	1.000	0.906	0.906	0.906	0.938	0.906	0.906	0.781	0.781	0.906	0.938	0.906	0.875
21	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	1.000	1.000	0.938	0.844	0.906	0.938	0.938	0.969	0.969	0.938	0.906	1.000	1.000	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.969
22	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	1.000	1.000	0.938	0.844	0.906	0.938	0.938	0.969	0.969	0.938	0.906	1.000	1.000	0.938	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.969
23	0.938	0.906	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	0.844	0.938	0.969	0.969	0.938	0.906	0.938	0.938	1.000	0.969	0.938	0.938	0.813	0.813	0.938	0.969	0.938	0.906
24	0.969	0.875	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.938	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
25	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	0.938	0.938	0.969	0.969	1.000	0.906	0.938	0.938	0.938	0.969	1.000	0.938	0.813	0.875	0.938	0.969	0.938	0.906
26	0.938	0.844	0.938	0.969	0.906	0.969	0.938	0.938	0.969	0.938	0.938	0.938	0.844	0.844	0.938	1.000	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	1.000	0.875	0.813	0.938	0.969	1.000	0.906
27	0.813	0.781	0.813	0.844	0.781	0.844	0.813	0.813	0.844	0.813	0.813	0.813	0.719	0.719	0.875	0.875	0.844	0.844	0.813	0.781	0.813	0.813	0.813	0.844	0.813	0.875	1.000	0.688	0.813	0.844	0.875	0.781
28	0.875	0.719	0.813	0.844	0.781	0.844	0.813	0.813	0.781	0.813	0.813	0.813	0.781	0.719	0.813	0.813	0.844	0.844	0.875	0.781	0.813	0.813	0.813	0.844	0.875	0.813	0.688	1.000	0.813	0.844	0.813	0.781
29	0.938	0.844	0.875	0.969	0.906	0.969	0.938	0.938	0.906	0.938	0.938	0.938	0.844	0.844	0.938	0.938	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	0.938	0.813	0.813	1.000	0.969	0.938	0.906
30	0.969	0.875	0.906	1.000	0.938	1.000	0.969	0.969	0.938	0.969	0.969	0.969	0.875	0.875	0.969	0.969	1.000	1.000	0.969	0.969	0.969	0.969	0.969	1.000	0.969	0.969	0.844	0.844	0.969	1.000	0.969	0.938
31	0.938	0.844	0.938	0.969	0.906	0.969	0.938	0.938	0.969	0.938	0.938	0.938	0.844	0.844	0.938	1.000	0.969	0.969	0.938	0.906	0.938	0.938	0.938	0.969	0.938	1.000	0.875	0.813	0.938	0.969	1.000	0.906
32	0.906	0.813	0.844	0.938	0.875	0.938	0.906	0.906	0.875	0.969	0.969	0.906	0.813	0.875	0.906	0.906	0.938	0.938	0.906	0.875	0.969	0.969	0.906	0.938	0.906	0.906	0.781	0.781	0.906	0.938	0.906	1.000

In this case, conducive environment creates trust and safety (Devitt et al., 2016) for other actors to work and offer service in pig farming. By avoiding this condition, farmers obtain better service from related and relevant stakeholders. A clique (clique census, CLQs) is the largest subgroup of actors in the social network that are directly connected to each other (maximal complete subgraph). The analysis output in supplement no.7 explained showed the group with two related actors from no 1 to 23 (Table 1). Meanwhile, actors with three groups were found in the number 24 (24 with actors 7, 6 & 5) to 32 (actors 30, 27 & 2) (see Figure 3).

Figure 3

Information flown and shared inside and outside actors of pig business (analysis referred to supplement data no. 8, i.e., Information centrality, IC). Small and big cycles determined the flows of information. Changed red to green and blue colours shows the importance and strategies of actors from high to sub-dominant subjected to information.

Information Centrality (IC) showed the information flow through all paths between actors weighted by strength of tie and distance. The IC ranges from 0 to ∞. The actors with IC’ = 1 identified the government. The output data from supplement 8 highlighted IC close to 1, i.e., actors of shipping (0.931), food court (0.931), retailers (0.927), retribution officer (0.908), breeder (0.926), and feeder (0.929). The power centrality (PC) index is the sum of the sizes of all Nth-order neighborhoods with weight 1/n. The PC ranges from 0 to 31, and when PC’ is equal to 1, then the node is connected to all. The output from Table 6. depicted local community, crop farmers, private credit business, and village officer with the value of 1.00.

Table 6

Power centrality (PC).

Node	Label	PC	PC′	%PC′
1	Consumers	7.226	0.3142	31.42
2	Geverment	5.833	0.6481	64.81
3	Extensionist	9.9	0.45	45
4	Irresponsible men	0	0	0
5	Local community	2	1	100
6	Banks	0	0	0
7	Private credit bureau	1	1	100
8	Buthcers	8.116	0.3689	36.89
9	Shipping	5.316	0.4833	48.33
10	Harbour porter	4.367	0.3638	36.38
11	Vehicles	4.367	0.3638	36.38
12	Crop farmers	1	1	100
13	Food cour	9.683	0.4401	44.01
14	Retailer	9.483	0.431	43.1
15	Other farmers	7.216	0.328	32.8
16	Fishermen	4.316	0.4316	43.16
17	Market officer	0	0	0
18	Univ. Researcher	0	0	0
19	Student communitty	2	0.6667	66.67
20	National research grant	3.167	0.6333	63.33
21	Quarantine officer	4.367	0.3638	36.38
22	Harbour inspectors	4.367	0.3638	36.38
23	Village officer	1	1	100
24	Subdisctric officer	0	0	0
25	Community security	2	0.6666	66.66
26	Retribution officer	4.316	0.4316	43.16
27	Breeder	11.583	0.5378	53.78
28	Feeder	11.583	0.5265	52.65
29	Labour	4.019	0.3091	30.91
30	Inseminator	0	0	0
31	Veterinarian	4.316	0.4316	43.16
32	Police	5.367	0.4128	41.28

Dynamic performance of stakeholders

All stakeholders were grouped into local community, government, banks, markets, private transportation, and university (Brookes et al., 2014). The local community organization consisted of crop farmers, consumers, fisherman, irresponsible men, breeder, feeder, harbor porter, labor, etc. These stakeholders were grouped based on working activities, i.e., production rate in the business process. At the level of production, involved stakeholders were breeders, feeders, laborers, and crop farmers.

In the business process, the custodian involved were consumers, and harbor porter. Related and interlinked stakeholders around the government are market, harbor, inseminator, income office region, sub district, village, extensionist, quarantine, veterinarian, community security, and police officers. Banks provide credit or loans. Market provides retailers and food courts. Private transportation provides shipping and vehicles. University provides researchers and student community service. Stakeholders where organized to work under national administratives, namely Ministries of Agriculture and Technology, Research and Higher Education, Rural and Underdeveloped Regions, Public Works, Internal Affairs, Commerce, Transportation, Manpower, Fishery and Marine, Defence and Security, as well as Health. Similar findings was reported by Brookes et al. (2014) in Australia. Private organization involvement was also identified, such as banks and restaurants.

These actors work to make sure no threat evolved, and to ensure safety (Devitt et al., 2016) in the community. The effect of small-scale pig business experienced not obeying of rules, legislation (Kanis et al. 2003), and biosecurity, as reported from Scotland (Correia-Gomes et al., 2017). And also experienced in Danish pig business, as reported by Fynbo and Jensen (2018). All ministries are working interlinked, sharing similar resources between programs, budgets, humans, and facilities even in laws, as reported by Sysak et al. (2012).

Private organization involvement was also identified, such as financial institutions, private transportation, and restaurants. Banks belonging to national, provide branches up to the local province and regency levels. National banks generally belong to the state, region, and private. The private institution has transportation for conveying products of animals and farmers from one to other regencies. As well as restaurants, such as KFC, besides, there are small-scale private types. Interrelated multi sectors and layer actors were identified ranging from central to local levels. Therefore, actors in pig sectors were grouped into four layers, namely 1st, 2nd, 3rd, and 4th as local, regional, national, and international, respectively. In the first level, the local actors involved were crop farmers, extensionist, consumers, community, butchers, harbor porters, irresponsible men, vehicles, other farmers, village officers, and fisherman. In the 2nd layer, the regional actors involved were banks, private credit business, harbor inspector, retailers, inseminators, veterinarian, community security, and student community services. In the 3rd layer, the national actors involved were banks, police, national research grant institution, (central) government, and quarantine officers. While in the 4th layer, the actors involved were breeders and feeders. The actors play vital roles in improving pig breeding and genetic programs (Leroy et al., 2017). Feeders play a vital role in providing appropriate feeds for the pigs.

As observed from the path of informal and formal values of chains and market chances, pig actors based on the informal system under West Papua circumstances are crop and pig farmers, as well as consumers. However, in the formal system, actors consist of pig farmers, middle men, retailers, outlets/markets, restaurants, and consumers.

From the stakeholder's mapping on the development of pig sectors in West Papua (Figure 4), concordians were grouped into power and interests (1st quadrant up to 4th). The 1st quadrant with low power and high interest was not identified, and categorized into zero actor involvement. In the 2nd quadrant with sub-dominant actors, only two important actors were found, namely the government and crop farmers. Therefore, the government and crops farmers were the two fold actors that play vital roles in supporting direct development of formal value chain (Govoeyi et al., 2019) of pig sectors in West Papua. The finding of actors' distribution on stakeholder map showed that in the 3rd and 4th quadrant, actors have interest related to the development of pig sector. The 3rd quadrant was designed to measure actors that have power with lack of interest, and found 15 actors including extensionist, shipping, consumers, local community, harbor porter, etc. While in the 4th quadrant, 15 actors were found with low power and low interest.

Figure 4

Stakeholders' mapping on pig farming systems in West Papua.

In general, the development of pig sector in West Papua run slowly due to the actors' low power and interest on which pig sectors depends. Therefore, the roles and responsibility of 30 actors in the 3rd and 4th quadrant need improvement. Access, services, and resources should be provided by all related stakeholders. Actors in the 2nd quadrant, namely the government should play vital and strategic roles in promoting local resources.

Intervention and innovation

Intervention needs to assure the sustainability of pig beneficiary business (Table 7). In policy sector, the 26 stakeholders (81.25%) carried out intervention policy. In budgeting sector, 10 concordians (31.25%) needed intervention, while 16 required spacing, and 12 needed intervention for time resource. Therefore, more than 80% actors (29) needed access. Some actors (16) needed knowledge intervention, while less than 5% (2) required skills. More than 50% of the actors (17) needed intervention related to threats encountered. Few actors needed power, while some were requested for sustaining pig business beneficiary. Besides intervention, the innovations needed are questionable and should be addressed to obtain clear concept and programs for improving pig business in West Papua. Innovations are needed to ensure the sustainability of pig business. In financial sector, nine stakeholders (31.25%) needed innovation, as highlighted by Mollenhorst and de Boer (2010) under egg production systems in the Netherlands. Providing easy process and access of loans, as well as services needed further study based on financial institutions.

Table 7

Intervention and innovation inventoried in developing pig sectors.

Factors	Sum	Proportion (%)
Intervention
Policy	26	81.25
Financial	10	31.25
Space	16	50.00
Time	12	37.50
Access	28	87.50
Satisfaction	5	15.63
Knowledge	16	50.00
Skills	2	6.25
Threat	16	50.00
Power	7	21.86
Innovation
Policy	6	18.75
Financial	9	28.13
Space	15	46.87
Time	12	37.50
Access	28	87.50
Satisfaction	5	15.63
Knowledge	20	62.50
Skills	16	50.00
Threat	2	6.25
Power	16	50.00

This relationships need collaboration among science and industry (Schodl et al. 2015). The application of technology (paper less) allow farmers to apply for loans, while some actors needed innovation of space (15), time (12), access (38), knowledge (19), skills (16), and powers (16). Few actors needed innovation in satisfying services and avoiding threats.

The constraints encountered under pig production

Several constraints were encountered, which were not made possible by all stakeholders. They include lack of services, programs, budgets, human resources, community services, loans, and facilities, such as slaughtering house (Govoeyi et al., 2019). And also lack of shipping structures, restaurant, market, business unit, mini feed meal, rules and regulations, technical pig production, such as breeding (Greef et al., 2011). As well as inefficient policy, such as importation, taxes, retribution and safety of business, such as killed animals, diseases (Brookes et al., 2014), theft, and mortality (Baxter and Edwards, 2017). Community services made farmers feel confidence with their business. Therefore, the skills and new innovation provided, enable farmers to keep up their production and business scales (Prado-lorenzo and Gallego-a 2011; Blok et al., 2015; Günther and Hüske 2014; Leroy et al., 2017; Stephen P Borgatti et al., 2003; Sysak et al., 2012; Nguthi 2007; Iyai 2010; Crossley et al., 2009; Menard and Klein 2004). Programs from other stakeholders are easy to practice in the fields, while the budget provided ensure development and drive business scales. Human resources, such as extensions and supervision are carried out properly in the field. By doing this, farmers have partners to consult and to obtain advices for technical issues and constraints faced. Facilities, such as slaughtering house, shipping structure (Averós et al., 2008), restaurant, markets, business unit, and mini feed mill drive farmers to achieve certain work hours and capacity to live up the business process of pig farming production. Rules and regulation implemented also enable farmers to develop their production. Technical pig production with related knowledge of animal aggression (behaviour) (Camerlink and Turner, 2017), enable farmers to sustain optimal pig production.

Therefore, modern knowledge and skills in terms of how to offer quality feeds, reproduction management, breeding selection (Kanis et al., 2003), and artificial insemination are the priority known by farmers. This was also reported by Montsho and Moreki (2012) in Bostwana. Policy in supporting pig business is different from that of small-scale farming production (Figure 5). Farmers do not obtain adequate policy and regulation that drive their business to achieve optimal opportunity by beneficiary as stated by Leroy et al. (2017). Government and private actors, such as banks have to make sure the policy created enable farmers to have future business prospect. Losses due to safety of business cause bankruptcy of beneficiary, and eventually, farmers fail to run their business appropriately. Finally, actors with the responsibility to assure safety and security should involve in pig farming business. The lack of actor's involvement induce failure, as well as several constraints encountered by pig farmers as reported in Uganda (Muhanguzi et al., 2012).

Figure 5

Stages of actors' involvement in pig business process at West Papua.

Conclusions

Several key stakeholders were identified by government, both in the national and local levels. Some concordians were interlinked with multi-disciplinary actors in developing pig-farming systems. The weaknesses encountered in the process of pig farming development include poor community services, lack of loans from banks and local government, facilities, such as slaughtering houses, shipping structures, restaurant, market, mini feed mill, breeding, low imported pig products and its sale-cuts, illegal taxes and retributions, killed pigs, thief, death of young pigs, and low still birth population. Therefore, actors with the responsibility to assure safety and security should be involve in pig farming business.

Large scale cooperations with the potential to establish mutual relationship were identified. The characters of actors' organization that benefited every mutual relationship were recorded. It was observed that in delivering and sharing resources, power, and intervention, each actor has similar understanding and trust in promoting better sustainable pig development. This article presents broad and specific actors' relationship, particularly in pig business. It was found that the concept of this interlinked actors’ relationship gave new insight in mapping complexity of organizations and institution involvement. The concept employed is related to livestock development in general, particularly for pig farming, and also in investigating actors' characteristics, addressing resources, power, and intervention. Further analyses is suggested by computing more indepth relationships within the innovation and intervention shared, for improving performances of pig production business on small-scale systems. Future research is suggested for interlinked actors using the Correlation Coefficient of Pearson (PCC) and mapping the interest, including the power in two dimensional graph. As well as inventorying all intervention and innovation that are achievable and applicable.

Declarations

Author contribution statement

Deny Anjelus Iyai, Irba Warsono, Yubelince Runtuboi, Hieronymus Yohanes: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Dwi Nurhayati, Desni Saragih, Dwi Rahardjo, Hanike Monim, Marlyn Lekitoo: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Maria Arim, Muhammad Djunaedi, Sangle Yohanes Randa, Stepanus Pakage, Mulyadi, Elfira Suawa, Frandz Pawere, Angelina Tethool, Onesimus Yoku, Lamberthus Nuhuyanan: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Yafed Syufi, Sintje Rumetor: Conceived and designed the experiments; Performed the experiments; Wrote the paper.

Agustinus Murwanto, Rizki Arizona: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Alnita Baaka, Daniel Seseray: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Martha Kayadoe: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. Muhammad Jen Wajo: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Margaretha Orisu: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

The data that has been used is confidential.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.