Introduction

As the impacts of climate change and depletion of natural resources are threatening our habitats and lifestyle1, it is widely recognized that the requirement today is to adopt a lifestyle that highlights sustainability2. The handicrafts can play important role in protecting the natural and social balances and offer ways of sustainable living. Handicraft is described by the use of simple raw materials and traditional techniques with very low impact on the environment. Hand-crafted products can be reused and recycled for years, without harming the natural environment3.

Hand-made carpet is a special art-industry with cultural and economic capability in Iran, which has fulfilled the aesthetic and practical needs of the people of this land and other countries for many centuries. The importance of this art becomes clear when it forms the largest non-oil export of our country from the economic point of view and is the foundation for creating many jobs around the self-made4. Hand-made carpet is one of the most well-known handicrafts around the world. Iranian hand-made carpet is known as a luxury product in the domestic and international markets due to its strength and product value5. The position of Iranian carpets in the world has put us in a sensitive situation, because it has taken a part of our culture, in such a way that to maintain its position, it is more important to pay attention to the category of innovation. Iranian carpet has high competitiveness in terms of texture and structural techniques, but it has weaknesses in terms of design and coloring. Examining several decades of activity of this industry shows a weakness in paying attention to the category of innovation in this field.

Toghraei, Navidzadeh, Attafar and Zakariyayi Kermani (2014) showed that there are various obstacles in this field that can be attributed to lack of attention to market needs, lack of designers’ information, non-acceptance of innovation, lack of necessary training, and lack of updated information. Although the handwoven carpet industry to have little competitive power in the international markets over the past few decades, but most of the experts and officials in this field admit that this industry can gain its position in the global markets if it has a written and clear strategy6,7.

One of the prerequisites for maintaining the status and value of hand-made carpets is attention to innovation, and continuous product development and innovation are vital requirements for the continued growth and survival of this industry. The weakness of innovation in products and production procedures or technology in this industry has caused the Iranian hand-made carpet to give up its position to competitors in some markets8. Sharing information through different channels is the first step in adopting innovations. When knowledge is accepted by beneficiaries, the effect of information appears in various forms and helps to increase performance, reduce production costs, increase product prices, increase income, and increase adoption of technologies9.

Knowledge and information are essential components to increase production and productivity. Information is a critical resource in investment operations and management. Effective delivery of information requires knowing the needs of producers and determining the best method to provide the information they need. Access to the right information at the right time in the right format and from the right source may change the balance between manufacturer success and failure. The communication of production information is a vital factor in the process of changing the producer society. For this reason, research results constitute an important knowledge base that should be made available to the manufacturer through sources whose characteristics are acceptable. Therefore, the identification of different information resources/information services used by users is needed to reveal the relationship of these resources/services, as well as the superiority/priority assigned to different types of resources and services10.

Supply chain management systems currently face several serious problems such as product tampering, poor traceability, delays and lack of real-time information sharing11.

Considering the importance of information in manufacturing industries and the acceptance of innovations, and the importance of the hand-made carpet industry and the need to pay attention to innovation in this industry, it is necessary to investigate the information network of hand-made carpet weavers with other actors in the hand-madeKISSHC production. These actors include public and private organizations, market and local actors. The question that is raised here is how and with what approach can the network of receiving information between weavers and other actors be examined? Weaver households receive more information from which types of actors? Is there a difference in the information receiving network of weavers in different production methods? In each of the types of actors (government and private organizations, market and local actors), which actor has the most frequency in providing information to the weavers?

In the past years, research in the field of social network analysis has increased. Initially, the field of research was limited to sociology and anthropology, but now it is used in many fields12. Network analysis and visualization of large complex real-world networks (any range from social networks, collaboration networks, Internet, World Wide Web to biological networks, etc.) is an active area of research in recent years. The strength of network analysis is in abstracting the complex relationships between system members in the form of a graph with nodes (including the main members) and boundaries (weighted or single weight as well as directed or undirected, depending on the nature of the interactions) and studying the characteristics of the graph according to one or Several criteria (such as node degree, diameter, clustering coefficient, etc.)13.

A social network is usually represented as a graph where individual or single nodes are represented as vertices and relationships between pairs of individuals are represented as boundaries14.

Network analysis can be divided into three groups15:

  1. 1.

    Analysis of interactions between actors with each other in the network.

  2. 2.

    Analysis of the position of actors in relation to the entire network.

  3. 3.

    Analysis of the arrangement of relationships or the structure of the entire network.

The unit of analysis is a relationship, for example a kinship relationship between individuals. The interesting feature of a relationship is its pattern: not age, sex, religion, income, attitudes; Although these may be traits of people who a relationship between them exists16.

Studies have been done in the field of information sources in the production of hand-made carpets. In the study of MirzaKarimi Isfahani, Bassam, & Hassanpoor Nami (2015), it was done to evaluate the customer’s style and how to obtain information about the customer’s style and needs in Isfahan handwoven carpet, concluded that successful manufacturers rely more on personal sales evaluations17. According to the designers, the assessment of the customer’s needs and tastes is based on the type of order and the amount of order received from the designs. It is also to visit internal and external furniture and decoration exhibitions. A small number of designers are in contact with businessman and sales intermediaries, so they mostly rely on information from manufacturers and do not have contact with customers. Ahmadi & Hematabadi (2022) considered the absence and exit of manufacturing companies in the field of hand-made carpets as a heavy impact to the body of Kerman carpets, and they believe that self-employed weavers have caused the decline of Kerman carpets, and they call self-employed production system as kurbafi, which has caused the production not to be purposeful and high quality18. Because in their opinion, self-employed production system causes the lack of professional marketing and the lack of weave new designs, and the way out of this situation is to enter industrial parks.

The results of the study by Vazirian, Karimian, Ghorbani, & Afshani (2021) showed that key actors have a high social position, authority and influence and are responsible for controlling and mediating in the village, as a result, they can act as local leaders in the decision-making process, coordinating people, establishing communication between others, accessing resources and information and the speed of their exchange, resolving disputes, developing trust and thus increasing social capital play a fundamental role19.

The results of the study by MohammadiKangarani, MirzadehKouhshahi, Nasrabadi, & RafieShahmabadi (2017) showed that the created networks have made it possible for the ecotourism to communicate with the local people of the island without intermediaries and there is no communication gap between them20.

Pantic and et al. (2022) with a study they conducted on creating a sense of agency for teachers, concluded that when teachers act as agents of change, their social networks are larger, more diverse, and more collaborative than in situations where they act as actors21. Examining the symptom structure of obsessive-compulsive disorder, cervin et al. (2022) showed that incompleteness and intrusive thoughts were the most central (i.e., they had the most unique relationship with other dimensions)22.

In a study that was conducted to evaluate the information resources of farmers in the field of soil, they found that although farmers regularly used online resources to access soil resources, online information and communication was not the main factor influencing the change of their practices. Farmers trusted other farmers the most, and they trusted agricultural experts and researchers the least23.

Ahmadifard and Karmidehkordi (2016), by examining the production methods of silk hand-made carpets in Zanjan city, concluded that in self-employed production system, the most important sources of information in the pre-production stage were private teachers, Basij, village council and family members24. During the production stage, lessors and sellers of inputs, village council and local brokers were the most important sources of information. In the post-production stage, SWOI and Imam Khomeini Relief Foundation and lint Collector, etc. were introduced as the most important resources. In the employer production system (shared-based production system & family-owned production system), in the pre-production stage, the village council, private teachers, employers, etc. were introduced as the most important source. During the production stage of the health house, employers, SWOI and Imam Khomeini Relief Foundation were introduced. According to the findings, they achieve to the conclusion that having enough and new information in the field of design and marketing makes the weavers tend to be self-employed production system. Mansouri, Zarmehr & Kazemi (2020), by Investigating the social capital of public library organization, concluded that the maximum scientific communication among the employees of the public library organization is one percent25. Investigating the centrality indicators of the network analysis such as degree, betweenness and proximity of the role of public library employees are 35%, 20% and 25% respectively. Therefore, considering the low cohesion and betweenness centrality, it can be claimed that the social capital is small.

Research methodology and data collection

The research adopted a survey-based quantitative method with the aim of analyzing the network of receiving information by weavers from other actors in the KISSHC production. Data were collected using structured interviews with rural silk weaver households (Fig. 1).

In this research, multi-stage stratified sampling method was used. In the first stage, due to cost and time constraints, three cities with the highest number of silk weavers (according to the statistics of the Carpet Office of Ministry of Industry, Mine and Trade of Zanjan province) including Zanjan, Tarem and Khodabande were selected. By referring to the Ministry of Industry, Mine and Trade of Tehran province, the villages with the highest number of weavers in three cities were introduced. By referring to the prefectural and also the provincial government of Zanjan province, the number of villagers in the specified villages was obtained, and through the village head, the information of some of the most important and key weavers in each village who had the most social relationships and were active in the field of silk hand-made carpets was obtained. We reached other weavers in a snowball technique and were able to get an estimate of the number of silk weavers in each village. After selecting the city, one or more sectors with the highest number of weavers were selected from each city. In the next step, one or more rural district with the highest number of weavers were selected from each sector. In the last stage of selection villages were selected based on 4 groups with one or more than 100 weavers. And based on the formula of Karjisi and Morgan and with an error of 5%, 270 households were selected as samples from a total of 3312 silk weavers in three cities.

In this study, network theory has been used to analyze the structure of the main weavers’ pattern of receiving information from other actors in the knowledge and innovation system. Network analysis provides important information about the relationships between elements in a network. Network analysis assumes that nodes are clustered together because they are connected to each other in some state. In network analysis, links are known as “boundaries”. The presence of significant links does not assume that the nodes are influenced by some contextual factor such as a latent variable, but network analysis assumes that the nodes may directly influence each other26.

There are numerous variables that can be calculated for NA and can be used depending on the research goal.

Fig. 1
figure 1

Stages of research implementation.

Full size image

Concepts in network analysis

Centrality indices

An essential tool for analyzing social networks are centrality indices defined on graph vertices. They are designed to rank actors according to their position in the network and are interpreted as the dominance of actors embedded in a social structure. Most centrality indices are based on the shortest paths connecting pairs of actors, such as measuring the average distance from other actors, or the rate of shortest paths an actor is on. Many network analysis studies rely at least partially on the evaluation of these indicators27.

  1. 1.

    Degree Centrality: Degree centrality is defined as a measure of the number of connections belonging to a node or the number of relationship degrees connected to a node28,4. Kretschmer (2007) has pointed out that in each relationship of a social network case, there is always a weight that indicates the closeness or intensity of interaction between actors29. This method was introduced by involving the weight when analyzing the relationships formed between the actors. From the relationships formed about the social network, the weight indicates the proximity and frequency of interaction between the nodes. Weighted relationships or relationship weight in Kretschmer’s method makes the results of social network research more accurate compared to unweighted relationships in measuring degree centrality. Degree centrality is defined by Eq. 130.

$$\:\text{d}\text{i}=\:\frac{{d}_{i}}{n-1}$$
(1)

 

di = number of ties incident upon node I, n = Total nodes.

  1. 2.

    Betweenness centrality: Betweenness centrality is a shortest path-based centrality measure that is widely used in the analysis of complex networks. Accurate determination of the betweenness is computationally expensive .

Betweenness centrality is usually determined in two steps27:

  1. 1.

    Calculate the length and number of shortest paths between all pairs.

  2. 2.

    Sum of all pair dependencies.

Betweenness centrality is defined by Eq. 230.

$$\:{\text{b}}_{k}=\frac{1}{2}{\sum\:}_{i\ne\:k}^{n}{\sum\:}_{j\ne\:k,i}^{n}\frac{{g}_{ikj}}{gij}\:\:$$
(2)

gij = Number of geodesic paths from node i to node j, gikj= number of geodesic paths from i to j that pass-through k.

  1. 3.

    Closeness centrality: It means measuring the total distance of a node with other nodes. If the length of the shortest paths of node N with other nodes in the network is small, then node N has high closeness centrality31,32. Closeness centrality is defined by Eq. 330.

$$\:\text{C}\text{i}=\:\frac{{C}_{i}}{n-1}$$
(3)

Ci= sum of geodesic distances from a node to all others, n-1 = Total distance to all others.

  1. 4.

    Eigenvector Centrality: The eigenvector centrality represents the most influential node in the network. The most influential/important node is the node with the highest eigenvector among other nodes in a network. The basic factor in determining the most influential node in a network with centrality scale using eigenvector centrality is the eigenvector value in its vicinity33,34. So even if there is a node with low weight or degree centrality, it can still be the most influential node with eigenvector centrality. By considering the value of the eigenvector centrality of its adjacent, we hope to increase the accuracy of determining the most influential work in a network35. Eigenvector centrality is defined by Eq. 4.

$$\:{\uplambda\:}\text{v}=Av$$
(4)

A = The adjacency matrix of the graph, λ = a constant (the eigenvalue), and v = The eigenvector.

Continuity indicators

  1. 1.

    Density: Network density is another important feature that affects the productivity of innovation networks. However, understanding how affect network density in the productivity of innovation networks is still unclear and even controversial36.

  2. 2.

    Transitivity: This index is used to analyze the level of stability and balance of relationships in a network and is variable between zero and one. Although it can also be displayed as a percentage. The meaning of this index can be expressed as if actor A has a link with actor B and actor B has a link with actor C, then transference is the opportunity and chance that A has to link with C37.

  3. 3.

    Fragmentation: Using the concept of cut point, fragmentation is defined as the proportion of mutually accessible nodes such that each node is removed from the network33,38.

  4. 4.

    Diameter: The diameter of a graph is one of its most basic parameters. Since several years ago, diameter calculation for large graphs has become a fundamental issue in the field of complex network analysis39. It is the longest distance between any two nodes in a network35.

  5. 5.

    Radius: The lowest amount of centrifugal is the whole graph40.

  6. 6.

    Average Distance: The average distance shows how can with many steps to reach each node in each network41.

  7. 7.

    Norm Distance: Norm distance is the degree to which knowledge transfer departments share the same organizational culture and value systems. Early research on technology transfer has shown that differences in work values and organizational cultures significantly hinder knowledge transfer. The reason is that similar cultures and value systems allow smooth working relationships between knowledge transfer parties. After all, culture and shared norms determine what is acceptable and what is not acceptable in a workplace. Shared norms not only provide predictability and understanding between the parties, but also ensure that a common approach will be adopted in the transfer process36,42.

In this research, network theory has been used to analyze the structure of the weavers’ information receiving pattern from other actors in the KISSHC industry. All mathematical calculations were done using UCINET software. The intended graphs were drawn in Netdraw software (from UCINET auxiliary tools).

In order to investigate the network of receiving information of weaver households from other actors in the system, due to the large number of weaver households (270 households), the studied villages and production method were used as network analysis criteria;

Receiving information on weaver households from other actors in each village based on three ranges: weak (households without any relationship = 0, and households with relationship = 1), Moderate (households with no relationship and one or more times a year = 0, and households with more than one or How many times a relationship in a year = 1), and strong (households without a relationship and one or more times a year and season = 0, and households with more than one or more relationships in a season = 1) were evaluated;

Results

Descriptive analysis of the amount of information received by weaver households from other actors in the KISSHCs

Examining the results showed that the most amount of information received is from the department of vocational education Zanjan province. Also, the results showed that although the weavers have a lot of contact with the Imam Khomeini relief foundation, the amount of information received from it is 16.4%.

Also, the largest amount of information received by weavers from non-governmental and private organizations is related to the union of rural HC cooperative company of Zanjan province with 42.7%. The analysis of the network of information received from market actors showed that the most information received from businessmen or employers of HCs.

is with 96.7% and the least information received is from the designer.

In response to the research question regarding the most active actors providing information to weaving households, the results showed that, the highest amount of information received by weavers from local actors is related to family members (FMs) and weavers in the same neighborhood or village or with kinship relations (WNVKRs), and the lowest amount of information received from village council members is 25.8%.

Descriptive analysis of the amount of information received by weaver households from other actors in the KISSHC in different production methods

Examining the results showed that there is a difference between weaver households in the amount of receiving information from different actors in the KISHCs in different production methods. The investigation of communication showed that because the weaver households in the rural areas under review have no connection with the national formal actors in different production methods, as a result, the weavers did not receive any information from this group of actors. The most communication is with local actors and in all production methods.

Examining the results of the Kruskal-Wallis test showed that the only significant difference in the amount of information received by weaver households from actors in the KISHCs is with market actors in different production methods.

The network of receiving weavers’ information from other actors in the KISSHC production

Examining the results of Table 1 in the field of continuity indicators of the amount of information received by weaver households from other actors in the KISHCs is as follows;

The highest level of density in the state of weak relationship with actors is equal to 0.126, and with the strengthening of the relationship and in case of having a close and strong relationship, the amount of this index decreases and is equal to 0.004.

The transitivity indicates the stability of the network. The highest amount in the state of strong relationship is equal to 0.805, which indicates the high stability of the network in the situation of receiving information from weaver’s families, more than once or more times in a season from other actors based on the transitivity index. The comparison of the fragmentation index in three states showed that by strengthening the information received from the actors, the amount of this index increased and was equal to 0.994. The diameter, which represents the longest distance between two nodes in a network, is equal to 4 in all three states, and the radius is equal to 2 in all three relational states.

The amount of average distance has decreased with the strengthening of the relationship and is equal to 1.905 in the state of strong relationship, which indicates that in the state of strong relationship, the amount of receiving information directly by the weaver households from other actors increases.

The norm distance refers to the amount to which people in a network have the same organizational culture, value systems, or common language. By strengthening the relationship and when strong relationships are considered, a large number of people who do not have a strong relationship are thrown out of the network and therefore this distance increases. In fact, as the distance between people increases, the norm distance also increases, which in This network is equal to 139/800.

Table 1 The cohesion indicators of the 2-mode network of receiving information on weavers from other actors in KISHCs.
Full size table

Centrality indicators for information received by weaver households in different rural areas with different production methods

Examining the results of Table 2 in the field of centrality indicators of the amount of information received by weaver households from other actors in the KISHC showed that, the degree centrality of the weavers in the investigated rural areas and in different production methods in the network of information received from actors in the KISHC is different. The division of the amount of information received by weavers from other actors into three spectrums, weak, moderate and strong, showed that in the weak state (weavers have any amount of information received), weavers in Valyaran village of Zanjan city, with the self-employed production system and frequency of 0.262 have the highest number of connections in the network of receiving information from the actors in the KISHCs (according to Fig. 2). Examining the degree centrality in the moderate state (weavers with partly and high information) showed that the weavers in Koloeim village in Tarom city, with the self-employed production system and frequency of 0.214 have the highest degree centrality (Fig. 3). Examining the degree centrality in the strong state (weavers with a lot of received information) showed that the weavers in Koloeim village in Tarom city, with the self-sufficient production method with a frequency of 0.095, have the highest amount of communication compared to other weavers in the network of receiving information from actors (Fig. 4).

The eigenvector centrality of weavers in the investigated rural areas is different in different production methods in the network of receiving information from actors in the KISHC. Dividing the amount of information received by weavers showed that in the weak state, the most influential for weavers in Valyaran village of Zanjan city, is with the self-employed production system and frequency of 0.208. Examining the amount of information received in the moderate state showed that the most influential weavers in Koloeim village in Tarom city are with the self-employed production system and frequency of 0.287. Investigating the influence of weavers in the network of information received from actors in the KISHC in the strong state showed that the weavers in Koloeim village in Tarom city have the most influence in the network with the self-employed production system and frequency of 0.805.

The betweenness centrality of weaver households in receiving information from actors in the investigated rural areas, and in different production methods, is different. The betweenness centrality focuses attention on actor, which plays an important role in the flow of information. The amount of information received by weavers in the weak state, showed that the weavers’ households in Jezla village in Zanjan city, with the self-employed production system and frequency of 0.029, have the most control power and mediation. Examining the control power of weaver households in the moderate state showed that the weavers in Koloeim village in Tarom city have the highest betweenness centrality with the self-employed production system and frequency of 0.057. Investigating the control power of weavers in a strong state showed that the weavers in Koloeim village in Tarom city have the highest betweenness centrality in the information receiving network, with the self-employed production system and frequency of 0.003.

The Closeness centrality of weaver households with other actors in the information receiving network in the investigated rural areas and in different production methods, is different. The amount of information received by the weavers in the weak state showed that, the highest speed of access to others has by the weavers’ households in Valyaran village in Zanjan city, with the self-employed production system and the frequency of 0.560. Examining the Closeness centrality in the moderate state showed that the weavers in Koloeim village in Tarom city have a higher speed of access with a frequency of 0.474. The examination of weavers’ access to information in a strong state showed that weavers in Koloeim village in Tarom city have the most access to others, with a self-employed production system and frequency of 0.396.

Table 2 CIs for network of receiving information on weavers with different rural areas and production system in KISHCs.
Full size table

Centrality indicators for the information network received from other actors in the KISSHC

Examining the results of Table 3 in the context of the centrality indicators the amount receiving information of the weaver households from other actors in the KISHC showed that the degree centrality of the actors in the network of information received by weavers from actors in the KISHC is different. The amount of information received by actors in a weak state showed that “WNVKRs” and “FMs” with a frequency of 1 have the highest degree centrality. Examining the average mode showed that “FMs” with a frequency of 0.756 have the highest degree centrality. Examining the degree centrality, in the strong relationship state, showed that “FMs” with 0.067 has the highest frequency.

The eigenvector centrality of actors in the network of information provided to the weavers in the KISHC is different. The amount of information provided in the weak state showed that the most influential actors in the network are related to “WNVKRs” and “FMs” with a frequency of 0.530. The examination of the moderate state showed that “FMs” with a frequency of 0.723 have the most influence in the network. Examining the eigenvector centrality in the state of strong relationship, showed that the most influential actors in the network are “FMs” with a frequency of 0.678.

The amount of betweenness centrality the actors in the field of providing information to the weavers in the KISHC is different. The amount of information provided by the weavers to other activists showed that the highest mediation power is related to “WNVKRs” and “FMs” with a frequency of 0.166. Examining the moderate state showed that the highest betweenness centrality is related to “FMs” with a frequency of 0.211. Examining the amount of betweenness centrality, in the state of strong relationship, showed that “FMs” with a frequency of 0.002 have the highest mediation power.

The amount of closeness centrality of actors in the network of providing information to weavers in the KISHC in the investigated rural areas and in different production systems is different. The amount of information provided in the weak state showed that the highest access speed is related to “WNVKRs” and “FMs” with a frequency of 0.679. The examination of the moderate state showed that the highest access speed in the network is related to “FMs” with a frequency of 0.506. Examining the amount of closeness centrality, in the state of strong relationship, showed that “FMs” with a frequency of 0.386 have faster access speed.

Table 3 CIs for network of receiving information on weavers from different actors in KISHCs.
Full size table
Fig. 2
figure 2

Network of receiving information on weavers from actors and different rural areas in KISHCs (Weak Connection).

Full size image
Fig. 3
figure 3

Network of receiving information on weavers from actors and different rural areas in KISHCs (Moderate Connection).

Full size image
Fig. 4
figure 4

Network of receiving information on weavers from actors and different rural areas in KISHCs (Strong Connection).

Full size image

Discussion and conclusion

It is very important to pay attention to the prosperity of strategic products such as hand-made carpets in order to gain competitive advantages for this important product. To stay competitive and gain a income advantage, stakeholders spend time and effort gathering information, evaluating alternatives, and choosing the best option for their business. This decision-making requires gathering information from various sources, which helps to reduce our incompetence in knowledge of basic manufacturing operations. Therefore, for the prosperity of hand-made carpets, it is important to identify the available information sources and the amount of information received from that source and the difference of these sources in production systems and different regions. Therefore, the main goal of this study is to analyze the information receiving network of weaver rural households from other actors in the KISSHC. The results showed that the weavers’ network of receiving information from other actors such as governmental organizations, educational-promotional organizations, local organizations, market and local actors has a low density and cohesion, which shows that they have little social capital, which is with the study results of Mansouri, Zarmehr and Kazemi (2020) are consonant25. The investigation of the information network of weavers based on the type of production system (family-owned, shared-based and self-employment) showed that the most communication and receiving information by weavers is in the self-employment production system. While the most frequent production method in the studied society is the Family-owned method, a system in which weavers get to know employers through local intermediaries such as FMs and fellow villagers, and after getting to know and starting weave for that employer, due to receiving wages and not having responsibility in the field of sales, they only focus on the weave and do not need information on its sales and market, they only have to deliver a high-quality carpet in a certain time and if there is a problem in the weave, they must report to the employer and get help from him, that this finding is consistent with the results of Ahmadifard and Karmidehkordi’s study (2017) regarding the employer’s role in the employer production system and its most important source of information24. Also, don’t give permission to imitate the map of the carpet being woven to the weaver’s self-employed (such as taking a photo of the carpet map being woven, or lending a part of the map of the woven carpet). All the mentioned cases have caused the reduction and weakness of the weavers’ communication network.

The low density and communication network of weavers is also due to the difference between the production methods in two different cases. While of the weavers were employed in weave in a self-employed production system, they also had a strong information receiving network. Because the nature of self-employing production system is such that the weaver must have sufficient information in all stages of production. While of the family-owned production system (the dominant production system in the studied areas), the weaver must only have sufficient skill in weave, and he/she usually acquired his/her skill from childhood and from FMs and WNVKRs, that this finding is consistent with the results of the study by Vazirian, Karimian, Ghorbani, & Afshani (2021) regarding the role of key actors in obtaining information sources19; And weavers have more trust in local actors in the field of weave, which is consistent with the results of the study by Rust & et al. (2022)23. The weaver’s information on the Family-owned production system in the field of input quality, sales and marketing location, marketability of the map, etc., does not affect the number of wages he/she receives. Therefore, weave with self-employed production system, while the weaver is familiar with the right sources of information and obtains up-to-date and correct information in the field of all production stages, it will strengthen the weaver’s information network and network cohesion, and finally increase social capital, which this finding is consistent with the results of the study. Ahmadifard and Karamidehkordi (2017), based on the role of correct information sources in the tendency towards self-employed production system24; Also, in this situation, the weaver will get a better profit from production and be more satisfied of own activity, which will reduce migrations and prevent the creation of fake jobs. Also, due to the elimination of middlemen and brokers from the production chain, the finished price of the carpet will decrease compared to the previous state and will increase its sales and exports; As a result, not only the self-employment production system has not caused the decline of the hand-made carpet industry, but it has also led to the prosperity of this industry. This finding is not consistent with the results of Ahmadi, and Hematabadi’s (2022)18. Although in the current situation, most of the self-employed weavers produce carpets that are less marketable and are sold at a lower price, but this is due to the lack of proper management and insufficient support for the self-employed weavers, and the self-employed production system itself has no problems.

According to the results of this research, the most important limitation of weavers is the lack of correct information and in fact incorrect information sources. which has caused most of the weavers, whose dominant way of livelihood is carpet weaving, to ensure the sale of their produced carpets, turn to the home wage method, which in the long run, due to receiving low wages in comparison to the difficulty of weaving in this method, causes the weavers to migrate and they have turned to fake jobs. Therefore, it is suggested to focus on the sources of information and training of weavers and provide them with sufficient support, so that the weaver knows about the appropriate input, weave and market at any time, which will reduce the middlemen and reduce the added value of the produced carpets, and finally cause the increase of domestic and foreign markets.

Among the most important limitations of this research is the limited studies in the field of knowledge system and innovation of handmade carpets, which led to limitations in comparing the results in the conclusion section. It is recommended that due to the important role of local actors in the information network received by weavers, strengthening the knowledge network of local actors by the public and private sectors in the field of innovations, facilities, etc. is of great importance. Moreover, given the role of the Self-employed production system in motivating weavers to produce, due to the difference in profit margins received in this method compared to other methods, the operators of this industry should strengthen the Self-employed production system by supporting weavers in providing up-to-date inputs and creating a market for carpets produced using this method.