Networking Behaviour of Agri-entrepreneurs and Systematic Challenges

Abstract

Networking helps entrepreneurs in opportunity identification, resource acquisition, overcoming barriers and ensuring sustainability and growth. However, agri-entrepreneurs are locally embedded, and networking can provide limited options to respond to challenges. In a developing economy, agriculture has various systemic risks such as disasters, policy changes, market fluctuations and government policies. Given the large employment and emerging concerns in agriculture, managing such systemic challenges becomes significant. While the literature broadly indicates that the networking behaviour of entrepreneurs helps, little is known if such behaviour helps in mitigating challenges. This study responds to the literature gap by identifying the association between networking behaviour and systemic challenges faced by one of the less developed regions of India. The study considered 14 systematic challenges from the PESTEL framework and 15 levels of networking from the literature. Entrepreneurs from the agriculture sector (N = 402) were contacted for their responses. Findings suggest that networking behaviour is significantly associated with various systematic challenges faced by entrepreneurs in the agriculture sector. The networking behaviour also varies with the demography of the entrepreneurs. The study contributes to social networking theory, structuration and risk management theory. Networking happens under a social structure, and social networking helps in managing systematic risk. Entrepreneurs, policymakers and educators can understand and facilitate such networking.

Keywords

Agriculture entrepreneur systemic risk PESTEL social networking

Introduction

A typical small and medium enterprise (SME) has location specificity that characterises its behaviour. This factor is more prominent for agriculture firms where land is a primary resource. The agri-supply chain is also geographically embedded. Location specificity is also associated with the political, economic, social, technological, legal and environmental (PESTEL) characteristics of a specific location. These characteristics are expected to influence the behaviour of firms and entrepreneurs.

The study region, Odisha, is the largest producer of rice in India. Some major crops cultivated in the state also include jute, oilseeds, pulses, coconut and others. Odisha has 4 geographic zones and 10 agro-climatic zones (NITI Aayog, n.d.), indicating geographic endowments. It was the first state to bring an agriculture budget and climate budget (Pradhan et al., 2023). Some of the drivers of agriculture in the context of this state include inputs (e.g., subsidies, agriculture credit), farm mechanisation technology, extension services, price incentives and infrastructural facilities (Hoda et al., 2021). However, the state is comparatively poorer, with a large number of farmers and agri-entrepreneurs. Frequent natural calamities affect the agriculture value chain substantially. Literature suggests low growth and productivity to be of concern. Some have suggested alternative methods and policy implementation efficiency for overall improvement (Pradhan et al., 2023). While emphasising the role of natural endowment, calamities and government, the literature has not adequately emphasised how entrepreneurs individually attempt to overcome different challenges. Entrepreneurs bear the risk from different sources, but how is not adequately explained. Given the limitations of individual entrepreneurial resources, entrepreneurs have to fall back on their family, friends, suppliers and customers, among others. In effect, entrepreneurs have to depend on their network to manage risks. Does each network agent contribute to various risks or challenges arising in the course of business? This research investigates risks or challenges specific to agriculture and its association with different network agents.

Literature Review

A network is a part of the entrepreneurial ecosystem. Industrial clusters and regional innovation systems are part of the network approach to understanding entrepreneurship (Spigel & Harrison, 2018). Entrepreneurs’ network helps in opportunity discovery (Shu et al., 2018). A network can have spatial characteristics, but not necessarily. While networks can be of multiple types, a business network was proposed as a cartel of independent firms responsible for growth (Johannisson, 2017). However, the characteristics, use and outcome of a network can have grey areas. The social network of entrepreneurs gave business advice, emotional support and business resources, thereby influencing business growth (Arregle et al., 2015). Crowdfunding is the use of social networks to raise capital (Hossain & Oparaocha, 2017). The online presence, network and interactions are also called the online social capital of entrepreneurs (Smith et al., 2017).

The networking behaviour of women also differs. Women use their network to overcome gender bias, suggesting a concept called gender capital in networking (McAdam et al., 2019). They overcome patriarchal barriers, including access to capital, financial information, resources and new business opportunities, through networking (Kalafatoglu & Mendoza, 2017).

Endogenous growth theory and knowledge spillover theory suggest that regional growth, entrepreneurship and innovation can be explained by network dynamics. Particularly, strategic relationships mediate the relationship between innovation-driven regional growth and entrepreneurship (Huggins & Thompson, 2015). The spatially embedded network of rural entrepreneurs and a strategic non-local network help rural entrepreneurs discover opportunities (Korsgaard et al., 2015).

Entrepreneurs face several challenges. Social or family approval for the choice of career, securing finance, administrative and legal processes in venture creation, marketing their products or services and firm survival are some of the known challenges. The challenges at the initial stages of a firm are known as the burden of newness. It is generally believed that challenges are growth-limiting aspects. At the extreme level, challenges can give rise to the quit intention of entrepreneurs. Challenges have a demographic aspect as well. Gender, prior work experience, enterprise age, family occupation, sources of capital, financial challenges, prior training, administrative and legal challenges and social preference for entrepreneurship have statistically significant impacts on the quit intention of young entrepreneurs (Kar & Ahmed, 2021). However, entrepreneurs who perceive higher challenges report a higher level of firm performance (Kar & Ahmed, 2019). Further, various entrepreneurial challenges are not isolated. Challenges are experienced by entrepreneurs as a group, and these are strongly correlated.

The personality type of entrepreneurs predisposes them to perceive different challenges. This indicates that challenges are subjective perceptions. Thus, personality factors and challenges are found to have significant correlations (Kar & Ahmed, 2019).

Networking is one of the ways to respond to challenges. Network members provide information, advice, support and resources, among other things to entrepreneurs. Family members, friends, social networks and business professionals form various levels of networks with whom an entrepreneur interacts. Each level is expected to transact different resources for the firm or entrepreneur. However, it is reported that only 40 per cent of entrepreneurs reported support from their family members. The occupation of the family also predisposes them to support the entrepreneur. The support is also associated with business performance and sociocultural challenges. Younger entrepreneurs with less education and prior experience were supported to a greater extent by their families. But irrespective of family support, entrepreneurs from family business backgrounds performed better (Kar & Ahmed, 2022). Thus, the impact of family networks on performance was inconclusive.

Formal or informal mentoring is also associated with new ventures or entrepreneurs. Mentor–mentee dyads form a network characterised by capacity building, connection, collaboration, concreteness, time and trust (Kar & Sarangi, 2020). Apparently, the role of a network is not always material or linked to business performance. Possibly, networking acts as a moderator for performance.

Bricolage in entrepreneurship typically presupposes the availability of a network. Similarly, resiliency as an individual entrepreneurial characteristic depends on the business network, more so in the case of a natural disaster (Pathak et al., 2023). The role of the network is accentuated in a crisis situation. The COVID-19 crisis was marked by fluidity and inadequate information, which prevented entrepreneurs from planning. Most sought information and help from their business network to decide their courses of action, including the strategic decision of a possible switching of sectors (Pathak et al., 2022). Business associations are networking mechanisms that help members acquire resources or coordinate with other stakeholders.

PESTEL Framework

In general, challenges arise from the political, economic, social and technological (PEST) factors, which were subsequently enhanced to include environmental, and legal (PESTEL) factors, presenting a broad framework within which entrepreneurship operates. This framework is also used to assess the quality of the entrepreneurial ecosystem (Lee & Cho, 2022), understand the influence of human factors on internationalisation of firms (Agwu & Onwuegbuzie, 2018), analyse tourism entrepreneurship (Roy & Chowdhury, 2021; Susilo, 2020), assess the environment for decision making (Sigcha et al., 2021) and find out opportunities (Maime & Rambe, 2023).

It is apparent from the literature review that the PESTEL factors have been studied extensively, either independently or in combinations of factors. Government policies (Bradley et al., 2021), regulations, complex social networks, family dynamics and cultural norms influence the sustainability of SMEs (Emon & Khan, 2023). The rule of law and regulatory quality also influence it (Agostino et al., 2020). These factors have been conceptualised as part of the entrepreneurial ecosystem (Guerrero et al., 2021), including Isenberg’s entrepreneurial ecosystem model (Mason & Brown, 2014).

The importance of PESTEL factors is specific to an industry or sector (Belas et al., 2020). The agriculture sector is more complex as it influences related sectors such as food, fertilisers and agriculture machinery, among others. Large employment in agriculture, food distribution, sustainability requirements and spatial embeddedness are some of the important characteristics, especially in developing economies. These characteristics and associated challenges of agriculture indicate the less amenability of networking as a dynamic response to challenges. This research tested what kinds of challenges are associated with different levels of networking in the case of agriculture entrepreneurship.

Theoretical Basis

The agribusiness supply chain is subjected to multiple theoretical aspects due to the involvement of multiple agents (entrepreneur, customer, supplier, social network and government). Behaviour at multiple levels of the supply chain is explained through agency theory (government as the principal implementing policy through agents). The business network structure is also expected to influence the behaviour as per the agency-structure theory or the theory of structuration. Risk management theory proposes that business risks be identified, assessed and responded to. However, the ‘how to’ part is open to entrepreneurs. Contract theory suggests entrepreneurs use contracts for coordination in an imperfect market such as agriculture. The literature also acknowledges the absence of a theory of cooperation in business (Ford & Mouzas, 2013). The lack of market convergence in agriculture is acknowledged in poststructuralist theory, indicating the presence of multiple realities. This research proposes that building networks is an effective response to various risks or challenges and tests their association in the context of agribusiness.

Research Gap

Research has proposed that challenges create conditions and experiences that motivate particular adaptive mechanisms such as social and network skills and creativity (Miller & Le Breton-Miller, 2017). However, the nature and dynamics of entrepreneurial networking activity are less understood by different demographic segments, including gender (Kalafatoglu & Mendoza, 2017). A similar observation was that the role of networks in regional innovation and growth is less formally examined (Huggins & Thompson, 2015).

Agriculture in the eastern part of India, particularly Odisha, is affected by multiple challenges. Odisha is typically a disaster-prone state and has lower levels of development compared to other states in India. The state depends on other states for food grains and vegetables; inadequate agricultural infrastructure, lack of agro-processing facilities, low farm mechanisation, inadequate government support and a lack of entrepreneurial mindset among people are general challenges indicated in various reports and discussed in different forums. The literature review indicated additional and fine-grained challenges in agriculture.

Objectives

This research hypothesised that various challenges and different networking behaviours of agricultural entrepreneurs are not associated. Challenges are independent and generated in the context of the business. Similarly, networking behaviours are innate to entrepreneurs and are not directed by different types of challenges in agriculture. SMEs involved in agriculture have localised operations and therefore possibly lack the dynamic characteristics necessitating an effort to reorient the entrepreneurial network.

Methodology

A list of 500 agriculture entrepreneurs with their contact details was prepared from a well-known business directory in Odisha (vignapan.com). The entrepreneurs were reached by telephone and email, requesting their responses to a structured questionnaire. The questionnaire contained a demographic profile, business profile, networking behaviour and perceptions towards various challenges to agriculture in Odisha. Challenges and networking agents were drawn from the existing literature indicated earlier. The questionnaire sought responses to the question, ‘I network with the following for my business’ with the response ‘Yes/No’. The following list of network agents was given: friends, family members, community leaders, social events, religious places/people, managers of other companies, professionals in the same business, industry associations, customers, suppliers, attend business events, bankers/creditors, chartered accountants, government officials and political leaders. The challenge question was to capture if entrepreneurs think the following are challenges to agriculture in Odisha: natural disasters, lack of infrastructure, low demand for products/services, price fluctuation, agri-worker wage, poor work culture, unavailability of financing, lack of agro-processing facilities, low farm mechanisation, inadequate government support, politics, government policy, poor administrative support and a lack of entrepreneurial mindset. The responses were captured in the categorical variable ‘Yes/No’.

Besides the descriptive analysis, the chi-square test in SPSS was used to analyse responses.

Analysis

Table 1 indicates that 84 per cent of the sample (N = 402) was male, married (88 per cent) and in the age group of 34–41 years (32 per cent).

Table 1.

Demographic Characteristics of the Sample.

Gender	Male	Female	Total
N (%)	339 (84.3)	63 (15.7)	402 (100.0)
Marital status	Unmarried	Married	Total
N (%)	48 (11.9)	354 (88.1)	402 (100.0)
Age in years	18–25	26–33	34–41	42–49	≥50	Total
N (%)	15 (3.7)	79 (19.7)	129 (32.1)	97 (24.1)	82 (20.4)	402 (100.0)

Figure 1 shows the networking behaviour of different age groups in the sample (those who responded ‘yes’ to the question). Networking behaviour is significantly associated with the age group of the entrepreneur. Entrepreneurs do not have significant variation in networking with family (p = .183) and suppliers (p = .171) by their age group, and these are rated higher compared to others. In social networking groups, senior entrepreneurs (>41 years) networked less with friends and religious events but have higher networking through social events and community. In the business network category, senior entrepreneurs have higher engagement with managers of other companies, business events, bankers or creditors, industry associations, business professionals and accountants. They also displayed higher levels of networking with government officials and political leaders.

Figure 1.

Note: The percentage is by age group.

Figure 2 indicates the association between perceived challenges and the age group of entrepreneurs. The perception of challenges differs significantly by age group. It can be seen from the graph that the perception of challenges, except disasters, politics, government policy, administrative support and lack of entrepreneurial mindset, peaks in the middle age groups (34–41 years). Senior agricultural entrepreneurs feel that disasters and a lack of mindset are more significant compared to other challenges. The graph also indicates that disasters are perceived to be more challenging. Higher agriculture wages and price fluctuations are considered higher challenges by respondents in the middle age groups.

Figure 2.

Challenges and Its Association with Age Group.

Entrepreneurs in different age groups are likely to have different levels of experience with agricultural operations which exposes them to different challenges. The nature and degree of the challenges are experiential. Second, challenges that are beyond control are perceived to be higher.

Networking Types and Challenges by Gender, Marital Status and Education

Gender: The networking style was found to have a significant association between genders. About 92 per cent of the women networked with their friends compared to about 82 per cent of men, and this association was significant (ꭓ² (1, N = 402) = 4.1, p ≤ .05). Ninety-six per cent of the men networked with their family members, whereas 91 per cent of women did so. This association was significant (ꭓ² (1, N = 402) = 4.45, p ≤ .05).

Ninety-six per cent of men networked with their suppliers, whereas 79 per cent of the women networked with their suppliers. The association was significant (ꭓ² (1, N = 402) = 23.1, p ≤ .01). Seventy-eight per cent of the men attend business events compared to 56 per cent of women. The association was significant (ꭓ² (1, N = 402) = 14.35, p ≤ .01). Eighty-one per cent of men compared to 56 per cent of women network with professionals in the same business group (ꭓ² (1, N = 402) = 20.288, p ≤ .01). Networking with bankers and creditors differed by gender. Ninety per cent of men networked with bankers compared to 78 per cent of women (ꭓ² (1, N = 402) = 7.5, p ≤ .01). Similarly, 84 per cent of men networked with their industry association compared to 59 per cent of women (ꭓ² (1, N = 402) = 20.713, p ≤ .01). Networking with accountants also differed between genders (men = 75 per cent, women = 44 per cent, ꭓ² (1, N = 402) = 23.01, p ≤ .01). Networking with political leaders differed (men = 40 per cent, women = 18 per cent, ꭓ² (1, N = 402) = 11.49, p ≤ .01).

Ninety-five per cent of men thought natural disasters were a challenge compared to 91 per cent of women agri-entrepreneurs (ꭓ² (2, N = 402) = 7.013, p ≤ .05). More men (79.4 per cent) thought lack of infrastructure was a problem compared to women (49 per cent) (ꭓ² (2, N = 402) = 25.499, p ≤ .01). More men (73 per cent) thought poor work culture was a problem compared to women (56 per cent) (ꭓ² (2, N = 402) = 8.65, p ≤ .05). Sixty-six per cent of men felt a lack of government support is a problem compared to 48 per cent of women (ꭓ² (2, N = 402) = 7.342, p ≤ .05).

Marital status: The marital status of entrepreneurs was also associated with their networking style and their perception of challenges. Ninety-two per cent of unmarried persons networked with friends compared to 82 per cent of married persons networked with their friends (ꭓ² (1, N = 402) = 4.258, p ≤ .05). Ninety-eight per cent of unmarried people compared to 78 per cent of married persons networked with their customers (ꭓ² (1, N = 402) = 10.456, p ≤ .01).

Married persons (60 per cent) attended more social events compared to unmarried persons (38 per cent) (ꭓ² (1, N = 402) = 8.425, p ≤ .01). Sixty per cent of the married networked with company professionals compared to 31 per cent of the unmarried persons (ꭓ² (1, N = 402) = 13.807, p ≤ .01). Seventy-seven per cent of married persons compared to 60 per cent of unmarried persons (ꭓ² (1, N = 402) = 5.813, p ≤ .05) attended business events. Networking with industry associations also differed by marital status (married = 82 per cent, unmarried = 63 per cent, ꭓ² (1, N = 402) = 10.199, p ≤ .01). Married persons (39 per cent) networked more compared to unmarried (19 per cent) with political leaders (ꭓ² (1, N = 402) = 7.274, p ≤ .01).

A few challenges were also perceived differently by marital status. Unmarried persons (85 per cent) thought agriculture wages were a challenge compared to married persons (75 per cent) (ꭓ² (1, N = 402) = 15.881, p ≤ .01). Poor work culture was thought to be a challenge (unmarried = 85 per cent, married = 68 per cent, ꭓ² (2, N = 402) = 18.596, p ≤ .01) by unmarried more compared to married. Similar pattern was observed for low mechanisation (unmarried = 77 per cent, married = 62 per cent, ꭓ² (2, N = 402) = 24.708, p ≤ .01), lack of agro-processing facility (unmarried = 69 per cent, married = 63 per cent, ꭓ² (2, N = 402) = 27.384, p ≤ .01), price fluctuation (unmarried = 83 per cent, married= 70 per cent, ꭓ² (2, N = 402) = 34.208, p ≤ .01), lack of government support (unmarried = 65 per cent, married = 62 per cent, ꭓ² (2, N = 402) = 23.531, p ≤ .01), low demand (unmarried = 83 per cent, married = 67 per cent, ꭓ² (2, N = 402) = 26.21, p ≤ .01), unavailability of finance (unmarried = 81 per cent, married = 68 per cent, ꭓ² (2, N = 402) = 33.42, p ≤ .01),

Married (95 per cent) thought natural disasters affected agriculture compared to unmarried (90 per cent) (ꭓ² (2, N = 402) = 17.724, p ≤ .01). Married (75 per cent) compared to unmarried (73 per cent) thought the lack of infrastructure to be a challenge (ꭓ² (2, N = 402) = 12.975, p ≤ .01). Government policy (married = 34 per cent, unmarried = 27 per cent, ꭓ² (2, N = 402) = 30.31, p ≤ .01), politics (married = 29 per cent, unmarried = 19 per cent, ꭓ² (2, N = 402) = 21.283, p ≤ .01), administrative support (married = 36 per cent, unmarried = 33 per cent, ꭓ² (2, N = 402) = 14.85, p ≤ .01) and lack of entrepreneurial mindset (married = 57 per cent, unmarried = 50 per cent, ꭓ² (2, N = 402) = 18.003, p ≤ .01).

Education

The networking behaviour and perceived challenges differed by entrepreneurs’ education (Table 2).

Table 2.

Significant Associations between Networking Behaviour and Challenges by Education.

		Education
Social Networking		No School		Up to 12		Graduate		Postgraduate or More		Total		ꭓ², df, p
Social Networking		n	%	n	%	n	%	n	%	n	%	ꭓ², df, p
Manager of other companies	No	5	100.0	90	45.0	67	46.2	14	26.9	176	43.8	12.892, df = 3, p = .005
Manager of other companies	Yes	0	0.0	110	55.0	78	53.8	38	73.1	226	56.2	12.892, df = 3, p = .005
Community leader	No	4	80.0	118	59.0	73	50.3	12	23.1	207	51.5	23.026, df = 3, p = .000
Community leader	Yes	1	20.0	82	41.0	72	49.7	40	76.9	195	48.5	23.026, df = 3, p = .000
Government official	No	4	80.0	110	55.0	81	55.9	19	36.5	214	53.2	7.914, df = 3, p = .048
Government official	Yes	1	20.0	90	45.0	64	44.1	33	63.5	188	46.8	7.914, df = 3, p = .048
Suppliers	No	1	20.0	21	10.5	2	1.4	3	5.8	27	6.7	12.645, df = 3, p = .005
Suppliers	Yes	4	80.0	179	89.5	143	98.6	49	94.2	375	93.3	12.645, df = 3, p = .005
Attend business event	No	2	40.0	63	31.5	24	16.6	13	25.0	102	25.4	10.493, df = 3, p = .015
Attend business event	Yes	3	60.0	137	68.5	121	83.4	39	75.0	300	74.6	10.493, df = 3, p = .015
Attend social events	No	5	100.0	91	45.5	60	41.4	17	32.7	173	43.0	9.545, df = 3, p = .023
Attend social events	Yes	0	0.0	109	54.5	85	58.6	35	67.3	229	57.0	9.545, df = 3, p = .023
Business professionals	No	3	60.0	57	28.5	21	14.5	10	19.2	91	22.6	13.761, df = 3, p = .003
Business professionals	Yes	2	40.0	143	71.5	124	85.5	42	80.8	311	77.4	13.761, df = 3, p = .003
Bankers/creditors	No	1	20.0	34	17.0	8	5.5	5	9.6	48	11.9	11.135, df = 3, p = .011
Bankers/creditors	Yes	4	80.0	166	83.0	137	94.5	47	90.4	354	88.1	11.135, df = 3, p = .011
Accountants	No	3	60.0	81	40.5	30	20.7	7	13.5	121	30.1	25.351, df = 3, p = .000
Accountants	Yes	2	40.0	119	59.5	115	79.3	45	86.5	281	69.9	25.351, df = 3, p = .000
Political leader	No	5	100.0	136	68.0	81	55.9	34	65.4	256	63.7	8.363, df = 3, p = .039
Political leader	Yes	0	0.0	64	32.0	64	44.1	18	34.6	146	36.3	8.363, df = 3, p = .039
Religious places/people	No	2	40.0	123	61.5	61	42.1	19	36.5	205	51.0	18.045, df = 3, p = .000
Religious places/people	Yes	3	60.0	77	38.5	84	57.9	33	63.5	197	49.0	18.045, df = 3, p = .000
Low mechanisation	No	2	40	75	37.5	32	22.1	15	28.8	124	30.8	18.250, df = 6, p = .006
Low mechanisation	Yes	3	60.0	125	62.5	113	77.9	37	71.2	278	69.2	18.250, df = 6, p = .006
No agro-process facility	No	1	20	77	38.5	33	22.8	10	19.2	121	30.8	17.407, df = 6, p = .008
No agro-process facility	Yes	4	80.0	123	61.5	112	77.2	42	80.8	281	69.9	17.407, df = 6, p = .008
Price fluctuation	No	0	0	65	32.5	26	17.9	9	17.3	100	30.8	17.544, df = 6, p = .007
Price fluctuation	Yes	5	100.0	135	67.5	119	82.1	43	82.7	302	75.1	17.544, df = 6, p = .007
Lack of government support	No	1	20	75	37.5	38	26.2	11	21.2	125	30.8	12.594, df = 6, p = .050
Lack of government support	Yes	4	80.0	125	62.5	107	73.8	41	78.8	277	68.9	12.594, df = 6, p = .050
Administrative support	No	3	60	124	62.0	76	52.4	15	28.8	218	30.8	26.097, df = 6, p = .000
Administrative support	Yes	2	40.0	76	38.0	69	47.6	37	71.2	184	45.8	26.097, df = 6, p = .000
Low demand	No	0	0	63	31.5	34	23.4	9	17.3	106	30.8	16.107, df = 6, p = .013
Low demand	Yes	5	100.0	137	68.5	111	76.6	43	82.7	296	73.6	16.107, df = 6, p = .013
Unavailability of finance	No	0	0	67	33.5	32	22.1	8	15.4	107	30.8	17.495, df = 6, p = .008
Unavailability of finance	Yes	5	100.0	133	66.5	113	77.9	44	84.6	295	73.4	17.495, df = 6, p = .008

Higher education increased networking with managers of other companies, attending social events, meeting or visiting religious places, community leaders, government officials, suppliers, attending business events, meeting business professionals, bankers and creditors and accountants, but reduced networking with political leaders.

Higher-educated entrepreneurs thought low mechanisation, a lack of agro-processing industries, price fluctuation, a lack of government and administrative support, low demand for the product and the unavailability of finance as significant challenges.

Table 3 indicates the significant association of networking behaviour and perceived challenges.

Table 3.

Association of Networking Behaviour and Perceived Challenges.

Friends

Family

Community Leaders

Attend Social Events

Visit Religious Places/People

Managers of Other Companies

Professionals in the Same Business

Industry Associations

Customers

Suppliers

Attend Business Events

Bankers, Creditors

CA/Accountants

Government Officials

Political Leaders

Total Row

Natural disasters

Lack of infrastructure

Low demand for products/services

Price fluctuation

Agri-worker wage

Poor work culture

Unavailability of financing

Lack of agro-processing facilities

Low farm mechanisation

Inadequate government support

Politics

Government policy

Poor administrative support

Lack of entrepreneurial mindset

Total column

Note: *Significant at p ≤ .05 level, **significant at p ≤ .01 level.

It can be seen that attending business events, industry associations and networking with accountants are the most important networking behaviours that have a relationship with all challenges. Networking at social events and association with suppliers is the next best behaviour exhibited by agri-entrepreneurs. Networking with political leaders is the least preferred behaviour, whereas networking with bankers and creditors and professionals in the same business are next to the least preferred networking agents.

Natural disasters, a lack of infrastructure and a lack of entrepreneurial mindset are challenges with decreasing order, which could be least helped by any network agents. Challenges that were associated with all networking behaviours or agents are as follows: price fluctuation, poor work culture, unavailability of financing and the lack of agro-processing facilities.

Discussion

The variation in networking behaviour by demography indicated peculiarities of the agriculture sector. Beyond subsidies and minimum support prices (MSP) for farmers, the government also has policies for women groups (self-help group (SHG)), primarily associated with minor agro-processing activities. Policies targeted towards micro, small and medium enterprises (MSMEs) also overlap with the agriculture sector. The government regularly covers losses due to natural disasters through insurance or disaster-specific assistance.

Interestingly, senior entrepreneurs show less networking with customers. This aspect could indicate the role of subsidies and other benefits extended by government agencies, thus having higher importance.

Women preferred more non-business, non-family-related social networking compared to men. The nature, scope and possible degree of control over their businesses could explain such observations. Growth intention and perception of challenges could also explain this networking behaviour. The nature of agribusiness by women and inherently less mechanisation or labour-intensive practices can explain why fewer women feel a lack of infrastructure as a problem. Pro-women and women SHG policies of the government could explain why more men feel a lack of government support to be a challenge.

Marriage in general, and particularly in India, reorients the networking style of individuals. It indicates a life stage linked to age, additional responsibilities, income and resource requirements. Unmarried individuals networked with their friends and customers more compared to the other group, but married individuals attended more social events and networked with political leaders. This could be due to the influence of women SHGs actively supported by the political party in power. Challenges also differed by marital status. Unmarried people perceived more challenges due to operational factors (agricultural wage, poor work culture, low mechanisation, lack of agro-processing facility, price fluctuation, lack of government support, low demand and unavailability of finance). Whereas, married individuals thought challenges such as natural disasters, lack of infrastructure, government policy, politics, administrative support and entrepreneurial mindsets were more challenging.

Social network theory indicates that the network is an element of social capital and cultural capital. The entrepreneurial network structure operates within the social structure and norms. When the network is limited to geography, it can indicate a community, which is a likely situation for agri-entrepreneurs. Network support can influence the positive aspect, for example, firm growth, or it can reduce the negative impact (minimise loss in a disaster). The positive or negative aspects can be a perception or actual. Further, it can be material, mental, informational (e.g., learning), friendship-oriented and confirmation-related. As a part of social capital, investment in networking can give access to other capital required. Social cohesion, support and reciprocity, among others, are characteristics of social networks. Networking operates at the micro (individual), meso (institutional, group) and macro levels, and the relationships have different levels of intensity (weak or strong).

Conclusion

This research focused on how agri-entrepreneurs’ networking behaviour is oriented towards systematic challenges. The study finds that networking behaviour has demographic variation, and there is significant evidence that the behaviour is associated with various systematic challenges. The demographic variation suggests psychographic variation as well. Thereby, it contributes to the social networking theory, which indicates that network agents help entrepreneurs navigate systematic challenges. Findings suggest that entrepreneurs can engage with different social, business and institutional agents to mitigate risks arising from systematic challenges.

Limitation

This research is a static representation of networking behaviour and does not consider how the same individual changes the networking behaviour over a period of time. Second, the association between networked agents and challenges does not indicate how the challenges are mitigated through networking. The intensity of engagement with the network is not considered in the present research and can be considered in the future. It is possible that the networking is emergent and not directed. There could be possible differential outcomes due to the levels of engagement in the network.

Future Scope of Research

A process view of networking indicates the network can have uncertainty, not be identifiable in advance, and the outcomes may be uncertain (Engel et al., 2017). Thus, networking as an emergent process and its ability to respond to various systematic challenges are worth investigating. Typically, networking would involve mediating or moderating various aspects of an organisation’s triple bottom line. Several methodological changes, such as a graphical network analysis, can be incorporated into research. The relationship between psychographic aspects can also bring deeper insight into how networking helps in various facets of entrepreneurial outcomes.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Brajaballav Kar

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