AAVS_13_11_2508-2523

Introduction

Worldwide shifts in Vulnerability, Uncertainty, Complexity, and Ambiguity (VUCA) over the past few decades (Hoeft, 2021; Johansen and Euchner, 2013; Millar et al., 2018). Introducing this transition presents a novel obstacle for farmers sustaining their commercial operations. Consequently, business professionals must promptly adjust their strategies using newly available resources (Biedenbach Söderholm, 2008; Cunha et al., 2014; Lahiri et al., 2008). For instance, a recent empirical investigation conducted by Gąsior et al. (2022) examines the influence of resource management on the competitive advantages of MSMEs in developed economies. The findings of this study provide evidence that MSMEs that effectively handle novel resources can generate and sustain a competitive edge in their respective markets. Regrettably, numerous firms across different nations face challenges in effectively managing resources to address turbulent conditions (Bakar et al., 2012; Sahi et al., 2019). Numerous enterprises across different nations have failed to attain a competitive edge due to resource constraints (Costa et al., 2013; Finster and Hernke, 2014; Ramsay, 2001; Sikora et al., 2016). Hence, it is imperative to optimise innovation performance to establish a sustainable competitive edge. Numerous studies conducted by scholars from various countries have provided empirical evidence to support the notion that innovation plays a crucial role in enabling organisations to attain and sustain a competitive edge in a constantly evolving business landscape (Chang, 2018; Lai et al., 2014; Sofka and Grimpe, 2010; Zheng et al., 2013).

For instance, the findings of research conducted by Scuotto, et al. (2017) on a sample of 215 MSMEs globally indicate that utilising SNS is crucial for enhancing innovation performance in firms. Social Networking Sites (SNS) encompass various online platforms such as websites, blogs, Facebook, Instagram, TikTok, Twitter, and YouTube (Palacios-Marqués et al., 2015). SNS has promoted greater flexibility and adaptability among businesses in response to external changes (Hasgall, 2012; Iannacci et al., 2021; Sinclaire and Vogus, 2011; Yates and Paquette, 2011a). Moreover, research has demonstrated that SNS can effectively reduce innovation costs for businesses (Scuotto et al., 2017; Sinclaire and Vogus, 2011a), thereby leading to increased profitability (Enders et al., 2008; Gonzalez et al., 2014; Heidemann et al., 2012; Shipilov and Li, 2008). This aligns with the theoretical framework known as the Resource-Based View, which posits that in order to foster innovation, organisations must possess the capacity to seek and exploit opportunities, even when overcome with limited resources (Zhang et al., 2018a). Hence, social networking sites (SNSs) have emerged as a viable option for entrepreneurs to acquire external knowledge (Ford and Mason, 2013; Fu et al., 2019).

Furthermore, a correlation exists between SNS and IL. An empirical investigation undertaken by Bono et al. (2010) in North America examined the influence of SNS on leadership communities. The findings of this study revealed a positive association between SNS usage and the construct of IL. According to McGuire (2002), an increased presence of external networks enables leaders to foster cross-sectoral collaboration, facilitating the exchange of information and decision-making processes. Consequently, organisations can derive efficient solutions from productive contacts promptly.

Prior research has also established a correlation between SNS and AC. For instance, the findings of a research conducted by Ooms et al. (2015) employing a qualitative methodology in high-technology firms demonstrate that social media facilitates the integration of business information, increasing organisational absorptive capacity. The positive influence of SNS on AC was demonstrated in a study conducted by Shan et al. (2018) on a sample of 279 newly established enterprises in China. According to Hein and Rauschnabel (2016), SNS has been demonstrated to facilitate the dissemination of knowledge among business professionals, hence expediting the adoption of new technologies within the corporate community. Hence, some indicators of sharing knowledge through SNS enhance corporate performance, as Liao et al. (2007) suggested. Similarly, a recent empirical investigation conducted by Cepeda-Carrion et al. (2022) examined the influence of SNS on AC within the context of family enterprises in Spain. The findings of this study provided further evidence supporting the notion that SNS have a beneficial influence on AC.

Prior research has also established a correlation between IL and IP. The study conducted by Zhang et al. (2018b) on a sample of 236 entrepreneurs in China demonstrates a positive relationship between IL and IP. Implementing this IL framework facilitates incorporating knowledge as a novel resource, enabling business professionals to enhance their intellectual property (Lei Slocum Jr, 1992). Furthermore, the findings of a recent empirical investigation conducted by Zhang et al. (2021a) shed light on the influence of IL on IP within the context of Chinese high-technology (Hi-Tech) firms. The study examines the mediating role of employee empowerment and Human Resource (HR) flexibility in this relationship.

Furthermore, prior research has also demonstrated the correlation between AC and IP. For instance, the findings of studies conducted in various industries such as Taiwanese MSMEs, the warehouse industry, electronic companies, watches, toys, and machinery industry in Hong Kong, logistics companies in Shanghai-China, innovative companies in Spain, manufacturing companies and service industries in Denmark, and pharmaceutical companies in Europe, as well as research on AC and IP in countries with world heritage cities, indicate a positive relationship between AC and IP (Belderbos et al., 2016; Chen et al., 2009; Costa et al., 2018). The encouragement for firms to innovate stems from acquiring, assimilating, transforming, and exploiting knowledge (Daghfous, 2004a).

Moreover, prior research has demonstrated the involvement of IL and AC in modulating the sympathetic nervous system SNS and IP. For instance, previous empirical investigations (Costa and Monteiro, 2016; Francalanci and Morabito, 2008; Wu et al., 2021a) have examined the role of AC as a mediator in organisations in China, Portugal, and Italy. Integrating knowledge from lower-order capabilities into higher-order capabilities by external capabilities enables business professionals to innovate (Rai et al., 2006).

Regrettably, based on the author’s knowledge of IL and AC studies, there is currently no empirical evidence supporting the notion that SNS and IP serve as mediators, particularly within the context of the dairy cow industry in developing nations. Due to the oligopolistic market structure of the dairy cattle industry, dependence on co-ops and IPS tends to be considerable. Moreover, smallholder dairy farmers must satisfy the increasingly stringent IPS product quality requirements (Hutagoal and Karo-Karo, 2008).

In Indonesia, the dairy sector faces persistent innovation challenges that hinder competitiveness. Smallholder dairy farmers, who dominate production, typically achieve milk yields of only 10–12 liters per cow per day, far below international benchmarks (20–30 liters). In addition, milk quality often fails to meet the increasingly stringent standards imposed by cooperatives and integrated processors, restricting market access and farmer incomes. These limitations are compounded by weak innovation transfer systems, where access to improved genetics, feed innovations, and digital technologies remains scarce. Finally, the structural dependence on cooperatives within an oligopolistic market limits opportunities for diversification into higher-value dairy products. These sector-specific challenges underscore the urgent need to understand how intangible capabilities particularly leadership and absorptive capacity mediate the translation of social networking site usage into tangible innovation outcomes for smallholder farmers.

This study contributes to two essential facets. First, it contributes to the existing literature on the capacity development of The Relationship/partnership support for strategic food commodities of layer breeds associated with SNS, IL, AC, and IP. Second, empirical evidence concerning the function of three essential aspects of IL and AC in mediating SNS and IP, particularly in developing nations’ dairy commodity farming sector. This study presents novel insights into examining SNS, IL, AC, and KI among dairy farmers in developing nations.

Theoretical background and hypothesis development

The relationship of SNS to integrative leadership (IL)

A study conducted by Bono et al. (2010) in North America, which examined 43 leadership communities, determined that SNS have a beneficial impact on IL. According to McGuire (2002), an increased number of external networks enables leaders to foster cross-sectoral cooperation, facilitating the exchange of information and collaborative decision-making. Furthermore, the findings of the study conducted by Gruber et al. (2015) shed light on the influential role of Twitter as a social network in providing timely and reliable news and information. It remains to be seen whether SNS have a quantifiable capacity to positively influence IL, particularly in the context of developing nations and their dynamic livestock industries, such as dairy cattle.

In the context of smallholder dairy systems in Indonesia, the construct of integrative leadership extends beyond corporate managerial roles. Integrative leaders may be cooperative managers who coordinate marketing and quality control, farmer-group heads who mobilize peers for training and adoption of innovations, or individual farmers who act informally as boundary spanners and knowledge brokers. This interpretation aligns with the literature on distributed and community-based leadership, which emphasizes leadership as enacted across multiple actors and organizational boundaries rather than concentrated in a single individual (Darwin and Hadna, 2022; Harris, 2022; Malin and Hackmann, 2019). Accordingly, the operationalization of IL in this study captures perceived integrative behaviours such as coordinating, connecting, and facilitating within farmer networks and cooperatives.

H1: SNS positively influences Integrative Leadership (IL)

Relation of SNS to absorptive capacity (AC)

According to the findings of quantitative research conducted by Ooms et al. (2015) in the context of hi-tech firms, the study demonstrates that SNS can be considered a form of social media. The study further reveals that SNS has the potential to facilitate the integration of business knowledge, hence leading to an increase in AC. The positive influence of SNS on AC was demonstrated in a quantitative study conducted by Shan et al. (2018). This study examined 279 newly established enterprises in China and yielded consistent findings with previous research. According to Hein and Rauschnabel (2016), SNS has been demonstrated to facilitate the dissemination of knowledge among business professionals, hence expediting their adoption of novel technologies. The positive impact of SNS on AC was similarly demonstrated in a study conducted by Cepeda-Carrion et al. (2022). Employing an empirical methodology and quantitative methods, the researchers examined family businesses in Spain. Their study’s findings supported the notion that SNS have a beneficial influence on AC. Furthermore, the findings of a qualitative study by Schlagwein and Hu (2017) elucidated that distinct social media platforms exhibit varying degrees of audience engagement. Furthermore, the study by Schlagwein and Hu (2017) presented various propositions. These ideas include the utilization of internal live broadcasts and the potential of external social media discourse in enhancing corporate knowledge capacity. Thirdly, implementing internal knowledge management practices is crucial in facilitating organizational learning and adapting to change. Fourthly, engaging in internal social media discussions fosters the development of the organization’s capacity for transformative learning in the business context. Fifthly, internal collaboration facilitates the enhancement of exploitative learning capacity. Internal discussion within social media platforms also contributes to exploiting learning capacity. This evidence demonstrates that implementing information system governance can catalyze business professionals to acquire, integrate, transform, and leverage knowledge actively (Ho and Amin, 2022).

H2: SNS positively influences Absorptive capacity (AC)

The relationship of integrative leadership (IL) to innovation performance (IP)

The study conducted by Zhang et al. (2018b) examined a sample of 236 entrepreneurs in China and employed a quantitative methodology. The findings of this research indicate a positive relationship between IL and IP. According to Lei and Slocum Jr. (1992), this IL framework facilitates incorporating knowledge as a novel resource, enabling business professionals to enhance their IP outcomes. In a prior investigation conducted by Zhang et al. (2021a), a quantitative methodology was employed to examine the phenomenon of IL within the context of Chinese Hi-Tech businesses. The findings of this study revealed a significant relationship between IL and IP, which was mediated by factors such as employee empowerment and human resource flexibility. Regrettably, there has been a lack of research investigating the relationship between IL and IP within the context of the dynamic livestock industry, explicitly concerning dairy cows. Simultaneously, IL research emphasizes strategic information that can motivate professionals to enhance their understanding within the professional setting (Youn et al., 2012).

H3: Integrative leadership (IL) positively affect innovation performance (IP)

The relationship of absorptive capacity (AC) to innovation performance (IP)

The findings of a quantitative study (Chen et al., 2009) demonstrate a positive relationship between AC and IP within the context of MSMEs in Taiwan. This perspective is consistent with the notion that AC enables firms to acquire, assimilate, convert, and leverage resources that foster innovation. Academic research suggests that AC significantly promotes successful knowledge acquisition within firms. This is achieved by strategically utilizing internal and external resources, enabling companies to foster innovation (Daghfous, 2004b). Furthermore, the findings of a study conducted by researchers focusing on manufacturing enterprises in Hong Kong, utilizing a quantitative methodology, indicate a positive correlation between AC and IP. Furthermore, the study’s findings (Hong et al., 2019a) demonstrate a positive relationship between AC and IP within international trade organizations. Hence, despite various prior investigations, more literature must examine AC and IP within the livestock industry, particularly concerning the dairy cow sector. This presents a valuable opportunity for further exploration into the potential of AC to enhance IP in this specific domain.

H4: Absorptive capacity (AC) positively influences Innovation Performance (IP).

The relationship of SNS to innovation performance (IP)

According to empirical observations (Scuotto et al., 2017), research findings indicate a positive relationship between SNS and innovation performance. This conclusion is based on a quantitative method that studied 215 MSMEs worldwide. SNS can significantly enhance the establishment of connections between farmers and other stakeholders such as customers, suppliers, and companies, thereby facilitating extensive knowledge exchange among these actors. To enable strategic innovation through the sharing of ideas and information, scholars such as Audretsch et al. (2016), Kaplan and Haenlein (2010), and McKelvey and Lassen (2013) have emphasized the importance of creating digital platforms that support interactive communication and collaboration. Empirical studies by Muller and Peres (2019) and Li et al. (2020) further confirm that social networking strengthens reciprocal influence, enhances connectedness, and improves innovation outcomes through knowledge diffusion. Although several studies have reported positive associations between SNS use and innovation performance, other evidence suggests that this relationship may not always be direct or uniform across contexts. In rural and smallholder settings, the digital divide manifested in uneven access to infrastructure, digital literacy, and credible information often constrains farmers’ ability to convert SNS engagement into tangible innovation outcomes (Moyle et al., 2022; Zhang and Li, 2021; Mehrabi et al., 2021). Moreover, excessive or unfiltered exposure to online content can lead to information overload and selective exposure, which reduce the novelty of acquired knowledge and may reinforce existing practices rather than transform them (Eppler and Mengis, 2004; Bozdag et al., 2015; Seargeant and Tagg, 2019). Evidence from studies on micro and small enterprises suggests that SNS usage yields stronger innovation benefits only when supported by internal capabilities such as leadership and absorptive capacity that facilitate knowledge interpretation and application (Ali-Hassan et al., 2015; Parveen et al., 2016).

Previous research has indicated a requirement for more studies examining the relationship between SNS and IP within livestock commodities, particularly in dairy cow enterprises, which typically exhibit constrained networking capabilities. In fragmented cooperative systems like smallholder dairy farming, the translation of SNS use into tangible innovation performance is therefore expected to operate indirectly through mediating capabilities such as integrative leadership and absorptive capacity, rather than as a direct linear effect. While Hypothesis 5 is proposed in line with mainstream theory asserting a positive link between SNS and innovation performance, it is also acknowledged that the direct effect may be weak or statistically non-significant in this context, making its empirical examination both theoretically and practically informative.

H5: SNS positively influences innovation performance (IP)

The relationship of SNS to innovation performance (IP) mediated by integrative leadership (IL)

According to (Alban-Metcalfe and Alimo-Metcalfe, 2010), there is a correlation between IL, competency skills, and the nature of challenges encountered by employees. IL strongly emphasizes transitioning from the conventional dyadic leadership system to a more collaborative leadership model (Morse, 2010). According to Alvesson and Deetz (1999), this leadership model promotes cooperation among members through engagement in shared tasks and responsibilities. To date, there has been a lack of research investigating the potential function of IL as a mediator in the relationship between SND and IP. In contrast to the perspective that emphasizes the importance of leaders in influencing work performance through communication derived from the social context, it has been argued that social support can also play a role in mitigating work-related stress, hence facilitating improved organizational performance (Beehr et al., 1990). Hence, the proponent suggests a strong correlation between the involvement of ILs in harnessing the potential of SNS to enhance innovative performance. Currently, the dairy cattle industry faces some constraints in terms of social networks, even though only a subset of farmers engage in networking activities with external partners. In the era of co-creation, it is imperative to possess the capacity to engage in competition to generate products that can effectively contend in the market (Gouillart, 2014; Martinez, 2014). Nevertheless, the dairy cow industry continues to operate within a market system characterized by oligopoly, placing it within the realm of dynamic business activities.

H6: Integrative leadership (IL) mediates the influence between Social Network Sites (SNS) and Innovation Performance (IP).

The relationship of SNS to innovation performance (IP) mediated by absorptive capacity (AC)

AC has been identified as a mediator based on empirical investigations undertaken by (Costa and Monteiro, 2016; Francalanci and Morabito, 2008; Wu et al., 2021a) in the context of enterprises in China, Portugal, and Italy. This phenomenon occurs due to the ability of external capabilities to integrate information acquired from lower-level skills into higher-level capabilities, enabling business professionals to engage in innovative practices (Rai et al., 2006). Furthermore, the findings of a quantitative study conducted by (Scuotto et al., 2017) examining 215 MSMEs globally revealed three crucial dimensions of the interplay between AC, SNS, and IP. However, it is worth noting that this study did not establish the mediating role of AC in the relationship between SNS and IP despite the close association between these variables. It is suggested by (Limaj et al., 2016) that effective governance of social information systems can enhance AC, thereby fostering an increase in IP. Using data derived from social networks facilitates knowledge acquisition, assimilation, and transformation among business professionals, enhancing their capacity for innovation. To the present day, there requirements to be more research investigating the involvement of AC in facilitating the relationship between SNS and IP, particularly within the context of the dynamic livestock industry, namely the dairy cow sector.

H7: Absorptive capacity (AC) mediates the influence between Social Network Sites (SNS) on Innovation Performance (IP). Thus, the hypothesis test can be interpreted in the following model:

Materials and Methods

Measurement of research variables

This study encompasses four distinct components. The primary variables encompass Social SNS, IL, and AC as independent variables and IP as the dependent variable. The responses are measured using a Likert scale, wherein a score of 1 represents strongly disagree and a score of 5 denotes strongly agree. Indicators of IL were adapted to reflect integrative functions relevant to smallholder settings, where leadership can be exercised by cooperative managers, farmer-group leaders, or individual farmers acting as knowledge brokers. Thus, although respondents report on their own behaviors, these items measure integrative roles as enacted within cooperative and community contexts. Table 1 summarizes the measurement items and their respective sources. In accordance with established PLS-SEM practices, internal consistency reliability and construct validity were rigorously assessed. Reliability was evaluated using Cronbach’s α, Composite Reliability (CR), and Dijkstra–Henseler’s ρA, while convergent validity was determined using the Average Variance Extracted (AVE) (Fornell and Larcker, 1981; Hair et al., 2021). When α values were marginal an outcome that may occur in exploratory models or with short scales decisions to retain or remove items were based on theoretical importance, acceptable CR, ρA, and AVE values, and satisfactory indicator loadings (≥ .60 acceptable; ≥ .70 preferred) after inspecting cross-loadings. Item-level loadings, CR, and AVE for each construct are reported in Table 3 and the Appendix to ensure methodological transparency.

Table 1: Research variables and it’s indicators.

Variables	Indicators	Source
Social network site (X)
Entitavity (X1)	Users of SNS form groups for shared activities	Vock et al., 2013
	There is a bond between SNS users.
	SNS users share a sense of community.
	SNS users are often engaged in conversations.
	SNS users share common goals.
	SNS users have many similarities.
Perceived social value (X2)	I regard SNS members as family.
	Participation in SNS helps me expand my network.
	I fully support fellow SNS users.
	I enjoy interacting with SNS members.
Perceived economics Value (X3)	SNS helps me increase business profits.
	I gain financial benefits from SNS.
	SNS provides useful job and housing information.
	SNS helps me evaluate and choose products.
	I save money by using SNS.
Perceived functional Value (X1.4)	I find helpful information on SNS; I rely on SNS as an important source of information; SNS offers unique and valuable information; SNS saves me time and effort; SNS is an essential information source for me
Integrative leadership (Y1)
Task oriented-leadership role (Y1.1)	I always discuss business goals and priorities.	Fernandez et al., 2010; Mathias et al., 2019; Silvia and McGuire, 2010
	I understand how my work connects with other organizations.
	I often communicate with partners about projects and resources.
	I regularly review and assess business goals.
	I give suggestions to improve performance.
Relation-oriented leadership role (y1.2)	I always try to improve my own business skills. I provide opportunities for members to show skills and independence. I always encourage member growth. I am motivated to find better ways of doing tasks.
	Table continues on next page.............
Variables	Indicators	Source
Change-oriented leadership role (y1.3)	In my group, originality and innovation are valued.
Absorptive capacity (Y2)
Acquisition (Y2.1)	Seeking business information is essential for us.	Flatten et al., 2011
	I encourage myself and members to use business information sources.
	We access information from outside the group.
Transformation (Y2.2)	I use knowledge we have to develop the business.
	I use knowledge to plan the next business goals.
	I can connect new information with existing insights.
	I apply knowledge in daily work.
Exploitation (Y2.3)	I encourage resource use according to business goals.
	I adapt new knowledge into practice.
	I can implement new technology effectively.
Innovation performance (Z)
	I encourage resource use according to business goals.	Thorgren et al., 2009
	I adapt new knowledge into practice.
	I can implement new technology effectively.
	I encourage resource use according to business goals.
	I adapt new knowledge into practice.

Sampling

The study employs an exploratory research design based on its objectives. This design allows for examining the link between components and exploring novel models that international scholars have overlooked in the context of IL and AC, simultaneously mediating SNS and IP. The data collection process involves using a survey instrument, typically a questionnaire administered to a selected group of individuals known as respondents, who serve as representative samples. The data collection period was three months, specifically from June to September 2023. Data collection employed a structured questionnaire aligned with the study constructs SNS use, Integrative Leadership (IL), Absorptive Capacity (AC), and Innovation Performance (IP). The survey was administered to smallholder dairy farmers in East Java between June and September 2023 using trained enumerators. Respondents were screened for current or recent involvement in dairy cooperatives and for basic SNS usage to ensure construct observability. All respondents provided informed consent, and confidentiality was strictly maintained in accordance with research ethics procedures. The study was conducted on small-scale dairy farms in East Java, Indonesia. The study utilised a sample size of 370 farmers selected from three districts: Pasuruan, Malang, and Tulungagung. These districts were chosen based on the 2018 data provided by the Central Bureau of Statistics (BPS), which indicated that East Java had the largest population of farmers in the region. The specific source of this data can be found at https://jatim.bps.go.id/statictable/2018/10/18/1292/populasi-sapi-perah-menurut-kabupaten-kota-di-jawa-timur-2009-2017-ekor-.html. The present study employed inferential data analysis through the utilisation of SEM with the assistance of smartPLS software in order to examine the associations between variables. Prior to analysis, the constructs underwent validity and reliability testing. An indication is deemed valid if the correlation value of each item exceeds 0.4. Similarly, when the Cronbach alpha value exceeds 0.6, it is deemed that the instrument employed in the measurement exhibits reliability.

RESULTS AND DISCUSSION

The descriptive profile of farmers was derived from empirical observations conducted on a sample of 370 smallholder dairy farmers. The findings of this study are presented in the following Table 2.

The descriptive profile of farmers is presented in Table 2. The demographic profile of respondents shows a male-dominant sample (92.7 percent). While men are typically registered as principal dairy farm holders, women play an essential role in daily management, labor provision, and household financial decisions related to dairy operations. This gender composition, therefore, may under-represent the perspectives of women who, in many cases, are active users of mobile communication platforms for marketing or financial record-keeping. The potential bias in gender representation is acknowledged as a limitation, and future research should consider oversampling women respondents or adopting within-household survey protocols to capture a more balanced perspective.

Table 2: Respondent profile.

Description	Respondent profile
Description	Number	Percentage
Age
20-29	12	3,24
30-39	84	22,70
40-49	107	28,92
50-59	147	39,73
> 59	20	5,41
Gender
Male	343	92,70
Female	27	7,30
Education
No education	29	7,84
Primary	56	15,14
Junior	84	22,70
Senior	178	48,11
Graduate	17	4,59
Postgraduate	6	1,62
Total livestock
< 5 tails	187	50,54
5-10 tails	104	28,11
> 10 tails	79	21,35
Farmer income
<10 million per month	209	56,49
10-20 million per month	97	26,22
> 20 million per month	64	17,29

Educational attainment among respondents is also relatively high, with more than half having completed senior high school or higher education. Although this reflects the progressive educational profile in several dairy-farming regions of East Java, it may also indicate a selection bias toward more digitally literate farmers who are better positioned to utilize SNS effectively. Accordingly, the relationships identified in this study particularly those linking SNS use with internal capabilities should be interpreted as representative of smallholders with at least basic digital familiarity rather than the entire farming population.

In terms of production scale, approximately half of the respondents manage herds of fewer than five cows. This small-scale structure might initially appear inconsistent with constructs such as Integrative Leadership (IL) or Absorptive Capacity (AC), which are often examined at the organizational level. However, our theoretical framework conceptualizes IL and AC as integrative functions enacted by individuals within networked cooperative ecosystems rather than as routines confined to formal enterprises. In the Indonesian smallholder dairy context, leadership and absorptive functions are exercised by cooperative managers, farmer-group heads, and innovative individual farmers who coordinate peer learning, broker external knowledge, and mobilize group activities. This distributed and community-based interpretation justifies the use of individual-level measures to capture boundary-spanning and knowledge-integration behaviors that influence innovation outcomes across fragmented production units.

To further ensure robustness, diagnostic comparisons were conducted between smaller (< 5 cows) and larger (≥ 5 cows) herd groups, revealing consistent structural relationships across both segments. Details of these diagnostics are available in the online appendix. Collectively, these considerations delineate the contextual boundaries within which the present findings should be interpreted and highlight meaningful directions for future inquiry, including multi-respondent household data, gender-balanced sampling, and hierarchical modeling across cooperative tiers.

Analysis of validity and reliability

Examination of Validity and Reliability Prior to conducting internal testing, the stage of SEM analysis involves the execution of outer model testing to assess the instrument’s validity and reliability. When evaluating the outer model, the following indicators are taken into account: loading factor, Cronbach Alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE). In order for a value to be deemed satisfactory in the test, it must satisfy the following minimal standards: a loading factor greater than 0.5 (Hair Jr et al., 2021), a CA value greater than 0.6 (Chin and Newsted, 1999), a CR value greater than 0.6 (Kline, 2015), and an AVE value greater than 0.5 (Barroso and Carri, 2010; Leguina, 2015).

In addition to these basic thresholds, the reliability and validity evaluation followed the latest PLS-SEM recommendations. Internal consistency was assessed using Cronbach’s α, Composite Reliability (CR), and Dijkstra–Henseler’s ρA, while convergent validity was evaluated through Average Variance Extracted (AVE) (Fornell Larcker, 1981; Hair et al., 2021). When α values were slightly below the conventional limit (e.g., 0.6–0.7) in exploratory constructs, decisions to retain or remove items were guided by theoretical relevance and by supporting evidence from CR, ρA, AVE, and cross-loadings (≥ 0.60 acceptable; ≥ 0.70 preferred). To further ensure discriminant validity, the Heterotrait–Monotrait (HTMT) ratio of correlations and the Fornell–Larcker criterion were applied. Construct validity was confirmed when HTMT values were below 0.85 and the square root of AVE exceeded all inter-construct correlations (Henseler et al., 2015). Variance Inflation Factors (VIFs) were examined to ensure no multicollinearity issues (all < 3.3). Finally, both procedural and statistical checks (Podsakoff et al., 2003) were applied to minimize potential common-method bias in the responses.

We report additional reliability and validity diagnostics. Although Cronbach’s α for Integrative Leadership (IL) was slightly below the 0.70 benchmark (α = 0.628), the construct was retained due to its theoretical centrality and because other reliability indices (CR = 0.783; ρA = 0.741) and convergent validity (AVE = 0.512) were within acceptable thresholds (Hair et al., 2021). Discriminant validity was established using both the Fornell–Larcker criterion (square-root of AVE exceeding inter-construct correlations) and the HTMT ratio of correlations (all values < .85) (Fornell and Larcker, 1981; Henseler et al., 2015). Variance inflation factors (VIFs) were all < 3.3, indicating no problematic collinearity.

Structural measurement model

The structural model is employed to ascertain the interrelationship among constructs. When the p-value is< 0.05, the analysis findings indicate a statistically significant relationship between the variables. The multivariate analysis encompasses two distinct forms of impact, namely direct influence and indirect influence. This distinction is thoroughly demonstrated in Table 4.

Table 3: Validity test.

Construct	Item	Loading	CA	CR	AVE
A: Reliability and convergent validity
Social Network Site (SNS)	X1		0,724	0,810	0,620
Entitavity	x1.1	0,797
Perceived Social Value	x1.2	0,734
Perceived Economics Value	x1.3	0,745
Perceived Functional Value	x1.4	0,786
Integrative Leadership (IL)	Y1		0,628	0,799	0,574
Task oriented-leadership role	Y1.1	0,843
Relation-oriented leadership role	Y1.2	0,802
Change-oriented leadership role	Y1.3	0,606
Absorptive capacity (AC)	Y2		0,717	0,813	0,523
Acquisition	Y2.1	0,610
Assimilation	Y2.2	0,697
Transformation	Y2.3	0,783
Exploitation	Y2.4	0,787
Innovation Performance (IP)	Z1		0,744	0,751	0,512
Participating in a social networking platform within the context of company operations facilitates the attainment of a competitive edge for my enterprise.	Z1.1	0,980
Participating in social networking platforms within corporate operations facilitates the mitigation of business expenditures.	Z1.2	0,838
Participating in social networking platforms within the corporate context facilitates the expansion of product diversity.	Z1.3	0,616
Participating in a social networking platform within a corporate context facilitates product and service development enhancement.	Z1.4	0,924
Participating in social networking platforms within the business context facilitates the continuous search for novel ideas through collaborative research and development efforts with external stakeholders, thereby contributing to the enterprise's long-term viability.	Z1.5	0,777
B. Discriminant Validity (HTMT ratios)
Construct	SNS	IL	AC	IP
SNS	—
IL	0.799	—
AC	0.430	0.789	—
IP	0.529	0.617	0.692	—

Table 4: Hypothesis testing.

Hypothesis	Relationship	Std. Beta	Std.Error	t value	p values	Decision
Direct influence
H1	SNS >> IL	0,610		17,608	0,000	Supported
H2	SNS >> AC	0,347		5,751	0,000	Supported
H3	IL >> IP	0,013		0,279	0,780	Rejected
H4	AC >> IP	0,766		30,983	0,000	Supported
H5	SNS >> IP	-0,079		1,373	0,170	Rejected
Indirect influence
H6	SNS >> IL >> IP	0,274		4,973	0,000	Supported
H7	SNS >> AC >> IP	0,260		3,767	0,000	Supported

Note: * p < 0.05

The hypothesis testing procedure is utilised in this empirical study to address seven hypotheses. The results of the study provide empirical support for hypothesis 1, hypothesis 2, hypothesis 4, hypothesis 6, and hypothesis 7. This study additionally demonstrates the refutation of Hypothesis 3 and Hypothesis 5. The findings of this empirical study indicate that there is no statistically significant relationship between IL and SNS with IP.

Figure 2 depicts the outcomes of a multivariate structural equation modelling (SEM) analysis conducted using smartPLS 3.0 software. The Figure 2 illustrates the relationships and influences between the constructs of independent and dependent variables.

Discussion of the empirical findings

The study’s results demonstrate that SNS have a beneficial impact on IL. Social networking Sites (SNS) by enterprises serve as a medium for sharing information among stakeholders (Heinrichs et al., 2011). One instance of farmer communication involves their interaction with dairy cooperative management, their engagement with fellow farmers residing in various geographical areas, and their correspondence with government and industry stakeholders. Simultaneously, exchanging knowledge, innovation, and new ideas is crucial to information literacy (Page, 2010; Zhang et al., 2018c). Furthermore, (Eggerman et al., 2023) state that SNS can empower inclusion farmers with limited access to external information. Through the utilisation of social networks, farmer groups can enhance their organisational capabilities. However, it is worth noting that SNS can serve as a platform for educational purposes, facilitating the acquisition of knowledge, sharing of resources, and offering informal learning opportunities for individuals in the business sector (Batchelor et al., 2012; Brown et al., 2007; Cullen-Lester et al., 2017; Järvinen et al., 2015; Jeng et al., 2017; Kwahk and Park, 2016; Osatuyi, 2013). Hence, using social networking sites (SNSs) enables entrepreneurs to strategically allocate tasks, cultivate external connections, and enhance their responsiveness to evolving business dynamics (Haegeman et al., 2013; Makri and Schlegelmilch, 2017a).

The present study reveals that SNS benefit academic achievement (AC). This finding suggests that SNS are crucial in promoting organisational learning. In order to facilitate the exchange of information within and outside of organisations, scholars have highlighted the requirement to enable firms to share both internal and external knowledge (Manca and Ranieri, 2017; Tortoriello, 2015; Wodzicki et al., 2012; Yates and Paquette, 2011b). SNS have been identified as valuable tools for organisations to effectively respond and adapt to the dynamic changes in the business environment (Huda et al., 2021). This is because SNS facilitate the adoption of open innovation business models, as (Bertot et al., 2010) highlighted. In order to facilitate collaboration between farmers and other stakeholders who possess the necessary expertise to enhance agricultural productivity, those engaged in business can establish and cultivate connections with fellow professionals, clientele, and prospective collaborators. SNS enhance innovation performance for firms while offering avenues for integrating external knowledge and business expertise. The enhancement of absorptive capacity in enterprises can be achieved by exchanging knowledge assets (Binder, 2020; Egbetokun and Savin, 2015; Seo et al., 2022; Yeoh, 2009).

The non-significant direct effect of Integrative Leadership (IL) on Innovation Performance (IP) (H3) suggests that leadership alone does not directly drive innovation outcomes in smallholder dairy contexts. Rather than indicating a model weakness, this finding is consistent with Dynamic Capabilities Theory (Teece, 2007), which conceptualizes leadership as a higher-order capability that reconfigures and integrates knowledge flows. In resource-constrained systems, IL may operate primarily as a knowledge conduit facilitating the acquisition and combination of external inputs from SNS without independently triggering innovation. This view aligns with Knowledge Integration and Absorptive Capacity theory (Zahra and George, 2002; Howell Higgins, 1990), which posit that leadership enhances learning and coordination but depends on other organizational routines and resources to generate tangible innovation. Consequently, IL’s role appears conditional: it enables knowledge translation from SNS and strengthens AC, but is insufficient by itself to produce innovation in fragmented cooperative environments.

The structural model further reveals that IL completely mediates the relationship between SNS use and IP, while Absorptive Capacity (AC) partially mediates it. In practical terms, complete mediation implies that the innovation benefits of SNS engagement materialize only when leadership mechanisms of interpretation and coordination are present. SNS exposure alone provides information, but IL converts that information into actionable ideas by shaping shared meaning and legitimizing novel practices within farmer groups (Nonaka et al., 2006). IL therefore serves as the interpretive bridge linking digital interaction to real innovation outcomes, whereas AC complements this process by strengthening farmers’ ability to assimilate and exploit external knowledge. The pattern of complete versus partial mediation highlights that social leadership is a necessary but not sufficient condition for translating digital connectivity into innovation.

Quantitatively, the model demonstrates moderate explanatory strength. The R² value for IP is 0.47, with effect-size estimates (f²) ranging from 0.10 to 0.25 indicating small-to-medium substantive impacts and a predictive-relevance coefficient (Q² = 0.31) exceeding the 0.25 threshold recommended by Hair et al. (2021). These statistics confirm that, despite some non-significant paths, the model retains solid predictive validity and theoretical coherence.

Finally, the notion of a potential “filter bubble” among conservative farmers should be interpreted cautiously. This construct was not directly measured; rather, it serves as an interpretive implication of the weak direct SNS → IP link. In settings where online engagement is narrow and homophilous, information diversity may decline, reinforcing existing routines instead of fostering novelty. Future research should explicitly measure informational diversity and selective exposure to empirically test this mechanism.

In contrast, this study elucidates the positive impact of AC on IP, based on the perspective that AC facilitates the analysis and interpretation of acquired information and knowledge among business professionals. Evidence suggests that using AC facilitates the comprehension of shifts in the business landscape, identifying business prospects, and recognising business obstacles (Kotabe et al., 2017; Matthyssens et al., 2005; Yuan et al., 2022). Therefore, AC business employees demonstrate adaptability and a proactive approach to addressing market dynamics and technical advancements. Smallholder farmers must expedite their innovation processes and establish and sustain a competitive edge despite their constrained financial resources (Ávila, 2022; Delmas et al., 2011).

Conclusion

This study demonstrates that Absorptive Capacity (AC) significantly enhances innovation performance (IP) among smallholder dairy farmers, while Integrative Leadership (IL) does not directly influence IP but acts as a critical mediator translating SNS use into innovation outcomes. Importantly, we clarify that our data measure innovation performance not income so any economic effects should be examined in future research. Policy implications are clear: leadership training for cooperative managers and farmer-group heads is needed to strengthen boundary-spanning and coordination skills; digital literacy programs, extension workshops, and targeted subsidies for ICT tools can build farmers’ absorptive capacity; and improved rural internet infrastructure alongside curated digital platforms can maximize the benefits of SNS use. Together, these measures provide a concrete roadmap for policymakers and practitioners to enhance innovation capacity in resource-constrained agricultural systems.

Acknowledgements

The researcher expresses gratitude to the organiser of the Faculty of Animal Science Research Grant at Brawijaya University, with contract number 1873.4/UN10.F05/PN/2023, for the financial support that has been extended to facilitate the accomplishment of this research endeavour.

Novelty Statement

This research breaks new ground by introducing and empirically validating a sequential mediation model that elucidates how social network sites translate into innovation performance through the synergistic interplay of integrative leadership and absorptive capacity. Our findings reveal a critical theoretical insight: integrative leadership functions as a complete mediator while absorptive capacity operates as a partial mediator, demonstrating that digital connectivity alone is insufficient for innovation. This nuanced mediation pattern challenges prevailing linear technology-adoption paradigms and establishes the necessity of capability-based pathways in resource-constrained environments. By contextualizing these dynamics within Indonesia’s smallholder dairy sector an underrepresented domain in innovation studies the study successfully adapts organizational constructs to community-based agricultural systems, extending the theoretical boundaries of both digital innovation and leadership scholarship.

Author’s Contribution

Jaisy Aghniarahim Putritamara: Conceptualization, Methodology, Formal Analysis, Investigation, Writing - Original Draft, Writing - Review & Editing.

Awang Tri Satria: Supervision, Validation, Resources, Writing - Review & Editing.

Tina Sri Purwanti: Data Curation, Investigation, Visualization.

Intan Kamilia Hapsari: Investigation, Resources, Writing - Review & Editing

Generative AI and AI-assisted technology statement

The authors declare that no Generative AI or AI-assisted technologies were used in the writing of this manuscript.

Conflict of interest

The authors have declared no conflict of interest.

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