Entrepreneurial Innovation and Mechanization Adoption among Wheat Farmers in Pakistan

Background to study

Entrepreneurial innovation is now an essential part of the interconnected, fast-paced global economy (Dossou et al., 2023). With rapidly evolving products, services, and technologies, enterprises must implement innovative strategies to stay competitive and relevant (Akomea et al., 2023; Osei et al., 2024). This is particularly true for agribusinesses linked to strategic trade routes such as the southern corridor of China-Pakistan Economic Corridor (CPEC) that links areas, including Kohat, Karak, Lakki Marwat, Bannu, Tank and Dera Ismail Khan, in southern Khyber Pakhtunkhwa (KP), Pakistan (Hassan et al., 2022; McCartney, 2022; Shaikh et al., 2025).

Innovation in conjunction with agribusiness is not simply a matter of creating new products and/or innovations, but also entails administrative innovation that enhances internal processes and the capacity of an organization to deliver sustainable competitive advantage (Dossou et al., 2023; Mursid et al., 2018). In relation to CPEC, as market access and connectivity across the region are driving change outside the rural economy, the critical importance of innovation is further reinforced (Zhang et al., 2024). Agribusinesses that operate along the value chain are fundamental to economic development (Goulas, 2025; Katchova and Enlow, 2013). Globally, agribusiness is a significant part of economic activity, particularly in developing countries (Kruja, 2020; Shaikh et al., 2025).

These enterprises contribute significantly to Gross Domestic Product (GDP), and are important sources of employment (Goulas, 2025). Within the CPEC corridor, however, especially in southern KP, agribusinesses, particularly wheat farming, play a vital role in rural livelihoods and in the economic resilience of the entire region (McCartney, 2022; Shah, 2025). Given this context, policy frameworks and regional development strategies must foster entrepreneurial innovation among wheat farmers and agribusiness operators in CPEC-linked regions. Such innovation encompasses new products or services, novel production technologies, improved organizational structures, or strategic planning systems that help agribusinesses achieve a competitive edge, boost productivity, and succeed in increasingly dynamic markets.

Research problem and knowledge gap

Despite expanding scholarly interest in agricultural innovation, there is limited evidence on how technological, administrative, and product innovations interact with farmers’ characteristics and mechanization practices in wheat-based farming systems. This gap is particularly relevant in southern Khyber Pakhtunkhwa, where traditional, semi-mechanized, and mechanized farming systems coexist. Therefore, this study evaluates entrepreneurial innovation and mechanization adoption among wheat farmers to better understand the factors associated with innovation-oriented agricultural development.

Objectives of the study

This study aims to examine entrepreneurial innovation and mechanization adoption among wheat farmers in southern Khyber Pakhtunkhwa. Specifically, the study seeks to:

• (i) assess product, technological, and administrative innovation among wheat farmers;

• (ii) examine the relationships of technological and administrative innovation with product innovation; and

• (iii) assess how farmer and farm characteristics are associated with mechanization adoption.

Research hypothesis

• H1: Technological innovation is positively associated with product innovation.

• H2: Administrative innovation is positively associated with product innovation.

• H3: Farmer characteristics are significantly associated with mechanization adoption.

• H4: Farming-system characteristics are significantly associated with mechanization adoption.

• H5: Institutional/cooperative support is significantly associated with mechanization adoption.

Conceptual review

Theoretical review

Empirical review

Empirical evidence from around the world attests to the positive correlation between innovation and firm performance, especially in the agriculture and SME sectors (Cheah et al., 2018). Cheah et al. (2018) examined SMEs and found moderate levels of innovation, with product and process innovations positively affecting performance. Similarly, Cheah et al. (2018) argue that the introduction of technological and organizational innovations significantly improves the output and efficiency of firms (Cheah et al., 2018). Awan and Ali (2022), with the aid of OLS models, were able to show that innovative activities (especially product-based) of SMEs significantly improved their productivity and profitability (Awan and Ali, 2022). Finally, Shaikh et al. (2025) also found strong correlations between innovative elements and better business outcomes.

From an Asian perspective, Hair et al. (2011) discuss SMEs based PLS-SEM, establishing that entrepreneurial innovativeness has a strong positive impact on non-financial and growth-related performance metrics (Cheah et al., 2018; Hair et al., 2011; Sarstedt et al., 2021). Similarly, Sarstedt et al. (2021) confirm the positive effects of product, process, and administrative innovation on an organization’s overall performance. In their 2018 study, Cheah et al. found that innovation across multiple dimensions (product, organizational, market, and process) in Ghana’s banking sector was directly associated with improved performance. Their structural model also indicates that innovation capability moderates the relationship between innovation type and firm output. Based on the literature and theoretical framework reviewed, the relationships examined in the present study are summarized in Figure 1.

Fig. 1.

In the case of Pakistan, Haq and Farooq (2016) discovered that wheat farmers adopting the digital and innovative seed technology under government-supported CPEC schemes in D.I. Khan and Bannu noticeably improved in yield, access to markets, and food security (Haq and Farooq, 2016). Likewise, Shah (2025) observed that the introducing administrative innovations (record-keeping, marketing collaboration) improved operational efficiency and profitability for wheat farmers in southern KP. In contrast, Hassan et al. (2022) found that administrative innovations also increased operational efficiency and profitability among the wheat farmers in southern KP. Furthermore, the interaction between innovation, access to finance, and firm size was highlighted by showing that innovation positively mediates access to finance and negatively moderates firm size (Ilić et al., 2024). This finding is evident in the structure of smallholders in the wheat-farming system of southern KP. These perceptual knowledge findings significantly support the current study’s premise: entrepreneurial innovation, particularly product innovation, is crucial for improving the performance of agribusiness/food security in the CPEC’s southern corridor.

Cluster analysis

The K-means clustering method was used to determine farmer typology based on five variables: age, education, years in farming, farm size, and farming system. The method aims to divide observations into k clusters so that each observation is as close as possible to its assigned cluster. The objective function is:

• C_i – is the set of points in cluster i

• and μ_i – is the centroid point of cluster i.

The variables used for clustering included age, education, years in wheat farming, farm size, and farming system. The categorical and ordinal variables were converted to numerical values and standardized before the K-means analysis to minimize scale effects. K-means clustering was performed with k = 3. To visualize the resulting cluster structure, principal component analysis (PCA) was applied to the standardized variables, and the first three principal components were used to display the three farmer clusters in three-dimensional space.

Structural model

The structural model is modelled as:

• y – dependent variable (agribusiness performance)

• i – intercept vector

• X – matrix of independent (predictor) variable (innovation types)

• β – coefficient vector

• e – error term (residuals).

This methodological approach enabled rigorous hypothesis testing and provided insights into the effects of different types of innovation on wheat agribusiness performance along the CPEC corridor.

SHAP-based machine learning model

To complement PLS-SEM, a Random Forest model was used:

• X – socio-economic and farm variables

• ŷ – predicted agribusiness performance.

SHAP values were computed as:

• ϕ_i – contribution of feature i,

• S – subset of features

• F – full feature set.

The Random Forest model was implemented using 500 trees (n_estimators = 500). The dataset was randomly partitioned into 80% for training and 20% for testing, with random_state = 42 to ensure reproducibility. Model fit was assessed using the coefficient of determination (R²) and root mean square error (RMSE). The training-set diagnostic produced R² = 0.819 and RMSE = 0.193. SHAP values were subsequently used to quantify the relative contribution of socio-economic and farm-level predictors to model predictions (Fig. S1 in Supplementary).

Respondent characteristics and farm characteristics

The demographic and background characteristics of the respondents are summarized in Table 1. The table included an analysis of responses from 381 wheat-growing farmers in south Khyber Pakhtunkhwa. A considerable number (34.1%) were between 36 and 45 years of age, with only 4.7% between 18 and 25 years of age. The vast majority were male (97.1%). There was a range of educational levels, with 31.8% having primary education and 21.0% having a postgraduate degree. Farming experience was distributed, with 33.1% having been away from wheat for over 20 years. The sizes of farms varied greatly; 28.9% were 5–10 acres, and 25.2% were over 20 acres. Regarding farming systems, semi-mechanized systems dominated (36.7%), followed by traditional (26.2%), mechanized (21.0%), and mixed (16.0%).

Cluster analysis groups the farmers into 3 distinct groups. Cluster 0 (31.2%) included experienced farmers with a moderate level of education and semi-mechanization. Cluster 1 (33.3%) was the most progressive group, with higher education, larger farms, and more developed farming systems (Fig. 3). Cluster 2 (35.4%) comprised younger, less educated farmers with smaller landholdings, who were dependent on traditional methods. These clusters encompass a range of farming backgrounds and can help inform tailored interventions and policy strategies.

Fig. 3.

Presenting and analyzing the data

Figure 4 shows the results of the Partial Least Squares Structural Equation Modelling (PLS-SEM) for the factors that affect Adoption of mechanization used by 381 wheat farmers. The model comprises four latent key factors: Farmer Profile, Farming system used, Orseessor (interpreted here as institutional/extension support), and Adoption of mechanization.

Fig. 4.

The results show that Farming system used has the most potent direct effect on Adoption of mechanization, with a path coefficient of 0.504, followed by the Orseessor (β = 0.426) and Farmer profile (β = 0.326). This suggests that the adoption of mechanization depends greatly on the structure and nature of farming practices, the provision of institutional support, and the individual characteristics of farmers.

All the observed indicators showed high loadings on their respective latent constructs, with factor loadings ranging from 0.673 to 0.813, confirming that the test items were good measures of these latent constructs. For instance, Education (0.754) and Age (0.806) were highly loaded on Farmer profile, while Farm size (0.673) and Farming system (0.754) were associated with Farming system used. Similarly, the loadings for Adoption of mechanization were highest for Systems 3 and 4, that is, 0.717 and 0.666, respectively. Overall, the model shows that a range of personal, operational, and institutional factors significantly affect mechanization adoption among wheat farmers in the current study area.

Furthermore, a bootstrapping analysis (Fig. 5) confirmed that Product innovation had a substantial and significant effect on Product quality (t = 45.235). Technological innovation (t = 2.736) and Administrative innovation (t = 3.747) had a significant impact on Product innovation, but their direct effects on Product quality were weak. All the measurement indicators showed high t-values indicating good reliability. These results emphasize Product innovation as a significant mediator of the relationship between innovation efforts and product quality.

Fig. 5.

The measurement model is depicted in Figure 4, with additional information in Table 2, which presents the path coefficients, t-values, and standard errors associated with the tested hypotheses. These statistical measures were used to provide a basis for evaluating support for each hypothesis. A bootstrapping resampling procedure was performed to increase reliability. This strategy was adopted to produce empirical distributions of the model parameters, thus allowing for more accurate estimation of the standard errors. The bootstrapping analysis was performed using data gathered from 381 wheat farmers across different districts in the south of Khyber Pakhtunkhwa province, thereby strengthening the statistical validity of the structural model. For each parameter, the standard deviation of the empirical distribution was used to estimate the standard error, and the standard error was used in turn to estimate the t-values required for hypothesis testing.

The bootstrapping analysis (Fig. 5) showed that Product innovation has a substantial and significant impact on Product quality (t = 45.235). Technological innovation (t = 2.736) and Administrative innovation (t = 3.747) had a significant impact on Product innovation, but their direct effects on Product quality were weak. All the measurement indicators showed high t-values indicating good reliability. These results emphasize Product innovation as a significant mediator of the relationship between innovation efforts and product quality.

Product innovation had the strongest and statistically significant relationship with perceived wheat product quality (β = 0.946, t = 45.285, p < 0.001), confirming its central role in explaining variations in perceived product quality (Table 2). Technological innovation had a significant impact on Product innovation (β = 0.306, t = 2.756, p = 0.006), which in turn had an indirect impact on product quality. However, its direct effect on product quality was insignificant (β = 0.036, t = 0.1607, p = 0.109). Similarly, Administrative innovation had a significant overall influence on agribusiness (β = 0.347, t = 3.747, p = 0.000), but had minimal (direct) effects on product quality, which were statistically insignificant (β = 0.028, t = 0.934, p = 0.351).

The coefficient of determination (R²) is an important measure for diagnosing the explanatory power of structural models in PLS-SEM. It assesses the degree to which the independent variables explain the dependent variable, product quality. As Hair et al. (2011) and Henseler et al. (2012) note, R² provides important information about model strength. Different scholars propose different benchmarks: according to Falk and Miller (1992), the minimum acceptable R² should be 0.10, whereas, according to Chin (1998), R² values of 0.67, 0.33, and 0.19 are considered substantial, moderate, and weak, respectively. Hair et al. (2011) further recommend 0.75, 0.50, and 0.25 as high, moderate, and low indicators of explanatory power, respectively. Cohen (1988) also provides alternative thresholds, assigning values of 0.27, 0.13, and 0.02 to strong, moderate, and weak, respectively. Based on these criteria, the R² value for product quality, representing the direct impact of innovation variables, is shown in Table 3, revealing the model’s power to explain product quality. The R² of 96.5% indicates that the independent variables of product innovation, technological innovation, and administrative innovation explain 96.5% of the variance in product quality. This high R² value exceeds the thresholds proposed by Falk and Miller (1992) and Chin (1998) to establish the robustness of the model in explaining endogenous construction. These results provide strong empirical support for the premise that innovative practices improve product quality in wheat agribusinesses. However, this very high R² should be interpreted with regard to the strong association between product innovation and product quality, and the conceptual proximity of the innovation-related constructs. As the measures were obtained from the same cross-sectional survey, the value primarily represents strong in-sample explanatory power and should not be interpreted as equivalent evidence of out-of-sample predictive accuracy. The bootstrapping results show that technological innovation was significantly associated with product innovation, supporting H1. Administrative innovation is also significantly associated with product innovation, supporting H2. The results concerning farmer characteristics, farming systems, and institutional support were subsequently evaluated against H3–H5 in the mechanization-adoption model.

SHapley Additive exPlanations based on machine learning

The application of SHAP analysis provides a quantitative, interpretable evaluation of the variables that affect the performance of the agribusiness. The findings show that the most significant variables are the farming system and the size of the farmland, followed by years of farm experience and education level (Fig. 6). These variables always have high SHAP values, indicating that they contribute substantially to the model’s predictions. On the other hand, demographic variables like gender register insignificant impacts on performance variability. The SHAP value distribution further demonstrates that higher levels of mechanization and larger farm sizes positively contribute to agribusiness performance, whereas lower levels are associated with lower outcomes.

Fig. 6.