Objective: The present study was conducted with the aim of identifying and prioritizing the factors affecting the adoption of smart marketing management for agricultural products by rural residents of Lorestan Province and providing practical solutions to increase this adoption. Given the key role of the agricultural sector in the economy of Lorestan Province and the serious challenges in the marketing of agricultural products, identifying the factors influencing the adoption of modern marketing approaches is of particular importance.
Methodology: This applied research is descriptive-survey in nature, conducted using a sequential exploratory mixed-methods approach (qualitative-quantitative). In the qualitative phase, 18 experts and activists in agricultural marketing in Lorestan Province were interviewed using purposive sampling with the snowball technique. Qualitative data were analyzed using thematic analysis with MAXQDA 2020 software. In the quantitative phase, the statistical population consisted of farmers and rural residents of Lorestan Province, from which 396 valid questionnaires were collected using multi-stage cluster sampling. The research instrument was a researcher-made questionnaire, whose validity was confirmed through content and construct validity (exploratory factor analysis), and reliability was confirmed using Cronbach's alpha coefficient. Quantitative data were analyzed using SPSS version 26, employing exploratory factor analysis, Friedman test, one-way ANOVA, and multiple regression.
Findings: The qualitative results led to the identification of five main factors affecting the adoption of smart marketing management: economic factors, socio-cultural factors, psychological factors (attitude), infrastructural factors, and institutional factors. In the quantitative phase, the results of exploratory factor analysis indicated that 25 items were categorized into five factors with eigenvalues greater than one, collectively explaining 68.72% of the total variance, indicating a good model fit. The largest share of variance explanation was related to economic factors at 18.17%. The Friedman test results showed that from the respondents' perspective and based on mean ranks, economic factors (mean rank: 4.32) ranked first, infrastructural factors (mean rank: 3.51) ranked second, institutional factors (mean rank: 3.34) ranked third, socio-cultural factors (mean rank: 2.98) ranked fourth, and psychological factors (mean rank: 1.65) ranked fifth, reflecting the respondents' subjective prioritization. One-way ANOVA results indicated significant differences among different educational groups of respondents regarding the importance assigned to each of the five factors, with the greatest differences observed between postgraduate groups and those with education below a diploma. Multiple regression results showed that the five factors entered into the model collectively explained 65.8% of the variance in the dependent variable (R²=0.658). Based on standardized beta coefficients, which indicate the explanatory contribution of each factor in predicting adoption, economic factors (β=0.278) had the largest share, followed by infrastructural factors (β=0.254), institutional factors (β=0.212), socio-cultural factors (β=0.176), and psychological factors (β=0.134).
Conclusion: The findings indicate that the adoption of smart marketing management among rural residents of Lorestan Province is primarily influenced by economic, infrastructural, and institutional factors, while psychological and socio-cultural variables are of secondary importance. Regression results and respondents' subjective prioritization both confirm that economic factors have the greatest contribution to predicting adoption. This suggests that structural, economic, and institutional barriers are the most significant challenges to adopting modern marketing technologies in rural areas. Accordingly, it is recommended that policymakers first provide the necessary infrastructure by strengthening farmers' economic foundations (through establishing credit funds, low-interest facilities, and sustainable markets), developing physical and communication infrastructure (including high-speed internet and transportation networks), and strengthening extension institutions. Subsequently, designing educational programs tailored to different literacy levels, considering differences among educational groups, and promoting a culture of using modern marketing technologies can facilitate broader adoption of smart marketing management in Lorestan Province.
Methodology: This applied research is descriptive-survey in nature, conducted using a sequential exploratory mixed-methods approach (qualitative-quantitative). In the qualitative phase, 18 experts and activists in agricultural marketing in Lorestan Province were interviewed using purposive sampling with the snowball technique. Qualitative data were analyzed using thematic analysis with MAXQDA 2020 software. In the quantitative phase, the statistical population consisted of farmers and rural residents of Lorestan Province, from which 396 valid questionnaires were collected using multi-stage cluster sampling. The research instrument was a researcher-made questionnaire, whose validity was confirmed through content and construct validity (exploratory factor analysis), and reliability was confirmed using Cronbach's alpha coefficient. Quantitative data were analyzed using SPSS version 26, employing exploratory factor analysis, Friedman test, one-way ANOVA, and multiple regression.