Binary Probit Research Articles

Determinants of smallholder crop farmers’ access to climate services in Elundini local municipality, Eastern Cape province, South Africa

Climate variability presents significant implications for agricultural production and overall food security, leading to seeking better access to climate services that can improve farmers’ decision-making in combating climate change impacts. The study examined the factors influencing smallholder crop farmers’ access to climate services in Elundini local municipality, Eastern Cape province. The study adopted a quantitative method using a cross-sectional survey approach. A multistage sampling procedure was employed to select 217 smallholder crop farmers. The study used primary data collected through structured questionnaires and face-to-face interviews. To analyze the data the paper employed a binary Probit model. The study results indicated that access to both short-term weather and seasonal forecasts is positively influenced by ownership of mobile phones and access to extension services. Similarly, access to short-term weather forecasts is positively influenced by age, monthly income, ownership of radio, timely climate information, and perceiving that climate change has negative effects on crop production. The study further revealed that land size, knowledge of climate change, and climate services accuracy are positive and significant factors in access to seasonal forecasts. On the other hand, being educated negatively influenced access to seasonal forecasts while land size had a negative and significant effect on short-term weather forecasts. The study concludes that age, land size, ownership of mobile phones, and access to extension services were significant factors that determine both farmers’ access to short-term weather and seasonal forecasts. Therefore, the study recommends that if the access and uptake of climate services are to be improved, government stakeholders, researchers, and forecast producers should collaborate and offer training sessions and workshops on climate services relevance and how to acquire and interpret them, particularly for elderly farmers.

Bridging Financial Inclusion to Water and Sanitation: Insights from Indonesia

Lack of access to improved drinking water and sanitation has been a major worldwide challenge, especially in Indonesia. This study investigates the impact of financial inclusion on access to improved drinking water and sanitation among households in Indonesia. Using binary probit regression analysis complemented with dominance analysis, we assess the relative importance of various factors, including financial inclusion, socio-economic status, and residence, in determining access to improved drinking water and sanitation services. Our findings highlight the critical role of urban residence in facilitating access to improved drinking water and sanitation, with urban areas demonstrating significantly higher access rates. Furthermore, participation in savings emerges as a more influential factor compared to credit participation, particularly in improving access to sanitation facilities. This suggests that while both savings and credit contribute to improved access, savings play a more pronounced role in addressing the higher upfront costs associated with sanitation infrastructure. Additionally, socio-economic factors such as household expenditure and education level are found to significantly influence access to improved water and sanitation, underscoring the importance of addressing broader socio-economic disparities. Overall, our study provides valuable insights and emphasizing the need for comprehensive strategies for policymakers to enhance essential access to clean water and sanitation.

Climate-smart agriculture and food security in climate-vulnerable coastal areas of Bangladesh

The issue of global climate change is increasingly worrisome, particularly for countries heavily reliant on agriculture. To reduce the negative impact of climate change on agriculture, farmers of Bangladesh started adopting different climate smart agriculture (CSA) practices. The CSA sustainably increases productivity, resilience, and food security, which can contribute to the achievement of a number of sustainable development goals (SDGs). However, the adoption of CSA is low especially in the climate-vulnerable coastal areas of Bangladesh. Therefore, this study was conducted to identify the factors affecting the adoption of CSA and its influence on coastal household’s food security. A total of 327 sample farmers from three coastal districts of Bangladesh were interviewed. The collected data were then analyzed by using the binary probit and ordered probit model. The findings indicated that highest 65% of farmers adopted early planting of rice as one of CSA practices. The adoption of CSA practices positively affected by the household annual income, extension services and awareness regarding CSA practices. Moreover, the adopters of CSA were more food secure than non-adopters. For instance, adoption of one additional CSA practice leads to an increase in the likelihood of being food secure by 4.3%. In terms of policy perspective, the adoption of CSA in the coastal areas can be increased through creation of employment opportunities, increasing access to extension services, and broadcasting of CSA-related programs on mass media.

Is technological innovation good or bad? An empirical investigation of technology startups

Digitalization has brought several unpredictable changes in the field of technology and innovation management. Despite several studies on how digital innovation stands out in terms of a firm's performance, there is an evident research gap for empirical studies on technology startups from developing countries. Therefore, this research investigates the impact of digitalization in three categories (digital interconnectedness, digitalized value chains and big data analytics) on exploratory and exploitative innovations under the moderation of absorptive capacity in technology startups of three different sizes operating in Pakistan. The study used the binary probit model and the results reveal the impact of digitalization on exploratory and exploitative innovations is heterogenous in startups of different sizes along with a significant moderating effect of absorptive capacity. The findings suggest that technological innovation plays a different role among different sizes of startups under the moderation of absorptive capacity.

Uptake determinants of climate-smart agricultural practice for greening smallholder groundnut value chain: Evidence from Malawi

Crop value chain greening projects can increase sustainability amidst the numerous difficulties facing agroecosystems due to degradation caused by overuse and climate change. Over the last few decades, external aid/support has been the main driver of food value chain greening efforts. One such initiative is the Sustainable Agriculture Production and Marketing for Rural Transformation (SAPMaRT) project, implemented to green Malawi's groundnut value chain. There is limited evidence on determinants of project participation and uptake of innovative practices among groundnut smallholders in Malawi. What determines agricultural project participation and the uptake level and intensity of promoted greening practices? We collected data from 244 households that participated in two SAPMaRT project districts, Kasungu and Mzimba. The binary probit and poisson regression models were used to assess the factors influencing project participation and groundnut greening practices (GPs) uptake. We found project participation's positive and significant effect on GPs’ uptake level and intensity. Education level, household size, climate awareness, and extension positively influenced project participation. Project participation, education level, age, and extension positively influenced the uptake level of GPs. However, the age of the household head was a negative determinant of the uptake level of GPs. The key determinants of uptake intensity were household head project participation, education level, land size, and extension. Our findings suggested that funded project participation enhances the uptake of agricultural interventions among smallholders.

Determinants of driver injury severity in electric vehicle crashes: A random parameters binary probit model with heterogeneity in means and variances

This study aims to investigate the factors significantly affecting occupant injury severity in electric vehicle (EV) accidents, with a particular focus on traffic flow characteristics before accidents, which have often been overlooked in previous research. Using data on EV accidents from January 1, 2021, to December 31, 2021, in Guangdong Province, China, this study considers various vehicle, road, weather, and pre-accident traffic flow characteristics. The research employs the random parameters binary probit model with heterogeneity in means and variances, comparing it with the binary probit model and the random parameters binary probit model to comprehensively address unobserved heterogeneity and further explain the factors contributing to the randomness of random variables. The results indicate that the random parameters binary probit model with heterogeneity in means and variances provides a superior fit. The findings reveal that variables such as the time of day, type of vehicle involved in the collision, color of EV, average morning/evening peak traveling hours significantly impact the severity of injuries in EV accidents. Additionally, the black indicator exhibits heterogeneity in mean and variance during the afternoon period. This study offers an in-depth understanding of the mechanisms behind driver injury severity in EV accidents and contributes to the development of effective countermeasures to protect drivers from severe injuries.

Present bias and its influence on financial behaviours amongst Indians

PurposePresent bias (PB) is a cognitive bias that stimulates the individual decision-maker to favour the present reward even over the higher reward in the future to avoid the uncertainty attached to the reward in an uncertain future. The article attempts to examine the prevalence of PB amongst Indians and the effect of such bias on savings and borrowings.Design/methodology/approachSecondary data on 47,132 respondents from the Financial Inclusion Insights, 2017 database was used in the study. The theory of self-control, which is captured by the widely accepted hyperbolic discounting model, was used to explore the presence of PB. Suitable statistical techniques and the binary probit regression model were employed to attain the objectives of the study.FindingsThe prevalence of PB was found amongst 8.2% of the sample respondents. The outcome of the study endorses the view of previous researchers that present-biased people tend to save less and borrow more.Originality/valueAlthough the exploration of the role of various cognitive biases on financial behaviour is gaining momentum in recent times, there is a dearth of studies exploring the prevalence of PB and its implication towards financial behaviour, especially in the context of the emerging economy of India. The study makes an original contribution in this regard by using a very rich dataset of 47,132 individuals in the Indian context for the first time.

Evaluating the barriers to adoption of improved biomass cookstoves in Benue state, Nigeria

The prevalent use of inefficient traditional stoves for biomass cooking energy in Nigeria contributes to high rates of biomass consumption, deforestation, and harmful pollutant emissions. This study aimed to assess the constraints hindering the adoption of improved biomass cookstoves (IBCS) in Benue State, Nigeria. Data were randomly collected from 135 households and analyzed using descriptive (frequency, mean and percentage) as well as inferential statistics (binary probit regression). Findings revealed that 88% of households rely on fuelwood for cooking, with approximately 35% still using traditional stoves. However, 46% of households have adopted IBCS and recognized its advantages, including improved cooking speed, fuel economy, time savings in fuelwood collection, cost savings, and reduced smoke emissions. Additionally, various factors such as age, household size, access to credit, location, distance to market, membership of women's associations, fuelwood collection distance, and fuelwood sources were identified as constraints to IBCS adoption. To promote IBCS adoption, we recommend that development planners and extension officers raise awareness among households and provide them credit incentives through government and NGO support. This research significantly contributes to pertinent knowledge by shedding light on the challenges and opportunities associated with IBCS adoption in Nigeria, thereby informing policy and development initiatives in the country.

The Effects of Health Insurance on Demand for Healthcare Services in Tanzania

ABSTRACT: Consistent healthcare financing systems within a particular nation or community have been regarded as vital for making universal healthcare access. This study intended to examine the influence of health insurance on the demand for healthcare services and the influence of socio-economic, demographic and hospital-related factors on demand for health care services in Tanzania context. The study employed secondary data, which were analysed by descriptive statistics and the results were presented in tables and percentages. Econometric analysis was also done using binary probit regression model to determine the factors influencing demand for health care services in Tanzania. The instrumental variable was applied to address the problem of endogeneity of health insurance. The regression results revealed that gender, education level, household size, income, treatment cost, perceived health quality and health insurance type were significant and positively linked with demand for health care services. However, marital status was also significant but negatively associated with the demand for healthcare services. The study concluded that, despite of the efforts made by the government and other stakeholders on emphasis over having health insurance, the enrolment to health insurance schemes is still low although it has been noted to significantly influence the demand for health care services. Lastly, the paper recommends that the government and other stakeholders are required to formulate or strengthen the formulated policy in practice, purposively to improve access, affordability and the quality of health care services. Governmental and non-governmental organizations (NGO`s) and other stakeholders should put more effective strategies to improve coverage but also reduce the financial burden caused by illness. Also, the government needs to subsidize the healthcare sector, especially to the poor and provide subsidies to private healthcare facilities, improvement of insurance type, specifically, the NHIF which needs to increase coverage. Furthermore, health care managers should educate other people and make a clear observation on factors that make individuals demand for health care because they have a great role in the health care sector as well as strategies to improve household`s income to meet the health needs in Tanzania.

Performance of agricultural extension agents in Indonesia: evidence from a nationally representative survey

ABSTRACT Purpose This study analyses the performance of field extension agents in Indonesia using a multi-dimensional performance indicator. Methodology The study uses data from an online survey of a nationally representative sample of 1,974 field extension agents. Binary and ordinal probit models are utilised to analyse the performance of extension agents and the factors influencing it based on a multi-dimensional measurement of performance. Findings and practical implications Improved performance is associated with increased career progression, availability of external funds, the opportunity to collaborate, involvement in government programs, implementation of disciplinary measures and a diversified use of advisory methods. To promote performance, the government should encourage and improve the institutional arrangements. Theoretical implications This study introduces a multidimensional measurement that covers timeliness, targeting and content dimensions while capturing both the institutional arrangement and demographic characteristics in explaining performance. Originality/value This study introduces an online survey for extension agents that encompasses a nationally representative sample, thereby demonstrating this cost effective approach designed for rapid data collection and facilitating the measurement of performance during unforeseen events, such as the Covid-19 pandemic. Its broad applicability allows for a thorough analysis of agricultural extension agents’ performance in a variety of contexts.

Adaptation of rice farmers to aging in Thailand

Abstract As one of the fastest-aging countries globally, Thailand faces unique challenges in its agricultural landscape due to the accelerated aging of its farming population. As rice is the most important crop, it is essential to understand the perceptions of farmers regarding the effects of aging on rice farming and how they adapt to aging. This research examined the adjustments that rice farmers had made in their practices over the past 5 years to cope with this demographic shift. The study utilized survey data collected from 300 rice farmers across two prominent rice-producing provinces in Thailand. The findings revealed that 32% of the surveyed rice farmers had adapted to the challenges posed by aging. The top two adaptive strategies allowed descendants to be more involved in rice farming activities and utilization of labor-saving machinery. To analyze the factors influencing the decision to adapt to aging, a binary probit model was employed. The results indicated that several factors played a positive and significant role in the rice farmers’ decisions. These included the farmer’s age, the size of agricultural land, access to irrigation, land ownership, the farmer’s perception of the impact of aging on rice farming, willingness to learn to use and embrace innovative technology for planting and harvesting, and a reduced number of farming activities performed through custom hiring services. Among these factors, irrigation, land ownership, and the farmer’s perception of the aging impact were found to have a strong influence on the farmer’s adaptation.

The influence of the industrial organization on farmers' willingness to adopt water-saving irrigation technologies: a perspective of organizational support

ABSTRACT Water scarcity and inefficiency hinder China's sustainable agriculture. Water-saving irrigation (WSI) technology, as a crucial means to improve water efficiency, has relatively low adoption rates. In this context, this study utilizes survey data from 1,178 farmers in the Yellow River Basin in China, employing both the Binary Probit model and the Iv-Probit model to deeply explore the influence and mechanism of industrial organizations on farmers' willingness to adopt WSI technology. Our study shows that organizational participation behavior and degree of organizational support can effectively enhance farmers' willingness to adopt WSI technology. Organizational support indirectly enhances farmers' willingness to adopt WSI technology by standardizing production, providing agricultural training, and being proactive in learning agricultural techniques. Furthermore, organizational support has a significant impact on farmers with low education, young age, large land management areas, and poor agricultural production conditions. Our study also found that organizational support and government support can play a synergistic role in promoting farmers' willingness to adopt WSI technology. Therefore, it is necessary to strengthen the cultivation of industrial organizations, improve the governance structure of industrial organizations, strengthen the institutional guarantee of industrial organizations, and pay attention to the heterogeneity of farmer groups when leveraging the role of industrial organizations.

COVID-19 pandemic, household welfare and diversification strategies of smallholder farmers in Uganda

Agricultural activities in many sub-Saharan African (SSA) countries are subject to various risk factors that the COVID-19 compounds. Earlier studies on the effect of COVID-19 on smallholders neglect the issue of comparison with non-farm households. The study uses micro-level household datasets to explore the impact of the COVID-19 pandemic on household welfare, with a focus on farm households relative to their non-farm counterparts. We employed a binary probit model and Propensity Score Matching (PSM) approach and demonstrated that farm households witnessed important income reductions during the COVID-19 pandemic in Uganda. The study contributes to the design of evidence-based approaches to reducing farmers’ vulnerabilities to agricultural risks and pandemic-related shocks.

Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model.

Dioxins are ubiquitous endocrine-disrupting substances, but determining the effects and benchmark doses in situations of coexposure is highly challenging. The objective of this study was to assess the relationship between dioxin andgestational diabetes mellitus (GDM), calculate the benchmark dose (BMD) of dioxin in coexposure scenarios, and derive a daily exposure threshold using an optimized physiologically based toxicokinetic (PBTK) model. Based on a nested case-control study including 77 cases with GDM and 154 controls, serum levels of 29 dioxin-like compounds (DLCs) along with 10 perfluoroalkyl acids (PFAAs), seven polybrominated diphenyl ethers (PBDEs), and five non-dioxin-like polychlorinated biphenyls (ndl-PCBs) were measured at 9-16 weeks of gestation. Bayesian machine kernel regression (BKMR) was employed to identify significant chemicals, and probit and logistic models were used to calculate BMD adjusted for significant chemicals. A physiologically based toxicokinetic (PBTK) model was optimized using polyfluorinated dibenzo-p-dioxins and dibenzofurans (PFDD/Fs) data by the Bayesian-Monte Carlo Markov chain method and was used to determine the daily dietary exposure threshold. The median serum level of total dioxin toxic equivalent (TEQ) was 7.72 pg TEQ/g fat. Logistic regression analysis revealed that individuals in the fifth quantile of total TEQ level had significantly higher odds of developing GDM compared to those in the first quantile (OR, 8.87; 95% CI 3.19, 27.58). The BKMR analysis identified dioxin TEQ and BDE-153 as the compounds with the greatest influence. The binary logistic and probit models showed that the BMD10 (benchmark dose corresponding to a 10% extra risk) and BMDL10 (lower bound on the BMD10) were 3.71 and 3.46 pg TEQ/g fat, respectively, when accounting for coexposure to BDE-153 up to the 80% level. Using the optimized PBTK model and modifying factor, it was estimated that daily exposure should be below 4.34 pg TEQ kg-1 bw week-1 in order to not reach a harmful serum concentration for GDM. Further studies should utilize coexposure statistical methods and physiologically based pharmacokinetic (PBTK) models in reference dose calculation.

A Study on the Socio-economic Factors Influencing Women’s Involvement in SHG Programmes in Ambedkar Nagar District of Uttar Pradesh, India

In the present paper, an attempt has been made to understand the socio-economic conditions of the SHG members and factors determining women’s participation in the SHG programme, utilizing pre-structured and pre-tested schedules to collect information from Ambekar Nagar district. Employed proportionate random sampling as well as purposive technique, 120 respondents were selected from two blocks i.e., Akbarpur and Bhiti based on higher number of SHGs. The data were analyzed with the help of suitable statistical tools like percentage, mean and binary probit models, to find the factors that effect the women’s participation in SHG programme. Results revealed that 40% of the respondents were illiterate, with the remainder having varying levels of education, predominantly at the primary level. Family size was primarily medium (5-6 members), and landholdings averaged 1.4 hectares, reflecting a predominantly agricultural setting. Livestock ownership, particularly buffaloes and goats, contributed significantly to household livelihoods. The study employed a probit regression model to analyze the determinants of women’s participation in SHGs, with factors such as age, family size, landholding, education, and net non-farm income examined. The results indicated that age and net non-farm income were statistically significant in influencing participation, with older women and those with higher non-farm income being less likely to engage in SHGs. Conversely, larger family size positively impacted participation. The findings suggest that rural development initiatives should prioritize younger women, expand education opportunities, and promote farm-based income generation to enhance SHG participation and, consequently, women's empowerment.

Combined Influence of Multiple Factors on Lung Cancer and Anxiety: A Probit Model Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide, significantly impacting public health. Concurrently, anxiety is a prevalent psychological disorder known to influence the development and progression of various cancers. This research paper aims to examine the combined influence of various factors, including demographic characteristics, environmental exposures, and physical conditioning data, on the incidence of lung cancer and anxiety. Given the binary nature of both lung cancer and anxiety outcomes, the analyses will first employ separate binary probit regression models to identify significant predictors for each condition independently. Then, joint modeling techniques will be implemented with dual purposes. First, by comparing the results of the individual and joint models, the reliability and robustness of the findings will be enhanced through cross-validation. Second, joint models will enable an investigation into potential endogeneity between lung cancer and anxiety. Addressing this endogeneity is crucial as it can potentially improve model robustness and provide deeper insights into the interrelationship between these two health outcomes. For the study purpose, the demographic factors considered include age and gender. Environmental factors encompass smoking history, alcohol consumption, and peer pressure. Physical conditioning data includes pre-existing health conditions such as yellow fingers, anxiety, chronic disease, fatigue, allergy, wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain. By leveraging advanced statistical modeling techniques, this research seeks to uncover nuanced relationships and potential causal pathways that may exist between lung cancer and anxiety. The findings from this study will contribute to the existing body of knowledge by providing additional case study showing the relationship among a multitude of factors on lung cancer and anxiety, respectively. Also, the endogeneity checking among lung cancer and anxiety may enhance the efficiency of models done by others in the future. The research paper's analysis on lung cancer incidences reveals that age and wheezing are significant predictors. In examining anxiety levels, smoking emerged as a significant predictor, indicating a higher likelihood of anxiety among smokers. In addition, the joint probit models confirmed these findings, with age and wheezing significantly predicting lung cancer incidence, and smoking significantly predicting anxiety levels. No significant endogeneity was observed between lung cancer and anxiety, suggesting that these health outcomes are influenced by different sets of factors. These findings underscore the importance of considering demographic, environmental, and physical conditioning data in understanding and addressing lung cancer and anxiety, and checking their potential endogeneity

Correlates of stocking naloxone: a cross-sectional survey of community pharmacists

BackgroundProvision of take-home naloxone (THN) and overdose education reduces opioid-related mortality. In Australia, from July 2022, all Australian community pharmacies were eligible to supply naloxone for free through the national THN Program.AimThis study aimed to identify naloxone stocking rates and correlates of stocking naloxone across Australian pharmacies.MethodData were collected from a representative sample of Australian pharmacists in Victoria, New South Wales, Queensland and Western Australia via an online survey. Data collected included pharmacy and pharmacist characteristics and services offered within the pharmacy, including needle and syringe programs, opioid agonist treatment (OAT) and stocking naloxone. Binary probit regression analysis was used to identify correlates of stocking naloxone after controlling for key covariates.ResultsData from 530 pharmacists were analysed. In total, 321 pharmacies (60.6%) reported stocking naloxone. Chain pharmacies and pharmacies that provided OAT had a greater probability of stocking naloxone (B = 0.307, 95%CI: [0.057, 0.556], and B = 0.543, 95%CI: [0.308, 0.777] respectively). Most (61.7%) pharmacists felt comfortable discussing overdose prevention with patients who use prescription opioids, and this comfort was associated with a higher probability of stocking naloxone (B = 0.392, 95%CI: 0.128, 0.655). Comfort discussing overdose prevention with people who use illicit opioids was lower (49.4%) and was not associated with stocking naloxone.ConclusionThere is scope to increase stocking of naloxone and comfort with overdose prevention, particularly through addressing comfort working with higher risk groups such as people who use illicit opioids.

Crisis interpreting and Deaf community understanding during the COVID-19 pandemic: results from a South Korea-based survey.

This study explores the South Korean Deaf community's response to sign language interpreting during the COVID-19 (coronavirus disease 2019) health crisis, focusing on individual factors affecting the signers' comprehension. The data were collected from a mobile-based questionnaire survey conducted among 401 Deaf adults; binary probit modelling was adopted to analyse the data. The major findings are: (i) 59.9 per cent of the respondents understood less than 70 per cent of the interpreting; (ii) males and urban residents tend to understand better; (iii) younger people (less than 50 years) and signers with a Bachelor's degree or higher are likely to have lower comprehension; and (iv) Deaf adults who visited a doctor after the COVID-19 outbreak tended to have lower comprehension. The findings demonstrate that individual characteristics, including age, impact significantly on the extent to which Deaf individuals understand the sign language interpreting of COVID-19 information, indicating that steps are needed to achieve a Deaf-inclusive society during a health disaster.

Exploring the edges of clientelism: ideology, ethnicity, and partisanship in Belize

ABSTRACT Studies focusing on Belize reveal that clientelism is the glue that makes the party system stick. We explore whether ideology and ethnicity account for partisanship within that deeply clientelist setting. We use four AmericasBarometer survey waves by the Latin American Public Opinion Project (LAPOP) to study the individual-level correlates of partisan attachments with the People’s United Party (PUP) and United Democratic Party (UDP). Using binary and multinomial probit maximum likelihood estimators, we find that ethnicity correlates to partisanship with the PUP, and ideology correlates to partisanship with the UDP. The robustness checks, which focus on declared vote choices in previous national elections, corroborate our findings. The Belizean case shows how clientelist parties can develop alternative support sources and that these can vary substantially.

The impact of internet use and cognitive conflict on farmer IPM technology adoption: evidence from China.

Integrated pest management (IPM) plays a crucial role in protecting agricultural environments and enhancing the quality of agricultural products. However, a major challenge in China is the conflicting understanding of IPM among farmers, leading to low adoption rates. This undermines farmers' ability to control pests and diseases while increasing risks to agricultural quality and safety. This study aimed to investigate the impact of cognitive conflicts on farmers' adoption of IPM in kiwifruit farms in Shaanxi and Sichuan provinces. Additionally, the study explored the moderating role of internet use in the relationship between cognitive conflicts and farmer adoption of IPM. Data were collected from 686 kiwifruit farms through field surveys in 2018. The binary Probit model and moderating effect models were used to assess the influence of internet use and cognitive conflict on farmer adoption of IPM. The study found that cognitive conflicts significantly hindered farmers' adoption of IPM. Higher levels of cognitive conflict were associated with lower likelihoods of adopting IPM. Internet use and frequency had positive effects on farmer adoption of IPM, promoting its implementation. Moreover, internet use and frequency helped alleviate the inhibitory effect of cognitive conflicts on farmer adoption IPM. This research enhances our understanding of cognitive conflicts among farmers when promoting IPM and provides viable strategies to improve the effectiveness of public sector promotion and stimulate farmers' willingness to adopt IPM. It emphasizes the importance of addressing cognitive conflicts and utilizing internet resources to enhance IPM adoption among kiwifruit growers in China. © 2024 Society of Chemical Industry.