Attitude on high tech production: TPB proves that individual attitude is a crucial factor that directly affects intentional behavior, and this hypothesis has been verified through various research papers in technological application behaviors (Ajzen, 1991; Khonje et al., 2015). Basically, an individual positive or negative attitude toward a behavior is related to his evaluation of the outcome of that behavior (Baumgartetz et al., 2012; Kristjanson et al., 2015). Elmustapha et al. (2018) found that farmer’ attitudes toward high tech production relating to their evaluation about the form, price, process and benefits of the application. In addition, Dima (2013) indicated that attitudes not only include subjective perceptions and personal feelings about the advantages or disadvantages of a solution but also involve the compatibility between the values that the solution may bring with that individual’s expectations. When people have a positive attitude toward technology, their likelihood of accepting the use of technology may also increase (Gadenne et al., 2011; Kassie et al., 2015; Negatu and Parikh, 2019).

Perceived benefits: Perceived benefits are related to the willingness to adopt something new compared to traditional practice (Kristjanson et al., 2015; Elmustapha et al., 2018). User behavior is shaped by the perception of higher benefits achieving through the use of a specific solution (Gadenne et al., 2011). Mottaleb et al. (2016) argued in their theoretical framework that perceived benefits can be defined as the extent to which users believe that using products/services will yield significant effectiveness for them. In the case of applying technology to agricultural production, Mukasa (2018) believed that the characteristics of the technology play an essential role in determining its application. When farmers consider technology adoption, they decide whether the technology has positive, efficient, and profitable (Muthumanickam et al., 2022). Additionally, farmers expect high farm income to increase their capital, enabling them to increase such as improved cultivars, seeds, and fertilizer quality. The relationship between perceived benefits and technology adoption plays a vital role in household decisions about technology adoption in agriculture production (Negatu and Parikh, 2019).

Subjective norm: Ajzen (1991) defined subjective norms, also known as social influence, as the perceptions of influencers who think that an individual should or should not perform a behavior. Subjective norms can be described as an individual’s perception of social pressures to perform or not perform a behavior (Dima, 2013). According to TPB, subjective norms can be formed through sensing normative beliefs from people or social factors that influence consumers (such as family, friends, colleagues, media...). The degree of impact of subjective normative belief factors on consumers’ buying tendency depends on: (1) the level of support/opposition for the consumer’s purchase and (2) the consumer’s motivation (Gadenne et al., 2011; Liu et al., 2018). In case of farming, farmers may follow the wishes of influencers. The degree of influence of related people on application behavioral trends and the motivation to follow related people are two basic factors to evaluate subjective norms. The stronger the level of intimacy of the people involved with the individual, the greater the influence on their high tech application. Researches by Meijer et al. (2015) and Moser and Barrett (2016) found that social factors such as influence from family and society are important sources affecting people’s interest in applying new technology. Some other studies built and tested a model based on TPB with the effects of social agents (family, society, government, media, and communication) in addition to attitudes also show significant relation with actual behaviors (Matuschke and Qaim, 2001; Njuki et al., 2018; Negatu and Parikh, 2019).

Perceived behavioral control: Each individual’s perceived behavioral control is related to their self-assessment of the difficulty or ease of performing a behavior (Pardey et al., 2016a,b; Elmustapha et al., 2018). According to Ajzen (1991), this perceived control factor comes from the confidence of the individual who intends to perform the behavior and the easy and favorable conditions for performing the behavior. The more resources and opportunities they have, the less resistance they think there will be, and the greater the perceived control over behavior will be. Ntshangase et al. (2018) believed that perceived behavioral control is measured through the person intending to perform the behavior’s awareness of having sufficient information and other necessary conditions for his or her decision.

Membership of agricultural extension organizations: Agricultural extension activities have an important impact on farmers’ technology adoption behavior (Mulema et al., 2022). In developing countries, local agricultural extension associations are the main focal points for disseminating information about new technologies in agricultural production and the benefits of these technologies. When farmers join agricultural extension associations, they have a higher chance of receiving technology information and technical assistance. Agricultural extension organizations also connect farmers with technology distributors for direct consultation and installation, thereby increasing the ability of farmers to apply technology in production. Many previous studies have demonstrated this positive relationship in developing countries (Marenya and Barrett, 2011; Grabowski et al., 2016; Lam et al., 2018).

Access to credit: Access to credit influenced a farmer’s decision to adopt improved technology positively in many experimental studies (Fischer, 2016; Floro et al., 2018; Dissanayake et al., 2022). Farmers having access to credit are more likely to adopt new technology normally, partly because new technologies will come with investments and increased costs such as labor and fuel. Receiving financial support will help farmers reduce the burden of initial investment as well as in the process of operating technology. This leads to a higher likelihood of adopting their technology. Ogundari and Bolarinwa (2018) showed that credit access had a positive relationship with technology adoption by farmers but to different degrees and this is the most important factor determining technology adoption by farmers. New technology is often associated with an initial investment and will bring long-term profits to farmers. This economic problem will determine the acceptance of technology by farmers. And better access to credit will help make economic aspects more feasible (Baumgartetz et al., 2012; Simtowe et al., 2019).

Access to information about new technology: Farmers will carefully study existing technology and the effectiveness of new technologies before deciding whether to adopt them (Kabunga et al., 2012; Burton, 2014; Dissanayake et al., 2022). They do not simply apply but also proceed from the initial step of awareness, then learn about the technology before finally deciding its application in their agricultural production (Di Falco et al., 2018). There is a positive relationship between access to information and technology adoption by farmers in studies by Di Falco and Bulte (2013) and Brown et al. (2019). Access to information can be done in traditional ways such as meeting farmers directly to disseminate techniques at agricultural fairs, agricultural extension associations, farmer associations or projects to strengthen the capacity of farmers. However, accessing information can also be done indirectly through television, promotional programs and especially social networks in today’s modern society.

Since 2009, Hanoi’s high-tech agricultural application program has been promoted. After more than 14 years of implementation, it has had a positive impact on the growth of Hanoi’s agricultural industry, reaching an average growth rate of 8.5%/year (Department of Agricultural and Rural Development, 2022). High-tech agricultural production achieved an average income more than 2 times higher than the average production value of the entire city, typically: high-quality vegetables reach about 450–500 million VND/ha/year, many models reach 2 billion VND/hectare/year. Currently, the area of high-tech vegetables alone reached 13,655 hectares, accounting for nearly 22.1% of the total cultivated vegetable area. To achieve the above achievements, Hanoi has invested in research, built models, and applied new technical solutions and technologies. Common technologies applied included biotechnology in breeding (in vitro plant tissue culture, vegetable grafting technology), net house technology, drip irrigation technology with pressurization and fertilizer supply system, post-harvest technologies, heat drying technology, film forming technology using automatic or semi-automatic machines in harvesting products. In addition, application of water-saving irrigation technology is also a relatively common model of use in vegetable growing areas with irrigation forms such as drip, local sprinkler and local underground irrigation. The advantage of this system is that it saves 30–60% of water compared to traditional methods, reduces labor, improves crop productivity and quality, and can especially provide fertilizer through a small irrigation system.

Survey results in Table 2 showed that within 450 producers out of a total of 436 respondents, 53.9% were male, and 46.1% were female; the ratio of men and women participating in the interview was quite balanced. The average age of respondents was 42.1, of which the age group from 41 to 50 accounts for the highest proportion (51.2%), this is also the common age group of household heads and the main labor force of farming households in Hanoi. On average, each household participating in the interview had 4.4 people. Over 59% of respondents had graduated from secondary school and the average number of years of schooling was 9.8 years. In the research sample, 34.1% of households applied technology in vegetable production and 65.9% applied traditional methods. The average vegetable growing area per household ranged from 2 to 20 sao (1 sao = 1,000 m2). Up to 62.5% of households had average income, high income households accounted for only 5.7% and low income households accounted for 17.6%.

Table 3 showed the results of analyzing the relationship between independent variables and the probability adoption of vegetable production in study area. We used binary logit regression with maximum likelihood estimation. Firstly, the results of model goodness of fit had significance value of 0.000 < 0.05. Thus, the regression model was consistent. The −2 Log likelihood (−2LL) measured how well the model fitted the data. In this case, the -2LL for the empty model (no independent variables) was 134.32. This significant reduction in -2LL indicated that the independent variables included in the model improved their fit compared to the empty model. Furthermore, the Cox & Snell R² and Nagelkerke R² values were greater than 0.5, suggesting that the regression model had a good fit and explained a substantial portion of the variability of the dependent variable.

Logit regression results indicated that there were 8 factors having a significant influence on the application of technology in vegetable production in Hanoi including ATT, PBC, FSIZE, EDU, MEO, ACRE and ACIF, in which ACIF had the strongest impact on farmers’ decisions to apply technology (Table 3).

The logit regression equation is written as follows:

From the results, farmer households’ attitude toward technology was the factor that had the strongest influence on their decision to apply high technology in vegetable production. This result is consistent with the TPB model, which says that the attitude factor is crucial in individual intention-making. This result was also discovered in previous studies on technological application behavior of farmers (Gadenne et al., 2011; Kassie et al., 2015; Negatu and Parikh, 2019). The results show that when attitude increases by 1 point, technology acceptance increases by 13.2%. According to Kristjanson et al. (2015), attitude was farmers’ perception of the application of technology. The more they understand the superiority of technology, the attitude will improve. Study by Elmustapha et al. (2018) in Malawi also found that demonstration field visits help improve farmers’ attitudes toward technology adoption. Dima (2013) found similar results in their study on the determinants of technology adoption by vegetable farmers in Malaysia. Technology demonstrations give farmers the opportunity to evaluate new technology before deciding whether to adopt it. Additionally, field demonstrations promote positive adaptive behavior among farmers as they have the opportunity to interact closely with technology enablers, thereby dispelling any doubts they may have about a particular technology. This result was also consistent with researches by Kristjanson et al. (2015) and Baumgartetz et al. (2012).

Access to information is the second most influential factor in farmers’ decisions to apply technology in production. Applying new technology is a complex process and the more information farmers have, the more confident they are in their decision-making process. In previous studies by Kabunga et al. (2012), Burton (2014), access to information was also proven to be one of the most important factors in the decision-making process to apply technology for farming households. There are many channels that can provide information about technology applications in production to people. Dissanayake et al. (2022) showed that traditional forms of providing information such as agricultural associations, training and disseminating agricultural knowledge directly have a stronger impact than indirect channels such as community media. However, Brown et al. (2019) in the study in Asia pointed out those social networks were the most important channel for disseminating knowledge and information for farmers. Whether provided directly or indirectly, access to information is always an important factor in promoting technology adoption behaviors whether in developed or developing countries (Di Falco and Bulte, 2013; Lam et al., 2018).

Participation in local agricultural extension organizations was the third most influential factor in the application of technology in vegetable growing by farmers. The positive sign of the coefficient was as expected that when farmers join agricultural extension organizations, the level of technology adoption would increase. This result is consistent with other findings from studies by Marenya and Barrett (2011), Grabowski et al. (2016), and Lam et al. (2018). Extension agencies were expected to increase awareness among farmers about techniques in agriculture production. Furthermore, adequate accompaniment of farmers by extension officers and other technical experts is crucial for technology adoption because their presence increases farmers’ confidence in the applied technologies.

Farm size was proposed to have a positive influence on farmers’ technology adoption decisions. As expected, this variable was statistically significant at the 5% significance level. This means that the larger farm, the more likely farmers were to adopt new technology. Although the vegetable growing area in the study area was fairly small, it was common for farmers to have multiple vegetable growing areas in different locations for different crops. This might spread production risks. Other studies also supported this hypothesis such as Lowder et al. (2016), Ojiako et al. (2017) and Noack and Larsen (2019). However, Noack and Larsen (2019) found a negative significant relationship between farm size and farmers’ technology adoption.

Access to credit had also a positive effect on farmers’ decision to apply advanced technology in our analysis. This result was consistent with initial expectation that access to credit had a direct impact on technology adoption. Farmers with access to credit were more likely to adopt new technologies (Fischer, 2016; Floro et al., 2018; Dissanayake et al., 2022). This makes sense because some technologies involve additional production costs, higher demands on labor and resources. Therefore, farmers’ ability to cover these additional costs by taking out loans was an important factor in their decision to adopt the production technology.

The educational attainment of farmers was also recognized as a positive influence on the decision to embrace new technologies. A higher level of education equips farmers with the capability to access and utilize information on the adoption of new technology effectively. This phenomenon can be attributed to the fact that a robust educational background tends to foster broader and more analytical thinking, enabling individuals to assess the advantages presented by innovative technology more comprehensively. The observed relationship between education level and the decision to apply advanced vegetable technology further substantiates this notion. Specifically, it becomes evident that farmers with higher levels of education are inclined to adopt a more expansive and accurate perspective, which enables them to make informed decisions regarding technology adoption. This aligns coherently with a similar study on factors influencing agricultural technology adoption in Asia and Africa such as Ndiritu et al. (2014), Ogada et al. (2014), and Aung et al. (2021).

Finally, perceived behavioral control was the factor with the weakest significant influence on the adoption of technology by farmers. For every 1 point increased in this variable, the likelihood of people accepting the technology increased by 5.6%. Perceived behavioral control is an individual’s perception of how easy or difficult it is to perform a behavior; it represents the degree of control over performing the behavior, not the outcome of the behavior (Ajzen, 1991). This outcome aligned harmoniously with a study on the impact of farmers’ use of information technology by Pardey et al. (2016a,b), Elmustapha et al. (2018), and Negatu and Parikh (2019). It implied a universal trend where farmers are more likely to recognize the advantages of applying technology; they tend to accept higher technology in agricultural production.