Extending TAM: interactivity, efficiency, and trust in ICT adoption among millennial farmers

Extending TAM: interactivity, efficiency, and trust in ICT adoption among millennial farmers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extending TAM: interactivity, efficiency, and trust in ICT adoption among millennial farmers Hari Otang Sasmita, Amiruddin Saleh, Wahyu Budi Priatna, Pudji Muljono This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5920374/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The adoption of information and communication technologies (ICTs) in agriculture can enhance productivity and reduce costs through technological advancements and digitization. However, an imbalance exists between high internet penetration and its use in Indonesian agriculture, especially among younger generations. This study investigated the impact of ICT factors on young small-scale farmers' ICT utilization to improve productive performance. This study extends the Technology Acceptance Model (TAM) by incorporating perceived interactivity, efficiency, and trust in ICT as additional constructs. A survey was conducted in Bogor District, Indonesia, using structural equation modeling for data analysis. The results show that interactivity significantly influences communication and information acquisition, while efficiency and trust affect communication, but not information acquisition. Communication and information significantly influence productivity. Mediation analysis revealed the crucial role of communication in translating efficiency, interactivity, and trust into productivity. The findings highlight the importance of designing ICT platforms that enable seamless two-way communication and the need for tailored training programs and localized content to enhance farmers' confidence in using ICT for information seeking. This study contributes to the literature on ICT adoption in agriculture by emphasizing the mediating role of communication in driving productivity and offers implications for policymakers, agricultural extension services, and ICT developers aiming to enhance farmers' productivity through technology adoption. Interactivity online participation adoption young farmers performance Figures Figure 1 Figure 2 Introduction Agriculture plays an important role in Indonesia’s economy. However, agriculture in the country is currently facing critical issues owing to climate change and the shrinking quantity and quality of land and human resources. According to Indonesia's Youth Statistics for 2023, approximately 19.23 percent or 12.31 million youth of a total of 64 million people are involved in agricultural activities 1 . However, over the past decade, the number of young people working in the agricultural sector has decreased by almost 10 percent compared to 2012. This decline reflects the low interest of younger generations in choosing agriculture as a career path 2 , even for agricultural schools 3,4 or for those who have agricultural career opportunities through their family inheritance 5 . The widespread recognition of information and communication technologies (ICTs) potential to enhance agricultural productivity and reduce production costs through technological advancements, innovation, and digitization has highlighted its numerous benefits for farmers. These include facilitating social interactions, connecting with intermediaries in product marketing, and accessing immediate agricultural guidance from experts 6–8 . Smartphone technology has provided new ways to disseminate information in interactive and multimedia formats 9 , helping young small-scale farmers to make informed decisions by accessing better market information 10 . Furthermore, ICTs possess the capacity to facilitate the implementation of sustainable agricultural practices, such as precision farming and climate-smart agriculture, by providing farmers with access to information, decision support tools, and monitoring systems. 11–13 . Consequently, ICT-mediated communication has become an important moderator in the digital communication era because it can provide immediacy, accessibility, interactivity, trustworthiness, and dynamism through various virtual platforms 14 . However, in Indonesia’s ICT utilization system, there is an imbalance between high Internet penetration and its use in the agricultural sector, especially among the younger generations. This is revealed by data from Indonesian Youth Statistics in 2023, which shows that the majority of youth use the internet for social media or social networking purposes at 84.37 percent, looking for information or news at 84.28 percent, and entertainment at 83.78 percent 15 . Meanwhile, youth accessing ICT, especially smartphones, reached 96.28 percent and internet 94.16 percent 1 . It should be noted that the majority of existing internet users in Indonesia are young people under the age of 35 who have been shown to spend more time each day on the internet 16 . Therefore, it is important to consider some of the controversies surrounding these data. First, what type of ICTs can help or detract young farmers from adoption? Second, it is crucial to understand how young farmers use ICTs to maintain communication and increase their capacity for agriculture. Exploring the key factors of ICTs that influence ICT utilization is not only important for improving the productive performance of young small-scale farmers in Indonesia but also a cornerstone for achieving the policy objectives of farmer empowerment programs both in Indonesia and in other developing countries. The adoption of ICT in agriculture has positively impacted market participation, rural entrepreneurship, and overall household income. ICT adoption among farmers is influenced by various factors across technological, individual, and environmental dimensions. From a technological perspective, perceived usefulness and relative advantage positively impact ICT adoption intention 17,18 . Additionally, studies show that factors such as performance expectancy and convenience play a significant role in shaping farmers' intention to adopt and use agricultural fintech products and services 19 . The ease of use of ICT tools also plays a significant role, particularly for entrepreneurs with no previous experience 18 . These factors are related to the farmers’ perceptions of the usefulness and ease of use of ICT solutions in agriculture. Nevertheless, further examination of the literature emphasizes that while the role of ICTs in facilitating information dissemination and communication has been acknowledged, investigation into ICT factors such as interactivity, efficiency, and trustworthiness in young farmers' ICT adoption is insufficient, particularly among young small-scale farmers. There appears to be a considerable research void regarding ICT factors and utilization that hinders the effectiveness of digital interventions in rural smallholder farming environments in Indonesia. This study aims to fill this gap by proposing a new research model that investigates the impact of ICTs factors on young small-scale farmers' ICT utilization to improve productive performance. This study aims to extend the technology acceptance model (TAM) by incorporating perceived interactivity as perceived ease of use, efficiency as perceived usefulness, and trustworthiness as the extended construct to examine the influence of ICTs factors on its utilization among young small-scale farmers. Bogor District was selected as the research site because of its status as an area that actively runs young farmer empowerment programs from the provincial-level government called as “Program Petani Milenial” (Millennial Farmers Program) and at the national level from Ministry of Agriculture of the Republic of Indonesia called Youth Entrepreneur and Employment Support Services (YESS). These programs are expected to produce strong and high-quality millennial entrepreneurs 20 . In this context, millennial farmers have been identified as key actors in the transformation of Indonesia’s agriculture sector. When they tend to be more open to technology, they have a great potential to adopt technology and carry out sustainable agricultural practices. This context provides an optimal framework for examining the influence of digital communication media on ICTs utilization among young small-scale farmers. The objectives of this study are as follows: (1) To propose a novel theoretical model that incorporates perceived trustworthiness into the Technology Acceptance Model (TAM) by analyzing the effects of digital communication media on young farmers' online participation. (2) The efficacy of the extended TAM in predicting and elucidating young farmers' online participation in productive performance was evaluated using structural equation modeling. (3) We analyzed survey data to explain how digital communication media factors influence young farmers' online participation to improve productive performance and offer insights to improve future adoption rates. Research theory and hypotheses This study integrates the technology acceptance model (TAM) 21 with an ICT-mediated communication model 22 to predict young farmers’ acceptance of and intention to use ICT in their online participation to improve productive performance (Figure 1). In this study, online participation was defined as the activities of access to agricultural information and communication to improve capacity. TAM has been widely used to predict people's acceptance and intention to use ICT across various domains. The core constructs of TAM - perceived usefulness (PU) and perceived ease of use (PEOU) - consistently show significant positive effects on technology adoption intention 13,23–25 . PEOU refers to an individual's perception of the utilization of a novel technology, encompassing the perceived difficulty of learning and ease of operation. When a new technology is perceived as user-friendly, individuals are more likely to adopt and utilize it. PU pertains to an individual's cognitive assessment of whether a novel technology can provide tangible value and benefits. If individuals perceive that a new technology is advantageous, they will be more inclined to accept and employ it. Additional factors, such as interactivity, efficiency, trustworthiness, social influence, and facilitating conditions, have been incorporated to extend the TAM and enhance its explanatory power. 23,25,26 . Based on the provided papers, there is a limited direct mention of perceived ease of use (PEOU) being measured using perceived of interactivity (PEI). However, some studies have examined interactivity as a factor that influencing PEOU. Researh 27 specifically examines how online social networking (OSN) experience factors, including interactivity, influence the actual use of social media for purchasing theme park services through perceived ease of use and perceived usefulness. Research 28 explored system interactivity (SI) as a factor impacting PEOU in e-learning adoption during the COVID-19 pandemic and found a positive relationship between SI and PEOU. While these papers demonstrate a connection between interactivity and PEOU, they do not explicitly measure PEOU using a "perceived of interactivity" construct. Instead, interactivity is typically treated as a separate factor that influences PEOU. In conclusion, while interactivity is recognized as an important factor related to PEOU in technology adoption models, the studies provided do not specifically measure PEOU by using a perceived interactivity construct. Further research is required to further explore this relationship. According to Davis' TAM definition, in predicting ICT utilization, perceived ease of use (PEOU) refers to whether farmers perceive the technology as simple, convenient, and easy to operate. Several studies have applied TAM to investigate ICT adoption in agriculture. Farmers are more likely to adopt ICT if they perceive it as useful and easy to use 26,29,30 . Research on ICT adoption among rural entrepreneurs has found that perceived usefulness and perceived ease of use positively influence attitudes toward ICT adoption 18,31 . For entrepreneurs lacking prior experience, perceived ease of use significantly influenced their attitudes toward ICT adoption 18 . Research 32 has reported that perceived ease of use has a positive effect on farmers' intention to adopt rice-shrimp crop technology, both directly and indirectly, through behavioral attitudes. The interactivity and efficiency of ICT tools (ICT-mediated communication) enables farmers to access real-time weather forecasts and market information, thereby enhancing their decision-making capabilities. For instance, IoT sensing platforms can provide farmers with crucial information about soil and environmental conditions, support better crop management, and increase crop yields 33 . Furthermore, smartphone-based agricultural extension services have been shown to boost rural income growth 34 . This improved access to information through interactive ICT tools empowers farmers to make informed decisions regarding their agricultural practices. Therefore, in this study interactivity refers to the technological affordance that enables users to interact with one another by utilizing the same technology to directly exert influence on each other's communication and content creation 35 . Compared with more familiar terrestrial communication technologies, if ICT operations are too complicated, farmers may lose confidence in learning the technology, and vice versa. The perceived usefulness of information and communication technologies (ICTs) can enhance farmers' online participation through social communication and access to agricultural information 36 . Several studies have demonstrated the positive impact of ICTs on farmers' adoption of sustainable agricultural practices and access to information. For instance, the use of WeChat application significantly increased the adoption of soil testing and formula fertilization among farmers in Zhejiang, China 37 . This finding highlights that digital extension services can promote sustainable agriculture by improving access to information. Similarly, broadcast media content on dry season agriculture in Nigeria's Federal Capital Territory was highly accessible (94.2%), exposing the majority of farmers (83.1%) to information on dry season farming practices 38 . Research 39 has demonstrated that perceived usefulness, subjective norms, and perceived behavioral control positively influence the adoption of digital extension services. In conclusion, ICTs has shown significant potential for enhancing farmers' online participation and access to agricultural information. Perceived trustworthiness in ICT enhances farmers' online participation through social communication and access to agricultural information. Studies have shown that ICT adoption, particularly through smartphone and internet use, can significantly impact rural and agricultural development. Perceived trust played a crucial role in this process. Prior research indicates that trustworthiness serves as a positive moderator in the relationship between factors such as perceived awareness, information acquisition, social influence, and the intention to utilize ICT 14 . Studies have shown that ICT adoption increases the probability of rural households' access to credit and empowers rural women and farm households in less-developed regions 40 . Furthermore, the implementation of contemporary technology in a co-production process, in conjunction with targeted training and monitoring, has the potential to substantially enhance smallholder farmers' access to and utilization of weather and climate forecasting information. 41 . This suggests that, as farmers develop trust in ICT systems, they are more likely to engage in online activities and access agricultural information. Although ICT-based market information sources significantly influenced market participation among smallholder livestock farmers, their effect on the intensity of market participation was not found to be significant 42 . This highlights the complexity of ICT adoption in agricultural contexts, and suggests that trust alone may not be sufficient to drive intensive engagement. Perceived trust in ICT can enhance farmers' online participation by facilitating social communication and accessing agricultural information. Factors such as perceptions of risks and costs, may hinder ICT adoption 29,43 . Access to information, training, and awareness of ICT benefits also positively influence adoption behavior 36 . To promote ICT adoption among young farmers, it is essential to address these factors through targeted interventions including tailored training programs, improved access to information, and supportive government policies 36,44 . Online participation and access to information technology can significantly enhance farmers’ productive performance by improving their knowledge, skills, and decision-making abilities. Digital extension services and mobile applications provide personalized information on crop selection, input usage, and cultivation methods, leading to increased input intensity, production diversity, crop productivity, and income 45 . These services offer timely access to appropriate agricultural information, enabling better decision making and effective responses to challenges and opportunities 46 . Farmers with access to various information sources, particularly government and private extension providers, demonstrate increased capacity and capability to adopt agricultural innovations 47 . The adoption of ICTs by farmers, such as smartphones and computers connected to the internet, has been shown to increase commercial orientation, land productivity by 21.3 percent, and labor productivity by 28.2 percent 48 . The use of ICT has also been shown to increase the technical efficiency of agriculture, with the technical efficiency score of ICT users reaching 0.64 compared to 0.57 for non-users 49 . In addition, video-based extension services have significantly increased the adoption of recommended agricultural technologies and practices 50 . This improvement in productivity is particularly significant for young, and small-scale farmers. A study of smallholder livestock farmers revealed that the utilization of ICT-based market information sources significantly influenced market participation, although the effect on the intensity of participation was not significant. 42 . In conclusion, online participation through various ICT tools and services can significantly improve farmers' productivity by providing timely and personalized information and facilitating better decision making. Methodology Data Collection. The research protocol was approved by the Ethics Committee of Indonesia's National Research and Innovation Agency. The investigation strictly adhered to all applicable guidelines and regulations. Each participant provided informed consent after being thoroughly briefed on the study's objectives and assured confidentiality of the survey. The findings of this research will be employed solely for scientific purposes, with an unwavering dedication to safeguard the privacy of personal data. This study targeted millennial farmers who are actively involved in the Millennial Farmer Empowerment Program and the Youth Entrepreneurship and Employment Support Services Programme (YESS) in Bogor Regency, Indonesia. Local agricultural authorities provided researchers with demographic and operational information about the millennial farmers who met the study criteria. To ensure the representativeness and diversity of the collected data, the study included farmers with varying levels of business size and digital engagement from all working areas of the Agricultural Extension Agency in Bogor. Data were collected using an online questionnaire distributed on Google Forms. In total, 382 questionnaires and 345 valid responses were collected, yielding an effective response rate of 90.3 percent. Measurement. A questionnaire was used as a research instrument. To develop the questionnaire, latent variables were operationalized based on a review of relevant literature. This study considered three media factors: interactivity, efficiency and trust 22,35 , online participation 51–56 , and farmers’ individual productive performance 57,58 . To ensure the accuracy and reliability of the data, the measurement of the variables in this study was designed to capture the engagement of millennial farmers with digital ICT’s. Variables were assigned based on the utilization of specific digital agricultural tools and technologies such as smartphones, the Internet, and digital platforms for online participation. Structural Equation Modeling (SEM) was conducted using SmartPLS 3 software to analyze the relationships among the variables, including the examination of mediating effects. All other variables were measured using a five-point Likert scale, where 1 represented "strongly disagree," 2 represented "disagree," 3 represented "neutral," 4 represented "agree," and 5 represented "strongly agree." The survey included 32 items designed to measure six latent variables (see Table 1 for detailed variable descriptions). Results Measurement model The measurement model comprised three evaluations: reliability, convergent validity, and discriminant validity. Reliability assesses how consistently a group of indicators measures the aggregate constructs 59 . This investigation utilized Composite Reliability (CR) and Cronbach's alpha coefficients to assess the internal consistency of indicators within each construct, with values exceeding 0.70 deemed acceptable for both measures 59 . Convergent validity assesses the degree to which indicators measuring the same construct converge. Two methodologies were employed to verify convergent validity: first, the indicator's standard loading factor for each construct, which should exceed 0.70, and second, the average variance extracted (AVE), which should exceed 0.50. 59,60 . Table 1 presents all values, including the loading factors, Cronbach's alpha coefficients, CR, and AVE. Table 1. Measurement model data Laten Variable Loading Factor Cronbach's Alpha Composite Reliability Average Variance Extracted (AVE) Cut-Off Value >0.7 >0.7 >0.7 >0.5 Communication 0.866-917 0.877 0.924 0.803 Efficiency 0.796-803 0.729 0.846 0.646 Information 0.763-861 0.927 0.940 0.663 Interactivity 0.864-902 0.905 0.934 0.779 Productivity 0.748-857 0.918 0.935 0.672 Trust 0.851-908 0.900 0.931 0.770 To assess discriminant validity (Table 2), researchers examined the relationship between the square root of the Average Variance Extracted (AVE) for individual constructs and their corresponding inter-construct correlations. The criterion for establishing discriminant validity was met when a construct's square root of the AVE surpassed its correlation with any other construct within the model framework 61 . Additionally, the factor loadings shown in Table 2 were analyzed to ensure that all indicators loaded significantly onto their respective constructs, with values above 0.70 considered satisfactory 62 . Analysis of the measurement model confirmed the constructs' validity and reliability, thus increasing the confidence in the study's measurement instrument. Tabl e 2. Discriminant validity: Fornell-Larcker criterion and cross-loading analysis Fornell-Larcker Com Eff Inf Int Pdv Tst Communication 0.896 Efficiency 0.485 0.804 Information 0.489 0.229 0.814 Interactivity 0.557 0.504 0.416 0.882 Productivity 0.581 0.554 0.378 0.664 0.820 Trust 0.565 0.562 0.367 0.834 0.685 0.878 Cross-loading analysis Com Eff Inf Int Pdv Tst COM1 0.866 0.381 0.410 0.466 0.464 0.484 COM2 0.917 0.438 0.445 0.543 0.553 0.534 COM3 0.904 0.480 0.458 0.486 0.540 0.498 EFF1 0.378 0.812 0.143 0.290 0.379 0.324 EFF2 0.437 0.803 0.227 0.569 0.501 0.623 EFF3 0.341 0.796 0.171 0.315 0.442 0.367 INF10 0.374 0.082 0.764 0.285 0.294 0.254 INF2 0.402 0.202 0.830 0.331 0.288 0.317 INF3 0.408 0.214 0.845 0.338 0.295 0.346 INF4 0.402 0.247 0.843 0.378 0.294 0.317 INF5 0.425 0.240 0.850 0.360 0.308 0.337 INF6 0.368 0.220 0.861 0.371 0.311 0.334 INF8 0.396 0.166 0.763 0.373 0.361 0.259 INF9 0.410 0.090 0.749 0.250 0.307 0.213 INT1 0.527 0.490 0.383 0.868 0.603 0.678 INT2 0.463 0.414 0.340 0.902 0.551 0.757 INT3 0.502 0.422 0.352 0.895 0.612 0.763 INT4 0.469 0.447 0.391 0.864 0.570 0.748 PDV1 0.492 0.425 0.330 0.472 0.824 0.500 PDV2 0.483 0.446 0.352 0.524 0.846 0.541 PDV3 0.519 0.450 0.344 0.556 0.857 0.578 PDV4 0.512 0.456 0.278 0.583 0.849 0.579 PDV5 0.495 0.461 0.330 0.627 0.834 0.631 PDV7 0.427 0.521 0.269 0.510 0.774 0.538 PDV8 0.388 0.432 0.256 0.539 0.748 0.571 TST1 0.460 0.473 0.302 0.723 0.609 0.874 TST2 0.527 0.516 0.328 0.717 0.606 0.876 TST3 0.536 0.489 0.322 0.766 0.617 0.908 TST4 0.451 0.495 0.337 0.722 0.572 0.851 Com Communication, Eff Efficiency, Inf Information, Int Interactivity, Pdv Productivity, Tst Trust Structural model evaluation The direct effect evaluation of the structural model assessment yielded significant associations between the multiple constructs. Table 3 presents the findings from the hypothesis testing and the corresponding path coefficients (β). The influences of interactivity, efficiency, and trustworthiness on communication and information, as well as their subsequent impact on productive performance, were analyzed in depth. Interactivity significantly influenced communication (β=0.258, t=3.263, p<0.01) and information (β=0.360, t=3.997, p<0.001). Efficiency significantly impacts communication (β=0.232, t=4.500, p<0.001), but does not have a significant effect on information (β=0.014, t=0.231, p=0.817). Digital trust demonstrated a significant positive effect on communication (β=0.219, t=2.605, p<0.01) but showed no significant effect on information (β=0.059, t=0.641, p=0.522). Furthermore, communication significantly influenced productive performance (β=0.521, t=9.791, p<0.001), and information had a significant positive effect on productive performance (β=0.124, t=2.168, p Productivity 0.521 0.053 9.791 H1 Supported Information -> Productivity 0.124 0.057 2.168 H2 Supported Interactivity -> Communication 0.258 0.079 3.263 H3a Supported Interactivity -> Information 0.360 0.090 3.997 H3b Supported Efficiency -> Communication 0.232 0.052 4.500 H4a Supported Efficiency -> Information 0.014 0.063 0.231 H4b Not Supported Trust -> Communication 0.219 0.084 2.605 H5a Supported Trust -> Information 0.059 0.092 0.641 H5b Not Supported A mediation analysis using SmartPLS revealed significant mediation effects on productive performance through communication and information. The indirect effect of efficiency on productive performance through communication (β=0.121, p=0.000) demonstrated a significant mediation pathway, indicating that communication plays a crucial role in translating efficiency into productive performance. Similarly, the indirect effect of interactivity on productivity through communication (β=0.134, p=0.002) showed a significant positive mediation effect, highlighting the importance of communication in this relationship. Trust also exhibited a significant indirect effect on productive performance through communication (β=0.114, p=0.019), suggesting that trust indirectly affects productive peformance through enhanced communication. Conversely, indirect effects through information pathways were less pronounced. The indirect effect of efficiency on productive performance via information (β=0.002, p=0.843) is not significant, indicating that the acquisition of information does not mediate the relationship between efficiency and productive performance. Similarly, the indirect effect of interactivity on productive performance through information (β=0.045, p=0.068) approached significance but remained statistically non-significant. Trust’s indirect effect on productive performance via information (β=0.007, p=0.582) was also insignificant, suggesting limited mediation through this pathway. These results underscore the stronger mediating role of communication compared with information on the relationships between efficiency, interactivity, and trust with productivity. Table 4 presents a summary of mediation effects . Table 4. Mediating effect Indirect Effect β SD T P Decision Efficiency -> Communication -> Productivity 0.121 0.032 3.797 0.000 Supported Interactivity -> Communication -> Productivity 0.134 0.044 3.027 0.002 Supported Trust -> Communication -> Productivity 0.114 0.048 2.352 0.019 Supported Efficiency -> Information -> Productivity 0.002 0.009 0.198 0.843 Not Supported Interactivity -> Information -> Productivity 0.045 0.024 1.828 0.068 Not Supported Trust -> Information -> Productivity 0.007 0.013 0.550 0.582 Not Supported Model Fit Several fit indices were used to assess the model's overall fit and predictive power. The analysis demonstrates that the proposed model effectively predicts the observed data, as evidenced by the Q² values, which measure predictive relevance. Predictive relevance was indicated by Q² values greater than 0, with values of 0.02, 0.15, and 0.35 signifying minimal, moderate, and strong predictive relevance, respectively 63 . In this model, c ommunication had a Q² value of 0.296 , indicating moderate predictive relevance, whereas i nformation showed minimal predictive relevance with a Q² value of 0.112 . Similarly, p roductivity demonstrated moderate predictive relevance with a Q² value of 0.230 . The R² values further confirm the explanatory power of the model. According to 64 , R² values were categorized as low (0.19), moderate (0.33), or high (0.60). In this analysis, c ommunication exhibited moderate explanatory power with an R² of 0.380 . This i nformation showed low explanatory power, with an R² value of 0.175 . Productivity also demonstrated moderate explanatory power, with an R² of 0.349 . These values suggest that the proposed model accounts for a reasonable amount of variance in endogenous variables, particularly communication and p roductive performance . The Standardized Root Mean Square Residual (SRMR) value, a measure of model fit, was 0.064 , which is within the acceptable range of <0.08 65 . This indicates a good fit between the proposed model and observed data. The f² values, which represent the effect size of the endogenous latent variables, provide further insight into the model's fit. For instance, c ommunication has a substantial effect on p roductivity ( f²=0.317 ). Efficiency exhibited a small effect size on c ommunication ( f²=0.059 ). Interactivity demonstrated a small effect size for both c ommunication ( f²=0.033 ) and information ( f²=0.048 ). Finally, the I mportance Performance M ap A nalysis (IPMA) values highlighst the importance of specific constructs in predicting productive performance. Interactivity and t rust had the highest IPMA values of 74.486 and 73.690 , respectively, indicating their critical roles in enhancing productivity (Figure 2). Communication and e fficiency also demonstrated notable importance with IPMA values of 64.101 and 64.845 , respectively. Collectively, the fit indices presented herein demonstrate that the proposed model exhibits adequate fit to the data, offering a dependable framework for elucidating the interrelationships among the variables examined in this study ( Table 5) . The findings validate the robustness and predictive accuracy of the model, supporting its utility in explaining the dynamics between communication, interactivity, efficiency, trust, and productivity. Table 5. Assesment of model fit Effect Size Coefficient of Determination SRMR Construct SSO SSE Q² (=1-SSE/SSO) R 2 Adj. R 2 Communication 1035.000 728.315 0.296 0.380 0.375 0.064 Information 2760.000 2449.852 0.112 0.175 0.168 Productivity 2415.000 1860.445 0.230 0.349 0.346 f 2 COM EFF INF INT PDV TST IPMA values Communication - - - - 0.317 - 64.101 Efficiency 0.059 - 0.000 - - - 64.845 Information - - - - 0.018 - 54.357 Interactivity 0.033 - 0.048 - - - 74.486 Productivity - - - - - - 69.520 Trust 0.021 - 0.001 - - - 73.690 Discussion and Implications Discussion This study examined the interplay between interactivity, efficiency, trust, communication, information, and productive performance in the context of small-scale millenial farmers' ICT adoption and utilization. The findings highlight the critical role of communication in bridging the relationship between farmers' perceptions of ICT (interactivity, efficiency, and trust) they used and productive performance. These insights shed light on how millenial farmers have engaged in ICT to enhance their agricultural practices and productivity. The results corroborated several hypothesized relationships, emphasizing the significance of communication and interactivity in the successful adoption and utilization of ICT among farmers. H1, H2, and H3a are supported, indicating that communication serves as a crucial mediating factor in translating the benefits of ICT-related perceptions into productivity outcomes. Millennial farmers who actively engage in online participation such as communicating with agricultural extension workers or other farmers, demonstrate significantly higher productive performance. This finding reinforces the idea that ICT-driven communication fosters collaboration, knowledge sharing, and problem-solving, which are essential for improving agricultural performance 49,66 . Interactivity emerged as a key factor influencing both communication ( H3a ) and information seeking behavior ( H3b ). Farmers perceive ICT as a platform that allows real-time two-way interactions, which enhance their ability to exchange ideas, seek advice, and share best practices. Interactive platforms, such as eKisaan, provide real-time information and have been linked to increased farmer incomes and productivity 67 . Other studies show that the use of audiovisual messages (video) and interactive voice response (IVR) services has been found to significantly improves farmers' knowledge, adoption of recommended practices, and crop yields 68 . This finding that ICT can effectively deliver agricultural information and enable farmers to apply new techniques. This interactive capability not only strengthens communication but also encourages farmers to seek relevant agricultural information actively. The findings suggest that interactivity is a critical driver of ICT adoption as it directly impacts farmers' ability to engage effectively with others in their community and with agricultural experts. The role of efficiency is also supported by its impact on communication ( H4a ), but its direct influence on information ( H4b ) is insignificant. This indicates that farmers perceive ICT as a tool that saves time, effort, and costs, primarily in the context of communication rather than information retrieval. For instance, efficient communication through ICT allows farmers to coordinate activities, access advice, and share updates with minimal resource expenditure, thereby indirectly enhancing productivity 34,69 . However, the limited impact of efficiency on information-seeking behavior suggests that small-scale millennial farmers may still rely on traditional methods and offline resources to acquire detailed agricultural knowledge. Although many digital platforms in Indonesia provide agricultural information services, only a few millennial farmers use these services. One of them is low digital literacy, which is the main obstacle that causes millennial farmers to lack adequate skills in searching, processing, managing, and presenting the agricultural information needed in this era of information abundance. A study on smallholder farmers' digital literacy revealed that while they demonstrated high proficiency in accessing and communicating information, they showed low digital literacy in managing, integrating, evaluating, and creating information 70 . Trust was found to significantly influence communication ( H5a ) but not information-seeking behavior ( H5b ). Farmers who trust ICT perceive it as reliable and widely accepted in their communities, which encourages them to engage in communication activities such as consultations with agricultural extension officers or discussions with peers. Studies have shown that perceived trust plays a crucial role in shaping user perceptions and behaviors in both traditional and digital media environments 71–74 . This suggests that media transparency could indeed influence communication and information-seeking behavior. In the context of e-government, research 75 indicates that transparency from the perspective of good governance aspect enhances citizens' trust, which is the principal predictor of achieving citizens' participation. This implies that media transparency in government communication could positively influence citizens' information-seeking behavior and engagement with public services. However, trust alone does not appear to directly drive farmers to seek agricultural information online, indicating that other factors such as perceived relevance or accessibility of information may play a more significant role in this regard. Interestingly, the hypothesized role of information in driving productive performance ( H2 ) is weaker than expected. Although millennial farmers actively seek and acquire agricultural information through ICT, the findings suggest that information alone may not directly translate directly into higher productivity. Instead, it is likely that the effectiveness of the information depends on how it is integrated into communication processes or applied to specific farming practices. This finding underscores the need for tailored information delivery mechanisms that align with the millennial farmers’ unique needs and contexts. In addition, millennial farmers at the research site rely more on ICT to communicate directly with extension workers and fellow farmers to obtain information and advice to improve their skills and capacities. Practical Implications This study contributes to the literature on ICT adoption in agriculture by emphasizing the mediating role of communication in driving productive performance. The findings extend the application of TAM by demonstrating how relational dynamics, such as interactivity and trustworthiness, enhance communication and ultimately improve performance outcomes in agricultural contexts. Moreover, this study highlights the critical role of interactivity as a driver of ICT engagement, reinforcing its importance in fostering two-way communication and active participation among young farmers. The results also challenge the traditional assumptions about the role of information in productivity. While previous studies have often positioned information as a direct enabler of performance, this research suggests that its impact is contingent on contextual factors such as communication quality, accessibility, and relevance. This requires a more nuanced understanding of how information interacts with other ICT-enabled processes to influence productive performance. The findings offer several practical implications for policymakers, agricultural extension services, and ICT developers who aim to enhance farmers' productive performance through technology adoption. First, the validation of H1 and H3a highlights the need to prioritize communication strategies that leverage ICT. For example, platforms that facilitate real-time interactive communication between millennial farmers and agricultural extension workers can amplify the positive effects of ICT on productive performance. Policymakers should invest in initiatives that promote digital literacy and foster trust in ICT among farmers, enabling them to fully utilize these tools for communication and collaboration. Second, the strong influence of interactivity ( H3a and H3b ) underscores the importance of designing ICT platforms that enable seamless two-way communications. Features such as live chats, forums, and video consultations can enhance farmers' ability to engage with their peers and agricultural experts, leading to more effective knowledge sharing and decision making. Third, while trustworthiness plays a significant role in communication ( H5a ), it is important to address the barriers that may limit farmers' ability to seek information through ICT ( H5b ). Efforts to build trust should focus on demonstrating reliability, accessibility, and community acceptance of ICT platforms. Additionally, tailored training programs and localized content delivery can enhance farmers' confidence in ICT use for information-seeking purposes. Finally, the limited role of efficiency in driving information-seeking behavior ( H4b ) suggests that farmers may require additional support or incentives to use ICT for this purpose. For example, integrating information-sharing tools with communication platforms or providing curated, context-specific information can improve farmers’ perceived efficiency and utility of ICT. Conclusion This study sheds light on the dynamics of interactivity, efficiency, trust, communication, information, and productivity in the context of farmers’ ICT adoption. By highlighting the central role of communication, these findings provide a robust framework to understand how relational and structural factors contribute to agricultural productivity. This study underscores the importance of fostering interactive, trust-driven communication strategies while challenging traditional assumptions about the direct role of information. These insights offer valuable guidance to enhance ICT adoption and productivity in agricultural communities. Declarations Data availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgment: The first author is supported by the Indonesia Endowment Fund for Education/Lembaga Pengelola Dana Pendidikan (LPDP) of the Republic of Indonesia and the Higher Education Financing Center/Balai Pembiayaan Pendidikan Tinggi (BPPT) of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia under Beasiswa Pendidikan Indonesia (BPI). Conflict of Interest: The authors declare no conflicts of interest Funding information: We thank the BPPT and LPDP for funding the publication fees for this study. The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. Author contributions : HOS: Conceptualization of ideas, data collection, formal analysis, writing, review, and editing preparation; AMS: Conceptualization of ideas, advisers, supervisors of data collection and analysis and review of the manuscript; WBP and PM: Advisers, supervisors of data collection and review of the manuscript. Additional information Correspondence and requests for materials should be addressed to HOS. References BPS. Statistik Pemuda Indonesia 2023 . vol. 21 (BPS, Jakarta (ID), 2023). Effendy, L., Widyaastuti, N. & Lastri, H. The Millennial Farmers’ Interest in Succeeding the Family Agriculture for Hydroponic Application in Garut District, West Java Indonesia. Univers J Agric Res 10 , 266–274 (2022). Unay-Gailhard, İ., Bavorová, M., Bednaříková, Z. & Ponkina, E. V. “I Don’t Want to Work in Agriculture!” The Transition from Agricultural Education to the Labor Market in Rural Russia. Rural Sociol 84 , 315–349 (2019). Unay-Gailhard, İ. & Brennen, M. A. 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Do Social Media, Good Governance, and Public Trust Increase Citizens’ e-Government Participation? Dual Approach of PLS-SEM and fsQCA. Hum Behav Emerg Technol 2023 , 1–19 (2023). Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5920374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415419450,"identity":"40e9f91c-fab6-4d1f-8c0d-37a068a1e2fa","order_by":0,"name":"Hari Otang Sasmita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACAyA+wFAB4RxgbABRjA0ShLWcIVULA2MblAfRwsCAV4s5+9mHBz7Os8sz5z/AeLhwB4M8fwNz4w18Wix70g0OztyWXGw5I4Hh8MwzDIYzDjA2W+B12IE0hsO825gTNwBNPszbxsC4AehO/H45/wyock594obzB8Ba7AlruQGypeFw4oYDCWAtiURoecZwcMax40C/JDYA/SKRPOMwIb+cT2P+8KGmGhhihw9/LtxhY9vf3v4Qb4jBQIIBMFKYwTHCTIx6iBbiFY+CUTAKRsEIAwBGF09A+RRi1wAAAABJRU5ErkJggg==","orcid":"","institution":"IPB University","correspondingAuthor":true,"prefix":"","firstName":"Hari","middleName":"Otang","lastName":"Sasmita","suffix":""},{"id":415419451,"identity":"2ed17c3a-40ce-4865-840b-00cfc1bfc969","order_by":1,"name":"Amiruddin Saleh","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Amiruddin","middleName":"","lastName":"Saleh","suffix":""},{"id":415419452,"identity":"6060cfbc-9b56-48b4-bf88-ebd44a8f1464","order_by":2,"name":"Wahyu Budi Priatna","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Wahyu","middleName":"Budi","lastName":"Priatna","suffix":""},{"id":415419453,"identity":"7e627cd4-1ec5-48ae-8cd5-983e8295c28f","order_by":3,"name":"Pudji Muljono","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Pudji","middleName":"","lastName":"Muljono","suffix":""}],"badges":[],"createdAt":"2025-01-28 18:34:11","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5920374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5920374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76427762,"identity":"2fd2dbbe-ecbc-4920-a5a5-e28075399cfe","added_by":"auto","created_at":"2025-02-17 06:02:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82903,"visible":true,"origin":"","legend":"\u003cp\u003eResearch framework. This figure illustrates a research framework aimed at examining the factors that influence productive performance. The model identifies interactivity, efficiency, and trust as key factors that impact communication and information acquisition. Communication (H1) and information acquisition (H2) are seen as direct influencers of productive performance. The framework hypothesizes that interactivity (H3a, H3b), efficiency (H4a, H4b), and trust (H5a, H5b) directly impact both communication and information acquisition. These relationships demonstrate how the interplay of these factors contributes to enhancing productive performance. The structure of the model illustrates the flow of influence, where interactivity, efficiency, and trust shape the processes of communication and information acquisition, which subsequently drive productive performance. The arrows denote the hypothesized pathways of influence, reflecting the interconnected nature of these variables.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5920374/v1/934f8c797260336d50e58c0d.png"},{"id":76427756,"identity":"03712eb9-0bd4-427b-8d90-81e8b79a7ed6","added_by":"auto","created_at":"2025-02-17 06:02:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImportance Performance Map Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5920374/v1/147df799ddfa8bb28b66422c.png"},{"id":76428754,"identity":"9dd0929f-a6a6-42af-a7f5-2c3cd0da99d8","added_by":"auto","created_at":"2025-02-17 06:18:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1749428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5920374/v1/3d558105-db31-4c77-ac10-2bcfa0af9f19.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExtending TAM: interactivity, efficiency, and trust in ICT adoption among millennial farmers\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAgriculture plays an important role in Indonesia\u0026rsquo;s economy. However, agriculture in the country is currently facing critical issues owing to climate change and the shrinking quantity and quality of land and human resources. \u0026nbsp;According to Indonesia\u0026apos;s Youth Statistics for 2023, approximately 19.23 percent or 12.31 million youth of a total of 64 million people are involved in agricultural activities\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;However, over the past decade, the number of young people working in the agricultural sector has decreased by almost 10 percent compared to\u0026nbsp;2012. \u0026nbsp;This decline reflects the low interest of younger generations in choosing agriculture as a career path\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;even for agricultural schools\u003csup\u003e3,4\u003c/sup\u003e or for those who have agricultural career opportunities through their family inheritance\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;widespread recognition of information and communication technologies (ICTs) potential to enhance agricultural productivity and reduce production costs through technological advancements, innovation, and digitization has highlighted its numerous benefits for farmers. These include facilitating social interactions, connecting with intermediaries in product marketing, and accessing immediate agricultural guidance from experts\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. Smartphone technology has provided new ways to disseminate information in interactive and multimedia formats\u003csup\u003e9\u003c/sup\u003e, helping young small-scale farmers to make informed decisions by accessing better market information\u003csup\u003e10\u003c/sup\u003e. Furthermore, ICTs possess the capacity to facilitate the implementation of sustainable agricultural practices, such as precision farming and climate-smart agriculture, by providing farmers with access to information, decision support tools, and monitoring systems.\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Consequently, ICT-mediated communication has become an important moderator in the digital communication era because it can provide immediacy, accessibility, interactivity, trustworthiness,\u0026nbsp;and dynamism through various virtual platforms\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, in Indonesia\u0026rsquo;s ICT utilization system, there is an imbalance between high Internet penetration and its use in the agricultural sector, especially among the younger generations. This is revealed by data from Indonesian Youth Statistics in 2023, which shows that the majority of youth use the internet for social media or social networking purposes at 84.37 percent, looking for information or news at 84.28 percent, and entertainment at 83.78 percent\u003csup\u003e15\u003c/sup\u003e. Meanwhile, youth accessing ICT, especially smartphones, reached 96.28 percent and internet 94.16 percent\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;It should be noted that the majority of existing internet users in Indonesia are young people under the age of 35 who have been shown to spend more time each day on the internet\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTherefore, it is important to consider some of the controversies surrounding these data. First, what type of ICTs can help or detract young farmers from adoption? Second, it is crucial to understand how young farmers use ICTs to maintain communication and increase their capacity for agriculture.\u0026nbsp;Exploring the key factors of ICTs that influence ICT utilization is not only important for improving the productive performance of young small-scale farmers in Indonesia but also a cornerstone for achieving the policy objectives of farmer empowerment programs both in Indonesia and in other developing countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe adoption of ICT in agriculture has positively impacted market participation, rural entrepreneurship, and overall household income. ICT adoption among farmers is influenced by various factors across technological, individual, and environmental dimensions. From a technological perspective, perceived usefulness and relative advantage positively impact ICT adoption intention\u003csup\u003e17,18\u003c/sup\u003e. \u0026nbsp;Additionally, studies show that factors such as performance expectancy and convenience play a significant role in shaping farmers\u0026apos; intention to adopt and use agricultural fintech products and services\u003csup\u003e19\u003c/sup\u003e.\u0026nbsp;The ease of use of ICT tools also plays a significant role, particularly for entrepreneurs with no previous experience\u003csup\u003e18\u003c/sup\u003e. These factors are related to the farmers\u0026rsquo; perceptions of the usefulness and ease of use of ICT solutions in agriculture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, further examination of the literature emphasizes that while the role of ICTs in facilitating information dissemination and communication has been acknowledged, investigation into ICT factors such as interactivity, efficiency, and trustworthiness in young farmers\u0026apos; ICT adoption is insufficient, particularly among young small-scale farmers. There appears to be a considerable research void regarding ICT factors and utilization that hinders the effectiveness of digital interventions in rural smallholder farming environments in Indonesia. This study aims to fill this gap by proposing a new research model that investigates the impact of ICTs factors on young small-scale farmers\u0026apos; ICT utilization to improve productive performance.\u003c/p\u003e\n\u003cp\u003eThis study aims to extend the technology acceptance model (TAM) by incorporating perceived interactivity as perceived ease of use, efficiency as perceived usefulness, and trustworthiness as the extended construct to examine the influence of ICTs factors on its utilization among young small-scale farmers. Bogor District was selected as the research site because of its status as an area that actively runs young farmer empowerment programs from the provincial-level government called as \u0026ldquo;Program Petani Milenial\u0026rdquo; (Millennial Farmers Program) and at the national level from Ministry of Agriculture of the Republic of Indonesia called Youth Entrepreneur and Employment Support Services (YESS). These programs are expected to produce strong and high-quality millennial entrepreneurs\u003csup\u003e20\u003c/sup\u003e. In this context, millennial farmers have been identified as key actors in the transformation of Indonesia\u0026rsquo;s agriculture sector. When they tend to be more open to technology, they have a great potential to adopt technology and carry out sustainable agricultural practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis context provides an optimal framework for examining the influence of digital communication media on ICTs utilization among young small-scale farmers. The objectives of this study are as follows:\u003c/p\u003e\n\u003cp\u003e(1)\u0026nbsp;\u0026nbsp;To propose a novel theoretical model that incorporates perceived trustworthiness into the Technology Acceptance Model (TAM) by analyzing the effects of digital communication media on young farmers\u0026apos; online participation.\u003c/p\u003e\n\u003cp\u003e(2)\u0026nbsp;\u0026nbsp;The efficacy of the extended TAM in predicting and elucidating young farmers\u0026apos; online participation in productive performance was evaluated using structural equation modeling.\u003c/p\u003e\n\u003cp\u003e(3)\u0026nbsp;\u0026nbsp;We analyzed survey data to explain how digital communication media factors influence young farmers\u0026apos; online participation to improve productive performance and offer insights to improve future adoption rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch theory and hypotheses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study integrates the technology acceptance model (TAM)\u003csup\u003e21\u003c/sup\u003e with an ICT-mediated communication model\u003csup\u003e22\u003c/sup\u003e to predict young farmers\u0026rsquo; acceptance of and intention to use ICT in their online participation to improve productive performance (Figure 1). In this study, online participation was defined as the activities of access to agricultural information and communication to improve capacity. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTAM has been widely used to predict people\u0026apos;s acceptance and intention to use ICT across various domains. The core constructs of TAM - perceived usefulness (PU) and perceived ease of use (PEOU) - consistently show significant positive effects on technology adoption intention\u003csup\u003e13,23\u0026ndash;25\u003c/sup\u003e. PEOU refers to an individual\u0026apos;s perception of the utilization of a novel technology, encompassing the perceived difficulty of learning and ease of operation. When a new technology is perceived as user-friendly, individuals are more likely to adopt and utilize it. PU pertains to an individual\u0026apos;s cognitive assessment of whether a novel technology can provide tangible value and benefits. If individuals perceive that a new technology is advantageous, they will be more inclined to accept and employ it. Additional factors, such as interactivity, efficiency, trustworthiness, social influence, and facilitating conditions, have been incorporated to extend the TAM and enhance its explanatory power.\u003csup\u003e23,25,26\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the provided papers, there is a limited direct mention of perceived ease of use (PEOU) being measured using perceived of interactivity (PEI). However, some studies have examined interactivity as a factor that influencing PEOU. \u0026nbsp; Researh\u003csup\u003e27\u003c/sup\u003e specifically examines how online social networking (OSN) experience factors, including interactivity, influence the actual use of social media for purchasing theme park services through perceived ease of use and perceived usefulness.\u0026nbsp;Research\u003csup\u003e28\u0026nbsp;\u003c/sup\u003eexplored system interactivity (SI) as a factor impacting PEOU in e-learning adoption during the COVID-19 pandemic and found a positive relationship\u0026nbsp;between SI and PEOU. While these papers demonstrate a connection between interactivity and PEOU, they do not explicitly measure PEOU using a \u0026quot;perceived of interactivity\u0026quot; construct. Instead, interactivity is typically treated as a separate factor that influences PEOU. In conclusion, while interactivity is recognized as an important factor related to PEOU in technology adoption models, the\u0026nbsp;studies provided\u0026nbsp;do not specifically measure PEOU by using a perceived interactivity construct. Further research is required to further explore this relationship.\u003c/p\u003e\n\u003cp\u003eAccording to Davis\u0026apos; TAM definition, in predicting ICT utilization, perceived ease of use (PEOU) refers to whether farmers perceive the technology as simple, convenient, and easy to operate. Several studies have applied TAM to investigate ICT adoption in agriculture. Farmers are more likely to adopt ICT if they perceive it as useful and easy to use\u003csup\u003e26,29,30\u003c/sup\u003e. Research on ICT adoption among rural entrepreneurs has found that perceived usefulness and perceived ease of use positively influence attitudes toward ICT adoption\u003csup\u003e18,31\u003c/sup\u003e. For entrepreneurs lacking prior experience, perceived ease of use significantly influenced their attitudes toward ICT adoption\u003csup\u003e18\u003c/sup\u003e.\u0026nbsp; Research\u003csup\u003e32\u003c/sup\u003e has reported that perceived ease of use has a positive effect on farmers\u0026apos; intention to adopt rice-shrimp crop technology, both directly and indirectly, through behavioral attitudes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interactivity and efficiency of ICT tools (ICT-mediated communication) enables farmers to access real-time weather forecasts and market information, thereby enhancing their decision-making capabilities. For instance, IoT sensing platforms can provide farmers with crucial information about soil and environmental conditions, support better crop management, and increase crop yields\u003csup\u003e33\u003c/sup\u003e. Furthermore, smartphone-based agricultural extension services have been shown to boost rural income growth\u003csup\u003e34\u003c/sup\u003e. This improved access to information through interactive ICT tools empowers farmers to make informed decisions regarding their agricultural practices. Therefore, in this study interactivity refers to\u0026nbsp;the technological affordance that enables users to interact with one another by utilizing the same technology to directly exert influence on each other\u0026apos;s communication and content creation\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;Compared with more familiar terrestrial communication technologies, if ICT operations are too complicated, farmers may lose confidence in learning the technology, and vice versa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe perceived usefulness of information and communication technologies (ICTs) can enhance farmers\u0026apos; online participation through social communication and access to agricultural information\u003csup\u003e36\u003c/sup\u003e. Several studies have demonstrated the positive impact of ICTs on farmers\u0026apos; adoption of sustainable agricultural practices and access to information. For instance, the use of WeChat application significantly increased the adoption of soil testing and formula fertilization among farmers in Zhejiang, China\u003csup\u003e37\u003c/sup\u003e. This finding highlights that digital extension services can promote sustainable agriculture by improving access to information. Similarly, broadcast media content on dry season agriculture in Nigeria\u0026apos;s Federal Capital Territory was highly accessible (94.2%), exposing the majority of farmers (83.1%) to information on dry season farming practices\u003csup\u003e38\u003c/sup\u003e. Research\u003csup\u003e39\u003c/sup\u003e has demonstrated that perceived usefulness, subjective norms, and perceived behavioral control positively influence the adoption of digital extension services. In conclusion, ICTs has shown significant potential for enhancing farmers\u0026apos; online participation and access to agricultural information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePerceived trustworthiness in ICT enhances farmers\u0026apos; online participation through social communication and access to agricultural information. Studies have shown that ICT adoption, particularly through smartphone and internet use, can significantly impact rural and agricultural development. Perceived trust played a crucial role in this process. Prior research indicates that trustworthiness serves as a positive moderator in the relationship between factors such as perceived awareness, information acquisition, social influence, and the intention to utilize ICT\u003csup\u003e14\u003c/sup\u003e. Studies have shown that ICT adoption increases the probability of rural households\u0026apos; access to credit and empowers rural women and farm households in less-developed regions\u003csup\u003e40\u003c/sup\u003e. Furthermore, the implementation of contemporary technology in a co-production process, in conjunction with targeted training and monitoring, has the potential to substantially enhance smallholder farmers\u0026apos; access to and utilization of weather and climate forecasting information.\u003csup\u003e41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis suggests that, as farmers develop trust in ICT systems, they are more likely to engage in online activities and access agricultural information. Although ICT-based market information sources significantly influenced market participation among smallholder livestock farmers, their effect on the intensity of market participation was not found to be significant\u003csup\u003e42\u003c/sup\u003e. This highlights the complexity of ICT adoption in agricultural contexts, and suggests that trust alone may not be sufficient to drive intensive engagement.\u0026nbsp;Perceived trust in ICT can enhance farmers\u0026apos; online participation by facilitating social communication and accessing agricultural information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFactors such as perceptions of risks and costs, may hinder ICT adoption\u003csup\u003e29,43\u003c/sup\u003e. Access to information, training, and awareness of ICT benefits also positively influence adoption behavior\u003csup\u003e36\u003c/sup\u003e. To promote ICT adoption among young farmers, it is essential to address these factors through targeted interventions including tailored training programs, improved access to information, and supportive government policies\u003csup\u003e36,44\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnline participation and access to information technology can significantly enhance farmers\u0026rsquo; productive performance by improving their knowledge, skills, and decision-making abilities. Digital extension services and mobile applications provide personalized information on crop selection, input usage, and cultivation methods, leading to increased input intensity, production diversity, crop productivity, and income\u003csup\u003e45\u003c/sup\u003e. These services offer timely access to appropriate agricultural information, enabling better decision making and effective responses to challenges and opportunities\u003csup\u003e46\u003c/sup\u003e. Farmers with access to various information sources, particularly government and private extension providers, demonstrate increased capacity and capability to adopt agricultural innovations\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe adoption of ICTs by farmers, such as smartphones and computers connected to the internet, has been shown to increase commercial orientation, land productivity by 21.3 percent, and labor productivity by 28.2 percent\u003csup\u003e48\u003c/sup\u003e. The use of ICT has also been shown to increase the technical efficiency of agriculture, with the technical efficiency score of ICT users reaching 0.64 compared to 0.57 for non-users\u003csup\u003e49\u003c/sup\u003e. In addition, video-based extension services have significantly increased the adoption of recommended agricultural technologies and practices\u003csup\u003e50\u003c/sup\u003e. This improvement in productivity is particularly significant for young, and small-scale farmers. \u0026nbsp;A study of smallholder livestock farmers revealed that the utilization of ICT-based market information sources significantly influenced market participation, although the effect on the intensity of participation was not significant.\u003csup\u003e42\u003c/sup\u003e. In conclusion, online participation through various ICT tools and services can significantly improve farmers\u0026apos; productivity by providing timely and personalized information and facilitating better decision making.\u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eData Collection.\u0026nbsp;\u003c/strong\u003eThe research protocol was approved by the Ethics Committee of Indonesia's National Research and Innovation Agency. The investigation strictly adhered to all applicable guidelines and regulations. Each participant provided informed consent after being thoroughly briefed on the study's objectives and assured confidentiality of the survey. The findings of this research will be employed solely for scientific purposes, with an unwavering dedication to safeguard the privacy of personal data.\u003c/p\u003e\n\u003cp\u003eThis study targeted millennial farmers who are actively involved in the Millennial Farmer Empowerment Program and the Youth Entrepreneurship and Employment Support Services Programme (YESS) in Bogor Regency, Indonesia. Local agricultural authorities provided researchers with demographic and operational information about the millennial farmers who met the study criteria. To ensure the representativeness and diversity of the collected data, the study included farmers with varying levels of business size and digital engagement from all working areas of the Agricultural Extension Agency in Bogor. Data were collected using an online questionnaire distributed on Google Forms. In total, 382 questionnaires and 345 valid responses were collected, yielding an effective response rate of 90.3\u0026nbsp;percent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement.\u0026nbsp;\u003c/strong\u003eA questionnaire was used as a research instrument. To develop the questionnaire, latent variables were operationalized based on a review of relevant literature. This study considered three media factors: interactivity, efficiency and trust\u003csup\u003e22,35\u003c/sup\u003e, online participation\u003csup\u003e51–56\u003c/sup\u003e, and farmers’ individual productive performance\u003csup\u003e57,58\u003c/sup\u003e. To ensure the accuracy and reliability of the data, the measurement of the variables in this study was designed to capture the engagement of millennial farmers with digital ICT’s. Variables were assigned based on the utilization of specific digital agricultural tools and technologies such as smartphones, the Internet, and digital platforms for online participation.\u003c/p\u003e\n\u003cp\u003eStructural Equation Modeling (SEM) was conducted using SmartPLS 3 software to analyze the relationships among the variables, including the examination of mediating effects. All other variables were measured using a five-point Likert scale, where 1 represented \"strongly disagree,\" 2 represented \"disagree,\" 3 represented \"neutral,\" 4 represented \"agree,\" and 5 represented \"strongly agree.\" The survey included 32 items designed to measure six latent variables (see Table 1 for detailed variable descriptions).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cstrong\u003eMeasurement model\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe measurement model comprised three evaluations: reliability, convergent validity, and discriminant validity. Reliability assesses how consistently a group of indicators measures the aggregate constructs\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This investigation utilized Composite Reliability (CR) and Cronbach\u0026apos;s alpha coefficients to assess the internal consistency of indicators within each construct, with values exceeding 0.70 deemed acceptable for both measures\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Convergent validity assesses the degree to which indicators measuring the same construct converge. Two methodologies were employed to verify convergent validity: first, the indicator\u0026apos;s standard loading factor for each construct, which should exceed 0.70, and second, the average variance extracted (AVE), which should exceed 0.50.\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e59,60\u003c/span\u003e\u003c/sup\u003e. Table 1 presents all values, including the loading factors, Cronbach\u0026apos;s alpha coefficients, CR, and AVE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Measurement model data\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLaten Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eLoading Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCronbach\u0026apos;s Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eComposite Reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCut-Off Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026gt;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026gt;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026gt;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026gt;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.866-917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.796-803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eInformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.763-861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eInteractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.864-902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.748-857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.851-908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo assess discriminant validity (Table 2), researchers examined the relationship between the square root of the Average Variance Extracted (AVE) for individual constructs and their corresponding inter-construct correlations. The criterion for establishing discriminant validity was met when a construct\u0026apos;s square root of the AVE surpassed its correlation with any other construct within the model framework\u003csup\u003e61\u003c/sup\u003e. Additionally, the factor loadings shown in Table 2 were analyzed to ensure that all indicators loaded significantly onto their respective constructs, with values above 0.70 considered satisfactory\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Analysis of the measurement model confirmed the constructs\u0026apos; validity and reliability, thus increasing the confidence in the study\u0026apos;s measurement instrument.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTabl\u003cem\u003ee\u003c/em\u003e 2.\u003c/strong\u003e\u0026nbsp; Discriminant validity: \u003cem\u003eFornell-Larcker criterion and cross-loading analysis\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003eFornell-Larcker\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eInf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePdv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTst\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.896\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.804\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eInformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.814\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eInteractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.882\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.820\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.878\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCross-loading analysis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePdv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTst\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.866\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCOM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.917\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCOM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.904\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEFF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.812\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEFF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.803\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEFF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.796\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.764\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.830\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.845\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n 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valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.850\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.861\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n 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66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.749\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n 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\u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.864\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.824\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.846\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.857\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.849\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.834\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.774\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePDV8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.748\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTST1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.874\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTST2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.876\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTST3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTST4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.851\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eCom\u003cem\u003e\u0026nbsp;Communication,\u0026nbsp;\u003c/em\u003eEff\u003cem\u003e\u0026nbsp;Efficiency,\u0026nbsp;\u003c/em\u003eInf\u003cem\u003e\u0026nbsp;Information,\u0026nbsp;\u003c/em\u003eInt\u003cem\u003e\u0026nbsp;Interactivity,\u0026nbsp;\u003c/em\u003ePdv\u003cem\u003e\u0026nbsp;Productivity,\u0026nbsp;\u003c/em\u003eTst\u003cem\u003e\u0026nbsp;Trust\u003c/em\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eStructural model evaluation\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe direct effect evaluation of the structural model assessment yielded significant associations between the multiple constructs. Table 3 presents the findings from the hypothesis testing and the corresponding path coefficients (\u0026beta;). The influences of interactivity, efficiency, and trustworthiness on communication and information, as well as their subsequent impact on productive performance, were analyzed in depth. Interactivity significantly influenced communication (\u0026beta;=0.258, t=3.263, p\u0026lt;0.01) and information (\u0026beta;=0.360, t=3.997, p\u0026lt;0.001). Efficiency significantly impacts communication (\u0026beta;=0.232, t=4.500, p\u0026lt;0.001), but does not have a significant effect on information (\u0026beta;=0.014, t=0.231, p=0.817). Digital trust demonstrated a significant positive effect on communication (\u0026beta;=0.219, t=2.605, p\u0026lt;0.01) but showed no significant effect on information (\u0026beta;=0.059, t=0.641, p=0.522). Furthermore, communication significantly influenced productive performance (\u0026beta;=0.521, t=9.791, p\u0026lt;0.001), and information had a significant positive effect on productive performance (\u0026beta;=0.124, t=2.168, p\u0026lt;0.05). Table 3 summarizes the results of the direct effects.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u003cem\u003e\u0026nbsp;\u003c/em\u003e3.\u0026nbsp;\u003c/strong\u003ePath coefficient and hypotheses testing\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunication -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH1 Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eInformation -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH2 Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eInteractivity -\u0026gt; Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH3a Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eInteractivity -\u0026gt; Information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH3b Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eEfficiency -\u0026gt; Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH4a Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eEfficiency -\u0026gt; Information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH4b Not Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH5a Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; Information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eH5b Not Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA mediation analysis using SmartPLS revealed significant mediation effects on productive performance through communication and information. The indirect effect of \u003cstrong\u003eefficiency on productive performance through\u0026nbsp;\u003c/strong\u003ecommunication (\u0026beta;=0.121, p=0.000) demonstrated a significant mediation pathway, indicating that communication plays a crucial role in translating efficiency into productive performance. Similarly, \u003cstrong\u003ethe indirect effect\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof interactivity on productivity through communication\u003c/strong\u003e (\u0026beta;=0.134, p=0.002) showed a significant positive mediation effect, highlighting the importance of communication in this relationship. \u003cstrong\u003eTrust also exhibited a significant indirect effect on productive performance through communication\u003c/strong\u003e (\u0026beta;=0.114, p=0.019), suggesting that trust indirectly affects productive peformance through enhanced communication. Conversely, indirect effects through information pathways were less pronounced. The \u003cstrong\u003eindirect effect of efficiency on productive performance via information\u003c/strong\u003e (\u0026beta;=0.002, p=0.843) is not significant, indicating that the acquisition of information does not mediate the relationship between efficiency and productive performance. Similarly, \u003cstrong\u003ethe indirect effect\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof interactivity on productive performance through information\u003c/strong\u003e (\u0026beta;=0.045, p=0.068) approached significance but remained statistically non-significant. \u003cstrong\u003eTrust\u0026rsquo;s indirect effect on productive performance via information\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u0026beta;=0.007, p=0.582) was also insignificant, suggesting limited mediation through this pathway. These results underscore the stronger mediating role of communication compared with information on the relationships between efficiency, interactivity, and trust with productivity.\u0026nbsp;Table 4 presents\u0026nbsp;a summary of\u0026nbsp;mediation effects\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Mediating effect\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eEfficiency -\u0026gt; Communication -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eInteractivity -\u0026gt; Communication -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; Communication -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eEfficiency -\u0026gt; Information -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNot Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eInteractivity -\u0026gt; Information -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNot Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; Information -\u0026gt; Productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNot Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cstrong\u003eModel Fit\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSeveral fit indices were used to assess the model\u0026apos;s overall fit and predictive power. The analysis demonstrates that the proposed model effectively predicts the observed data, as evidenced by the \u003cstrong\u003eQ\u0026sup2;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evalues, which measure predictive relevance. Predictive relevance was indicated by \u003cstrong\u003eQ\u0026sup2;\u003c/strong\u003e values greater than 0, with values of 0.02, 0.15, and 0.35 signifying minimal, moderate, and strong predictive relevance, respectively\u003csup\u003e63\u003c/sup\u003e. In this model, c\u003cstrong\u003eommunication\u003c/strong\u003e had a Q\u0026sup2; value of \u003cstrong\u003e0.296\u003c/strong\u003e, indicating moderate predictive relevance, whereas i\u003cstrong\u003enformation\u003c/strong\u003e showed minimal predictive relevance with a Q\u0026sup2; value of \u003cstrong\u003e0.112\u003c/strong\u003e. Similarly, p\u003cstrong\u003eroductivity\u003c/strong\u003e demonstrated moderate predictive relevance with a Q\u0026sup2; value of \u003cstrong\u003e0.230\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The \u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e values further confirm the explanatory power of the model. According to\u003csup\u003e64\u003c/sup\u003e, R\u0026sup2; values were categorized as low (0.19), moderate (0.33), or high (0.60). In this analysis, c\u003cstrong\u003eommunication\u003c/strong\u003e exhibited moderate explanatory power with an \u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof \u003cstrong\u003e0.380\u003c/strong\u003e. This i\u003cstrong\u003enformation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eshowed low explanatory power, with an \u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e value of \u003cstrong\u003e0.175\u003c/strong\u003e\u003cstrong\u003e. \u003cstrong\u003eProductivity\u003c/strong\u003e\u003c/strong\u003e also demonstrated moderate explanatory power, with an \u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof \u003cstrong\u003e0.349\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThese values suggest that the proposed model accounts for a reasonable amount of variance in endogenous variables, particularly \u003cstrong\u003ecommunication\u003c/strong\u003e and p\u003cstrong\u003eroductive performance\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eStandardized Root Mean Square Residual (SRMR)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evalue, a measure of model fit, was \u003cstrong\u003e0.064\u003c/strong\u003e, which is within the acceptable range of \u003cstrong\u003e\u0026lt;0.08\u003c/strong\u003e\u003csup\u003e65\u003c/sup\u003e. This indicates a good fit between the proposed model and observed data. The \u003cstrong\u003ef\u0026sup2;\u003c/strong\u003e values, which represent the effect size of the endogenous latent variables, provide further insight into the model\u0026apos;s fit. For instance, c\u003cstrong\u003eommunication\u003c/strong\u003e has a substantial effect on p\u003cstrong\u003eroductivity\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003ef\u0026sup2;=0.317\u003c/strong\u003e). \u003cstrong\u003eEfficiency\u003c/strong\u003e exhibited a small effect size on c\u003cstrong\u003eommunication\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003ef\u0026sup2;=0.059\u003c/strong\u003e). \u003cstrong\u003eInteractivity\u003c/strong\u003e demonstrated a small effect size for both c\u003cstrong\u003eommunication\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003ef\u0026sup2;=0.033\u003c/strong\u003e) and \u003cstrong\u003einformation\u003c/strong\u003e (\u003cstrong\u003ef\u0026sup2;=0.048\u003c/strong\u003e). Finally, the \u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003emportance Performance\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003eap\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003enalysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(IPMA)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evalues highlighst the importance of specific constructs in predicting productive performance. \u003cstrong\u003eInteractivity\u003c/strong\u003e and t\u003cstrong\u003erust\u003c/strong\u003e had the highest IPMA values of \u003cstrong\u003e74.486\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003e73.690\u003c/strong\u003e, respectively, indicating their critical roles in enhancing productivity (Figure 2). \u003cstrong\u003eCommunication\u003c/strong\u003e and e\u003cstrong\u003efficiency\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ealso demonstrated notable importance with IPMA values of \u003cstrong\u003e64.101\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003e64.845\u003c/strong\u003e, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, the fit indices presented herein demonstrate that the proposed model exhibits adequate fit to the data, offering a dependable framework for elucidating the interrelationships among the variables examined in this study (\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5)\u003c/strong\u003e. The findings validate the robustness and predictive accuracy of the model, supporting its utility in explaining the dynamics between communication, interactivity, efficiency, trust, and productivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 5.\u003c/strong\u003e Assesment of model fit\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCoefficient of Determination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ\u0026sup2; (=1-SSE/SSO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAdj. \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1035.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e728.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2760.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e2449.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2415.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1860.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eCOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eEFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eINT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ePDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eIPMA values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e64.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e64.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e54.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInteractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e74.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e69.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e73.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion and Implications","content":"\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined the interplay between interactivity, efficiency, trust, communication, information, and productive performance in the context of small-scale millenial farmers' ICT adoption and utilization. The findings highlight the critical role of communication in bridging the relationship between farmers' perceptions of ICT (interactivity, efficiency, and trust) they used and productive performance. These insights shed light on how millenial farmers have engaged in ICT to enhance their agricultural practices and productivity.\u003c/p\u003e\n\u003cp\u003eThe results corroborated several hypothesized relationships, emphasizing the significance of communication and interactivity in the successful adoption and utilization of ICT among farmers. H1, H2, and H3a are supported, indicating that communication serves as a crucial mediating factor in translating the benefits of ICT-related perceptions into productivity outcomes. Millennial farmers who actively engage in online participation such as communicating with agricultural extension workers or other farmers, demonstrate significantly higher productive performance. This finding reinforces the idea that ICT-driven communication fosters collaboration, knowledge sharing, and problem-solving, which are essential for improving agricultural performance\u003csup\u003e49,66\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteractivity\u003c/strong\u003e emerged as a key factor influencing both communication (\u003cstrong\u003eH3a\u003c/strong\u003e) and information seeking behavior (\u003cstrong\u003eH3b\u003c/strong\u003e). Farmers perceive ICT as a platform that allows real-time two-way interactions, which enhance their ability to exchange ideas, seek advice, and share best practices. Interactive platforms, such as eKisaan, provide real-time information and have been linked to increased farmer incomes and productivity\u003csup\u003e67\u003c/sup\u003e. Other studies show that the use of audiovisual messages (video) and interactive voice response (IVR) services has been found to significantly improves farmers' knowledge, adoption of recommended practices, and crop yields\u003csup\u003e68\u003c/sup\u003e. This finding that ICT can effectively deliver agricultural information and enable farmers to apply new techniques. This interactive capability not only strengthens communication but also encourages farmers to seek relevant agricultural information actively. The findings suggest that interactivity is a critical driver of ICT adoption as it directly impacts farmers' ability to engage effectively with others in their community and with agricultural experts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe role of \u003cstrong\u003eefficiency\u003c/strong\u003eis also supported by its impact on communication (\u003cstrong\u003eH4a\u003c/strong\u003e), but its direct influence on information (\u003cstrong\u003eH4b\u003c/strong\u003e) is insignificant. This indicates that farmers perceive ICT as a tool that saves time, effort, and costs, primarily in the context of communication rather than information retrieval. For instance, efficient communication through ICT allows farmers to coordinate activities, access advice, and share updates with minimal resource expenditure, thereby indirectly enhancing productivity\u003csup\u003e34,69\u003c/sup\u003e. However, the limited impact of efficiency on information-seeking behavior suggests that small-scale millennial farmers may still rely on traditional methods and offline resources to acquire detailed agricultural knowledge. Although many digital platforms in Indonesia provide agricultural information services, only a few millennial farmers use these services. One of them is low digital literacy, which is the main obstacle that causes millennial farmers to lack adequate skills in searching, processing, managing, and presenting the agricultural information needed in this era of information abundance. A study on smallholder farmers' digital literacy revealed that while they demonstrated high proficiency in accessing and communicating information, they showed low digital literacy in managing, integrating, evaluating, and creating information\u003csup\u003e70\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrust\u0026nbsp;\u003c/strong\u003ewas found to significantly influence communication (\u003cstrong\u003eH5a\u003c/strong\u003e) but not information-seeking behavior (\u003cstrong\u003eH5b\u003c/strong\u003e). Farmers who trust ICT perceive it as reliable and widely accepted in their communities, which encourages them to engage in communication activities such as consultations with agricultural extension officers or discussions with peers. Studies have shown that perceived trust plays a crucial role in shaping user perceptions and behaviors in both traditional and digital media environments\u003csup\u003e71–74\u003c/sup\u003e. This suggests that media transparency could indeed influence communication and information-seeking behavior. In the context of e-government, research\u003csup\u003e75\u003c/sup\u003e indicates that transparency from the perspective of good governance aspect enhances citizens' trust, which is the principal predictor of achieving citizens' participation. This implies that media transparency in government communication could positively influence citizens' information-seeking behavior and engagement with public services. However, trust alone does not appear to directly drive farmers to seek agricultural information online, indicating that other factors such as perceived relevance or accessibility of information may play a more significant role in this regard.\u003c/p\u003e\n\u003cp\u003eInterestingly, the hypothesized role of \u003cstrong\u003einformation\u003c/strong\u003e in driving productive performance \u0026nbsp;(\u003cstrong\u003eH2\u003c/strong\u003e) is weaker than expected. Although millennial farmers actively seek and acquire agricultural information through ICT, the findings suggest that information alone may not directly translate directly into higher productivity. Instead, it is likely that the effectiveness of the information depends on how it is integrated into communication processes or applied to specific farming practices. This finding underscores the need for tailored information delivery mechanisms that align with the millennial farmers’ unique needs and contexts. In addition, millennial farmers at the research site rely more on ICT to communicate directly with extension workers and fellow farmers to obtain information and advice to improve their skills and capacities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study contributes to the literature on ICT adoption in agriculture by emphasizing the mediating role of communication in driving productive performance. The findings extend the application of TAM by demonstrating how relational dynamics, such as interactivity and trustworthiness, enhance communication and ultimately improve performance outcomes in agricultural contexts. Moreover, this study highlights the critical role of interactivity as a driver of ICT engagement, reinforcing its importance in fostering two-way communication and active participation among young farmers. The results also challenge the traditional assumptions about the role of information in productivity. While previous studies have often positioned information as a direct enabler of performance, this research suggests that its impact is contingent on contextual factors such as communication quality, accessibility, and relevance. This requires a more nuanced understanding of how information interacts with other ICT-enabled processes to influence productive performance.\u003c/p\u003e\n\u003cp\u003eThe findings offer several practical implications for policymakers, agricultural extension services, and ICT developers who aim to enhance farmers' productive performance through technology adoption. First, the validation of\u0026nbsp;\u003cstrong\u003eH1\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eH3a\u003c/strong\u003e highlights the need to prioritize communication strategies that leverage ICT. For example, platforms that facilitate real-time interactive communication between millennial farmers and agricultural extension workers can amplify the positive effects of ICT on productive performance. Policymakers should invest in initiatives that promote digital literacy and foster trust in ICT among farmers, enabling them to fully utilize these tools for communication and collaboration. Second, the strong influence of\u0026nbsp;\u003cstrong\u003einteractivity\u003c/strong\u003e (\u003cstrong\u003eH3a\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eH3b\u003c/strong\u003e) underscores the importance of designing ICT platforms that enable seamless two-way communications. Features such as live chats, forums, and video consultations can enhance farmers' ability to engage with their peers and agricultural experts, leading to more effective knowledge sharing and decision making. Third, while trustworthiness plays a significant role in communication (\u003cstrong\u003eH5a\u003c/strong\u003e), it is important to address the barriers that may limit farmers' ability to seek information through ICT (\u003cstrong\u003eH5b\u003c/strong\u003e). Efforts to build trust should focus on demonstrating reliability, accessibility, and community acceptance of ICT platforms. Additionally, tailored training programs and localized content delivery can enhance farmers' confidence in ICT use for information-seeking purposes.\u003c/p\u003e\n\u003cp\u003eFinally, the limited role of\u0026nbsp;\u003cstrong\u003eefficiency\u003c/strong\u003e in driving information-seeking behavior (\u003cstrong\u003eH4b\u003c/strong\u003e) suggests that farmers may require additional support or incentives to use ICT for this purpose. For example, integrating information-sharing tools with communication platforms or providing curated, context-specific information can improve farmers’ perceived efficiency and utility of ICT.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study sheds light on the dynamics of interactivity, efficiency, trust, communication, information, and productivity in the context of farmers’ ICT adoption. By highlighting the central role of communication, these findings provide a robust framework to understand how relational and structural factors contribute to agricultural productivity. This study underscores the importance of fostering interactive, trust-driven communication strategies while challenging traditional assumptions about the direct role of information. These insights offer valuable guidance to enhance ICT adoption and productivity in agricultural communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eThe first author is supported by the Indonesia Endowment Fund for Education/Lembaga Pengelola Dana Pendidikan (LPDP) of the Republic of Indonesia and the Higher Education Financing Center/Balai Pembiayaan Pendidikan Tinggi (BPPT) of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia under Beasiswa Pendidikan Indonesia (BPI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information:\u0026nbsp;\u003c/strong\u003eWe thank the BPPT and LPDP for funding the publication fees for this study. The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: HOS: Conceptualization of ideas, data collection, formal analysis, writing, review, and editing preparation; AMS: Conceptualization of ideas, advisers, supervisors of data collection and analysis and review of the manuscript; WBP and PM: Advisers, supervisors of data collection and review of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003eCorrespondence and requests for materials should be addressed to HOS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBPS. \u003cem\u003eStatistik Pemuda Indonesia 2023\u003c/em\u003e. vol. 21 (BPS, Jakarta (ID), 2023).\u003c/li\u003e\n\u003cli\u003eEffendy, L., Widyaastuti, N. \u0026amp; Lastri, H. 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Dual Approach of PLS-SEM and fsQCA. \u003cem\u003eHum Behav Emerg Technol\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, 1\u0026ndash;19 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bogor Agricultural University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Interactivity, online participation, adoption, young farmers, performance","lastPublishedDoi":"10.21203/rs.3.rs-5920374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5920374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe adoption of information and communication technologies (ICTs) in agriculture can enhance productivity and reduce costs through technological advancements and digitization. However, an imbalance exists between high internet penetration and its use in Indonesian agriculture, especially among younger generations. This study investigated the impact of ICT factors on young small-scale farmers' ICT utilization to improve productive performance. This study extends the Technology Acceptance Model (TAM) by incorporating perceived interactivity, efficiency, and trust in ICT as additional constructs. A survey was conducted in Bogor District, Indonesia, using structural equation modeling for data analysis. The results show that interactivity significantly influences communication and information acquisition, while efficiency and trust affect communication, but not information acquisition. Communication and information significantly influence productivity. Mediation analysis revealed the crucial role of communication in translating efficiency, interactivity, and trust into productivity. The findings highlight the importance of designing ICT platforms that enable seamless two-way communication and the need for tailored training programs and localized content to enhance farmers' confidence in using ICT for information seeking. This study contributes to the literature on ICT adoption in agriculture by emphasizing the mediating role of communication in driving productivity and offers implications for policymakers, agricultural extension services, and ICT developers aiming to enhance farmers' productivity through technology adoption.\u003c/p\u003e","manuscriptTitle":"Extending TAM: interactivity, efficiency, and trust in ICT adoption among millennial farmers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 06:02:00","doi":"10.21203/rs.3.rs-5920374/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6822a0cd-ee67-4551-b46d-959b5c84437a","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T06:02:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 06:02:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5920374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5920374","identity":"rs-5920374","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}