Technology Adoption Propensity Scale – The Journal of Innovation and Information (JIK) focuses on how we gain knowledge through innovation and how information encourages new forms of innovation. All innovations do not lead to information. In various fields that create theory and knowledge, only continuous innovation can be generalized. Jik welcomes articles on innovations that improve the quality of information or can be used to improve information. Innovation is a broad concept that deals with innovation processes, structures, results, old veterans and organizational behaviors in private and public sectors, as well as at individual, national and professional levels. Jik articles investigate the information that offers or promotes innovations to encourage the best practice in society.
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Technology Adoption Propensity Scale
Jik, theoretical articles, quantitative or qualitative methods, empirical studies, application -oriented articles, teaching -oriented articles, case studies, book investigations, conference reports, short articles about current trends in science and society, the latest innovation and shorter information publishes the content in a short time. Jik is investigating the world class on developing issues in the world of innovation and knowledge and appealing to wide readers. Editors welcomed the clues for special number topics. Jik articles should show common contextual differences when emphasizing lessons to a wider audience.
Impact Of Improved Variety Adoption On Rice Productivity And Farmers’ Income In Cameroon: Application Of Propensity Score Matching And Endogenous Switching Regression
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2023: The State Of Generative Ai In The Enterprise
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Despite the increasing adoption of digital technology, small and medium -sized enterprises (SMEs) continue to drift behind larger companies. This study integrates the technology, organization and environmental (foot) frame with Rogers’ Diffusion Theory (Doi) Rogers to investigate the factors affecting the adoption of the digital technology of SMEs. The data obtained from 419 SMEs were analyzed using partial structural equation modeling of partial frames (PLS-SAM), then an artificial nervous network (ANN) analysis to measure the importance of the defined variables. PLS-SEM results show that technological, institutional and environmental factors directly affect adoption. Supported variables include adoption costs, senior management support, human resources, digital culture and trade common weight. Some factors indirectly affect adoption through the support of the top management. This study also found that the international orientation of SMEs controls the relationship between digital culture and adoption behavior. Ann’s consequences, the most important foreseepers listed from the most effective digital culture, international orientation, maximum management support, trade partner weight, human resources and adoption costs, he says. This research contributes to the theoretical discourses on adopting technology by integrating the frame of the toes into the theory of Rogers. It distracts that no toes work separately. Findings provide practical guidance to SME managers by emphasizing the need to improve the support of human resources, digital culture and the highest managers. Governments can use these findings to determine ways to support the adoption of digital technology SMEs by providing subsidies to reduce the costs that remain as an obstacle.
10 86 20.53 Small 148 35.32 High School Education 164 39.14 Middle 28 6.92 Diploma 42 10.02 Final Sector Services 154 36.75 Bachelor 178 42.48 Production 178 42.48 Agenvelope Travel 36.52 Advanced Advanced Cities 332 79.24
Study On The Effect Of Digital Technology Adoption And Farmers’ Cognition On Fertilizer Reduction And Efficiency Improvement Behavior
50 33 33 7.88 In the entrepreneurship studies (Douglas et al., 2020) and firm behavioral research (eg. Chen et al., 2023). These areas usually contain data that often not distributed, which makes PLS-SAM a suitable choice, because it does not rely on the assumption of normal data distribution (Hair et al., 2020). In addition, PLS-SEM is particularly suitable for studies containing complex models, because it tends to obtain convergence of models when there are many hidden variables. This operation control variables can be used to use PLS-SEM to ensure a model convergence, as it leads to a complex model with several hidden variables, direct factors and moderation. The structural equation modeling of partial squares is useful for the development of theoretical frameworks (Hair et al., 2017) and therefore more compatible with the discovery of theory (Dash and Paul, 2021). Considering that this study combines two theories that contain various elements of world-class information, PLS-SEM is a suitable choice because the theoretical framework is still being developed. In addition, PLS-SEM can achieve reliable results even in small and medium sample sizes (Hair et al., 2020), which is particularly beneficial in the fields of research it has large examples (Cheah et al., 2023). Since the sample size of this study is limited to small business data in developing countries, PLS-SEM is a correct analytical method for these conditions. Finally, PLS-SEM is flexible for analyzes that contain formatting or reflective syntacticism (Dash and Paul, 2021). The use of PLS-SEM is suitable for this, because this study uses indicators based on the research research on CT, this study applies all syntacticism as reflecting and aligned with the literature precedent. The analysis was performed using SmartPLS 4.0.9.9 (Ringle et al., 2024). The artificial nuclear network (ANN) artificial neural network (Ann) method has been used to complete the PLS-SEM, because it can determine the predictive capacity of independent variables and can be sorted according to certain results (Abbasi et al., 2021) for addressing the research problem of this study. An artificial neural network is often used to measure the importance of every independent drawing, which has been proven to be statistically significant through sensitivity analysis (Lee et al., 2022). To estimate the results of the analysis, Ann uses an annse -to -one consecutive (FFBP) algorithm (FFBP), where the inputs are uploaded to a forward path and reversed errors (FFBP) (FFBP). Ann can learn from data and allow researchers to have more accurate estimates that contribute to strong research findings (Abbasi et al., 2021) and suitable for finishing the predictive power of any prejudice (Sharma, Joshi et al., 2024). Due to its many benefits, some academicians recommend that YSA measures be applied to compensate for PLS-SEM restrictions, and that they cannot treat non-linear relations (eg Leong et al., 2020). In this study, neural network analysis was performed using SPSS v.26 Neurological Network Module (IBM, 2019). The reliability of measurement measurement measurements was evaluated with Alpha Cronbach, RHO_A digkstra-densities, compound reliability (CR) and average variation (Ave). All results Hair’s et al. (2019) Suggestion for Alpha Cronbach assessment criteria, RHO_A and CR values over 0.7 and over 0.5 CR values and Ave values that can be found in Table 4. The results of the factor loads are attached, where the value for each indicator is more than 0.708 (Hair et al., 2019). Tacudeen et al. (2018), except for the indicator, unnecessary if it does not affect Ave or CR. Therefore, PB2 jumping was kept because it did not affect the results of Ave and CR. Average Cronbach Composite Reliability (α) Rod_a was removed (Ave) and 0.829 0.843 0.878 0.59 AI 0.91 0.91 0.944 0.848 Compb 0.86 0.899 0.641 CP 0.817 0.817 0.817 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.848 DC 0.872 0.873 0.912 0.723 GREGS 0.898 0.909 0.925 0.711 GRESS 0.902 0.907 0.927 0.717 HR 0.923 0.937 0.945 0.811 IO 0.908 0.908 0.9085 pb PB81 PB PB 0.868 0.902 0.649 TPP 0.86 0.882 0.897.
