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This study aims to examine smallholder farmers’ perceptions toward the adoption of climate-smart agriculture (CSA) in smallholder farmers in the Upper Blue Nile Highlands of Ethiopia. Available research focused on profitability and economic constraints alone, disregarding the farmers’ perception of the adoption of CSA innovations. There is relatively little empirical work on farmers’ perceptions of innovations. Hence, a critical research gap that will strengthen CSA innovation research and practice includes understanding farmers’ perceptions about CSA innovations and how these perceptions interact with their adoption.

A cross-sectional household survey was conducted among 424 smallholder farmers selected from five agro-ecosystems. A structured questionnaire was used to collect primary data and a review of literature and documents was used to collect secondary data. The study used a multivariate probit model to examine perception factors affecting the likelihood of adopting multiple CSA innovations. The dependent variables were eight CSA innovations, while the independent variables were crafted from the three pillars of CSA.

Major CSA innovations adopted by farmers include improved variety, crop residue management, crop rotation, compost, row planting, soil and water conservation, intercropping and agroforestry. Farmers’ perception toward CSA innovations includes: CSA innovations sustainably increase productivity and income; enhance soil fertility; diversify livestock feed and energy sources; reduce soil erosion, weed infestation and crop failure; enhance soil organic matter, reduce chemical fertilizer use and rehabilitate land. Farmers’ positive perceptions of the benefits of CSA innovations for increasing crop productivity, reducing agricultural vulnerability to climate change and lowering farm greenhouse gas emissions have boosted adoption.

Farmers’ perceptions toward CSA innovations must be enhanced to increase the adoption of CSA innovations in the smallholder agriculture system. The CSA innovation scale-up strategies should focus on farmers’ perception of CSA innovation benefits toward food security, climate change adaption and mitigation outcomes. Awareness of CSA needs the close collaboration of public extension as well as local institutions such as farmers’ training centers.

The study adopts a multivariate probit model that models farmers’ simultaneous CSA innovation choices. Hence, this study contributes to the literature in four significant areas. First, it argues for differential treatment of the perception of smallholder farmers about innovations is needed. Second, it recognizes the interdependence of the adoption of innovations. Third, it directly assesses the farmers’ perception, while others use proxies to measure it. Finally, there are limited or no studies that address the perception of innovations within the lens of adopter perception theory.

Hierarchically superior bodies develop normative instructions to induce the diffusion of innovations, stimulating the adoption of management practices in supervised public bodies and seeking public administration efficiency increase. Despite this, the effectiveness of these normative instructions is unknown, as well as its inducing and lasting effects in the diffusion of these innovations, especially in Brazil. This study aims to understand the effects of normative induction.

The adoption of risk management, integrity & ethics and information security practices was evaluated over a decade (2009 to 2019), including the adoption behavior of more than 200 Brazilian federal agencies. Public open data were collected and analyzed with multinomial logistic regression.

The normative instructions’ effectiveness in propagating the evaluated practices is remarkable; however, its mere development by the superior bodies cannot be considered enough since the general adoption index can be considered good but not excellent. No evaluated practice reached a saturation level above 75%.

This paper contributes to bringing the international literature’s generic knowledge on the adoption of innovation to the specific Brazilian public administration context, providing insightful implications for policymakers, public managers and researchers.

This work is unique, as it systematically analyzes multiple innovation adoption and presents excellent opportunities for future researchers by reproducing all scripts and automation developed. Furthermore, all data are available and hosted on public platforms with detailed steps and documentation.

The use of open data from governmental sources allows enhanced transparency and the discovery of affecting variables while observing innovation adoption in the public administration.

The presence of normative instructions and their adoption rate is rarely measured in the Brazilian public administration.

Adopting additive manufacturing (AM) on a large-scale requires an adoption in company value chains. This may happen through product innovation and require interorganizational cooperation, but the value-adding potential of cooperation and application recognition is still poorly understood. This study aims to investigate the progress of AM adoption in innovation projects featuring AM application recognition and interorganizational cooperation in the value chain.

A multiple-case study was implemented in successful metallic AM adoption examples to increase the understanding of AM adoption in value chains. Primary data were collected through interviews and documents in three AM projects, and the data were analyzed qualitatively.

All three AM projects showed evidence of successful AM value chain adoption. Identifying the right application and the added value of AM within it were crucial starting points for finding new value chains. Interorganizational collaboration facilitated both value-based designs and experimentation with new supply chains. Thereby, the focal manufacturing company did not need to invest in AM machines. The key activities of the new value chain actors are mapped in the process of AM adoption.

The cases are set in a business-to-business context, which narrows the transferability of the results. As a theoretical contribution, this paper introduces the concept of AM value chain adoption. The value-adding potential of AM is identified, and the required value-adding activities in collaborative innovation are reported. As a practical implication, the study reveals how companies can learn of AM and adopt AM value chains without investing in AM machines. They can instead leverage relationships with other companies that have the AM knowledge and infrastructure.

This paper introduces AM value chain adoption as a novel, highly interactive phase in the industry-wide adoption of metallic AM. AM value chain adoption is characterized in multi-company collaboration settings, which complements the single-company view dominant in previous research. Theory elaboration is offered through merging technology adoption with external integration from the information processing view, emphasizing the necessity of interorganizational cooperation in AM value chain adoption. Companies can benefit each other during AM adoption, starting with identifying the value-creating opportunities and applications for AM.

The need for context-specific adoption models led to the development of the firm technology adoption model (F-TAM) model. Among small to medium-scale enterprises (SMEs); however, firm-level factors were rather insignificant in engendering SME level adoption of technological innovation. This study aims to examine the effect of firm size and other moderating and mediating factors on the relationships between personal, firm, societal and technological factors proposed in the stakeholder-oriented F-TAM among SMEs.

A research instrument was developed, reviewed by experts, and pilot tested with a sample of 25 respondents. Data were purposively collected from four hundred (400) SMEs and analyzed with partial least squares structural equation modeling (PLS-SEM).

The study discovered that employees, societal and technological factors moderate the relationship between firm factors of adoption and firm adoption. Without these moderating effects, firm factors of adoption would have been insignificant at the SMEs’ level of organizational technology adoption. The study further discovered that firm size, as well as risk propensity, also affect the relationships proposed in the model.

Data was collected on voluntary adoption from the most cosmopolitan area of a developing country. It, therefore, needs further contextual validation across the country and different countries.

The engagement of innovations in firms must be planned with employees and society as major stakeholders.

The significance of this finding is the study’s emphasis on an eco-system approach for examining the phenomenon of innovation adoption. To the best of the authors’ knowledge, this study is the first to examine the effect of firm characteristics on is proposed eco-system of stakeholders.

The purpose of this study is to discuss how enhancing the role of local institutions (LI) and incorporating indigenous knowledge (IK) in climate change adaptation planning can improve adoption and scaling success of climate-smart agriculture innovations.

A review of relevant literature from sub-Saharan Africa was used to answer the study research questions.

Embracing IK and LI in climate change adaptation projects can enhance adoption and scaling success of climate-smart agriculture innovations in smallholder farming. Such efforts will improve: information gathering and dissemination, mobilization of resources, establishment of useful networks with relevant stakeholders, capacity building farmers on various fronts and provision of leadership in climate adaptation programs.

Fully embracing IK and LI can improve the scaling of climate-smart innovations only if development partners recognize IK systems that are to be transformed and build on them instead of trying to replace them. Also, participatory approaches in scaling innovations will enhance input from rural people in climate change adaptation programs.

Development interventions aimed at taking proven effective climate-smart innovations to scale must, therefore, engage local communities and their indigenous institutions as active stakeholders in designing, planning and implementation of their climate adaptation programs.

This paper aims to explore various factors associated with radio frequency identification (RFID) adoption with quantitative meta-analysis. More specifically, this paper attempts to measure key variables of RFID adoption derived from Rogers’ innovation theory and further examines how state intervention influences the process of RFID adoption. First, this paper compares, relying on a meta-analysis, various mean effect sizes among technological, organizational and environmental factors (i.e. government-driven policies) that Rogers suggested in his innovation model.

In mean effect size analysis, this paper finds that the technological factor is the most powerful factor that affects the RFID adoption. The technological factor is statistically significant across all regions, including North America, Europe and Asia. The organizational factor is significant only in developing countries like Southeast Asian countries and East Asian countries. Environmental factors like government intervention for facilitating RFID adoption are strong enough only in Southeast Asia and Europe.

This paper finds that government’s supportive policy is more effective in Europe but not in America, while external pressure is still more effective in Southeast Asia. These results implicate that developmentalism or government-driven policy can be effective not only in developing countries but also in the case of developed countries. In addition, this paper conducts a seemingly unrelated regression (SUR) analysis based on Fisher’s standardized score.

In SUR analysis, this paper finds that the correlations between RFID adoption intention and three innovation factors vary across industrial areas. More specifically, the manufacturing area shows negative moderating effect on all three equations where correlations between Rogers’ innovation factors and RFID adoption intention are meta-dependent variables. Also, RFID adoption is accelerated when the size of the firm is large or the location of the firm is in Southeast Asia. This result implicates that the aspect of technology adoption can be changed by region and type of industry.

The purpose of this paper is to provide insight into the students’ adoption rate of the online examinations at Universitas Terbuka (UT), which is a hotbed of technological innovation in higher education, by analyzing the students’ adoption rate of the online examinations related to their academic factors (program of study, GPA, credit earned), socioeconomic status (age, sex, marital status, employment status), and residential factor.

This is an in-depth case study of 1,540 first-year students for the period ranging from the start of the second semester of 2013 to the first semester of 2016 at Bengkulu Regional Office of UT. During the term, quantitative data were set up with ordinal regression to measure the extent to which the adoption rate categories were influenced by such demographic characteristics as academic attainment, personal background, and current residence.

The results showed that while the program of study, grade point average, credits earned, sex, and residence had systematic effects on adopter categories, employment and marital status did not. The highlights of the results were that students with GPA less than or equal to 2.9 were 2.02 times likely to fall into a higher adopter category as compared to those with GPA above 2.9, and students residing in city were 2.50 times more likely to hit the higher levels of adopter category, compared to those residing outside city.

As this is a case study of the students enrolled in the second semester of 2013 and because there has been a rapid change in the way people access information technology, further work should be done, in particular on the sample of students who have enrolled lately.

The institution of open and distance learning (ODL) should accelerate the introduction of new learning resources based on diffusion of innovation modeling.

It is also recommended to especially encourage the new students of ODL to have a feeling of easiness and self-confidence regarding online examinations, and understand their importance. To achieve this objective, regional office staffs can guide the students to try out the online examination in the orientation study activities for new students.

Despite the extensive research on diffusion model for the past decades, this field of study has much more to offer in terms of describing and incorporating such current innovation as an online examination in ODL platforms with which to associate students’ academic and demographic profiles.

This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.

The advent of technology has enabled frequent innovations in the digital modes of doing business. While some users are reluctant to adopt one, others have made it their way of life. The purpose of this study is to find out the approximate time frame of a digital user in an emerging economy like India to adopt their favorite digital application. The importance of an individual’s level of internet skills is explored in the aspect of adopting digital innovations.

The study follows a quantitative approach where data collected from both primary and secondary sources are analyzed to arrive at the results. In total, 837 responses collected with the help of a structured questionnaire are entered and analyzed using SPSS. Multinomial logistic regression is the statistical tool used in the research.

Five sets of adopter categories based on Rogers (1983) are created using an adoption score and are represented in a Time Horizon to understand better. The Unaffected adopter category is used as the reference point to compare the set of internet skills against each of the other five categories. The five sets of categories comprising the internet skills depict interesting results in the case of all the four comparisons made. Social skills are not seen to have any influence on a user being an Innovator or Early Adopter as compared to an Unaffected user. It is also established that Operational and Creative skills play important role in users’ probability of being an innovator, early adopter and early majority compared to being unaffected.

The study analyzes the role of internet skills in the process of adopting digital innovations. The role of internet skill although is associated with every aspect of the digital revolution taking place all over, its application including the direct score of a digital user’s level of internet skills is not found in the literature.

Agri-food supply chains are facing a number of challenges, which cause inefficiencies resulting in the waste of natural and economic resources, and in negative environmental and social impacts. Food waste (FW) is a result of such inefficiencies and supply chain actors search for economically viable innovations to prevent and reduce it. This study aims to analyse the drivers and the barriers that affect the decision of supply chain operators to adopt innovations (technological – TI, organisational – OI and marketing – MI) to reduce FW.

The analysis was carried out using a four-step approach that included: a literature review to identify factors affecting the decision to adopt innovations; analysis of FW drivers and reduction possibilities along agri-food supply chains through innovations; mapping the results of Steps 1 and 2 and deriving conclusions regarding the factors affecting the adoption of innovations to reduce and prevent FW.

Results show that different types of innovations have a high potential in reducing and preventing FW along the supply chain; however, they still must be economically feasible to be adopted by decision makers in the food supply chain. TI, OI and MI are often interrelated and can trigger each other. When it comes to a combination of different types of innovation to reduce and prevent FW, a good example of combining TI, OI and MI may be observed in the retail sector in Europe. Here, innovative smartphone apps (TI) to promote the sale of products nearing their expiration dates (OI in terms of organising the sales differently and MI in terms of marketing it differently) were developed and adopted via different retailing channels, leading to the creation of a new business model.

This study analyses the drivers of FW generation together with the factors affecting the decision to adopt innovations to reduce it and provides solutions to supply chain operators to prevent and reduce FW through different types of innovations.

Literature has not systematically addressed innovations aiming at the reduction of FW yet. This paper provides a comprehensive literature review of the determinants of innovation adoption and offers a novel view on the problem of FW reduction by means of innovation, by linking factors affecting the decision to innovate with FW drivers.