Adoption Decisions Research Articles

Long ties accelerate noisy threshold-based contagions.

In widely used models of biological contagion, interventions that randomly rewire edges (generally making them 'longer') accelerate spread. However, recent work has argued that highly clustered, rather than random, networks facilitate the spread of threshold-based contagions, such as those motivated by myopic best response for adoption of new innovations, norms and products in games of strategic complement. Here we show that minor modifications to this model reverse this result, thereby harmonizing qualitative facts about how network structure affects contagion. We analyse the rate of spread over circular lattices with rewired edges and show that having a small probability of adoption below the threshold probability is enough to ensure that random rewiring accelerates the spread of a noisy threshold-based contagion. This conclusion is verified in simulations of empirical networks and remains valid with partial but frequent enough rewiring and when adoption decisions are reversible but infrequently so, as well as in high-dimensional lattice structures.

The impact of environmental regulation and economic expectations on crop-livestock integration among hog farmers: a field study from China.

Decoupling of crop-livestock systems increases the risks of pollution, waste of nutrient resources, and biodiversity loss. Crop-livestock integration (CLI) is an effective solution to these problems, and motivating farmers to adopt CLI is the key. Many countries have implemented environmental regulations (ER) aiming to influence farmers' CLI adoption decisions. Based on a field study of 316 hog farmers from Shaanxi Province of China, this paper applies the triple-hurdle model to empirically examine the impacts of economic expectations (EE) and ER on CLI adoption decisions. It also verifies the income effect of CLI. The results are as follows: 90.5% of farmers are willing to adopt CLI, but the adoption rate is only 40.8% and the average integration degree is only 0.236; CLI not been widely popularized. EE and ER promote farmers' CLI adoption significantly, while the impact of interaction between EE and ER on CLI adoption differs. IER weakens the positive impact of EE on farmers' CLI integration degree, which has a "crowding out effect." GER negatively moderates the impact of EE on farmers' adoption willingness of CLI. CER strengthens the positive effect of EE on farmers' adoption behavior and CLI integration degree. CLI increases the farmers' income. These results contribute to our understanding of the mechanisms of CLI adoption decisions and sustainable policy optimization for green agricultural development.

Navigating Technology Adoption/Acceptance Models

The accelerated rate of technological progress has significantly altered how individuals and or-ganizations interact with technology. Understanding the process of technology adoption has be-come crucial for remaining competitive in a world that is becoming increasingly digital. This entry provides an overview of numerous technology adoption models, shedding light on the factors that influence the adoption of new technologies by individuals and organizations. This entry examines the Technology Acceptance Model (TAM) is examined, with an emphasis on perceived utility and usability. Focusing on performance expectation, effort expectation, social influence, and facilitat-ing conditions, the Unified Theory of Acceptance and Use of Technology (UTAUT) is presented. In addition, the Technology-Organization-Environment (TOE) Framework is discussed, taking in-to account the three factors that influence adoption decisions. The Innovation Adoption Lifecycle is investigated, with the characteristics and motivations of various adopter categories highlighted. Individual, organizational, and external factors that influence the adoption of technology are also examined. The entry concludes by addressing the challenges and limitations of existing models and highlighting the importance of technology adoption research in influencing the future of soci-ety and business.

ENERGY Pro: Spatially explicit agent-based model on achieving positive energy districts

This article describes the ENERGY Pro agent-based model using the Overview, Design Concept, and Details + Human Decision-making (ODD+D protocol). The model is empirically explicit and aims to investigate the adoption decisions of homeowners in Amsterdam on different energy-efficient retrofitting (EER) measures. Following the ODD+D protocol, this study uncovers the conceptual framework used for model construction, the spatial microsimulation process of expanding the data, and the model implementation details. The article also describes sensitivity analysis, validation results, and how to use and adapt the model. With this article, the authors aim to make the model replicable and accessible to other researchers and inspire them using the combination of social simulation and spatial microsimulation in studying the energy transition.•The agent-based model is described using the ODD+D protocol.•The combination of simulation methods is used for constructing an empirical model.•The model on energy transition can be adapted for other cities.

Resistance to online learning technology: Theoretical and empirical perspectives

The increasing popularity of online learning applications (OLAs) among institutions and individuals is an ongoing trend in information technology. While much of previous technology adoption research has been focused on adoption motivators or enablers, the lack of research on adoption inhibitors is the primary motivation for this study. The main focus is on dispositional resistance to change (RTC) and its role in individual OLA adoption decisions. A theoretical model integrating two fields of research, technology adoption and psychology, was developed to investigate the influence of RTC on key adoption predictors using a sample of 217 university-student OLA users. A combination of cognitive dissonance and self-verification theories was used to explain the mechanism of this influence, which was subsequently tested and verified. Findings indicate that RTC has a significant negative impact on effort expectancy and usage intention, but not on attitude and performance expectancy. This study contributes by theoretically underpinning and empirically validating the impact of RTC on OLA adoption decisions. Furthermore, it illustrates the importance of incorporating RTC into technology adoption studies in general and OLA studies in particular. This research provides the foundation for future work in this severely underexplored area.

Farmers’ Participation in Climate Smart Agriculture Initiatives and Adoption Decisions in the Arsi and East Shewa Zones of Oromia Region, Ethiopia

Climate change is a major challenge to sustainable agricultural production in the world. The intricacy of the problem requires farmers’ compliance with regenerative agricultural practices that can facilitate the conservation of fragile productive systems while sustaining their productivity. Accordingly, various climate change management strategies have been promoted in Ethiopia. This paper analyzed farmers’ participation in CSA practices and the determinants of adoption in the Oromia region of Ethiopia. The data were collected during the 2020 cropping season from 420 randomly selected farmers in the Arsi (highland) and East Shewa (lowland) zones of the Oromia region. The data was analyzed with descriptive and Logit regression. The results showed that of the total thirteen climate-smart agricultural practices that were assessed, about 6 and 4 were adopted by more than 50% of the farmers in the highland and lowland, respectively. Also, 21.0% and 42.4% of respondents from highland and lowland respectively participated in CSA programs or projects. Moreover, 35.5% and 34.8% of the highland and lowland agroecology farmers respectively adopted some CSA after project participation. In highland agroecology, crop rotation, crop residual management, crop diversification, and intercropping were the mostly used practices, while crop rotation, adjustment of planting time, minimum tillage, and planning of drought-resistant varieties were mostly used by lowland agroecology farmers. Moreover, logistic regression results reveal that CSA adoption is influenced by age, education, gender, family size, credit support, experience, extension support, non-agricultural income, and livestock holding. It was concluded that adoption of CSA was low, and promotion of extension support and CSA participation experiences will facilitate adoption of some CSA practices. More importantly, extension support should focus on CSA practices that were widely used by the farmers. Res. Agric. Livest. Fish. Vol. 11, No. 1, April 2024: 79-91

Impact of participation in collective action on farmers’ decisions and waiting time to adopt soil and water conservation measures

PurposeThis study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.Design/methodology/approachThe Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.FindingsThe findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.Practical implicationsThe results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.Originality/valueDrawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.

Identification of returns to new technologies under distinctive econometric approaches

ABSTRACT This article uses distinctive econometric models to investigate returns to new technologies and whether adoption decisions can be explained by comparative advantage. I consider three models: static and dynamic panel models of homogeneous returns to fertilizer, as well as the correlated random coefficient model of heterogeneous returns. I discuss both the benefits of using each of the models and their interpretations. I then use data from cocoa farming in Ghana to identify the returns to fertilizer by means of these three models. The estimated average returns in different models are positive, high and strongly significant statistically. I also find evidence of heterogeneous returns to new technology based on comparative advantage. My overall results suggest that adoption of new agricultural technologies may not necessarily benefit all farmers, and the adoption decision may be crop-specific, context-specific or technology-specific.

A Process for Institutional Adoption and Diffusion of Blended Learning in Higher Education

AbstractBlended learning has emerged as a prominent feature in higher education over the past decade, aiming to enhance students’ learning experiences and improve outcomes. It has been adopted at various levels within universities, with an increasing trend of institutional adoption. Despite its prevalence, scholars have expressed concerns about our limited understanding of blended learning beyond small-scale and individual applications. Drawing on Rogers’ diffusion of innovations theory, this case study explores the intricate process of adoption and implementation of blended learning within a university. Semi-structured, one-on-one interviews were conducted with 24 lecturers and six university executives involved in a university-wide initiative for blended learning. The interviews were analysed using a thematic pattern matching technique. In addition, a variety of relevant university documents were gathered and analysed using a content analysis method. Results reveal that despite some hesitancies among lecturers, the process of adoption of blended learning was supported by a well-justified strategy, investment in infrastructure, and provision of continued lecturer support. Viewing these results through the lens of Roger’s stage model highlights a lack of a proper ‘matching’ stage, reflecting a failure to engage lecturers in adoption decision-making and incorporate their feedback into the blended learning strategy.

Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework

Collaborative robots (cobots) are emerging in manufacturing as a response to the current mass customization production paradigm and the fifth industrial revolution. Before adopting this technology in production processes and benefiting from its advantages, manufacturers need to analyze the investment. Therefore, this study aims to develop a decision-making framework for cobot adoption, incorporating a comprehensive set of quantitative and qualitative criteria, to be used by decision-makers in manufacturing companies. To achieve that objective, a qualitative study was conducted by collecting data through interviews with key actors in the cobot (or advanced manufacturing technologies) adoption decision process in manufacturing companies. The main findings of this study include, firstly, an extensive list of decision criteria, as well as some indicators to be used by decision-makers, some of which are new to the literature. Secondly, a decision-making framework for cobot adoption is proposed, as well as a set of guidelines to use it. The framework is based on a weighted scoring method and can be customizable by the manufacturing company depending on its specific context, needs, and resources. The main contribution of this study consists in assisting decision-makers of manufacturing companies in performing more complete and sustained decision analyses regarding cobots adoption.

Social learning and the acquisition of information and knowledge—a network approach for the case of technology adoption

Abstract The complexity of agricultural innovations and heterogeneity of circumstances of technology application, outcomes, and social network structures have often led to obstacles in social learning and suboptimal adoption. In this article, we examine technology diffusion by focusing on the roles of peer adoption decision, peer experience, and network structures, using survey data from 500 farm households in Northern Ghana to generate social network contacts. Our results reveal that learning from peer experiences matter more in the diffusion process than just peer adoption decisions alone. We also find that social learning and the likelihood of adoption are higher when peers are central nodes, and particularly when they belong to cohesive subgroups, but lower when they are in highly segregated networks. Our results shed new light on the role of central agents, since highly cohesive neighbourhoods seem to promote diffusion more in high-modularity networks than central nodes.

Comparative Analysis of the Productivity and Efficiency of Cluster and Individual Farming in West Arsi Zone of Oromia

The main objective of this study was to compare the productivity and efficiency of clustered and individual farming; to identify factors affecting clusters farming practice and to know the view/perception of farmers for clustered farming approach in West Arsi zone. To conduct the study, primary data was collected from 152 randomly selected household heads through semi-structured questionnaire. Secondary data were also collected from different sources including CSA, ZOANR, DOANR, and from published and unpublished sources to supplement primary data. In this study both descriptive statistics and econometric analysis were employed. The primary data was analyzed using descriptive statistics and stochastic efficiency decomposition method to decompose TE. Stochastic Frontier Approach (SFA) was used for its ability to distinguish inefficiency from deviations that are caused by factors beyond the control of farmers. The productivity of wheat per hectare was 47.17 and 39.042 quintal for cluster and individual farming respectively which is statically significant at 1% level. The study result revealed that the mean of wheat TE was about 78.46%, for cluster farming and 69.25%, for individual farming as the Cobb-Douglas functional form indicate that. As the result of research analysis indicates that, the cumulative sum of farmers’ perception towards the compatibility of cluster farming with the socio-economic situational circumstances was 4.093 suggesting farmers perceive positively that it was compatibility with their socio-economic situational circumstances. The likelihood of farmers to practice cluster farming positively influenced by Sex of HH, age of HH, nearest market center, distance to FTC, participation on field visit and participation in social organization in West Arsi zone. The study suggested that farmer adoption decisions are affected by above mentioned factors and policies addressing each decision process and cross-cutting issues are required to improve farmer participation in cluster farming. In addition, the study suggested the need for policies to discourage land fragmentation and promote, participation in field visit, and strengthening social network of farmers, to increase participation of farmers in cluster farming in the zone.

Sustainable Suppliers-to-Consumers’ Sales Mode Selection for Perishable Goods Considering the Blockchain-Based Tracking System

Given the significant product spoilages of perishable goods transported over long distances, they are usually sold from suppliers to consumers through an offline direct channel. Sustainable suppliers can utilize the blockchain-based tracking system (BTS) to reduce product spoilages, enabling the spoilage reduction effect, and offer authentic information, triggering the premium effect. With the advent of e-commerce, they can now opt for an online direct channel, setting the online direct price as either non-different or different from the offline direct price, and have to face challenges in selecting the optimal sales mode. This paper addresses these complexities by developing a mathematical model to construct a sustainable suppliers-to-consumers pricing model, incorporating the BTS, in the perishable goods market. Our research reveals that the decision to adopt the BTS hinges on factors like the spoilage reduction effect, premium effect, production cost, and tag cost, with the premium effect outweighing the spoilage-reduction effect. The necessity of using the BTS grows with extended circulation times, where the BTS significantly reduces spoilages during transportation, fostering sustainable development. While sustainable suppliers may not always bear the tag cost independently, they can adjust their pricing strategies automatically and pass the tag cost to consumers for more profit. The BTS adoption decision does not influence the optimal sales mode selection strategy. The offline direct channel offers the highest profit for suppliers, followed by the Online to Offline (O2O) direct channel with differential pricing, and the O2O direct channel with non-differential pricing yields the lowest profit.

The Full Value of Vaccine Assessments Concept-Current Opportunities and Recommendations.

For vaccine development and adoption decisions, the 'Full Value of Vaccine Assessment' (FVVA) framework has been proposed by the WHO to expand the range of evidence available to support the prioritization of candidate vaccines for investment and eventual uptake by low- and middle-income countries. Recent applications of the FVVA framework have already shown benefits. Building on the success of these applications, we see important new opportunities to maximize the future utility of FVVAs to country and global stakeholders and provide a proof-of-concept for analyses in other areas of disease control and prevention. These opportunities include the following: (1) FVVA producers should aim to create evidence that explicitly meets the needs of multiple key FVVA consumers, (2) the WHO and other key stakeholders should develop standardized methodologies for FVVAs, as well as guidance for how different stakeholders can explicitly reflect their values within the FVVA framework, and (3) the WHO should convene experts to further develop and prioritize the research agenda for outcomes and benefits relevant to the FVVA and elucidate methodological approaches and opportunities for standardization not only for less well-established benefits, but also for any relevant research gaps. We encourage FVVA stakeholders to engage with these opportunities.

Sales mode selection and blockchain technology adoption decisions in a platform supply chain

In recent years, some large-scale e-commerce platforms have established blockchain systems (BSs) to improve consumer product quality trust degrees (QTDs). However, whether the BSs work eventually depends on the upstream manufacturer strategic decision on whether to join the BSs to provide product quality information authentication. The impacts of the platform sales mode selection between the agency and the reselling modes on the manufacturer blockchain technology (BT) adoption decision are examined, and the equilibrium strategy combinations between the platform sales mode selection and the manufacturer BT adoption decisions are obtained. Furthermore, the impacts of the platform and the manufacturer strategic decisions on the product quality decision are analyzed. The results show that the manufacturer may have more incentive to join the BS under the agency mode than under the reselling mode. In addition, the manufacturer should set a higher product quality level under the agency mode than under the reselling mode, after joining the BS only if the unit BT adoption cost is low. Finally, the QTD, the quality improvement cost and the unit BT adoption cost determine the equilibrium strategy combinations. The double marginalization effect in the channel and the platform channel power advantage are used to explain the above findings. Several extensions are provided to verify the robustness of the results and to broaden the applicability of the model and method.

Modeling the implications of policy reforms on pesticide risk for Switzerland

Growing public awareness of the negative effects of pesticides on the environment, ecosystems, and human health has led governments to set targets for reducing pesticide risk. Switzerland introduced in 2023 two new policy measures to reduce pesticide risk by 50 % by 2027: (1) voluntary direct payment programs supporting pesticide-reduced and pesticide-free but non-organic cropping systems for most crops on arable land, and (2) restrictions of harmful pesticides for farmers managing under Swiss cross-compliance standards. This study aims to (1) develop a method to assess pesticide risk on a national scale and (2) carry out an ex-ante impact assessment to predict whether these policies can effectively reduce pesticide risks in Switzerland. Therefore, we introduced crop-specific pesticide quantities and pesticide risk scores into a sample of 1907 bio-economic farm optimization models. The models were used to predict farmers' adoption decisions regarding voluntary direct payment programs from 2019 to 2030. By combining the bio-economic farm optimization models with an agent-based modeling approach, we assessed the evolution of pesticide-related risks at the national level. Simulations for pesticide risk from 2019 to 2022 reflected the observed pesticide risk monitored by the Swiss government. In surface waters and semi-natural habitats, achieving the target depends on reducing pyrethroids, a class of insecticides with high-risk potential. Further, we highlight significant uncertainty in projecting the risk potential for surface waters and semi-natural habitats due to uncertainty about the amounts of pyrethroid used for different crops. The results underline the need for comprehensive datasets on pesticide use in Switzerland.

The University Students’ Perception on Electric Vehicles and its Impact on their Willingness to Buy

EVs migration symbolizes the major transformation towards green transport track, interned by an aim to minimize emissions, improve energy efficiency, and minimize environmental damage. This work is focused on the changing trends in the automotive sector, which is caused by the introduction of the stricter emission standards and promotion of the electric mobility by countries worldwide. By having an emphasis on consumers' EVs perceptions, we see factors that affect their adoption decisions including demographics, accessibility, backgrounds, and types of mental disorders. An analysis of the most important research findings in this area provides the consumers and policy-makers with a perspective which will also be valuable to industry players. Respective issues in regard to electric vehicles, including infrastructure, policy, economics, and social impact, are also presented for a complex view on the barriers and opportunities of the electric vehicle market. Through finding the knowledge and perception gaps relating to EVs, this study seeks to make a positive difference towards an EV-powered future.

Investing in security-as-a-service for e-commerce infrastructure by small and medium enterprises: a Monte Carlo approach

PurposeThis paper aims to examine the conditions under which small and medium enterprises (SMEs) invest in security services when they migrate their e-commerce applications to the cloud environment. Using a risk management perspective, the paper assesses the impact of security service pricing, security incident prevalence and virulence to estimate SME security spending at the market level and draw out implications for SMEs and security service providers.Design/methodology/approachSecurity risks are inherently characterized by uncertainty. This study uses a Monte Carlo approach to understand the role of uncertainty in the decision to adopt security services. A model relating key security constructs is assembled based on key constructs from the domain. By manipulating security service costs and security incident types, the model estimates the market-level adoption of services, security incidents and damages incurred, along with measures of their relative dispersion.FindingsThree key findings emerge from this study. First, adoption of services and protection is higher when tiered security services are provided, indicating that SMEs prefer to choose their security services rather than accept uniformly priced products. Second, SMEs are considered price-sensitive, resulting in a maximum level of spending in the market. Third, results indicate that security incidents and damages can be much higher than the mean in some cases, and this should serve as a cautionary note to SMEs.Originality/valueSecurity spending has been modeled at the firm level. Adopting a market-level perspective represents a novel contribution. Additionally, the Monte Carlo approach provides managers with tangible measures of uncertainty, affording additional information and insight when making security service adoption decisions.

Perceived barriers to implementing building information modeling in Iranian Small and Medium-Sized Enterprises (SMEs): a Delphi survey of construction experts

ABSTRACT Building information modeling (BIM) is a disruptive information technology tool in the construction sector. Although this technology had a significant impact on the manufacturing industries, it, like any other technology, encountered several challenges when applied to the construction sector. Conversely, small and medium-sized enterprises (SMEs) in developing economies often face significant impediments when using innovative technologies. Thus, this paper seeks to determine and investigate the perceived barriers to applying BIM in construction SMEs based in Iran. Three rounds of Delphi surveys were carried out with 15 BIM experts engaged in construction SMEs to identify the key barriers to BIM implementation in SMEs. An empirical survey questionnaire comprising these identified barriers was subsequently designed and disseminated to the invited experts. Altogether, 56 valid survey responses were received and analyzed. The study’s findings revealed SMEs management’s hesitancy to adopt BIM, stakeholders’ reluctance to change their established methods, and a lack of technical understanding as the critical impediments to BIM adoption in construction SMEs. Also, the study identified four barrier dimensions – technology, legal, management, and financial. These BIM implementation barrier dimensions can be employed to better allocate resources and financing for BIM deployment and construction innovation in SMEs. The study will assist major project stakeholders and SMEs make better-informed BIM adoption decisions, particularly in developing nations like Iran.

Explaining drivers of farmers’ willingness for early adoption of enhanced irrigation technologies: case of Tunisia

The objective of this paper is to identify the influencing factors of farmers’ willingness for early adoption of enhanced irrigation technologies in Tunisia. We estimate a multinomial logit model with data from 931 farmers in Central (Chebika) and Northern (Fernana) of Tunisia. Regression results reveal that early adoption is positively influenced by levels of extension service quality, trust in farmers’ associations and extension agents, farmer’s perception towards the innovation, credit access, and off-farm income. However, it is negatively affected by market access issues, risk aversion, and age. Risk, trust, and perception towards technology are important factors in driving early adoption decision. The findings imply that farmers training on water conservation technologies, financial support for innovation adoption, awareness of young farmers about the opportunities of agricultural innovation, incentives to farmers’ associations in order to improve their market access, and inclusive participatory approaches during technology generation and transfer are all accelerators of early adoption speed of innovations by farmers.