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This paper aims to explore the open government data initiative in the Food Protection program area within the New York State’s Department of Health to assess the impacts of opening data in terms of data quality and public value. An ecosystem lens is used to explore the dynamics of actors and their interactions, the processes involved in the program and the consequences such interplay brought forth to data quality.

The data were collected through 15 semistructured interviews with multiple stakeholders from different sectors, such as county officials, administrators and technicians, food sanitarians, data journalists and restaurant owners. At the analysis stage, the ecosystem perspective helped to capture the big picture of the open data actor interrelationships within this community regarding the food service inspections datasets.

Prior research suggests that open data initiatives enhance data quality. However, this study shows how opening data can adversely affect the quality of data. Results are explained by competing dynamics and conflicting interests among open data actors, undermining the expected public value from open data initiatives.

The findings are in contrast with the mainstream open data literature and helps open data scholars to anticipate some currently unexpected results of open data initiatives. Limitations include potential biases associated to interpretation of interview data and that the results are based on a single case study.

This study makes governments and policymakers alert about the possibility of similar open data byproducts and unwanted outcomes and helps them to design more effective open data policies, hence gaining higher economic advantage while lowering costs of open data initiatives.

Detailed open data and open data case studies through the ecosystem perspective are still scarce and can enrich discussions about open data policy design and refinement in the public sector. The data used for this research are not used in any prior papers, and to the best of the authors’ knowledge, this is the first study to identify such adverse effects of data quality that have been reported.

There are limited studies addressing how choosing a maintenance strategy can contribute towards maximising outputs from given inputs, thereby minimising costs and improving a company’s competitiveness. The analytic hierarchy constant sum method (AHCSM) is used to access the appropriateness of maintenance strategies for improving the overall efficiency of a structural steel fabrication construction company.

A semi-structured interview was formulated with the stakeholders of the quality department to understand the company’s maintenance portfolio and its current functional capability. The information from this case study was then dissected to represent the factors that the company deemed appropriate for evaluating their maintenance strategy. The AHCSM approach provided a framework, which ranked the importance of factors that are sensitive to the construction industry and rank the suitability of maintenance strategies.

Factors affecting the selection of maintenance strategies to improve business efficiency are productivity, quality, reliability, cost, safety and work environment, morale, inventory and flexibility. Total productive maintenance strategy produces the most desirable outcome; however, the predictive or condition-based maintenance strategy provides an optimum solution for the case study company while considering the equipment usage, frequency of production and the current economic climate.

The approach presented allows practitioners to consider ways to increase the level of production and improve the efficiency of construction businesses without a high increase in investment. The findings can inform gaps in existing maintenance approaches in achieving business objectives.

The purpose of this paper is to carry out an empirical investigation of the role of various factors such as economics, social, marketing, cultivation and government in adoption of organic farming. Further, this study examines the factors that influence farmers’ choice of adopting organic farming, based on their demographic classification such as education level, farm size, farming experiences and land ownership of the organic farmers.

To address the research objectives, the primary data were collected with the help of a structured questionnaire from 200 respondents. In this study, the QUAL–QUAN sequence of mixed method design was used. Four focus groups were conducted to identify the factors of organic farming adoption. Further, multinomial regression analysis was applied to analyze the differential impact of these factors in relation to the farmers’ demographic classification.

The study found five major factors that affect the adoption of organic farming (economic, social, marketing, cultivation, government policy) in India. The study also observed that marketing and government policy factors were most crucial in influencing all types of farmers irrespective of their educational level. The farmers with more farming experience were more concerned about social factors. Similarly, the farmers using lease farms were found to be concerned about the economic viability of organic farming.

This study suggests that without government support, the adoption of organic agriculture seems to be a highly challenging task in a situation, where majority of the farmers fall under the small and marginal category. Hence, to promote organic farming in a developing country like India, the government has to invest more in schemes where farmers should get exclusive training and support to strengthen their intention behind the adoption of the organic farming.

Based on the collective insights from the studies, the different stakeholders with interest in organic agriculture may frame necessary strategies to promote organic farming.

The purpose of this paper is to investigate factors affecting Vietnamese farmer's intention toward organic agricultural production based on research model integrating theories: theory of planned behavior (TPB) and norm activation model (NAM).

After in-depth interviews with 5 agricultural researchers and 5 farmers, the authors determined the official research model and built a complete survey. Data were collected from 318 farmers in the Hanoi, Vietnam by directly survey. Statistical methods, such as Cronbach's alpha analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM), were used to analyze the data.

This paper shows that attitude, subjective norms, perceived behavioral control and personal norm were significantly related to intention of the farmers; awareness of consequences was positively related to ascription of responsibility, personal norm, attitude and subjective norms; ascription of responsibility has a positive relationship with personal norm. The TPB-NAM integration model is proven to be superior to the original TPB model when studying factors affecting Vietnamese farmer's intention toward organic agricultural production.

The main limitation of this paper is that the sampling method is not representative for the whole country and just stops at researching the intentions of farmers without understanding the practices of organic agricultural production.

The findings indicate that state management agencies in Vietnam need to pay attention to raise awareness among farmers about the role of organic farming, communicate to farmers through different channels about the benefits of organic farming compared to conventional agriculture and form information spillover groups between farmers who have produced organic agriculture and have not yet produced organic agriculture.

This paper contributes to the existing literature by focusing on integrating TPB and NAM when understanding farmers' intention toward organic agricultural production in Vietnam. This integrated model has proven the suitability of combining two approaches, a rational approach and an ethical approach, when studying farmer intentions.