An Exploration of Environmentally Sustainable Practices Associated with Alternative Grazing Management System Use for Horses, Ponies, Donkeys and Mules in the UK

Equestrian grazing management is a poorly researched area, despite potentially significant environmental impacts. This study explored keepers’ use of alternative grazing systems in the care of UK horses, donkeys and mules through an internet survey. The survey was available during the summer of 2020 and comprised closed and open questions, which were analysed with descriptive statistics and iterative thematic analysis, respectively. A total of 758 responses was incorporated into the analysis; the most popular system used were tracks (56.5%), Equicentral (19%), “other” (e.g., non-grass turnout) (12.5%), rewilding (7.5%) and turnout on either moorland (0.7%) or woodland (2.5%). The thematic analysis highlighted that equid keepers across the systems were highly engaged in exploring sustainable practices. Their approaches varied according to each system, yet all aimed to fulfil practices in three major categories, i.e., supporting diverse plant life (usually through restricting equid access to certain areas), supporting wildlife (through the creation of biodiverse environments) and sustainably managing droppings and helminths. Additionally, proponents of the Equicentral systems declared to be aiming to support soil health. These data provide a promising insight into equid keepers’ behaviour and attitudes to sustainability.

Revisiting Knowledge Management System Use

Knowledge Management Systems (KMS) are adopted with the aim of facilitating knowledge flow within the organization. However, it is seen that member participation on these platforms is limited. The objective of this work is to identify aspects that influence intention to seek knowledge on KMS. Antecedents to knowledge seeking behaviour were identified through a morphological review of literature. A conceptual model was proposed based on the Decomposed Theory of Planned Behaviour. Structural Equation Modelling was used to assess the adequacy of the model. Results show that seeking happens when the individual has an intrinsic motivation to learn and when the quality of knowledge available on KMS is perceived as having high content value. Interestingly, we find that top management has no bearing on one’s intention to seek. Findings reveal that HR activities need to identify people management practices, such as hiring people with a curious disposition and promoting seeking as a positive behaviour. KM practices need to focus on stimulating curiosity and learning amongst members.

Human Resource Information System Use, Satisfaction, and Success

With the advent of technology in the workplace, the applicability of the human resource information system (HRIS) within organizations has gained momentum widely. Indeed, employees' perceptions towards human resource information system has changed gradually. Human resource information system is influencing employees' work activities to such an extent that it has become imperative precedence for organizations' to maintain HRIS quality. Keeping this in the background, the study aims to examine the relationship of HRIS system quality, HRIS information quality, HRIS service quality, and HRIS perceived usefulness in determining HRIS system use and its users' satisfaction. Further, the study also aims to analyze the relationship of HRIS system use and HRIS users' satisfaction in determining HRIS success in Indian organizations. A sample of 116 HR staffs and managers working in IT-enabled service sector from National Capital Region (India) has been taken for step-wise regression analyses. The findings of the study revealed that HRIS service quality and perceived usefulness showed a significant positive relationship with HRIS system use. The results also revealed that HRIS system quality and perceived usefulness showed a significant positive relationship with users' satisfaction. Further, the findings also revealed that HRIS system use and HRIS users' satisfaction has a significant relationship with HRIS success.

Avatars Control System use Based Bioinformatics in E-Business Research of Entrepreneurs

This Avatars Control System use Based Bioinformatics in E-Business Research & deals with issues related to the construction and development of the digital economy in one particular region -Republic of Armenia. This country is considered, the digital sector of which may become the largest in the country in the next five years. The digital revolution affects all areas of the Armenian economy. Prior to this, the Armenian economy was known as the mining industry, a food producer and a food. Today, according to statistics, Armenia's leading mining and energy companies are already one step ahead of their colleagues in the economically successful implementation of digital technology. Naturally, in such successes of digital transformations, there are key components. The authors hope that the examples of development and thought about the driving springs of these transformations, chosen in consideration of the interests of the development of the Russian E-Business Research of Entrepreneurs , can be interesting and useful for Russian enterprises that have started their digital transformations.

Improving Halal Traceability Process in the Poultry Industry Utilizing Blockchain Technology: Use Case in Indonesia

Demand for Halal food has significantly increased with the growing Muslim population and society’s interest in sustainable food production. However, there has been an increase in concerns regarding the Halal food labeling transparency process, with misleading labels found across the world. Blockchain-based traceability systems are a potential solution for current limitations in monitoring the production process of food due to its inherent decentralization and immutable nature. The technology allows stakeholders, including consumers, to promote farm-to-fork transparency, where traceability is a core component. This paper will explore the blockchain-based traceability system use case implemented by the Indonesian poultry player PT Sreeya Sewu Indonesia Tbk. Utilizing a blockchain-based system as a foundation for traceability shows promising results: data throughout the process is recorded permanently and difficult to tamper. Although the system does not eliminate the possibility of incorrect information being recorded, the same immutability characteristics will keep the new knowledge of fraud permanent if found during the audit. This layer of accountability contributes to the transparency that benefits both the consumers and stakeholders of the value chain.

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Background Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public’s interests in such features of AI. Objective This study elicited the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. Methods We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents’ views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. Results Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents’ trust in health and technology, and respondents’ fears and hopes regarding AI, do not play a significant role in the majority of cases. Conclusions The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.

Association between induction of the self-management system for preventing readmission and disease severity and length of readmission in patients with heart failure

Objective We recently developed the self-management system using the HF points and instructions to visit hospitals or clinics when the points exceed the pre-specified levels. We found that the self-management system decreased the hospitalization for HF with an increase in unplanned visits and early intervention in the outpatient department. However, it is unclear whether we managed severe HF outpatients who should have been hospitalized. In this study, we aimed to compare HF severity in rehospitalized patients with regard to self-management system use. Results We retrospectively enrolled 306 patients (153 patients each in the system user and non-user groups) using propensity scores (PS). We compared HF severity and length of readmission in rehospitalized patients in both groups. During the 1-year follow-up period, 24 system users and 43 non-system users in the PS-matched cohort were hospitalized. There were no significant differences between the groups in terms of brain natriuretic peptide levels at readmission, maximum daily intravenous furosemide dose, percentage of patients requiring intravenous inotropes, duration of hospital stay and in-hospital mortality. These results suggest that the HF severity in rehospitalized patients was not different between the two groups.

Challenges of Quantifying PRISM 2.0 and Tablet Use

As with the PRISM 1.0 trial, an important outcome of the PRISM 2.0 trial is use of the PRISM system and use of the PRISM system compared to the control condition (a standard tablet without the PRISM software). Frequent use over time is an important measure of system success. Further, use data provide key measures of system usefulness and usability. What features do participants use most and how often? Within those features, what activities do they engage in? What are the patterns of use throughout the trial, and how does PRISM system use compare to the control condition? However, quantifying use is not an easy task. This talk presents the challenges of quantifying use of a complex, multi-faceted system, and of making meaningful comparisons in use between two very different systems. Analysis approaches and solutions are discussed.