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Effective professional development programs (PDPs) rely on well-defined goals. However, recent studies on PDPs have not explored the goals from a multi-stakeholder perspective. This study identifies the most important learning goals of PDPs at science research institutions as perceived by four groups of stakeholders, namely teachers, education researchers, government representatives, and research scientists. Altogether, over 100 stakeholders from 42 countries involved in PDPs at science research institutions in Europe and North America participated in a three-round Delphi study. In the first round, the stakeholders provided their opinions on what they thought the learning goals of PDPs should be through an open-ended questionnaire. In the second and third rounds, the stakeholders assessed the importance of the learning goals that emerged from the first round by rating and ranking them, respectively. The outcome of the study is a hierarchical list of the ten most important learning goals of PDPs at particle physics laboratories. The stakeholders identified enhancing teachers' knowledge of scientific concepts and models and enhancing their knowledge of the curricula as the most important learning goals. Furthermore, the results show strong agreement between all the stakeholder groups regarding the defined learning goals. Indeed, all groups ranked the learning goals by their perceived importance almost identically. These outcomes could help policymakers establish more specific policies for PDPs. Additionally, they provide PDP practitioners at science research institutions with a solid base for future research and planning endeavors.
Background
Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.
Objective
This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance.
Methods
To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers.
Results
The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately.
Conclusion
A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.
Background
Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.
Objective
This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance.
Methods
To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers.
Results
The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately.
Conclusion
A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.
Robo advisors represent a digital financial advice solution challenging traditional wealth and asset management, investment advice, retirement planning, and tax-loss harvesting. Based on algorithms, big data analysis, machine learning, and other technologies, these services minimize the necessity for human intervention. Based on an international three-stage Delphi study, we provide a plausible forecast of the development of the robo advisor industry, with regards to market development, competition, drivers of growth, customer segments, challenges, services, technologies, and societal change. The results suggest that the financial advice market will experience a further increase in the number of robo advisor services available. Existing and traditional financial advice players will be forced to adjust to the changing environment of the market. Due to low fees and ease of use, robo advisors will be made available to a broad cross section of society, and will cause significant market losses for traditional investment advice companies. Ten years from now, the predominant investment class will remain Exchange Traded Funds (ETFs). Even though degrees of human intervention are expected to vary considering the complexity of advice, automation will increase in significance when it comes to the development of robo advisors.