Happiness in Communities: How Neighborhoods, Cities and States Use Subjective Well-Being Metrics

The investigation of subjective well-being with the aim of steering tourism development towards improving host community well-being in World Heritage Sites is the principle behind Planet Happiness project. To complement the extant literature on the Happiness Index's use in different national contexts, this research outlines a methodological approach of the translation and localization of Happiness Index to Turkish language and cultural characteristics. Subsequently, additional items derived from scholarly literature to capture residents' perceptions of conservation efforts and tourism activity are proposed. Reinforcing these theoretical findings with insights of polling firms and through interviews with experts working in World Heritage Sites, this research suggests that well-being studies would benefit from a modular perspective that allows the reflection of contextual elements in the survey instrument and in the data collection process that are rooted in the geographical, legal, economic, and sociocultural attributes of the region or city in question. This methodological approach is tested in Cappadocia World Heritage Site in Turkey with 178 respondents chosen via convenience sampling. The findings of descriptive statistical procedures indicate a contextual incompatibility of several items of the Happiness Index in Turkey and suggest that future inquiries into subjective well-being would benefit from contextspecific modifications to the Happiness Index. By combining well-being with findings pertinent to additional tourism and conservation domains, the methodological implications for future well-being studies in Turkey's World Heritage Sites are discussed.

Section: Methodsmentioning

confidence: 99%

Section: Integration Of Tourism and Conservation Domains To The Himentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Subjective Well-Being in World Heritage Sites: Localization and Thematic Integration of the Happiness Index for Turkey

Kuzuoglu

Ata

Hatipoğlu

et al. 2020

“…We propose that before long, AI will be deeply embedded into many aspects of all kinds of communities and that assessing the impact of AI on communities could be included in assessments of community well-being. Well-being indicators have gained momentum as tools for assessing community and regional well-being (Durand 2018;Lee et al 2015;Musikanski et al 2017;Phillips and Wong 2017;Sirgy et al 2009;Sung and Phillips 2018). Community well-being indicators gather data that are usually reflective of what is valued by the community's residents (Phillips 2003), and thus can be used to understand the impacts of AI on community well-being as well as to aid in decision-making.…”

Section: Indicators For Assessing the Impact Of Ai On Community Well-mentioning

confidence: 99%

Artificial Intelligence and Community Well-being: A Proposal for an Emerging Area of Research

Musikanski¹,

Rakova

Bradbury³

et al. 2020

Self Cite

We are calling for a new area of research on the nexus of community well-being and artificial intelligence (AI). Three components of this research we propose are (1) the development and use of well-being metrics to measure the impacts of AI; (2) the use of community-based approaches in the development of AI; and (3) development of AI interventions to safeguard or improve community well-being. After providing definitions of community, well-being, and community well-being, we suggest a definition of AI for use by community well-being researchers, with brief explanations of types and uses of AI within this context. A brief summary of threats and opportunities facing community well-being for which AI could potentially present solutions or exacerbate problems is provided. The three components we propose are then discussed, followed by our call for cross-sector, interdisciplinary, transdisciplinary and systems-based approaches for the formation of this proposed area of research.

“…Some of these initiatives are the call from the United Nations (UN) General Assembly ( 2011) on member states to adopt happiness and well-being in terms of measurements and goals (p. 9); the issue of the World Happiness Report since 2012 (see World Happiness Report); the Global Happiness Policy Report first published in 2018 (Global Happiness Council 2018); and the Better Life Index developed by the Organisation for Economic Co-operation and Development (OECD) issued in 2011. Countries are using indicators of happiness and well-being, including subjective well-being (SWB) indicators, to gather data (Durand et al 2018;Musikanski and Polley 2016;O'Donnell et al 2014), and communities across the globe are measuring happiness and using the data to guide interventions (Musikanski et al 2017). Bhutan and the United Arab Emirates are forming policy specifically aimed at increasing the happiness of their populations (Centre for Bhutan Studies and GNH 2018;Musikanski 2014;Musikanski 2018).…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between the Sustainable Development Goals and Happiness Metrics

Iriarte¹,

Musikanski

2019

Self Cite

International institutions, national governments and communities are promoting and measuring happiness in various ways. However, as of the writing of this article, there is not an agreed upon happiness index that institutions, governments, and communities use to gather and compare data. On the other hand, the Sustainable Development Goals (SDGs), which have a set of indicators commonly shared by institutions, governments, and communities, do not explicitly consider happiness even though SDG Goal 3: Good Health and Well-being references well-being. In this article, we construct an Aggregated Happiness Index (AHI) based on five indices in use and applied at different governance levels. Based on common domains and indicators from these indices, the AHI is composed of twelve domains, thirty-one indicators and distinguishes between objective and subjective indicators. The AHI domains and indicators are benchmarked against the SDGs goals and indicators respectively using a grading schema based on a traffic light. Our analysis reveals that at the domain level the SDGs cover 66.7% of the AHI, however the coverage at indicator level drops to 48.6%. The SDGs indicators cover 61.1% of the AHI objective indicators and 17.9% of the AHI subjective indicators. Major gaps are found in the domains of community & social support, subjective well-being and time balance. We found a lack of subjective metrics in other domains, including economic standard of living and health. We discuss the opportunities and drawbacks of approaching SDGs and happiness metrics separately or synergistically. Given the potential benefits of integrating both approaches, we propose the framework we term SDGs for Happiness composed of 18 indicators of which 61.1% are subjective that should be considered in addition to the SDG indicators to measure happiness within the SDGs.