Applied Research in Quality of Life

, Volume 7, Issue 3, pp 239–261

Measuring Social Sustainability: A Community-Centred Approach

Article

DOI: 10.1007/s11482-012-9166-x

Cite this article as:
Magee, L., Scerri, A. & James, P. Applied Research Quality Life (2012) 7: 239. doi:10.1007/s11482-012-9166-x

Abstract

Efforts to measure social and community sustainability confront a series of methodological dilemmas. We present four key distinctions that tend to orient such efforts: between objective and subjective assessment; between “communities” as the sum-of-their-parts, or as holistic and distinct entities in themselves; between present and future aspects to be measured; and between use of “top–down” and “bottom–up” indicators. We then propose a questionnaire for sustainability assessment in light of these. We administered the questionnaire to various communities in the Middle East, South and South East Asia between 2006 and 2010, and present descriptive summaries and a factor analysis of the results here. The results serve two aims: to augment existing qualitative research conducted in the respective areas, and to test the validity and reliability of the instrument itself. Several limitations of the questionnaire emerged during analysis, which we discuss. The results also show strong correlation with national Human Development Index figures for the communities surveyed and moreover, point to several interesting attitudinal divergences between the communities sampled. We conclude with an outline of a revised sustainability assessment instrument that has application for research looking to bridge the gap between psychological orientations towards wellbeing, on the one hand, and sociological or organizational studies on sustainability, on the other hand.

Keywords

Community Wellbeing Quality of life Sustainability Indicators 

Introduction

Understanding the uneven resilience of communities has been a preoccupation of the social sciences since the nineteenth century. Classical social theory and sociology was preoccupied with themes and questions about the cohesion, stability and integration of communities. While terminology has changed, debates in this area are still to be resolved. Despite, or perhaps because of this lack of resolution, enquiry over the past two decades has shifted sideways to potentially more fruitful lines of inquiry. The task of understanding “society”, and more locally, “community”, has increasingly intersected with a new set of preoccupations—sustainability, wellbeing and quality of life. The underlying task of enquiry thus has moved, at least rhetorically, from questions of social structure, regulation and function, to more agency-focussed questions dealing with issues such as sense of sustainability, community, wellbeing, quality of life, security from ‘risk’ or inclusion and participation.

We identify four common dilemmas in the measurement of community sustainability. The first of these relates to what is measured by indicators—whether they measure objective conditions of a community, or to those conditions as subjectively experienced by its members (Diener 2006; McCrea et al. 2006). A second and associated dilemma concerns the ontological status of community itself. Is community as an entity reducible to the sum of its parts, or rather is it constituted as an integrated object beyond its parts (Sirgy 2010). A third dilemma concerns the temporal orientation of assessment. We suggest that an important distinction between wellbeing and quality of life, on the one hand, and sustainability studies, on the other, fundamentally concerns this temporal dimension—while notions of quality of life and wellbeing tend to assess past and present states of communities and individuals, sustainability can be broadly conceived as oriented towards future states. The fourth dilemma, epitomized in the distinction between so-called global, or “top–down”, and local, or “bottom–up” assessment approaches, concerns whether to apply universal indicators sets—which lead to comparability but tend to ignore local, community-based meanings of sustainability—or whether to devise context-specific indicators, selected by and relevant to the communities themselves, but requiring interpretation and translation in order to compare communities meaningfully (Agger 2010; Fraser et al. 2006).

It is in this context that we developed a questionnaire instrument that provides an integrated assessment of community sustainability. The particular instrument we introduce is oriented towards these dilemmas in the following ways. It aims to measure the subjective attitudes of a community towards sustainability. It is geared towards understanding these attitudes both individually and as they relate towards the community as a whole, thereby treating community as a distinct and irreducible entity. It focuses upon both present wellbeing and future sustainability of the community. Finally, it adopts a “top–down” approach, where variables are predefined. Hence, in this conception of sustainability, the kinds of theoretical distinctions introduced above—between global and local, objective and subjective, holistic and individualistic, and present and future “frames”—are both important to distinguish but invariably intertwined. Analytically, this suggests that contemporary communities need to be understood as upon reflection they would understand themselves—as enmeshed in global systems while striving for local autonomy; as entities that can be objectively studied but also with validly subjective interpretations of their conditions; as coherent but necessarily fragmentary collections; and with a present sense of wellbeing heavily conditioned by concerns about sustainability.

Although our variables are predefined in the questionnaire, we acknowledge the need in other work for complementary grounded or engaged theoretic approaches (James et al. 2011a, b), to elicit community-based definitions and indexes of wellbeing. Similarly, while here we pursue a strategy of eliciting individual responses through a sampling questionnaire, we also acknowledge that a rounded picture of community sustainability requires considerable further elaboration, for example through more specific economic, ecological, political and cultural sustainability indexes. We endeavour to reflect some macro-level features by extending our set of variables to measure participant attitudes not only towards their own personal, or subjective, wellbeing, but also towards the broader, intersubjective wellbeing of their community. The strength of this assessment tool lies, though, in the other two vectors of the dilemmas posed above. It does so, first, in assessing sustainability as experienced by the community itself; and, second, in combining existing wellbeing indicators with a series of supplementary variables aimed at eliciting responses to future community sustainability challenges. While the questionnaire does not measure sustainability definitively, by measuring a cross-section of community attitudes it can contribute towards better sustainability assessment, augmenting both qualitative studies and objective indices such as the Human Development Index (HDI).

We applied the tool in a series of pilot studies with various Global North and Global South communities, each with distinct characteristics. Here the distinction between North and South is treated as socio-economic distinction based on a geographical tendency for poorer countries to be located in the southern hemisphere. The studies were part of an ongoing theoretical and empirical engagement with at-risk communities that also included qualitative ethnographic studies. In particular, assessing the sustainability of communities in Global South regions requires sensitivity to how these features are constructed and measured. Not only are many of these communities spatially, politically and economically isolated, they are increasingly at risk from environmental, economic and cultural hazards brought about, at least in part, by ever-expanding global industrialization, and commensurate patterns of consumption and production. Growing awareness of these risks within communities themselves means that objective conditions are increasingly reflected—though not necessarily directly mirrored—by subjective experiences. Moreover, the present assessment of quality of life in these communities is heavily tempered by anxieties about how sustainable their ways of life can continue to be. Increasingly communities are agitating for assessment tools that enable them to manage inevitable transitions brought about by processes of globalization and climate change, and preserve what they can of their traditions.

The research presented in this article thus had several distinct aims. Firstly, we were interested in examining whether a generalizable questionnaire could accurately measure subjective attitudes of members across diverse sites and communities, and supplement information available from other sources. For this purpose we used two forms of control: ethnographic research conducted at the communities, and general HDI statistics for the countries in which the communities reside. Secondly, we investigated whether salient differences existed between communities in low and high-income countries towards different sustainability dimensions. In the context of the communities surveyed here, these differences further point to what may, given further study, prove to be important distinctions between communities in the North versus the South. Finally, the questionnaire is based upon an alternative theoretical conception of sustainability to the common “triple bottom line” paradigm, as we discuss further below. We therefore used the questionnaire as a form of extended pilot, during which this conception was further developed, and in turn led to a reformulation of the questionnaire itself, which we present below.

We begin in the next section by surveying briefly current trends in wellbeing and sustainability indicator development. We then introduce our own sustainability questionnaire with an overview of its theoretical basis, and discuss pilot studies using the questionnaire, conducted with a range of communities in the Middle East, South and Southeast Asia. We include a range of communities in the Global South, and also include several communities from Israel and Australia, for comparative purposes. These communities were surveyed between 2006 and 2010, in conjunction with qualitative studies. We then discuss our results, along with several limitations that emerged during these pilots. We conclude with an outline of a new version of the questionnaire, that includes some possible remedies, along with final observations.

Measuring Sustainability, Wellbeing and Quality of Life

The literature on sustainability, wellbeing and quality-of-life indicators has flourished over the past two decades. So has the number of projects attempting to quantify and qualify these concepts. In relation to wellbeing and quality-of-life, considerable eclecticism exists among proposed indicator systems, with variation introduced by scope (global, national or community-based), domain (life versus domain satisfaction (Diener 2006)), demographic, geographic and cultural factors (Martin et al. 2010), orientation (objective versus subjective (McCrea et al. 2006)), theoretical conceptions of wellbeing (McMahan and Estes 2011) and statistical interpretations (Rojas 2011). A further key distinction evident in early debates in the literature contrasts liveability with comparison theories of quality of life. While liveability theory held that persons’ judgements about quality of life referred to absolute standards or universalizable norms, comparison theory held that people make judgements about quality of life based upon comparison with some past experience, or with their own perceptions of the experiences of others (Veenhoven and Ehrhardt 1995). In recent years, researchers have attempted to combine the two theories by proposing the view that persons’ judgements about quality of life implicate both absolute standards and recent changes in quality of life (Hagerty 1999).

Quality-of-life and sustainability literatures have historically been somewhat divergent, with efforts to measure sustainability showing a similarly rich and complex tradition. The broadening out of conceptions of sustainability in the 1990s, most notably after the UNCED conference in 1992 (United Nations 1992a, b), led to various compositional definitions—including, most famously, the triple-bottom-line approach encompassing environmental, economic and social dimensions (Elkington 1997). Reflecting this diversity, in 2003, estimates of the number of sustainability indices alone already exceeded five-hundred sets (Parris and Kates 2003). As those authors and others have suggested, the proliferation of indicator sets stems from the many divergent views and corresponding definitions of sustainability (Bohringer and Jochem 2007; McCool and Stankey 2004; Munda 2005; Parris and Kates 2003). Among the operant distinctions are those between strong and weak definitions of sustainability (Atkinson 2000); between top–down (expert-driven) and bottom–up (community-driven) indicator development (Fraser et al. 2006); and between economic, ecological and holistic or indexical approaches towards measurement (Bramley and Power 2009; Singh et al. 2009).

Similarly, a host of different methods have been employed for measuring sustainable development, adopting a range of biophysical, econometric and human development models (Gasparatos et al. 2008; Parris and Kates 2003; Wilson et al. 2007). Finally, sustainability indicators have also integrated into a variety of broader frameworks oriented towards stakeholder engagement, policy and planning practices, including multi-criteria analysis (Balana et al. 2010; Munda 2005), integrated assessment (Hettelingh et al. 2009; Krajnc and Glavic 2005; Lee 2006), transition management (Schilperoord et al. 2008), and various forms of strategic environment assessments (Pope et al. 2004).

Recently, there has been some convergence between these large bodies of literature, as substantial research points to the often intimate connections between individuals’ perceptions of their own absolute and relative quality of life, and the sustainability of the cultural, communitarian and organizational contexts in which they find themselves (Holden 2007; Kilbourne 2006; Assche et al. 2010; Kruger 2010; Matarrita-Cascante 2010).

Community settings often make ideal units of analysis through which to study the overlay between individual wellbeing and social sustainability in particular, and have increasingly been studied in both respects (Agger 2010). Communities themselves have been widely studied, both as psychological collectives, and as strongly cohesive sociological entities. We draw here upon canonical representations of both conceptions in our understanding and subsequent measurement of community sustainability. In describing the psychological sense of community, McMillan and Chavis (1986) identify four components: membership, influence, integration and fulfilment of needs, and shared emotional connection. In a similar vein, but abstracted into more generalized sociological terms, Putnam’s reintroduction of “social capital” as a distinctive and distinguishing feature of social networks, and particularly of “bonding” capital in relation to homogenous social groups, offers a guiding if problematic notion for what it is that comprises communities, and what, consequently, needs sustaining for their continued survival.

Against this background of theoretical distinctions in the sustainability and wellbeing measurement literature we present results of a survey, the Social Sustainability Survey, alongside a series of indicators aimed at conveying a picture of the subjective attitudes of community members in relation to their expressed ideals of ‘sustainability’. In endeavouring to encompass multiple dimensions of sustainability, the indicator set is similar to other holistic assessment strategies. However, our approach differs in a number of respects. Firstly, we contrast the orientation of our model with triple-bottom-line approaches, both by revising the underlying structural basis of understanding sustainability, and by focussing on this understanding from the experiential standpoint of community participants. In this respect the survey shares common features with the psychometric perspectives of the Australian Unity Wellbeing Index (Cummins et al. 2003), World Values Survey (Inglehart and Basanez 2000) and World Database of Happiness (Veenhoven 2009), and indeed certain constructs of the Wellbeing Index and World Values Survey are incorporated into our indicator set. Secondly, we distinguish our approach from purely psychometric studies in focussing on the community as a level of analysis. Hence, although individuals’ responses concerning their own wellbeing are relevant, we also measure attitudinal assessment of the communities they belong to, reflecting Sirgy’s observation (2010) that community is both equal to and more than the sum of its parts. Finally, we differentiate our approach by looking to measure the intersubjective and future character of a community—how members of that community not only feel about themselves in the present, but also about their broader social and natural environment, and about the future prospects of that environment.

Methodology

The Social Sustainability Survey was first developed and administered to a number of rural and urban communities in Victoria, Australia in 2006. Over the next 4 years it was further administered to a number of diverse communities in the Southeast Asian, South Asian and Middle Eastern regions. When administered in urban and regional community settings in India and Sri Lanka, the questionnaires were used as auxiliaries to interviews and consultations with coastal rural communities affected by the 2004 Indian Ocean tsunami. Use of the questionnaire with the City of Melbourne cohort was through a combination of randomized street and online polling. In all other cases, questionnaires were administered as part of community consultation, and participants were selected through a combination of purposive and snowball sampling in those areas. In those cases, the questionnaire accompanied a more extensive qualitative engagement in the communities sampled, through a series of ethnographic, interview-based and observational inquiries into community well-being and sustainability (see, for example, Mulligan and Shaw 2007; Scerri et al. 2009). Hence the initial aim of the questionnaire was to supplement existing qualitative research, to identify areas of community concern, rather than to offer a basis for comparative assessment. Consistent with this aim, a number of supplementary questions were included in different community settings, regional, localized, project-based and time-based differences. For example, questionnaires administered in Sri Lanka and India after the tsunami included a module of additional questions on disaster recovery (Mulligan and Shaw 2007). A core set of variables was measured consistently throughout, with the notable exception of the City of Melbourne questionnaire administered in 2009. These are discussed further below.

The questionnaire is developed on a theoretical model, the Circles of Sustainability, elaborated in earlier work (Scerri and James 2010a, b). This model departs from conceptions such as the triple bottom line by treating sustainability under a broadly social constructionist and critical pragmatic paradigm. In this model, sustainability indicators measure the extent to which a community’s goals, desires and ambitions are being met. Accordingly, in contrast to triple bottom line, we treat the ‘social’ as an overall category that is integral to the very definition of sustainability. We then differentiate four rather than three conceptual domains—economy, ecology, politics, and culture—against which the sustainability of different forms of social practice and meaning can be assessed. Notably, in distinction to the triple bottom line, politics and culture are distinguished as two separate domains of social life that, governed by their own integral logics, warrant equal consideration and assessment as existing in a relationship with the forms of social life that appear to take place when regarded in terms of the economic and/or ecological domains. Aside from representing a more evenly weighted conceptualization, this approach also mitigates against a key line of critique of the triple bottom line approach—the invariable encroachment of economic relations, especially market relations, upon environmental and ‘social’ concerns. Instead, respondents’ perceptions of what counts as indicators of sustainable social relations, that is, economic, ecological, cultural and political relations, are treated as prerequisites for sustainability.

The variables we introduce in the questionnaire aim to measure such perceptions of community sustainability in each of these four domains—both in absolute and relative, atomistic and holistic, present and future and top–down and bottom–up terms. As the theoretical model itself was being developed during the pilot of the questionnaire, we have constructed retrospective proxy subscales to measure sustainability against these domains. One side-effect of this iterative process has been some early obfuscation between economic and ecological variables, and we opted to collapse these into a single subscale in our principal component analysis below. We also constructed a HDI Proxy subscale modelled on the Human Development Index (Human Development Report 2010), to examine how our results could be interpreted against a standardized index, and to test construct validity against the UNDP published HDI figures. Similarly, in line with our views that community sustainability can be treated as an extension of community wellbeing, we adopted a number of variables from the Wellbeing Index (Cummins et al. 2003). Other variables, as part of the ‘core’ set, were chosen to reflect broader intersubjective and future-oriented community attitudes. The remaining common survey questions capture administrative and demographic variables. The complete set of questions and the manifest variables they are measure are listed in Table 2 below.

A further interest was in how well our results at a community level, and measuring subjective attitudinal responses, could be compared with nation-level objective indices such as the Human Development Index (Human Development Report 2010). Though previous research suggests subjective and objective indicators do not always correlate strongly even under controlled circumstances (McCrea et al. 2006), a positive correlation for the self-assessment subscale of the questionnaire across different communities is at least suggestive of construct validity. Similarly, we also discussed results with researchers engaged in qualitative research with the surveyed communities, to establish anecdotally whether results are consistent with their findings.

Given that the administration of the questionnaire typically took place in the context of a range of very different community engagements, where often the very notion of ‘community’ was difficult to define, there are notable inconsistencies in the size and sampling strategies of the samples collected. We also note in our analysis that this is the first time a comparative study of the samples has been conducted. In addition to several difficulties harmonizing variant data sets, we became aware of several problems of construct validity and reliability, which we aim to address in future iterations of the questionnaire. Nonetheless, the exploratory analysis which follows demonstrates how further iterations might complement a sustainability assessor’s existing toolkit. The analysis also makes a contribution in its own right into understanding key factors and relationships of the sampled communities.

The particular countries, sites, communities, years and sample sizes of surveys conducted are listed in Table 1.
Table 1

Countries, years, sample sizes and percentages

Country

Year

Size (N)

Percent

Papua New Guinea

2006

1,062

31.5

Malaysia

2006

105

3.1

Sri Lanka

2007–2008

515

15.3

India

2008

181

5.4

Timor Leste

2008

615

18.3

Israel

2009

137

4.1

Australia

2006–2009

753

22.4

Total

 

3,368

100.0

Data Preparation and Analysis

The data was collected, recorded and coded into separate SPSS files after each administration of the questionnaire. In mid-2010, we began an intensive effort to collate, clean and consolidate the various data sets. During this process, we encountered several difficulties with both the data and constructs being measured. We outline the procedure used to prepare and analyse the data, as well as some of these difficulties, where they relate to our findings and interpretations below.

To prepare the data for analysis, results of all surveys were consolidated from a number of SPSS and Excel sources into a single SPSS file. A number of operations were then undertaken to correct for consistency issues described above. These steps included:
  1. 1.

    Coding was re-applied to variables consistently. Where variables had different levels of information (for example, where a combination of ten-point and five-point scales had been used), the lesser of the two options was adopted.

     
  2. 2.

    Variable and data types were specified for all variables. The majority were attitudinal variables measured by five-point Likert items; these were accordingly coded as ‘ordinal’.

     
  3. 3.

    Missing values were flagged explicitly either as ‘user missing’ or ‘system missing’.

     
  4. 4.

    Variable names and labels were made less ambiguous.

     
  5. 5.

    Values were cross-checked across all surveyed communities, to ensure broadly comparable range, median and frequency distribution values.

     
Once the data was aggregated in SPSS, a set of core variables was defined. These are presented in Table 2, and include those variables common to most of the surveys administered. Where a given item was not included in a particular survey, values were recorded as missing. For the purpose of the exploratory analysis, all Likert items are here treated as ordinal variable types.
Table 2

Sustainability measures, common variables

Variable

Domain

Variable kind

Variable type

Age

Demography

Characteristic

Interval

Gender

Demography

Characteristic

Nominal

Ethnicity

Demography

Characteristic

Nominal

Location

Demography

Characteristic

Nominal

Postcode

Demography

Characteristic

Nominal

Country

Demography

Characteristic

Nominal

Living_With

Demography

Characteristic

Nominal

Household_Size

Demography

Characteristic

Ratio

Country_of_Birth

Demography

Characteristic

Nominal

Years_lived_in_current_neighbourhood

Demography

Characteristic

Ratio

Years_lived_in_previous_neighbourhood

Demography

Characteristic

Ratio

Financial_Assessment

Economy

Characteristic

Ordinal

Health_Assessment

Culture

Characteristic

Ordinal

Level_of_Education

Culture

Characteristic

Ordinal

Identified_Community

Culture

Characteristic

Nominal

Integration_with_Community

Culture

Attitude

Ordinal

Environmental_Conditions

Ecology

Attitude

Ordinal

Life_as_a_Whole

Culture

Attitude

Ordinal

Personal_Relationships

Culture

Attitude

Ordinal

Sense_of_Safety

Culture

Attitude

Ordinal

Work_Life_Balance

Economy

Attitude

Ordinal

Influence_Authority

Politics

Attitude

Ordinal

Decisions_in_Interest_of_Whole_Community

Politics

Attitude

Ordinal

Experts_can_be_trusted

Politics

Attitude

Ordinal

Govt_make_good_laws

Politics

Attitude

Ordinal

Enjoy_meeting_others_with_differences

Politics

Attitude

Ordinal

Trustworthiness_of_others

Culture

Attitude

Ordinal

Influence_of_cultural_history

Culture

Attitude

Ordinal

Importance_of_technology

Culture

Attitude

Ordinal

Frequency_of_use_of_technology

Culture

Behaviour

Ordinal

A series of descriptive statistics were obtained for this variable set, both to observe tendencies in the data and to cross check the data-cleaning process, to ensure absence of “out-of-band” data. We also correlated pair-wise all scalar variables. We then conducted a factor analysis with varimax rotation, to view whether variables clustered together intelligibly. We hypothesized also that characteristic demographic data could be useful predictors for some of the behavioural and attitudinal data, and ran regression tests to test this. Finally, ANOVA and further correlation tests were administered to determine whether meaningful differences existed, for the core attitudinal variables, between the various communities participating in the survey, and also how strongly our own measures correlate with published HDI figures. The interpretation of these tests is discussed below.

Findings

After the data was consolidated, our total sample size was 3,368. Country distribution was heavily oriented towards Papua New Guinea, Australia, East Timor and Sri Lanka. Gender distribution was approximately even (Female = 49.4%; Male = 50.2%), while age distribution is skewed towards a younger demographic, with over 75% of respondents under the age of 50. Self-assessments of health, wealth and education—variables related to indices such as the HDI—reflect the application of the survey to large number of Global South countries. The majority of respondents described themselves financially as ‘Struggling’ (50%), with only 9.1% stating they were ‘Well-off’. 45.2% of respondents stated they had primary school or no formal education at all, while only 18.4% had completed secondary school. Conversely, against the health measurement-construct, 48.6% of respondents self-assessed as ‘my health is generally good’.

A proxy HDI index variable, termed ‘HDI Self-Assessment’ was composed out of the normalized values of health, financial and education self-assessment variables. The frequency distribution of this composite variable demonstrates that in fact the relative skews of these variables collectively cancel out, leaving a close approximation to a normal distribution, as shown in Fig. 1 below.
Fig. 1

Distribution of HDI self-assessment variables

Of the 15 common attitudinal variables listed in Table 2, all but three had median, and all but one had mode values of ‘Agree’ (4). As all Likert items were phrased in such a way that agreement tended to endorse the underlying variable being measured, this indicates a degree of correlation between responses is likely. The average mean value was 3.65, while the average standard deviation was 1.06, a relatively low dispersion, one that confirms the clustering of responses on the positive end of the scale. As the presentation of inferential tests below suggests, there are some interesting differences between communities sampled however.

Correlations

Both Spearman’s rho and Pearson’s correlation coefficient were obtained of all core scalar variables, 22 in total, and separately, of all attitudinal variables, 15 in total. Of 231 possible scalar correlations, 179 (77.5%) were significant at the 0.01 level, with a further eight significant at the 0.05 level (81.0%). Of the 105 possible correlations of the 15 attitudinal variables, 100 were significant at the 0.01 level. Together these results suggest a very high degree of dependence between the variables, a feature discussed further below in both the factor analysis and survey redesign sections. Given the sample size, use of five-point scales for attitudinal variables, and potential for skew in both wording of question probes and sampling strategy, such coalescence is perhaps not surprising.

Principal Component Analysis

A factor analysis was conducted on all attitudinal variables. Kaiser-Meyer-Olkin measure of sampling adequacy was 0.843, a very high level for conducting factor analysis (Field 2005). Varimax rotation was selected, due to potential dependencies between discovered factors (Field 2005). Table 3 tabulates the varimax-rotated factors, with the factors themselves interpolated as follows:
Table 3

Principal component analysis

 

Component 1

Component 2

Component 3

Integration_with_Community

.639

  

Environmental_Conditions

.674

  

Life_as_a_Whole

.711

  

Personal_Relationships

.669

  

Sense_of_Safety

.627

  

Work_Life_Balance

.629

  

Influence_Authority

 

.577

 

Decisions_in_Interest_of_Whole_Community

 

.711

 

Experts_can_be_trusted

 

.765

 

Govt_make_good_laws

 

.731

 

Enjoy_meeting_others_with_differences

  

.581

Trustworthiness_of_others

  

.551

Influence_of_cultural_history

  

.488

Importance_of_technology

  

.577

Frequency_of_use_of_technology

  

−.671

Principal component analysis is used as the extraction method. Rotation is conducted using varimax with Kaiser normalization, converging in 5 iterations. Only scores above 0.4 are recorded

  • Satisfaction with various aspects we have interpreted against our theoretical four-domain model as economic and ecological conditions (life as a whole, involvement with community, personal relationships, the environment, sense of safety, work/life balance).

  • Trust and confidence in political conditions (ability to influence authority, belief decisions are in interest of whole community, trust in experts and government)

  • Trust and confidence in cultural conditions (enjoy meeting and trust in others, influence of history, importance and use of technology)

The three factors are interpreted here as accounting for each of the four domains in the underlying model. The first factor combines all six satisfaction constructs, taken from the Australian Unity Wellbeing Index (Cummins et al. 2003). These have been—admittedly quite liberally—interpreted as reflecting general contentment with economic and ecological circumstances, where ‘ecology’ is considered as the intersection between the social and natural context. The following two factors more directly aggregate items reflecting political and cultural engagement, respectively.

Since missing values caused a large number of cases (1,593, or 47.3% of 3,368) to be ignored in the analysis, a separate analysis was conducted with mean values substituted back in. The analysis showed a weaker sampling adequacy result, but no change in the variables or factors identified. A series of composite indices, termed respectively Attitudes towards Economy and Ecology, Attitudes towards Politics and Attitudes towards Culture, was constructed from the normalized values of the relevant underlying indicators. These in turn were compiled into an overall Attitudinal Self-Assessment index, similar to the HDI Self-Assessment variable described above. All five computed variables were then used in subsequent regression and ANOVA tests.

Predicting Sustainability Assessments—Regression Results

Regression tests were conducted to note the significance and direction of relationships between the principal component clusters of attitudinal variables, and demographic and self-assessment characteristics. Results of these for all attitudinal variables are included in Table 4. For the Well-being Index satisfaction levels (interpreted, as suggested above, so as to cover economic and ecological domains), and attitudes relating to the political domain, only the financial self-assessment variable stands out as a strong—and negative—predictor, suggesting that those who assess themselves poorly nonetheless score highly against satisfaction and political engagement indicators. Conversely, all variables other than ‘Financial Assessment’ and ‘Years lived in previous neighbourhood’ have a strong predictive relationship on the aggregated cultural engagement indicator.
Table 4

Regressions

Model

Unstandardized coefficients

Standardized coefficients

t

Sig.

B

Std. Error

Beta

(Constant)

81.237

2.084

 

38.979

.000

Age

.612

.220

.089

2.781

.006

Household size

−.159

.119

−.043

−1.336

.182

Financial assessment

−2.649

.536

−.155

−4.946

.000

Health assessment

−.606

.502

−.039

−1.206

.228

Level of education

−.263

.272

−.031

−.966

.334

Years lived in current neighbourhood

−.812

.220

−.124

−3.687

.000

Years lived in previous neighbourhood

.198

.167

.039

1.189

.235

Comparing Communities—ANOVA and Correlation Results

An ANOVA test was also conducted using the community as the grouping variable. Of particular interest was whether the first three principal components identified in the component analysis had significant differences between communities. Similarly we examined the composite ‘Attitudinal Self-Assessment’ and ‘HDI Self-Assessment’ variables across the groups. The tabulated results of this test are included in Table 5. Each of the five computed variables showed significant differences across the different community groups at both 0.05 and 0.01 levels.
Table 5

ANOVA of composite variables across communities

 

Sum of squares

df

Mean square

F

Sig.

Attitudes towards economy and ecology

Between Groups

1763.205

4

440.801

27.496

.000

Within Groups

34612.283

2159

16.032

  

Total

36375.488

2163

   

Attitudes towards politics

Between Groups

1999.909

5

399.982

40.061

.000

Within Groups

26098.794

2614

9.984

  

Total

28098.704

2619

   

Attitudes towards culture

Between Groups

14879.615

5

2975.923

305.189

.000

Within Groups

26435.147

2711

9.751

  

Total

41314.762

2716

   

Attitudinal self-assessment

Between Groups

6620.378

4

1655.095

14.719

.000

Within Groups

199032.075

1770

112.448

  

Total

205652.453

1774

   

HDI Self-assessment

Between Groups

429736.973

6

71622.829

204.563

.000

Within Groups

1122152.037

3205

350.125

  

Total

1551889.010

3211

   
Table 6 compares both mean values and rank for the five composite variables across each of the seven communities (Melbourne (2009) and Timor Leste are incomplete due to certain items not being included in their respective surveys). As the ranks make clear, HDI self-assessment means appears to correlate with attitudes towards economy, ecology and culture, with ‘Australian Towns’ and ‘Be’er Sheva’ ranking highly for each of these four variables. Attitudes towards Politics, on the contrary, correlate inversely. This suggest that communities generally satisfied and confident regarding economic, ecological and cultural dimensions are sceptical of prevailing power systems and structures; those, on the other hand, who self-assess poorly and are dissatisfied with present material conditions nonetheless express greater trust and confidence in political mechanisms.
Table 6

Composite variable mean comparison

Mean values

Values

Attitudes towards Economy and Ecology

Attitudes towards Politics

Attitudes towards Culture

Attitudinal Self-Assessment

HDI Self-Assessment

2010 HDI Country Values a

Australian Towns

24.2

11.5

21.4

72.8

65.5

0.937

Be’er Sheva, Israel

24.3

11.7

22.7

74.8

78.4

0.872

Malaysia

21.4

13.1

15.8

65.2

46.5

0.744

Melbourne, Australia

.

.

.

.

53.6

0.937

Papua New Guinea

24.2

13.8

17.1

70.9

41.6

0.431

Sri Lanka

22.5

13.5

19.7

72.7

38.4

0.658

Timor Leste

.

14.0

15.3

.

36.0

0.502

Mean ranks

 

Attitudes towards Economy and Ecology

Attitudes towards Politics

Attitudes towards Culture

Attitudinal Self-Assessment

HDI Self-Assessment

2010 HDI Country Relative Ranks a

Australian Towns

3

6

2

2

2

1

Be’er Sheva

1

5

1

1

1

2

Malaysia

5

4

5

5

4

3

Melbourne, Australia

3

1

Papua New Guinea

2

2

4

4

5

7

Sri Lanka

4

3

3

3

6

5

Timor Leste

1

6

7

6

a (Human Development Report 2010)

A further pair-wise set of correlations was ran over the composite variables, which confirm the above findings across the whole data set—all variables correlate significantly at 0.05, 0.01 and 0.001 levels, with ‘Attitudes towards Politics’ the only variable correlating negatively with the others. We also plotted our HDI Self-assessment proxy variable against 2010 HDI values given by the UNDP for the corresponding countries. Here we noted a strong positive correlation (R = 0.756, p < 0.05).

Discussion

In the first instance, this article has reflected our interest in examining whether a generalizable questionnaire could accurately measure subjective attitudes of members across diverse sites and communities, and supplement information available from other sources. For this purpose we used two forms of control: the ethnographic research conducted at the communities, and general HDI statistics for the countries in which the communities reside. Secondly, we investigated whether salient differences existed between low and high-income communities towards different sustainability dimensions. In the context of the communities surveyed here, these point to distinctions between communities in the Global North versus the South. Finally, the questionnaire is based upon an alternative theoretical conception of sustainability to the common “triple bottom line” paradigm. We therefore used the questionnaire as a form of extended pilot, during which this conception was further developed, and in turn led to a reformulation of the questionnaire itself, which we present below.

Overall, results suggest that the questionnaire provides a useful and general instrument for measuring community attitudes towards what is perceived by community members to constitute ‘sustainability’. Administered over a broad range of communities—urban and rural, high and low-income, and those dealing with the aftermath of environmental (Sri Lanka), political (Timor Leste) and economic (Melbourne) upheaval—post facto regressions and component analyses demonstrate moderately coherent patterns. Such patterns are commensurate with what the theoretical model suggests is the case: that ‘sustainability’ combines absolute and relative subjective interpretations of prospects for individual and collective wellbeing, now and into the future, and requires input both by community members and authorities. The strong correlation result between our proxy HDI variable and actual HDI figures suggests both reasonable construct validity and high convergence between the subjective impressions and objective assessments among the communities sampled. This is particularly striking due to two potentially confounding elements during the periods examined. The Melbourne survey was conducted in 2009, at the height of concerns over the Global Financial Crisis, while the Sri Lanka survey was conducted relatively soon after, and in areas directly affected by, the tsunami in 2004. The survey results also confirmed data obtained from observations and interviews. Studies of affected communities in post-tsunami in Sri Lanka by Mulligan and Shaw (2007), for example, outline both the immense development challenges facing communities, and their resilient attitudes in response.

While the aims of the survey were exploratory—and emphatically not intended to introduce ranking considerations—the correlation, regression and ANOVA tests also do demonstrate significant relationships and high degrees of deviance between the various communities who have participated. The key finding from the exploration appears to be the inverse relationship between levels of political engagement and satisfaction and all other subjective indicators—economic, ecological and cultural. Results for Australian and Israeli communities, in particular, demonstrate that those with high levels of general satisfaction, education and material contentment tend to be more sceptical and pessimistic with regard to their involvement in structures of power. This clearly needs more robust study but points to a potential series of hypotheses to be tested in future rounds of the survey, and possibly calls for more robust theorization of the links between education, material contentment and ideals of political wellbeing.

From the point of view of establishing the theoretical model, of greatest interest in the results was the strong relationship between the first three factors of the factor analysis, and the four domains—economic, ecological, political and cultural—articulated in the theoretical model. This suggests that the survey instrument successfully measures community values are oriented towards different domains of ‘sustainability’: that is, sustainability is a social problem that encompasses economic, ecological, political and cultural relations, relations that are both reproduced in social structures but also open to pressure to change from social agents or actors.

This said, several confounds should be noted. Firstly, these factors all only account for 47.2% of the total variation—leaving a large amount of attitudinal variance still unexplained by the four-domain model. Secondly, the limitations around the survey design and administration discussed earlier suggest higher levels of significance testing are needed at the very least before results can be inferred to the broader community populations. Thirdly, both economic and ecological constructs were coalesced in the primary factor identified. Given a key claim of the four-domain model is that each of the domains is at least potentially in conflict with others, the moderate sample size and range of communities ought to bring out greater variation between constructs measuring each domain. Of course both the domain-construct relationship and the factor analysis have been conducted ex post; an important feature to exhibit in results of follow-up surveys would be a stronger correlation between ex ante and ex post alignment of variables to co-ordinating factors. Nevertheless, the coalescence of principal components with the independently derived domains suggests these remain a sound basis for the construction of future iterations of any resulting indicator set.

Measuring Social Sustainability, Version 2.0

As mentioned above, a range of difficulties were encountered with the data and their analysis. In part this is due to the initial aims of the questionnaire, which were to illustrate local areas of community concern, rather than to coerce commensurability across all applications of the questionnaire. Other issues relate to specific applications of the questionnaire—reliable translations of key constructs, inconsistent coding and varied sampling strategies employed—and these clearly limit the inferential power of the results. More generally, a more systematic organization of items and scales would improve the reliability and validity of results from future applications of the instrument.

To address these goals, we conducted a number of workshops in 2010 and 2011. A revised set of indicators/questions was drafted, with an associated set of ‘reference’ questions and responses. These retained consonance with the existing survey yet sought to address the identified limitations. Version 2.0 now measures sustainability explicitly against the four domains and their subdomains, which only formed the background to the original survey. More explicitly, community sustainability is assessed with reference to the following:
  • Economic prosperity—the extent to which the community can engage in activities relevant to their economic wellbeing and feel confident about the consequence of changing structures beyond their locale.

  • Ecological resilience—the extent to respondents perceive the ‘rates’ at which the surrounding natural environment can withstand and recover from the community’s actions.

  • Political engagement—the extent to which members of the community can participate and collaborate in structures and processes of power that affect them.

  • Cultural vitality—the extent to which the community is able to maintain and develop its beliefs, celebrate its practices and rituals, and cultivate narratives of meaning that define the community.

In total, the revised structure of 48 variables more closely measures community sustainability against our own Circles of Sustainability theoretical model. We have included eight items for each of the four domains, along with 10 demographic and six wellbeing items (the latter are again sourced from the Australian Unity Wellbeing Index). The domain items are further divided into subscales for sense of trust, concern and optimism about the future. We have also mapped the questions in the survey in relation to the Human Development (Creating Capabilities) approach (Nussbaum 2011), to provide more structured concordance with an existing widespread measure of sustainability. We plan to conduct further pilot studies of the instrument in 2012.

Conclusion

The Community Sustainability Survey has been applied to approximately 3,300 members of various communities in the Middle East, South and Southeast Asia between 2006 and 2010. Our results showed several interesting patterns: members of communities in countries with above average HDI scores (‘Australian towns’, ‘Be’er Sheva’, ‘Melbourne’) scored higher on all but one of our composite attitudinal scales (attitudes towards economy, ecology, culture, self-assessment and HDI proxy). The exception, Attitudes towards Politics, tentatively corroborates other findings confirming widespread disaffection with politics in economically advanced liberal democracies, such as those observed over several decades by Inglehart (1977, 1990, 1997). The relative stable political environments in these countries further suggests discrepancies between subjective assessments and objective conditions regarding specifically political sustainability. As we acknowledge though, this result may be the product of confounding variables and construct validity. Political scepticism can equally be taken as an indicator of a robust political environment rather than its converse, the failure of political processes. We also found a pleasing degree of correlation between our own HDI proxy variable and published UNDP HDI values, and anecdotal confirmation with qualitative research conducted at the same community sites.

In terms of the first of four dilemmas we introduced at the outset of this article, we note this instrument will sit alongside others piloted under the same project rubric, thus complementing the standardized, “top–down” indicators of sustainability outlined here with locally developed, “bottom–up” and issue-based indicators. In terms of the remaining three of the dilemmas, the results of the questionnaire provide a useful context for examining the relationship between alternative subjective, intersubjective and objective modes of measurement; between individuals and community; and between present wellbeing and future sustainability. The reformulation of variables will, we expect, allow us to better gauge these dimensions, and so provide a more robust instrument for understanding and assessing sustainability from a community’s own point of view.

Though initially intended as an augmented instrumental probe into qualitative modes of community engagement, the process of consolidating, cleaning and analysing the results of the survey suggests that the instrument has a potentially broader role to play as a tool for assessing a community’s own attitudes towards sustainability. Further work is required to formulate and pilot the revised survey. However, as the exploratory analysis shows, useful results have been extracted from the existing data set, including the inverse relations between political and other domain indicators, and a potential scoring mechanism for ranking communities’ self-assessments. We suggest that it may fill a gap between the current group of objective, techno-scientific indices, and subjective, psychometrically-oriented well-being and quality of life measures, focussing on sustainability as an intersubjective and future-oriented process between community members.

Acknowledgements

The people who have contributed to the development of this questionnaire are too numerous to list, but to give a sense of the reach of our indebtedness to others we list the researchers who were involved in the Papua New Guinea project: Albert Age, Sama Arua, Kelly Donati, Jean Eparo, Beno Erepan, Julie Foster-Smith, Betty Gali-Malpo, Andrew Kedu, Max Kep, Leo Kulumbu, Karen Malone, Ronnie Mamia, Lita Mugugia, Martin Mulligan, Yaso Nadarajah, Gibson Oeka, Jalal Paraha, Peter Phipps, Leonie Rakanangu, Isabel Salatiel, Chris Scanlon, Victoria Stead, Pou Toivita, Kema Vegala, Naup Waup, Mollie Willie, and Joe Yomba. In addition, given the issue that the PNG project involved many languages across 50 villages in five provinces, we need to thank in particular, Gerard Arua, Vanapa, Central Province; Monica Arua, Yule Island, Central Province; Viki Avei, Boera, Central Province; Sunema Bagita, Provisional Community Development Advisor, Milne Bay Province; Mago Doelegu, Alotau, Milne Bay Province; Clement Dogale, Vanagi, Central Province; Jerry Gomuma, Alepa, Central Province; Alfred Kaket, Simbukanam/Tokain, Madang Province; Yat Paol from the Bismark Ramu Group, Madang Province; Joseph Pulayasi, Omarakana, Milne Bay Province; Bing Sawanga, Yalu, Morobe Province; Alexia Tokau, Kananam, Madang Province; and Naup Waup, Wisini Village, Morobe Province. They became our formal research leaders in their respective locales and guides to language nuances.

Parts of this research were supported under Australian Research Council’s Linkage Projects funding scheme, and for that we thank the ARC.

We also gratefully acknowledge the comments and suggestions of three anonymous reviewers in the preparation of this article.

Copyright information

© Springer Science+Business Media B.V./The International Society for Quality-of-Life Studies (ISQOLS) 2012

Authors and Affiliations

  1. 1.School of Global Studies, Social Science and PlanningRMIT UniversityMelbourneAustralia
  2. 2.RMIT UniversityCarltonAustralia

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