The Citizen Satisfaction Index (CSI): Evidence for a four basic factor model in a German sample

Cities 31 (2013) 156–164

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The Citizen Satisfaction Index (CSI): Evidence for a four basic factor model in a German sample Sebastian Zenker a,⇑, Sibylle Petersen b, Andreas Aholt c a b c

Erasmus University Rotterdam, Erasmus School of Economics, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands University of Leuven, Department of Psychology, Tiensestraat 102, BE-3000 Leuven, Belgium Department of Economic Promotion, City of Hamburg, Wentorfer Str. 38a, 21029 Hamburg, Germany

a r t i c l e

i n f o

Article history: Received 10 August 2011 Received in revised form 25 January 2012 Accepted 26 February 2012 Available online 21 March 2012 Keywords: Cities Citizen satisfaction Scale development Place marketing

a b s t r a c t Where is the best place to live? The answer depends on how we ask the question and which scale we apply. Our study offers two contributions to the increasing comparability of research on citizen satisfaction: First, it combines together 18 different scales with items derived from qualitative research and then reduces those items to a set of 21 questions that we label Citizen Satisfaction Index (CSI). Second, we replicate four distinct dimensions of citizen satisfaction in two studies that employ different methodological approaches (explorative & confirmatory factor analysis, multidimensional scaling): Urbanity & diversity, nature & recreation, job opportunities, and cost-efficiency. These four dimensions establish a conceptual framework of relevant factors that may prove useful in comparative research on citizen satisfaction. Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved.

Introduction: place branding and citizens’ satisfaction In recent years, the branding of places (and cities in particular) has gained popularity among city officials (Anholt, 2010). As a result, place marketers devote a greater amount of focus to establishing the city as a brand (Braun, 2008) in an effort to promote their city to its target groups. In the past, external audiences ensnared the focus of place branding, but there is currently an effort to also encompass residents (Insch & Florek, 2008; Zenker, 2009), since they simultaneously fulfil different roles in place marketing. First, and most obviously, they are an important target group of ‘place customers.’ Less considered, perhaps, is that they shape the place brand with their characteristics and behavior (Freire, 2009; Kavaratzis, 2008). Furthermore, they serve as ambassadors for their place brand and grant credibility to any communicated message (Trueman, Klemm, & Giroud, 2004). Finally, they are citizens – in terms of an active part of the place – who are vital for the political legitimisation of place branding. Citizens1 are not just passive beneficiaries or place customers, but active partners and co-producers of public goods, services and policies. Thus, place marketers should foremost focus on establishing the city as a good place to live for citizens, rather than just a nice place to visit for travellers. This makes citizen satisfaction one of the most important outcomes of place

⇑ Corresponding author. Tel.: +31 10 4082740; fax: +31 10 4089153. E-mail addresses: [email protected] (S. Zenker), [email protected] euven.be (S. Petersen), [email protected] (A. Aholt). 1 It could be argued that this definition of citizens is too narrow. The authors want to state that this is only one way of defining citizens; however, from our point of view, it is a good fit for the concept of place marketing and branding.

management (Insch, 2010; Insch & Florek, 2008, 2010), as well as for place marketing and branding (Zenker & Martin, 2011). Place branding is a tool from the general field of marketing. It aims to shape customers’ mental brand representations and evaluations (Keller, 1993). Place marketing, by contrast, could be described as ‘‘the coordinated use of marketing tools supported by a shared customer-oriented philosophy, for creating, communicating, delivering, and exchanging urban offerings that have value for the city’s customers and the city’s community at large’’ (Braun, 2008, p. 43). Place marketing aims ‘‘to maximize the efficient social and economic functioning of the area concerned, in accordance with whatever wider goals have been established’’ (Ashworth & Voogd, 1990, p. 41). These definitions highlight one very important point: Even though place marketing features an economic intention, the increasing of social functions – like citizen satisfaction – also forms a major goal. In practice, as well as in theory, the definitions and concepts of place marketing often lack a proper definition and a consistent usage. As a result, place marketing is often mistaken as place selling (see for a discussion: Berglund & Olsson, 2010; Kavaratzis & Ashworth, 2005), focusing solely on the promotional aspects of marketing while disregarding the central aim and broader range of place marketing and branding – namely, to satisfy the consumer’s needs and wants (demand orientation). Place selling, on the other hand, describes a process that tries to find the right consumers for an existing product (supply orientation). While the two may work in conjunction, they cannot be used interchangeably. According to Kotler and colleagues, one of the major aims for place marketing is to ‘‘promote a place’s values and image so that potential users are fully aware of its distinctive advantages’’ (Kotler,

0264-2751/$ - see front matter Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.cities.2012.02.006

S. Zenker et al. / Cities 31 (2013) 156–164

Haider, & Rein, 1993, p. 18). This search for distinctive advantages (in terms of location factors) is another reason why satisfaction with a place of living becomes so important: It is a way to benchmark cities (Luque-Martinez & Munoz-Leiva, 2005), to make them comparable and measurable. But where is the best place to live? What makes a place a good place for living? What makes citizens happy? Those answers necessarily depend on the scale one chooses to apply. Here we are faced with a growing amount of empirical data (as reviewed below) that requires us to ask a few central questions: How can we condense this complex mass of data to a manageable item pool for research? What are the basic factors underlying how people distinguish cities from each other? How do these basic factors influence satisfaction with the place and the feeling of bond with a place? These are the questions we address in this paper. How to ‘measure’ a city? The existing place branding literature gives us three main directions for the question of how to measure city perception (Zenker, 2011): (1) in the form of place (brand) associations of target customers with qualitative methods, such as focus group interviews (e.g., Lodge, 2002; Morgan, Pritchard, & Piggott, 2002); (2) place attributes with quantitative methods like standardized questionnaires on different location factors (e.g., Grabow, 2005; Merrilees, Miller, & Herington, 2009); and (3) with mixed methods such as multidimensional scaling (MDS; e.g., Carrol & Green, 1997), network analyses (e.g., Henderson, Iacobucci, & Calder, 2002), the brand concept map method (John, Loken, Kim, & Monga, 2006) or the laddering technique based on means-end chain theory (e.g., Grunert & Grunert, 1995). While qualitative methods have the advantage of open questions and therefore offer the option to explore unique associations with a city, it is nearly impossible to compare two different cities with this data. The focus of this paper is on the comparison of cities, which cannot be achieved using unique associations. However, measuring the perception of a city with the help of a standardized questionnaire leads to other problems, such as the fact that results are strongly affected through the selection of attributes. As Grabow, Henckel, and Hollbach-Grömig (1995) indicated, the amount of items analyzed in a questionnaire strongly affects which items have a significant influence on the dependent variables. The current paper concentrates on building a manageable item pool for further research and tries to investigate, using a mixed approach, which location factors are basic factors for measuring city perception. As Kruskal and Wish (1994) discussed in their work on multidimensional scaling, it is neither possible nor desirable to build a model that reflects the ‘true’ dimensionality of a construct. Rather, it should be the goal of research to find fruitful and efficient models. We want to construct and test a usable model on how people evaluate their city and distinguish it from other cities. To build this model on an empirical basis, the two essential questions are: What to ask, and what to predict? We addressed these two questions in Study 1. In Study 2, we used an attribute-free approach with multidimensional scaling analysis to identify the latent dimensions of citizen satisfaction. Additionally, we embedded rankings of cities utilizing the factors identified in Study 1 via Preference Mapping. We used this different methodology in an independent sample to validate the findings of Study 1. Study 1 Item selection: what to ask? Different disciplines apply different foci on cities. Urban planners and architects often take a highly physical approach when

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describing the image of a city: the architecture, the housing market, the general degree of urban development or the landscape and natural environment are at the center of research (Jensen, 2007; Lynch, 1960). Furthermore, urban planners, as well as sociologists, emphasize diversity, tolerance and the openness of a city to different cultures and subcultures (Hoffman, 2003; Lynch, 1960; Tan, 2003; Trueman, Cook, & Cornelius, 2008). Other disciplines focus on the general economic growth of a region, the average costs of living, professional networks, or job opportunities at a specific place (Florida, 2004; Grabow et al., 1995; Hospers, 2003; Stolarick, 2005). From a tourism perspective, issues like cultural activities, entertainment and nightlife, the atmosphere of a city, degree of pollution, shopping opportunities and a wide range of outdoor events are valuable considerations for a city (Baker & Cameron, 2008; Evans, 2003; Lodge, 2002; Morgan et al., 2002). All together, the above could be described as a breadth of location factors. To condense this vast amount of different location factors that could be influencing citizen satisfaction, we collected all available place benchmark studies and selected those which fully described their measured location factors (similar to the procedure of Grabow et al., 1995). By merging the items of the 18 place surveys to which we had access, we excluded semantically redundant items and finally compiled a pool of 38 items for our first investigation. Before going into the field, we conducted interviews with 20 city-experts from different disciplines (five persons each working in the fields of science, business, politics and arts; all located in the city of Hamburg). In semi-structured and in-depth interviews, we asked which location factors were the most important for their satisfaction with a place. All aspects from our item pool were mentioned by each of the participants. Each expert additionally mentioned issues that applied to the City of Hamburg’s unique situation (e.g., being not only a city but also a federal state), or that could be integrated into existing categories (e.g., a variety of musicals that could also be seen as a variety of cultural events), so we did not need to change our item pool substantially. For the analysis of the underlying structure of these factors, we used explorative factor analysis in a first step, akin to Grabow et al. (1995). In a second step, we tested whether these factors could act as independent, explanatory variables in a structural equation model.

Dependent variables: what to predict? As Ashworth and Voogd (1990) suggested, the aim of place marketing is to maximize (among other variables) the social functioning of an area. However, how can we evaluate whether this goal was met? In the marketing disciplines, loyalty or commitment is a frequently used construct in different customer indices, including the American Customer Satisfaction Index ACSI (Fornell, Johnson, Anderson, Cha, & Bryant, 1996), the European Customer Satisfaction Index ECSI (Cassel & Ekloef, 2001), and the Swiss Index of Customer Satisfaction SWICS (Bruhn & Grund, 2000). Additionally, researchers of the organizational context commonly measure commitment as a consequence of satisfaction (e.g., Meyer, Allen, & Smith, 1993). To a certain extent, commitment also describes the attachment to a place, which is an important variable in the field of environmental psychology (Florek, 2011; Insch & Florek, 2008). It could also be seen as an indicator of positive citizenship behavior, since it measures the individual or group’s level of ‘‘psychological ownership’’ (Pierce, Kostova, & Dirks, 2003) for a place. From these diverse constructs (satisfaction, commitment, loyalty, attachment, etc.), we chose satisfaction and commitment to test our model, noting that commitment is a variable (including parts of loyalty and attachment) that results from satisfaction. Therefore we model commitment to a city (e.g., ‘‘feeling home and bound to a place’’) as a consequence of satisfaction with the city (e.g., ‘‘all to-

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gether I am satisfied with the city I live in’’), in line with consequences of other models on satisfaction (e.g., Insch & Florek, 2008). Data collection Procedure We conducted our study in Germany via online surveys in cooperation with different German media partners and via different Web forums. We cautiously arranged the surveys according to recommendations given in the field (Birnbaum, 2004; Kraut et al., 2004). For one, the surveys were server-side programmed (EFS Survey, Unipark) so that anyone with Internet access was able to participate, thus avoiding common technical selection biases which tend to exclude people who do not meet special browser recommendations. Moreover, we assigned each participant a session ID (‘cookie’) to keep the users from participating in the same survey again. Finally, we performed pre-tests of the survey on different browsers and different screen resolutions to ensure that the survey would look and behave the same way on all systems (Reips, 2002). To increase response rate, we included motivators such as feedback of the results and a lottery for Internet gift vouchers. At the end of the questionnaire participants were able to decide if they wanted to submit their data for scientific analysis or if they preferred to be deleted from the data pool. With all measures taken to prevent common shortcomings of online research (Tourangeau, 2004), we are confident that our data is of high quality. Sample Excluding participants who did not give final consent, 611 participants (54.7% women) completed the survey and met the technical requirements introduced in the above procedure. Mean age was 36.3 years (SD = 11.9), 30.6% of the sample had children and 30.9% were married. Furthermore, 6.1% had a so-called migration background and 43.2% of the participants had a university degree. Thus, the sample could not be seen as fully representative of the whole German population (which is generally older and less educated). Measures Our item pool was presented twice in the survey: First with the question of ‘‘how satisfied are you with the following city attribute in the city you live in’’ and ‘‘how important is the following city attribute for your place satisfaction and your choice of a place to

live at.’’ Items were measured with a Likert scale ranging from 1 (‘‘not at all’’) to 5 (‘‘fully’’). All questions were randomized across participants to avoid item context effects. For the measurement of the dependent variable ‘‘satisfaction,’’ we used the modified three-item measure on overall job satisfaction (Fields, 2002) combined with the one-item Kunin-Faces measure (Kunin, 1955). Both methods have been shown to be valid measures of satisfaction. We also created a Place Commitment Scale by modifying the German scale for job commitment from Allen and Meyer (Schmidt, 1998). These items (except the Kunin-Faces) were randomized and measured with a Likert scale ranging from 1 (‘‘I fully disagree’’) to 5 (‘‘I fully agree’’). At the end we asked about demographic attributes like age, family status, profession, and educational background.

Results We used the correlation matrix of all items to check for those that did not differ in the semantic understanding by participants. Three pairs of items had a significant high correlation (see below) and the contents of these item pairs were very similar, so we decided in all three cases to merge the pairs into one item each (by combining the means of both items). We combined the items ‘‘desirability of a city’’ and ‘‘vibe and energy of a city’’ from the original scales, which showed a correlation of r = .74 (p < .001), and formed the new item ‘‘desirability, vibe and energy of a city.’’ We did the same with the original items ‘‘a wide variety of high culture events’’ and ‘‘a wide variety of street culture events’’ (r = .75; p < .001), which we fused into the item ‘‘a wide variety of cultural events.’’ Furthermore, we combined the items ‘‘job opportunities’’ and ‘‘chance of job promotion’’ (r = .80; p < .001) to create ‘‘job and promotion opportunities.’’ Next, we performed an explorative factor analysis for the remaining 35 items in the pool. Similar to previous researchers (Grabow et al., 1995), we found no clear factor structure. We expected this problem in advance, because our list did not distinguish ex ante between items that had either a high or low relevance for the participants. In order to differentiate between major and minor aspects, we integrated an importance rating into the questionnaire. These ratings enabled us to disregard minor facets by slowly reducing the items one by one according to the importance ranking; eventually we arrived at a four-factor structure as we intended

Table 1 Explorative factor analyses – principal component analysis with varimax-rotation (Study 1).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Item

Urbanity and diversity

A wide range of cultural activities (theatre, etc.) A variety of shopping opportunities Many different cultures and subcultures The energy and atmosphere of the city Availability of different services The urban image of the city Openness and tolerance of the city A lot of nature and public green area Environmental quality (low pollution) A number of parks and open spaces A wide range of outdoor-activities Tranquillity of the place Cleanness of the city Access to water front The general level of wages Good job and promotion opportunities General economic growth of the particular region Professional networks in the city Housing market/cost of hiring The general price level in the city/costs of living Availability of apartments and houses

.85 .74 .73 .70 .59 .53 .53

Note: N = 611; only factor loads of .5 or more are shown.

Nature and recreation

Job opportunities

Cost-efficiency

.83 .74 .74 .64 .63 .55 .50 .78 .75 .64 .54 .90 .83 .82

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and commitment model with the help of the confirmatory factor analysis. Fig. 1 shows the results of our four basic factors model. For this model the common quality criteria all fell in an acceptable range: a Good Fit Index (GFI) of 0.91, greater than the benchmark of 0.90 (Hu & Bentler, 1999); an CMIN/DF (the minimum sample discrepancy divided by degrees of freedom) of 3.38 less than the benchmark of 5 (Arbuckle & Wothke, 1999); and an Root Mean Squared Error of Approximation (RMSEA) of 0.06 appropriately less than the benchmark of 0.08 (Browne & Cudeck, 1993). Overall the model fit is good. Testing the model with a multiple regression analysis, an Adjusted R-Square of 0.50 indicates that the model explains 50% of the variance in the citizens’ overall satisfaction. A variance of 50% is an acceptable result for a construct as complex as a city. To find further support for this four-dimensional factor structure, we had to use another method and a different sample.

for reasons of interpretability and manageability. The complete list of items ranked by importance is shown in Appendix A. With the help of the importance ranking, we reduced the item pool to 21 distinct items (Appendix B). The factor structure and factor loading are shown in Table 1. The means, standard division, Cronbach alpha and correlation between the four factors are shown in Table 2. The Cronbach alpha (i.e., the internal consistency) for all four factors was high (all alpha > .74). We labeled our first factor urbanity & diversity because on the one hand, it contains items related to the size and range of the services offered by a city to its customers (e.g., the urban image of a city, a wide range of shopping or cultural activities); and on the other hand, this factor comprised a couple of items, like dealing with multiple cultures, the general atmosphere, or the openness and tolerance of a city. Our second factor could be described as nature & recreation, as it contains environmental items like low pollution, access to rivers or watersides, the tranquillity of a place, parks and open spaces, and other recreation areas involving outdoor activities. The third factor was named job opportunities, due to the fact that all four items deal with the professional aspects of a city. The last factor could be described as a cost-efficiency factor, encompassing the items related to general price level and cost of living, rental costs, and the availability of apartments and houses. Our next step was to prove the findings of our place satisfaction

Study 2 Method Procedure For the second study we chose ordinal multidimensional scaling analysis (MDS) as our methodological approach. MDS differs from

Table 2 Spearman correlation matrix and Cronbach’s a for all factors (Study 1). Factors

Urban

Nature

Job

Cost

Urbanity and diversity Nature and recreation Job opportunities Cost-efficiency Overall satisfaction Commitment

(.88)

.60*** (.83)

.64*** .36*** (.81)

.13** .27*** .04 (.83)

Note: N = 611; Cronbach’s a shown in brackets. ** p < .01. *** p < .001.

Fig. 1. The CSI model (Study 1).

Satisfaction .68** .58*** .45*** .15*** (.89)

Commitment .33*** .28*** .24*** .04 .51*** (.74)

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factor analysis in its ability to explore underlying dimensions and exposes overlaps between objects, cities in this case, by accounting for their mutual similarities. MDS offers the advantage of an attribute-free approach: instead of selecting items or attributes for the participants, as in factor analysis, participants are asked to make global similarity judgments between pairs of objects. Thus, this approach identifies latent dimensions underlying the mental representation of the objects in questions without being biased by the choice the researcher makes when selecting attributes. Rather than a statistical method, MDS has been called a theory about mental representation, or a theory about perception (Torgerson, 1958). All participants completed a two-step survey: (1) attribute-free ratings of mutual similarities between cities and (2) ranking of cities based upon the 21 items identified in Study 1, reflecting the factors of citizen satisfaction: urbanity & diversity, nature & recreation, job opportunities and cost-efficiency. These rankings gave us the opportunity to explore the relevance of the four CSI factors for the MDS dimensions using External Preference Mapping (Chang & Carroll, 1989). In its simplest form presented here, External Preference Mapping is a multiple linear regression analysis that uses latent dimensions identified by MDS as predictors of evaluative rankings of the objects included within the MDS analysis. In the present study, we used MDS dimensions as predictors of the four CSI factors. We embedded four aggregated regression lines (or vectors) within the MDS maps (one for each CSI factor), using ranking values averaged across all participants. As in Study 1, we collected our sample via online surveys in cooperation with Web forums and with the same necessary diligence. Because people in this method had to compare real German cities, we additionally restricted the number of participants to ten people from each of the sixteen German federal states, to avoid a potential bias in localizations of citizens in cities or regions. Sample One hundred and sixty participants (10 per federal state) completed the survey. The sample’s mean age was 33 years (SD = 8.8) and women made up 51.9% of the sample. 26.3% of the sample had children and 21.3% were married. The percentage of participants with a so-called migration background was 3.2% and 57.5% had a university degree. Thus, like in Study 1, the sample population had a higher level of education and was younger than the general German population. Measures The participants were asked to make mutual comparisons between the ten largest German cities (the names of the cities can be found in Fig. 2), resulting in 45 paired comparisons for a Likert scale ranging from 1 (‘‘not similar at all’’) to 7 (‘‘fully similar’’). For the ranking of cities related to the four CSI factors, we used the item pool (21 items) developed in Study 1 and a Likert scale ranging from 1 (‘‘not at all’’) to 5 (‘‘fully’’). All items were randomized to

Table 4 Model fit for regression analysis (Study 2). df

R2

F-ratio

P

Dimensions 1 and 2 Urbanity and diversity Job opportunities

2 2

.90 .68

31.87 7.50

.000 .018

Dimensions 3 and 4 Cost efficiency Nature and recreation

2 2

.767 .66

11.51 6.69

.006 .024

avoid item context effects. These rankings were used to calculate a mean rank list of cities per CSI factor. Results Using Kruskal’s Stress as an index of fit (Kruskal et al., 1994), we found that a four-dimensional model showed the best fit to our data (Kruskal’s stress = .01). Decrease in stress compared to a three-dimensional solution (Kruskal’s stress = .05) was substantial. Table 3 shows the correlation matrix for the aggregated data. Fig. 2 shows the four basic factors as vectors within the four dimensions of the city perception identified with MDS. Results obtained with External Preference Mapping show that Dimension 3 and Dimension 4 can be interpreted as cost-efficiency and nature & recreation. The vectors for urbanity & diversity and job opportunities could not be interpreted as dimensional axes. However, the correlation between the rankings regarding these factors was only marginally significant (p = .06). Thus, the results confirmed our model of four basic factors of citizen satisfaction, although the two factors urbanity & diversity and job opportunities cannot be considered as completely independent. For both configurations, a good model fit for the regression analysis was obtained (Table 4). Discussion Simple enough or too simple? The general problem with implementing a scale involves choosing a level of complexity. Did we use enough different facets to explain citizens’ satisfaction? Is the item pool still manageable and useful for research? Is a four factor model the best solution? Unfortunately, there is no agreement in the literature about the ‘‘right’’ number of factors. Also, our research indicates that there should be more factors, since our four factors explain only half of the variance. There are personal factors which local authorities, urban planners or place marketers cannot influence, such as private and social networks like families and friends. Our study excluded other factors such as regional and international transport connections because they were not mentioned in our expert interviews prior

Table 3 Spearman correlation matrix for dimensions and factors (Study 2). Factors

Dim1

Dim2

Dim3

Dim4

Urban

Cost

Dimension 1 Dimension 2 Dimension 3 Dimension 4 Urbanity and diversity Cost-efficiency Job opportunities Nature and recreation

–

.48 –

.47 .31

.31 .43 .32

.87** .75* .49 .27 –

.07 .53 .15 .73* .22 –

Note: N = 160. * p < .05. ** p < .01.

–

Job .49 .72* .52 .77** .62 .87** –

Nature .72* .30 .87** .35 .61 .22 .60 –

S. Zenker et al. / Cities 31 (2013) 156–164

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Fig. 2. The four basic factors within the four dimensions of the city perception.

to Study 1, although those factors fall in the decision maker’s domain of responsibility. However, compared to other researchers in related fields – for example, Merrilees et al. (2009) who explained 51% of city brand attitude with ten different factors – measuring 50% variance with a 21-item scale using only four factors is a clear improvement. Empirically, both of our studies indicate that, when using the suggested item pool, a four factor solution is the best model. Developing a scale is always a trade-off between complexity and usability. While models for complex constructs such as cities can never be truly accurate, they need to be fruitful for research and theory development. The CSI shows a good explained variance of citizens’ satisfaction and still has a manageable amount of items and factors. Because the results of Study 2 derived from a completely different and attribute-free approach with an independent sample, we have faith in our data as well as in our model of four basic factors.

ing budget of 5 million Euros per annum (Jacobsen, 2009). Unfortunately, a proper success measurement in place marketing practice remains missing, thus raising questions regarding the efficient and effective use of the taxpayers’ money (Jacobsen, 2009). As Zenker and Martin (2011) state, citizen satisfaction could be one suitable measure of success that gives place marketers a practical solution for assessing and evaluating the impact of their work. Furthermore, the Citizen Satisfaction Index (CSI) could be used as a benchmark for cities to fulfil their main duty – improving the city for its citizens. Thus, it could not only be seen as a success measurement for place marketing activities, but also for the whole place development. Implementing the CSI for different cities on a regular basis could, ideally speaking, help to identify changes and problems in place development from a customer’s (citizen’s) point of view. Implication for place marketing research

The four basic factors Our first factor urbanity & diversity had the strongest impact on citizens’ satisfaction and represents a kind of metropolitan character. It seems that the majority of participants wanted to live in big cities with a wide range of opportunities, cultural events or shopping activities, but they also favoured a place that is tolerant and open to many different cultures and subcultures. Contrary to that, participants also expressed a desire for nature & recreation: low pollution, parks and open spaces, and the tranquillity of a place. In his book ‘‘The Image of a City,’’ Lynch (1960) described the conflict between those two human desires in our urban era. Even though job opportunities and the cost-efficiency had no direct (i.e., significant) influence on the citizens’ overall satisfaction, both factors are important because of their influence on the perception of the two other factors, as can be seen in the confirmatory factor analysis (Fig. 1). Additionally, they could wield strong influence on related questions – for example, people’s decision when moving to another place (see Zenker & Gollan, 2010). Implication for place marketing practice Cities invest a considerable amount of taxpayers’ money into their marketing activities: Berlin, for example, maintains a market-

Even though citizen (or place) satisfaction is widely discussed in practice and in theory, empirical research is scarce. This paper provides both a conceptual framework and a freely accessible and validated scale. The model summarizes factors that contribute to citizens’ satisfaction with their cities (and reasons for dissatisfaction). The four-dimensional structure gives researchers and practitioners clear guidelines, outlining what dimensions their own models should reflect. Furthermore, we provide a scale that combines items from different backgrounds (e.g., practical place marketing and scientific concepts) and disciplines (e.g., economics, urban planning, psychology, and sociology), and disregards items to which participants ascribed a low importance. This reduced scale of 21 items can be used to compare cities in a systematic way. Limitations and directions for future research Even though the aim of the research was to create an effective and manageable model, it excludes other possible influence factors on citizens’ satisfaction, such as private reasons for satisfaction. These factors were excluded because personal factors (such as family bonds, relationships and other private social network factors) are not accessible to urban planners, even though they might also be important factors when deciding on a preferred city. Thus, the

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CSI focuses on factors that are accessible to urban planning and place marketing. Secondly, one should take into account that the Citizen Satisfaction Index (CSI) only measures the individual citizen’s perception of location factor rather than city characteristics, such as the real rate of unemployment, which are commonly used in city rankings. Given this important difference, the results from a CSI do not necessarily indicate that a physical characteristic needs alteration, but rather that one has to work against prejudices and stereotypes about a city. The third limitation is the focus on a German sample. The cultural background of participants could influence the importance of different aspects of the city perception. Variations within the German culture with regard to the factors tested in this study might be higher than variations between West European cultures. However, even with this narrow cultural focus, we believe the paper is of strong value for place marketing practice and urban research in providing a conceptual framework and an efficient measurement instrument. We suggest that future research should explore three different directions: firstly, the validation of the Citizen Satisfaction Index (CSI) for different cultural samples; secondly, longitudinal studies for further validation of the CSI over time; and finally, obtaining a deeper understanding of the importance of different factors, especially for job opportunities and cost-efficiency factors, on citizens’ satisfaction and other dependant variables like personal mobility and willingness for migration. Ideally, this conceptual framework, along with the freely accessible and validated scale for factors underlying citizens’ satisfaction and commitment to a city, will be of great use for future urban research and place marketing by increasing the comparability of otherwise idiosyncratic results in our field.

Appendix A (continued) Item

Mean

SD

General economic growth of the particular region City size (number of citizens) Local public transit Universities and offerings for extension studies Crime rate Climate and weather of the region Medical services Percentage of singles Costs for energy, water, etc. Support and service from the local authorities Schools and children day care services Support for building your own business Local taxes and duties State subsidies (e.g. free children day care) Numbers of celebrities living in the city

2.59

1.29

2.98 2.92 2.89 2.49 2.49 2.48 2.43 2.34 2.33 2.29 2.24 2.04 2.02 1.43

2.01 1.86 1.97 1.89 1.25 1.39 1.40 1.27 1.21 1.94 1.30 1.12 .47 .80

Note: N = 611; from 1 = ‘‘not important at all’’ to 5 = ‘‘very important’’; reasons for exclusion from scale are shown in bold letters.

Appendix B. German and English CSI questionnaire

Factor

German item

Urbanity and diversity

Ein großes kulturelles Angebot (Theater, Clubs, etc.)

Nature and recreation

Viel Natur und Grünflächen

Acknowledgments The authors want to thank the editor Ali Modarres and the two anonymous reviewers for their constructive comments and the effort to enhance our work. Appendix A. Importance rankings for all used items (Study 1) Item

Mean

SD

A wide range of cultural activities (theatre, nightlife, etc.) The urban image of the city The energy and atmosphere of the city A lot of nature and public green area Openness and tolerance of the city Good job and promotion opportunities Access to water A number of parks and open spaces A wide range of outdoor-activities A variety of shopping opportunities Availability of different services Environmental quality (low pollution) Professional networks in the city Many different cultures and subcultures Availability of apartments and houses Housing market/cost of hiring The general price level in the city/costs of living Tranquility of the place Cleanness of the city The general level of wages

3.72

1.10

3.54 3.40 3.31 3.22 3.16 3.00 2.99 2.98 2.97 2.97 2.92 2.90 2.87 2.85 2.84 2.82 2.81 2.76 2.71

1.34 1.04 1.40 1.36 .99 1.40 1.38 1.38 1.37 1.37 1.38 1.34 1.33 1.35 1.40 1.34 1.35 1.30 1.35

English item

A wide range of cultural activities (theatre, nightlife, etc.) Angebot an A variety of Einkaufsmöglichkeiten shopping opportunities Vielfalt an Kulturen Many different und Subkulturen cultures and subcultures Die Atmosphäre und The energy and Energie der Stadt atmosphere of the city Angebot von Availability of Dienstleistungen different services Das urbane Image der The urban image Stadt of the city Offenheit und Toleranz Openness and in der Stadt tolerance of the city

Umweltqualitäten (geringe Schadstoffbelastung) Parks, Wanderwege, Spielplätze und freie Flächen Freizeitgestaltung an der frischen Luft (Outdoor-Aktivitäten) Beschaulichkeit des städtischen Lebens

A lot of nature and public green area Environmental quality (low pollution) A number of parks and open spaces A wide range of outdoor-activities Tranquility of the place

S. Zenker et al. / Cities 31 (2013) 156–164 Appendix B (continued)

Factor

German item

English item

Sauberkeit der Stadt

Cleanness of the city Access to water

Zugang zu Wasser (Flüsse, Seen oder Meer) Job Das allgemeine opportunities Gehaltsniveau Gute Beschäftigungsund Aufstiegschancen

The general level of wages Good job and promotion opportunities Wirtschaftspolitisches General economic Klima, growth of the Wachstumsdynamik particular region Berufliche Netzwerke Professional in der Stadt networks in the city

* **

Cost-efficiency

Kosten für Wohnraum und/oder Bauflächen Allgemeines Preisniveau/ Lebenshaltungskosten Verfügbarkeit von Wohnraum und/oder Bauflächen

Housing market/ cost of hiring The general price level in the city/ costs of living Availability of apartments and houses

Overall satisfaction

Alles in allem bin ich zufrieden mit der Stadt, in der ich lebe Im Allgemeinen mag ich die Stadt nicht, in der ich lebe* Im Allgemeinen lebe ich gerne in dieser Stadt Wie zufrieden sind Sie mit der Stadt, in der Sie leben? **

All together I am satisfied with the city I live in In general I did not like the city I live in* In general I like living in this city How satisfied are you with the city you live in?**

Item is negative coded and has to be recoded before building the factor load. This item is measured with a Kunin faces scale.

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