Capturing the Image of Second-Hand Stores: Investigating the Underlying Image Dimensions

ABSTRACT - Store image is one of the explanatory variables of store patronage. However, store image is not a one-dimensional construct, but consists of several dimensions. These image dimensions may vary for different types of stores due to the variety in products they carry. Since recycle stores are very different from the traditional retail outlets to which most of the studies pertain, the objective of the current study is to investigate the store dimensions that underlie the image of recycle stores. To this end two existing image scales, developed for the traditional retail market, are applied to the specific case of recycle stores. The results of the study indicate that the image of recycle stores can be captured in the same image dimensions as is the case for traditional outlets.

Citation:

Malaika Brengman, Maggie Geuens, and Tine Faseur (2002) ,"Capturing the Image of Second-Hand Stores: Investigating the Underlying Image Dimensions", in AP - Asia Pacific Advances in Consumer Research Volume 5, eds. Ramizwick and Tu Ping, Valdosta, GA : Association for Consumer Research, Pages: 387-393.

Asia Pacific Advances in Consumer Research Volume 5, 2002      Pages 387-393

CAPTURING THE IMAGE OF SECOND-HAND STORES: INVESTIGATING THE UNDERLYING IMAGE DIMENSIONS

Malaika Brengman, Limburg Centre and Ghent University, Belgium

Maggie Geuens, Vlerick Leuven Gent Management School, Ghent University, Belgium

Tine Faseur, Ghent University, Belgium

ABSTRACT -

Store image is one of the explanatory variables of store patronage. However, store image is not a one-dimensional construct, but consists of several dimensions. These image dimensions may vary for different types of stores due to the variety in products they carry. Since recycle stores are very different from the traditional retail outlets to which most of the studies pertain, the objective of the current study is to investigate the store dimensions that underlie the image of recycle stores. To this end two existing image scales, developed for the traditional retail market, are applied to the specific case of recycle stores. The results of the study indicate that the image of recycle stores can be captured in the same image dimensions as is the case for traditional outlets.

INTRODUCTION

Recently, the second order retail market has gained increasing attention. This trend can be explained by several factors such as the shortening o product life cycles and the growing awareness of environmental pollution. Notwithstanding this augmenting interest in second-hand stores, empirical research on this type of stores is still very scarce. Indeed, most studies are confined to traditional retail stores. Ample research is abound on determinants of store patronage of traditional outlets, but hardly any study on the topic can be found for second-hand stores. Results of studies in the traditional retail market show that image is one of the variables that have a significant impact on retail store patronage (eg. Arnold et al., 1983, Granbois, 1981). In this context, researchers propose that "the closer the store’s image to the consumer’s needs, the more positive the individual’s predispositions toward that store and the greater the probability that the consumer will shop in the store" (Assael, 1987, Monroe and Guiltinan, 1975). However, the question is whether image, and more specifically which aspects of store image, are of any importance in a second-order retail market. The behavior of people purchasing used articles may be guided by completely different motivations than that of traditional retail customers. Only few studies deal with store image, store choice processes and store patronage pertaining to second-order retailing (Riecken et al., 1979; O’Reilly et al., 1984; Darley & Lim, 1993, 1999). Unfortunately, they lead to contradictory results. In a first study, Darley and Lim (1993) find that consumer’s store image has no significant influence on second-hand store patronage, while other explanatory variables such as the general attitude toward the store type and perceived product quality do. Nevertheless, in a later study (1999) the same authors do observe a significant influence of image on second-hand store patronage. One of the reasons for the inconsistency in results may be that the authors used two different, not validated one-dimensional scales, containing only a few items. However, store image is not a one-dimensional construct, but consists of several dimensions. According to Peterson and Kerin (1983), store image dimensions vary across different types of stores, partly because of the different kinds of products carried by the stores. Therefore, it is not inconceivable that image dimensions revealed in the traditional retail market are unstable or unsuited for the second-order market. After all, there may be more difference between a second-order store and a traditional store than between traditional stores carrying different products. The latter shows the necessity for a study to validate image scales and the underlying image dimensions, developed to capture the image of traditional stores, in a second-order market.

Therefore, the objective of the current study is to apply two existing retail image scales to second-order stores and more specifically toward recycle stores. The main goal is to investigate whether similar image dimensions and a similar model fit can be found for recycle stores than is the case for traditional stores. The first scale is the twenty-nine Consumer Retail Store Image Scale (CIRS), proposed by Dickson and Albaum (1977), which consists of six underlying factors: prices; products; store, layout and facilities; service and personnel; promotion and others. The second scale is the 10-item Store Image Scale (SIS) proposed by Manolis, Keep, Joyce and Lambert (1994), which consists of three underlying dimensions: a general attribute dimension, an appearance related dimension and a salesperson-service dimension. Scores on both scales proved to be reliable and valid to measure the image of traditional retail stores (Baerden and Netemeyer, 1999). However, the question is whether the image of recycle stores can be captured in the same dimensions as the ones found for traditional shops.

THE SECOND-ORDER RETAIL MARKET

Recently, the second order retail market has gained increasing attention. In today’s consumption society, people do not bother anymore having broken durables fixed, because this is often too expensive. People are replacing them even before they are worn out, because they are running obsolete or out of fashion. Indeed, competitive innovation is responsible for shortening product life cycles and faster product replacement (Cordero, 1991). In Flanders, Recycle Centres have popped up, providing a solution to potential waste problems of this nature. 'Recycle Centres’ are companies that collect, process (sort, repair, dismantle) and sell waste products, convenient for reuse or recycling respectively (Lenders, 1998). Flemish Recycle Centres are not-for-profit organizations and their primary goals are to provide employment, to provide cheap goods for people in need and to help solve the waste problem. Recycle stores, where the goods collected by recycle centres are sold, resemble ordinary second-hand stores. However, they do not give any compensation for the goods they acquire. Since they provide a free pick-up service, the issue of acquiring enough goods does not pose any problems. Not only people with a low income, also people that want to tackle the environmental pollution are potential clients of recycle stores.

STORE IMAGE

Interest in the concept of store image dates back from 1958, from the moment when Pierre Martineau described the "personality of the retail store". He defined store image as "the way in which the store is defined in the shopper’s mind, partly by an aura of psychological attributes" (p. 47). From that moment onwards, the concept of store image has gained a lot of attention, and several definitions have been developed. A generally accepted definition of store image is an individual’s cognitions and emotions that are inferred from perceptions or memory inputs that are attached to a particular store and which represent what that store signifies to an individual (Baker et al, 1994; Mazursky and Jacoby, 1986).

Most researchers treat store image as a multidimensional concept. Store image is expressed as a function of the salient attributes of the store that are evaluated and weighted against each other. It is generally acknowledged that store image is comprised of functional (physical), as well as emotional attributes. These attributes are organized into perceptual frameworks by shoppers that determine the shopper’s expectations about a retailer’s overall policies and practices (Berman and Evans, 1989).

Over the years different authors have distinguished different store attributes or characteristics that are part of the overall image towards the store. For example, Lindquist (1974) combines items from nineteen studies and comes up with nine different store attributes: merchandise, service, clientele, physical facilities, comfort, promotion, store atmosphere, institutional and post-transaction satisfaction. According to Dickson and Albaum (1977), a consumer’s image of a retail store encompasses attitudes towards retail prices, products, store layout and facilities, service and personnel, promotion and "others". Manolis, et al. (1994) claim that store image consists of the following three dimensions: (a) a general store attributes dimension, (b) an appearance related dimension, and (c) a salesperson/service dimension.

MEASURING STORE IMAGE

Most researchers use semantic differential scales to measure store image. According to Zimmer and Golden (1988) the use of semantic differential scales holds several advantages: 1. The scales are easy to administer, 2. only a minimum amount of literacy is required, 3. responses are easy to code and analyse, 4. scores on the scales are very reliable, and 5. data can be treated as interval scaled. On the other hand, semantic differential scales are also characterised by several disadvantages. Firt of all, they have a structured format wherein unimportant dimensions may be included or important dimensions excluded. Another disadvantage mentioned by Zimmer and Golden is that semantic differentials make no distinction between a neutral rating and a "don’t know" answer. A final drawback is that semantic differential scales are unable to measure global or overall impressions, which is how image is often defined.

In order to solve the problem of what type of scale to use, Chowdhury, Reardon and Srivastava (1998) investigate whether store image is best measured by using a structured or an unstructured scale. Their results point to a very high degree of correspondence between structured store image scales and the variables derived from the coding of unstructured measures. Because of the many disadvantages associated with unstructured scales, the authors advise researchers to rely on the traditional structured scales.

Two examples of structured, semantic differential scales that have been developed and validated to measure the image of traditional stores are the Consumer Retail Store Image Scale (CIRS) and the Store Image Scale (SIS).

The Consumer Retail Store Image Scale (CIRS)

In order to develop a reliable semantic differential instrument to measure consumers’ images of retail stores, Dickson and Albaum (1977) use in-depth interviews to generate a representative sample of possible descriptors. From these interviews, twenty-nine bipolar items are deduced. Using factor analysis, the researchers try to determine whether basic image dimensions can be revealed that are common to four different store types, or whether one or more factors are rather situation specific. The four store types are supermarkets, discount stores, department stores and shoe stores. As far as supermarkets and discount stores are concerned, five factors account for more than fifty percent of the variance. In the case of department and shoe stores, six factors account for more than fifty percent of the variance. From these results the authors conclude that there is at least some, but no complete similarity in the latent factor structures for the four types of stores. The specific five and six factors, or the exact differences and similarities between the dimensions found for, on the one hand, supermarkets and discount stores, and, on the other, department and shoe stores, are not described by Dickson and Albaum (1977). Only a general conclusion is drawn. From the results of their study the authors infer that a consumer’s image of a retail store encompasses attitudes towards retail prices, store layout and facilities, service and personnel, promotion and 'others’.

The Store Image Scale (SIS)

Manolis et al. (1994) base themselves on the work of Zimmer and Golden (1988). As is the case for the latter authors, the former use an open, unstructured consumer based approach to establish a stable scale to measure store image. By means of depth interviews 45 items are generated. After pre-testing the items in a few focus groups (n=37) and comparing them to previous found items, 23 of the 45 items are withheld. This 23-item store image scale is intended to assess three factors of store image: (a) a general store attributes dimension, (b) an appearance related dimension, and (c) a salesperson/service dimension. These factors are consistent with both the pre-test data and existing image literature. Purification of the scale led to 13 additional items being dropped due to non-significant loadings and high error variances. Finally, ten items are retained and subjected to confirmatory factor analysis to test the dimensionality and internal consistency of the dimensions. The results of this analysis are satisfactory and confirm the three-dimensional factor structure (NFI and CFI>.9). Additionally, the stability of the three dimensional structure is tested for three different types of retail stores: self-service, limited-service and full-service stores. The factor structure did nt vary according to retail store type.

RESEARCH OBJECTIVE

Since the importance of second-hand stores has recently increased to a great extent, and since scientific knowledge on this type of stores is scarce, this study is confined to the second-order retail market, and more specifically to recycle stores. From previous studies it is clear that in the traditional retail market, store image is an important explanatory variable of store patronage. Importantly, store image is not a one-dimensional construct but seems to be composed of different dimensions, each with its own importance. In order to find out whether and to what extent store image and different image elements matter for second hand stores, first relevant image dimensions for this specific type of stores need to be defined. Therefore, the objective of the current study is to determine the underlying image dimensions of second-hand stores, and more specifically recycle stores, on the basis of two existing structured image scales (SIS and CIRS). Both scales are considered to be good and stable to measure the image of different types of retail stores. Although each scale encompasses a different number of dimensions, the underlying dimensions seem to hold for different types of traditional retail stores. The question is whether they also hold for recycle stores.

RESEARCH METHOD

Qualitative study

The objective of the study is to test the robustness of two existing store image scales for a different store type, namely recycle stores. First of all, a limited qualitative study involving fifteen unstructured depth interviews was performed, in order to examine whether the attributes covered in the structured scales are applicable to second-hand stores and whether no important dimensions are omitted. Probing respondents for attributes related to the image of second-hand stores and recycle stores more specifically, did not reveal any dimensions different from the ones included in the SIS and CIRS-scales. Moreover, the dimensions incorporated in the scales appeared to be relevant for this specific store type as well.

TABLE 1

GOODNESS-OF-FIT MEASURES FOR THE 6-FACTOR CIRS

Quantitative study

Stores and Respondents. Store image data are collected from a sample of 15 "Flemish Recycle Stores" spread over Flanders. Face-to-face interviews are conducted with 370 participants. Of the respondents 56% are female and 44% are male, while 32.2% fall in the age category 18-35 years old, 47.2% is aged between 36 and 55, and 20.6% is older than 55. The respondents are addressed inside or in the neighbourhood of the recycle stores. This way familiarity with the store is deemed sufficiently high for a clear and differentiated perception of the image of the store (cfr. Acito and Anderson 1979). The respondents are asked to answer questions about the image they have of the recycle store they are visiting at that time (or the nearest-by store).

Measures. Besides demographic questions such as age and gender, the questionnaire contains the items of the two semantic differential scales that have to be compared: the Consumer Retail Store Image Scale (CIRS) developed by Dickson and Albaum (1977) and the Store Image Scale (SIS) developed by Manolis et al. (1994). The items of the scales are translated from English into Dutch with a forward-backward procedure. Moreover, each item is tested for clarity. All the image items are measured by means of 7-point semantic differential scales, ranging from B3 to +3. Some of the items are reversed.

RESEARCH RESULTS

To investigate whether the image of recycle stores can be captured by the same dimensions as is the case for traditional stores, both exploratory and confirmatory factor analyses are conducted on the consumer retail store image scale (CIRS) and the store image scale (SIS).

Consumer Retail Store Image Scale (CIRS)

In order to explore the factor structure of the CIRS-scale, principal components analysis (PCA) with Varimax rotation is used. PCA extracts seven factors with Eigenvalues greater than unity, explaining almost 60% of the variance. The factors account for 29.9%, 6.5%, 5.6%, 5.2%, 4.3%, 3.7%, and 3.5% of the variance respectively. In view of the size of the variance explained by the first factor, the findings seem to suggest that one dominant factor underlies the structure of CIRS. Also the Scree plot points to a one-factor structure. However, in the traditional retail market six dimensions underlie store image. Therefore, it is deemed relevant to explore a one to six factor structure. The results of the exploratory analyses show that only a one- and a four-factor solution are meaningful. For other solutions similar items load on different factors or only one item loads on one of the factors. As a consequence, the one- and four-factor structure obtained by PCA and the six-factor structure proposed by Dickson and Albaum (1977) will be evaluated by conducting confirmatory factor analysis using LISREL 8.3.

As the fit indices in Table 1 show, the one-factor solution does not provide an acceptable fit. In Table 1 in column 2, acceptable values for a good fit are given (Marsh & Hovecar, 1985; Bentler, 1990; Bagozzi & Baumgartner, 1994; Sharma, 1996; Baumgartner & Homburg, 1996) [It has to be added that the chi square test is highly dependent on the sample size, and that its hypothesis of an exact reproduction of the sample covariance matrix by the implied covariance matrix of the model is often considered to be overly rigid. Models that fit the data well, often have to be rejected on the basis of the chi square value (Bollen and Long, 1993; Bagozzi and Baumgartner, 1994).], while the third column provides the fit indices for the 29-item, one-factor CIRS model. A possible reason for the bad fit may be that the specified factor is not one-dimensional. Therefore, the standardized residuals are carefully examined. To check for multi-dimensionality, the size and pattern of the standardized residuals are investigated. Standardized residuals should be smaller than |2.58|. Moreover, a subset of items having large negative standardized residuals with the other items that compose the same factor, and large positive residuals among each other, suggests multi-dimensionality of the factor in the sense that the subset of items constitute a separate factor (Steenkamp and van Trijp, 1991). Nineteen of the standardized residuals are larger than |2.58| and there seems to be a pattern pointing to multi-dimensionality. As a consequence, the one-factor structure has to be rejected.

FIGURE 1

MODEL OF THE THREE-FACTOR CIRS

The fit of a four-factor model is better, but still unacceptable (see Table 1, column 4). Moreover, eighteen of the standardized residuals are larger than |2.58| and again a pattern amongst the standardized residuals suggests a more than four factor structure leading to the rejection of the four-factor solution.

Also the six-factor structure proposed by Dickson and Albaum (1977) seems to provide an unacceptable fit (see Table 1, column 5). However, in this case no standardized residuals larger than |2.58| are found and no specific pattern among the standardized residuals can be revealed, which means that none of the items is misspecified and that each factor is one-dimensional (Steenkamp and van Trijp 1991). Another possible reason for the bad fit could be the lack of within-method convergent validity. A weak condition for within-method convergent validity is that the factor regression coefficient on an item is statistically significant. A stronger condition is that the correlation between the item and the constructs exceeds 0.50 (Steenkamp and van Trijp 1991). All the factor regression coefficients are statistically significant (t>2), but they are not substantial. After deleting ten items because of factor regression coefficients smaller than .50, the six-factor model obtains a reasonable fi. A final step is to investigate the reliability of the different factors by calculating Cronbach’s alpha or, in case the factor is composed of only two items, the correlation between the items for each factor. Pearson correlations for the factors "Prices", "Promotion", and "Others" are -.55, .62, and .39 (p<.001) respectively. Cronbach’s Alpha for the factors "Products", "Store layout and facilities", and "Service and Personnel" amount to .54, .87 and .78 respectively. An important remark is that the Cronbach’s Alpha of the factor "Products" is too small. Deleting one of the items does not improve Cronbach’s Alpha of that factor (alpha if item deleted<.54 for all three items). Since this factor does not prove to be reliable, the deletion of the whole factor is called for. A confirmatory factor analysis on the remaining five-factor model yields a good fit (see Table 1, column 6 and Figure 1).

In conclusion, scores on the five-factor CIRS scale prove to be valid and reliable; and the five dimensions underlying store image are very comparable to the ones obtained for traditional stores.

Store Image Scale (SIS)

The same procedure as for the CIRS-scale is followed for the SIS-scale. In this case PCA extracts two factors with Eigenvalues greater than unity, explaining 54% of the variance. The factors account for 43.1% and 11.3% of the variance respectively. In view of the size of the variance explained by the first factor, the findings seem to point to a one-dimensional structure of the SIS-scale. Also the Scree plot suggests a one-factor structure. However, in the traditional retail market a three-dimensional structure has been confirmed for different types of stores. Therefore, a one to three factor structure will be explored. As Table 2 (column 3) shows, no acceptable fit is obtained for the one-factor model. Taking a look at the standardized residuals suggests a more than one-factor model would better fit the data (the standardized residual between items 7 and 8 is larger than 4). Therefore, the one-factor model is rejected. A two-factor model does not result in an acceptable fit either (Table 2, column 4). Again, not all standardized residuals fulfil the criterion of being smaller than |2.58| and suggest that more factors underlie the data. Finally, the three-factor structure proposed by Manolis et al. (1994) is investigated. This yields acceptable goodness of fit measures (see Table 2, column 5). However, checking for one-dimensionality and convergent validity reveals that two items do not meet the stronger condition of within-method convergent reliability (factor regression coefficient<.5) (Steenkamp and van Trijp, 1991). Therefore, these two items are deleted from the model. Again, the model seems to fit the data well (see Table 2, column 6). Moreover, reliability of the factors proves to be satisfactory. Cronbach’s Alpha for the first factor, "General Store Attributes", amounts to .74, while Pearson Correlation for the factors "Appearance" and "Salesperson / Service" is .59 and .25 respectively (p<.001). The obtained model is shown in figure 2.

To conclude, although for both the CIRS and the SIS-scale PCA suggests a one-factor structure, the image data of recycle stores seem to best fit a factor-structure corresponding to the one found for traditional stores. Indeed, for CIRS five and for SIS three image dimensions similar to the ones obtained in the traditional market were detected. These results demonstrate the robustness of the original scales since they prove to be valid and reliable for a wide range of retail outlets.

TABLE 2

GOODNESS-OF-FIT MEASURES FOR THE SIS

DISCUSSION AND SUGGESTIONS FOR FURTHER RESEARCH

The objective of the current study was to investigate whether the image of second-hand stores can be captured by means of the same image scales and image dimensions as the ones developed and validated in a traditional retail context. To this end, two structured retail image scales (CIRS and SIS), that proved to be valid and reliable for different types of traditional stores, are applied to recycle stores.

Dickson and Albaum (1977) propose six dimensions underlying consumer’s image toward traditional retail stores. This six-factor structure is confirmed for department and shoe stores, while for supermarkets and discount stores only five dimensions can be revealed. On the basis of the current study, it can be concluded that very similar results are obtained for recycle stores, although several items and one of the six factors have to be deleted. Indeed, also in the present study five dimensions seem to underlie store image: 1. price (high or low prices, value for money); 2. store, layout and facilities (attractiveness of the store, the organization of the layout, ); 3. service and personnel (the friendliness and helpfulness of the personnel, the service, ); 4. promotion (whether there are bargains or special promotions) and 5. others (whether checkout goes slow or fast, whether the store attracts upper or lower class people).

The dimension "product" does not seem to be stable and is deleted. This may be due to the fact that the quality of the products is very heterogeneous, that some of the products are crammed while others are not, and that for some product categories a wide selection is available while for other categories only one or two products are available.

Manolis et al. (1994) suggest that the image of traditional stores can be captured in three dimensions, which proved to be true for self-service, limited-service and full-service stores. The results of the current study suggest the latter also holds for recycle stores. The first dimension refers to the general image of the recycle store: the overall impression the consumer has of the store, how the consumer evaluates the store’s assortment and the store’s layout, and whether the store is judged to be high class or low class. The second dimension refers to the appearance of the store: does the store have a good or a bad appearance and is it in good or in bad physical condition? Finally, the third dimension refers to the salespersons and the service of the store: does the store offer good or bad service and do the salespersons make a good or bad impression? The results show once more that the store image scale (SIS) and its three dimensions can capture a store’s image in a valid and reliable manner.

FIGURE 2

MODEL OF THE THREE-FACTOR SIS

In conclusion, in contrast to Peterson and Kerin’s (1983) suggestion that image dimensions vary across different types of stores, image dimensions encountered for traditional stores also seem to underlie the image of recycle stores. At least, the SIS and CIRS scales prove to be very robust and applicable to a wide range of retail outlets. As a consequence, in future studies it is justified to use these traditional scales to capture the image of second-hand outlets in order to relate image and its different components to store choice processes, store patronage, etc. The latter can reveal which dimensions really matter and which are of lesser importance in a second-order retail outlet.

A limitation of the present study is that only a small qualitative inquiry was carried out with regard to the specific attributes encompassing the image of second-hand stores. The current study investigated the underlying dimensions captured in existing, structured image scales. Although the qualitative interviews did not point to any dimensions that were lacking, future research could focus to a greater extent on unstructured techniques to assure that indeed all the dimensions are relevant and no important dimensions are missing.

Another limitation is that potential cultural effects could be reflected in our results. Although previous studies in the UK (Birtwistle, Clarcke and Freathy, 1999, Coshall, 1985 ), the Netherlands (Timmermans, Van Der Heyden and Westerveld, 1982) and Spain (Alonso and Muica, 1986) came up with pretty similar dimensions than the CIRS scale, cultural effects may conceivably bias the responses to the store image scales under consideration as they have not yet been cross-culturally validated. However, on the basis of a recent cross-cultural study it has been concluded that attributes reflecting store image may well be similar for several countries of the European Union (Davies and Flemmer, 1995). Although it was not the intent of the present study to cross-culturally validate the store image scales, the authors acknowledge that cross-cultural effects may have confounded the data and suggest that this may be a very interesting avenue for further investigation.

REFERENCES

Acito F. and Anderson R.D. (1979). "The influence of learning on store image differentiation: an empirical demonstration", Educators’ Conference Proceedings, American Marketing Association, Chicago, IL, 500-503.

Alonso J. and Mugica L. L. (1986), "Customer Store Image in Spain: an Empirical Study on Food Stores", International Journal of Retailing, 1 (2), 3-11.

Arnold S., Tae Oum H. and Tigert D. (1983), "Determinant attributes in retail patronage: seasonal, temporal, regional, and international comparisons", Journal of Marketing Research, 20 (May), 149-157.

Assael H. (1987), "Consumer behavior and marketing action", 3^rd ed., New York: Kent.

Baerden W.O. and Netemeyer R.G. (1999), "Handbook of marketing scales: Multi-item measures for marketing and consumer behavior research., 2^nd edition, SAGE Publications.

Bagozzi R.P. and Baumgartner H. (1994), "The evaluation of structural equation models and hypothesis testing." In: Bagozzi, R. P. (Ed.), Principles of Marketing Research (pp. 386-422), Cambridge, MA: Blackwell.

Baker J., Grewal D. and Parasuraman A. (1994), "The influence of store environment on quality inferences and store image", Journal of the Academy of Marketing Science, 22, 328-339.

Baumgartner H. and Homburg C. (1996), "Applications of structural equation modelling in marketing and consumer research: a review". International Journal of Research in Marketing, 13, 139-161.

Bentler P.M. (1990), "Comparative fit indexes in structural models" Psychological Bulletin, 107 (2), 238-246.

Berman J. and Evans J.R. (1989), "Retail Management: A Strategic Approach", 4^th edition, Macmillan Publishing Company, New York.

Birtwistle G., Clarke I. and Freathy P. (1999), "Store image in the UK fashion sector: consumer versus retailer perceptions" International Review of Retail, Distribution and Consumer Research, 9 (1), 1-16.

Chowdhury J., Reardon J. and Srivastava R. (1998), "Alternative Modes of Measuring Store Image: an Emperical Assessment of Structured versus Unstructured Measures", Journal of Marketing Theory and Practice, (Spring), 72-86.

Cordero R. (1991), "Managing for Speed to Avoid Product Obsolescence: a Survey of Techniques", Journal of Product Innovation Management, 8, 283-294.

Coshall J. (1985), "The Form of Micro-Spatial Consumer Cognition and Its Congruence With Search Behavior" Tijdschrift voor Economische en Sociale Geografie, 70, 345-355.

Darley W.K. and Lim J.S. (1993), "Store-Choice Behavior for Pre-owned Merchandise", Journal of Business Research, 27 (May), 17-31.

Darley W.K. and Lim J.S. (1999), "Effects of Store Image and Attitude Toward Second-Hand Stores on Shoppin Frequency and Distance Travelled", International Journal of Retail and Distribution Management, 27 (8), 311-318.

Davies B.J. and Flemmer M. (1995), "Consumer behavior convergence in the European Union", The Service Industries Journal, 15 (October), 177-186.

Dickson J. and Albaum G. (1977), "A Method for Developing Tailormade Semantic Differentials for Specific Marketing Content Areas", Journal of Marketing Research, 14 (February), 87B91.

Granbois D. (1981), "An integrated view of the store choice / patronage process", Advances in Consumer Research, 8, 693-695.

Linquist J.D. (1974), "Meaning of Image", Journal of Retailing, 50 (Winter), 29-38.

Lenders F. (1998), "Wat is een kringloopcentrum. Syllabus", Koepel van Vlaamse Kringloopcentra.

Manolis C., Keep W.W., Joyce M.L. and Lambert D.R. (1994), "Testing the underlying structure of a store image scale", Educational and Psychological Measurement, 54 (3), 628B645.

Marsh H.W. and Hovecar D. (1985), Application of Confirmatory Factor Analysis to the Study of Self-concept: First and Higher Order Factor Models and their Invariance across Groups." Psychological Bulletin, 97 (3), 562-582.

Martineau P. (1958), "The Personality of the Retail Store", Harvard Business Review, 36 (January-February), 47-55.

Mazursky D. and Jacoby J. (1986), "Exploring the Development of Store Images", Journal of Retailing, 62 (Summer), 145-165.

Monroe K.B. and Guiltinan J.B. (1975), "A path-analytic exploration of retail patronage influences", Journal of Consumer Research, 2 (June), 19-27.

O’Reilly L., Rucker M., Hughes R., Gorang M. and Hand S. (1984), "The relationship of psychological and situational variables to usage of a second-order marketing system", Journal of the Academy of Marketing Science, 12 (3), 53-76.

Peterson R.A. and Kerin R.A. (1983), "Store image measurement in patronage research: fact and artifact" in Darden, W.R. and Lusch R.F. (eds.) Patronage Behavior and Retail Management, North Holland, New York, 293-306.

Riecken G., Yavas U. and Battle C. (1979), "Pre-owned merchandise buying: a neglected retailing phenomenon", Development in Marketing Science, 2, 58-62.

Sharma, S. (1996), "Applied multivariate techniques", New York, John Wiley and Sons.

Steenkamp J.B.E.M. and van Trijp H.C.M. (1991), "The use of LISREL in Validating Marketing Constructs", Journal of Research in Marketing, 8, 283-299.

Timmermans H., Van Der Heijden R. and Westerveld H. (1982), "Cognition of urban retailing structures: a Dutch case study", Tijdschrift voor Economie en Sociale Geografie, 73, 2-12.

Zimmer M.R. and Golden L.L. (1988), "Impressions of Retail Stores: A Content Analysis of Consumer Images", Journal of Retailing, 64 (Fall), 265-293.

----------------------------------------