Food Choice Questionnaire (FCQ) revisited. Suggestions for the development of an enhanced general food motivation model

Appetite 52 (2009) 199–208

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Research report

Food Choice Questionnaire (FCQ) revisited. Suggestions for the development of an enhanced general food motivation model Christos Fotopoulos a, Athanasios Krystallis b, Marco Vassallo c, Anastasios Pagiaslis a,* a

Department of Food and Agri-Business Management, University of Ioannina (UoI), George Seferi 2, GR-30100, Agrinio, Greece MAPP, Department of Marketing and Statistics, Aarhus School of Business (ASB), University of Aarhus, Haslegaardsvej 10, DK-8210 Aarhus V, Denmark c Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione (INRAN), via Ardeatina 546, IT-00178, Roma, Italy b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 14 May 2008 Received in revised form 17 September 2008 Accepted 20 September 2008

Recognising the need for a more statistically robust instrument to investigate general food selection determinants, the research validates and confirms Food Choice Questionnaire (FCQ’s) factorial design, develops ad hoc a more robust FCQ version and tests its ability to discriminate between consumer segments in terms of the importance they assign to the FCQ motivational factors. The original FCQ appears to represent a comprehensive and reliable research instrument. However, the empirical data do not support the robustness of its 9-factorial design. On the other hand, segmentation results at the subpopulation level based on the enhanced FCQ version bring about an optimistic message for the FCQ’s ability to predict food selection behaviour. The paper concludes that some of the basic components of the original FCQ can be used as a basis for a new general food motivation typology. The development of such a new instrument, with fewer, of higher abstraction FCQ-based dimensions and fewer items per dimension, is a right step forward; yet such a step should be theory-driven, while a rigorous statistical testing across and within population would be necessary. ß 2008 Elsevier Ltd. All rights reserved.

Keywords: New food motivation typology Confirmatory factor analysis Cluster analysis

Introduction Food product choice is a complex function of preferences for sensory characteristics, combined with the influence of nonsensory factors, including food-related expectations and attitudes, health claims, price, ethical concerns and mood (Prescott, Young, O’Neil, Yau, & Stevens, 2002). A variety of social, cultural and economic factors thus contribute to the development, maintenance and change of dietary patterns. Intra-individual determinants, such as physiological and psychological factors, acquired food preferences, and knowledge can be distinguished from interpersonal or social factors, such as family and group influences (Eertmans, Baeyens, & Van den Bergh, 2001). Comprehensive models have been developed to sketch out the way people construct the process of choosing foods in general. For example, Frust, Connors, Bisogni, Sobal, and Winter Falk (1996) group the factors involved in food choice into three major components (life course, influences and personal systems), the particular relationship of these components to one another generating the process or pathway leading to the point of choice.

* Corresponding author. E-mail address: [email protected] (A. Pagiaslis). 0195-6663/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2008.09.014

Although such models document the full complexity of (food) choice,1 their comprehensive nature makes it difficult to make predictions about actual, general food choice behaviour (Eertmans et al., 2001). An instrument that past research points out as one that might have a potential to fulfil the aim of predicting general food choice based on its determinants is the Food Choice Questionnaire (FCQ) (Pollard, Steptoe, & Wardle, 1998; Steptoe, Polland, & Wardle, 1995; Steptoe & Wardle, 1999). Since its introduction, FCQ is adopted as a whole or partially at both the cross-national level (e.g. Eertmans et al., 2006; Prescott et al., 2002), as well as a country level, addressing general food selection determinants-related research around the globe (see, for instance, the work by Martins and Pliner (1998) in Canada; Glanz, Basil, Maibach, Goldberg, and Snyder (1998) in the US; Lindeman and Vaananen (2000) in Finland; Biloukha and Utermohlen (2000) in Ukraine; Lockie, Lyons, Lawrence, and Mummery (2002) in Australia; Ares and Gambaro (2007) in Uruguay; and Chryssohoidis, Krystallis, and Perreas (2007) in Greece).

1 For a comprehensive review of the concept of human motivation, as well as its application on the food-related research see Pincus (2004) and Eertmans, Victoir, Notelaers, Vansant, and Van den Bergh (2006).

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Despite its above-presented applications, however, the FCQ is not yet surrounded by the preferable amount of academic trust in its ability to determine food-related choices satisfactorily (Eertmans et al., 2006; Lindeman & Vaananen, 2000), due to its conceptual deficiencies that reflect on its limited statistical robustness. On the other hand, research on the relative contribution of the various determinants to human eating behaviour appears to be scarce (Eertmans et al., 2001). The food motivational literature seems surprisingly void of an improved typology (although there have been some very recent but useful attempts i.e. Byrd-Bredbenner & Abbot, 2008), which – seen from a multidisciplinary point of view – would determine food selection motives comprehensively and exhaustively and which would simultaneously constitute a robust multidimensional instrument to measure food selection determinants both across and within populations. Recognising the need for a more statistically robust multidimensional instrument to investigate general food selection determinants – despite the existence of a substantial number of models that attempt to predict specific food type-related behaviour in various well-defined decision making contexts, the present research explores the ability of the FCQ to perform that task by focusing on its apparent statistical deficiency. FCQ description, applications and criticism The FCQ contains 36 items representing search, experience and credence characteristics related to intrinsic and extrinsic food attributes that motivate consumers to make general food choices. Respondents are instructed to rate the importance of each FCQitem for their food choices ‘‘on a typical day’’ on a unipolar 4-point scale (1 = ‘‘not at all important’’, 2 = ‘‘little important’’, 3 = ‘‘moderately important’’, and 4 = ‘‘very important’’). The FCQ involves nine motivational dimensions (factors), each containing three to six items: 1. ‘‘health’’, 2. ‘‘mood’’, 3. ‘‘convenience’’, 4. ‘‘sensory appeal’’, 5. ‘‘natural content’’, 6. ‘‘price’’, 7. ‘‘weight control’’, 8. ‘‘familiarity’’, and 9. ‘‘ethical concern’’. In their UK samples, Steptoe et al. (1995) report that sensory appeal, health, convenience and price are rated as the most important among the food choice motives. They also report differences in food choice motives associated with gender, age and income, and found the FCQ factors to converge with measures of dietary restraint, eating style, the value of health, health locus of control, and personality factors. Steptoe et al. (1995) conclude that: ‘‘. . .within western urban populations, the FCQ provides the opportunity to assess a broad range of factors perceived as relevant to food selection. . .’’ (Steptoe et al., 1995, p. 282). More recently, the complete FCQ has been used in food-related research in non-English speaking populations. For instance, Lindeman and Vaananen (2000) assessed through the FCQ the ethical food choice motives of Finish respondents; Chryssohoidis et al. (2007) used three adapted (shorter) FCQ versions to explore Greek consumers’ perceptions about the quality of specific food products of Greek origin as opposed to the perceived quality of their foreign-made counterparts; and Ares and Gambaro (2007) assessed through the FCQ the influence of Uruguayan consumers’ motives underlying food choice on perceived healthiness and willingness to try functional foods. Moreover, cross-national FCQbased comparisons in food choice motives have also been made by Prescott et al. (2002) among consumers from Japan, Taiwan, Malaysia and New Zealand; and by Eertmans et al. (2006) among consumers from Italy, Belgium and Canada. The work by Eertmans et al. (2006) aims to examine the degree to which the 9-factorial structure of the FCQ is invariant across western urban populations, as postulated by Steptoe et al. (1995) in their normative work. Data were obtained from Italian, Belgian

and Canadian students. The factor solutions of those samples appeared to differ from the configuration obtained by Steptoe et al. (1995). Some divergences were rather small, whereas other incongruities were large enough to reinterpret factors. Overall, the results of the tests conducted by Eertmans et al. (2006) do not support Steptoe et al.’s (1995) assumption that the FCQ has a factorial structure that would generalize from the original UK samples across western urban populations (i.e. a factorial structure that would show at least configural invariance, Steenkamp & Baumgartener, 1998). Instead, the work by Eertmans et al. (2006) suggests that the FCQ-items and the underlying constructs may have different connotations in other western cultures, whether it concerns English speaking or non-English speaking countries. Moreover, recent research with a sample of Greek consumers (Krystallis & Chryssohoidis, 2006) also failed to confirm the initial FCQ factorial design. Research aims As mentioned previously, the present research recognises the need for a more statistically robust multidimensional instrument to investigate general food selection determinants, and the fact that, to date, there appears to be a scarcity of such general food selection motivation models in the relevant literature. Using as a point of departure past research that adopts a rather critical stance towards the conceptual completeness and corresponding statistical robustness of the FCQ (i.e. Chryssohoidis et al., 2007; Eertmans et al., 2006; Lindeman & Vaananen, 2000), the present research aims to explore the ability of the FCQ to determine food selection motives by adopting the full 36-item FCQ measured on bipolar 7-point Likert-type scales, validating and confirming its 9factorial design and ad hoc developing a more statistically robust FCQ version, which will still be embodied into the original FCQ conceptual framework. This latter aim is unique to the present research, since no previous effort has been made in the literature to come up with a more robust FCQ version – although some efforts have been made to develop food motivation typologies based on the initial FCQ version at the general food selection context (e.g. Martins & Pliner, 1998), or at a food type-specific context (e.g. Lockie et al., 2002, for organic food and Ares & Gambaro, 2007, for functional food). The present research further aims to explore the relative ability of the enhanced FCQ to discriminate among consumer segments with clear-cut socio-demographic profile in terms of the importance consumers in each cluster assign to different food selection motives. This type of analysis makes a great contribution to the food-related consumer motivation literature, since little empirical work has examined the functioning of the FCQ in various subpopulations. That is, research to date has examined its usefulness on a national or cross-population basis; however, the factors motivating food choices are likely different for segments of different socio-demographic profile within the wider population. Materials and methods Subjects and procedure The research data are yielded from a sample of 997 Greek households. Data are collected during the period May–June 2006 by a professional research agency in Athens. The questionnaire developed for data collection was self-administered, handed out and collected by the agency upon completion by a person aged 18 and above in each household, in charge of grocery shopping or sharing this responsibility equally with other members of the household. Respondents’ mean age is 42.88 years (S.D. = 12.15).

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Overall, the sample is nationally representative in terms of education, income and geographic distribution (Table 1). Material The questionnaire used in the research comprises five sections, one of which is the 7-point rated FCQ section (full version of the questionnaire available upon request). The last section of the questionnaire includes the socio-demographic profile of the respondents. The FCQ version that is used in the relevant section of the questionnaire includes the initial 36 items developed by Steptoe et al. (1995), already used in previous research in Greece (Chryssohoidis et al., 2007). For the purposes of the present study, however, the original FCQ is re-translated in Greek and back translated in English by two independent translators to ensure accuracy and maximise linguistic equivalence. Following the aims of the research as described above, participants rated the importance of the 36 FCQ items on a bipolar 7-point Likert type importance scale

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with points as follows: 1 = ‘‘extremely unimportant’’, 2 = ‘‘unimportant’’, 3 = ‘‘slightly unimportant’’, 4 = ‘‘neither unimportant nor important’’, 5 = ‘‘slightly important’’, 6 = ‘‘important’’ and 7 = ‘‘extremely important’’. It has been decided to use a bipolar 7-point scale instead of the original unipolar 4-point one for two reasons: first, to increase the number of response options within items (i.e. from 4 to 7) and thereby the opportunities to increase the variability of the measurement scale, that is to increase the mere quality of the scale (DeVellis, 1991); second, to take into account a central neutral point as a means of avoiding a forced preference on behalf of the subjects (DeVellis, 1991). The food choice decision-making is a process well known for its complexity and, as such, it naturally requires a substantial number of response options to allow respondents more latitude in describing their level of opinion. The use of 7-point Likert-type scales is not unseen in the food choice motivation literature. Martins and Pliner (1998) report the development of a 32-item FCQ-based scale (called Food Motivation Scale) measured on 7-point Likert-type importance scales.

Table 1 Sample’s socio-demographic profile (%, N = 997).

1: Central (8%), Crete (8%), South-Central (4.7%), West (4.0%); 2: Temporarily unemployed, pupil/student, pensioner; 3: Entrepreneur or CEO (4.6%), worker/technician (4.4%), pupil/student (2.8%), temporarily unemployed (1.6%), farmer (0.9%).

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Moreover, Prescott et al. (2002) report the use of the FCQ measured on 7-point Likert-type agreement scales. In both of these surveys, the 7-point scales show satisfactory statistical properties; however, no effort has been made in either study to confirm the statistical robustness of the FCQ’s factorial design when measured on 7-point scales. The descriptive statistics for the FCQ can be found in Table 2. The scores on each of the nine FCQ dimensions are computed by averaging item ratings per dimension. Data analysis Confirmatory factor analysis (CFA) is performed (LISREL 8.72) to confirm and validate the factorial pattern suggested for the FCQ by Steptoe et al. (1995). Many authors agree in the importance of a priori theory before implementing CFA (Hurley et al., 1997), whereas the use of exploratory factor analyses (EFA) is appropriate for new or ad hoc measures (Conway & Huffcutt, 2003). In this study, the measurement instrument is established a priori through the FCQ. Consequently, it is meaningful to directly confirm its original factorial design trough CFA. The internal consistency of the various constructs is assessed by Cronbach alpha coefficients (SPSS 15.0). The general CFA models’ fit is assessed by: (a) the chi-square test as a descriptive goodness-of-fit index for nested models; and (b) the Comparative Fit Index (CFI), the Non-Normed Fit Index (TLINNFI) and the root mean square error of approximation (RMSEA). To evaluate the parsimony of the hypothesized models, the Parsimonious Normed Fit Index (PNFI) is used to compare nested and non-nested 1st and 2nd-order models. Finally, the chi-square difference test, the Akaike’s Information Criterion (AIC) and the Consistent Akaike’s Information Criterion (CAIC) are applied to compare two or more competing models estimated from the same data. Hierarchical cluster analysis (HCA) is performed at the final stage of the analyses (SPSS 15.0) to determine a number of consumer segments (clusters) with different level of importance assigned to the enhanced FCQ’s motivational factors, after the enhanced FCQ model’s robustness being tested at the previous stage of analyses. The enhanced FCQ motivational factors are thus used as clustering criteria. Existence and number of the statistically significant differences among the emerging clusters in terms of the importance assigned to the FCQ motivational factors is assessed through post-hoc one-way ANOVA tests (p < 0.05) performed at the cluster level. The selection of the ideal clustering scenario (i.e. ideal number of clusters) is based on the above statistically significant differences, as well as on the convergence between the hierarchical and the k-means solutions per clustering scenario (Pearson correlations, p < 0.01). Finally, statistically significant socio-demographic differences among the motivational clusters emerged are assessed through chi-square tests at the cluster level (p < 0.05). Analysis and results Scale reliabilities and item statistics As hypothesized in the original FCQ, the nine motivational dimensions show moderate to good reliability (Cronbach alphas from a = 0.61 to a = 0.82; see Table 2), with the exception of the ‘‘ethical concern’’ dimension (a = 0.30), whereas the reliability of the overall FCQ typology is also very high (a = 0.93). Not surprisingly, the items 20, 32 and 19, which form the lowreliability ‘‘ethical concern’’ dimension, show the lowest item-tototal correlation (r). Correlations lower than 0.40 appear also in

Table 2 Descriptive statistics and reliabilities of the original FCQ with 7-point scales (N = 997). No.

FCQ item

Mean/S.D.

ra

b

It is important to me that the food I eat on a typical day : 1. Health 22 Contains a lot of vitamins and minerals 29 Keeps me healthy 10 Is nutritious 27 Is high in protein 30 Is good for my skin/teeth/hair/nails etc. 9 Is high in fibre and roughage

5.77/1.08 5.92/1.06 5.83/1.09 5.44/1.27 5.00/1.24 5.49/1.09

0.62 0.63 0.61 0.40 0.36c 0.49

4.76/1.36 6.12/1.12 4.97/1.36 5.57/1.11 5.11/1.30 5.29/1.19

0.43 0.31c 0.53 0.41 0.53 0.61

5.12/1.42 5.18/1.34 5.19/1.37 5.41/1.20

0.56 0.57 0.61 0.36c

5.47/1.20

0.41

5.58/1.15 5.01/1.31 5.76/1.10 5.85/1.10

0.51 0.28c 0.52 0.50

6.03/1.12 5.76/1.08 5.88/1.17

0.58 0.59 0.66

5.50/1.18 5.42/1.22 5.73/1.06

0.55 0.58 0.49

5.14/1.31 5.10/1.24 5.36/1.32

0.74 0.67 0.60

5.46/1.10 4.93/1.45 4.99/1.41

0.32c 0.44 0.51

4.60/1.63 5.60/1.21 5.27/1.13

0.09c 0.19c 0.24c

FCQ mean: 5.39

r: 0.93

Cronbach a: 0.771 2. Mood 16 Helps me cope with stress 34 Helps me cope with life 26 Helps me relax 24 Keeps me awake/alert 13 Cheers me up 31 Makes me feel good Cronbach a: 0.736 3. Convenience 1 Is easy to prepare 15 Can be cooked very simply 28 Takes no time to prepare 35 Can be bought in shops close to where I live or work 11 Is easily available in shops and supermarkets Cronbach a: 0.742 4. Sensory appeal 14 Smells nice 25 Looks nice 18 Has a pleasant texture 4 Tastes good Cronbach a: 0.668 5. Natural content 2 Contains no additives 5 Contains natural ingredients 23 Contains no artificial ingredients Cronbach a: 0.779 6. Price 6 Is not expensive 36 Is cheap 12 Is good value for money Cronbach a: 0.772 7. Weight control 3 Is low in calories 17 Helps me control my weight 7 Is low in fat Cronbach a: 0.820 8. Familiarity 33 Is what I usually eat 8 Is familiar 21 Is like the food I ate when I was a child Cronbach a: 0.613 9. Ethical concern 20 Comes from countries I approve of politically 32 Has the country of origin clearly marked 19 Is packaged in an environmentally friendly way Cronbach a: 0.304

Key: Bold characters correspond to the highest means among FCQ variables and all r > 0.60. a Item-total correlation. b 7-point bipolar scale with end-points 1 = ‘‘extremely unimportant’’ and 7 = ‘‘extremely important’’. c FCQ items with r < 0.40.

1131.45 224 0.064 0.96 0.78 0.96 1283.45 1732.21 4360.38 484 0.090 0.92 0.84 0.92 6164.74 6619.41 4218.11 480 0.088 0.93 0.83 0.92 5977.78 6456.06

6010.74 –

1403.08

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5815.78 CFA6 vs. CFA7: 194.96 Chi-sq (5, 0.001): 20.52

B. 2nd-order CFAs

CFA6: 33 items (excl. it. 19, 20, 32) 2 2nd order factors – 8 1st order factors

CFA7: 33 items (excl. it. 19, 20, 32) 1 2nd order factors – 8 1st order factors

C. Adapted CFA

relation to a number of other items, such as item 30 of ‘‘health’’; item 34 of ‘‘mood’’; item 35 of ‘‘convenience’’; item 28 of ‘‘sensory appeal’’ and item 33 of ‘‘familiarity’’. The average (un-weighted) importance assigned by the sample to the 36 items is 5.39 in the 1–7 scale. At the individual item level, the highest importance is assigned to the motives helps me cope with life (6.12) and contains no additives (6.03), whereas the motives keeps me healthy (5.92), contains no artificial ingredients (5.88), tastes good (5.85), and is nutritious (5.83) follow. For exploratory purposes only, it is worth noting that the highest mean importance is shown for ‘‘natural content’’ (5.98), and then for ‘‘convenience’’ (5.68), ‘‘health’’ (5.57), ‘‘sensory appeal’’ (5.55) and ‘‘price’’ (5.55). Moreover, ‘‘mood’’ is the most controversial construct in terms of assigned importance, since it combines both the highest and the second-lower scoring items of the whole FCQ. Furthermore, the lowest importance is assigned to ‘‘ethical concern’’ (5.16) and ‘‘familiarity’’ (5.03). Overall, the 36 FCQ variables are only slightly non-normal, with skewness and kurtosis lower or quite close to j1j, in spite of some indices fluctuating from higher than j1j, up to around j1.5j for skewness and j2.7j for kurtosis (results available upon request).

CFA8: 24 items (excl. items 30, 27, 34, 24, 35, 11, 25, 12, 33, 19, 20, 32) 8 1st order factors

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3791.16 467 0.085 0.94 0.84 0.93 5422.73 5977.77 4036.48 517 0.083 0.94 0.81 0.93 5818.72 6485.96 4179.76 542 0.082 0.94 0.80 0.93 6002.25 6734.44 4473.45 558 0.084 0.93 0.82 0.92 6365.89 7003.60 SBS Chi-sq Df RMSEA CFI PNFI TLI-NNFI AIC CAIC

4080.64 524 0.083 0.94 0.82 0.93 5875.96 6501.87

4435.96 – 5592.72 CFA4 vs. CFA5: 245.32 Chi-sq (50, 0.001): 86.66 5754.25 CFA3 vs. CFA4: 161.5 Chi-sq (25, 0.001): 52.62 CFA3 vs. CFA5: 1318.29 Chi-sq (75, 0.001): 118.60 5663.96 CFA2 vs. CFA3: 90.2 Chi-sq (18, 0.001): 42.3 CFA2 vs. CFA4: 71.2 Chi-sq (7, 0.001): 24.3 6149.89 – N  chi-sq D(N  chi-sq)

CFA4: 35 items (excl. it. 20) 8 factors – 2 items CFA3: 36 items 8 factors – 3 items CFA2: 35 items (excl. item 20) 9 factors CFA1: 36 items 9 factors

A. 1st-order CFAs Goodness-of-fit index

2nd-order CFAs The presence of substantial correlations at the 1st-order factor levels of CFAs 1–5 might point out to the existence of higher-order factors (Fabricar, Wegener, MacCallum, & Strahan, 1999). A hierarchical sequence of nested partial 2nd-order CFAs is therefore implemented (CFAs 6 and 7), based on the previously described 1st-order CFA5 model and its estimated correlations.

Table 3 Progress of CFAs conducted on the original FCQ with 7-point scales (N = 997).

1st-order CFAs Even though the non-normality of the data is only slight, the selected method of CFA model estimation is Maximum Likelihood (LISREL 8.72) with its robust correction for non-normality (Robust Maximum Likelihood – RML; Satorra & Bentler, 1994). The fit of the original 36-item CFA measured in 7-point scales – hereafter called CFA1 – is marginally accepted (Table 3A). Moreover, most of the standardized factor loadings result adequate, ranging from 0.47 to 0.87 (cut-off levels from 0.50 to 0.95 are adequate to assess convergent validity; Kline, 2005). However, the loadings of some items are quite low (e.g. item 16 = 0.39, item 25 = 0.40 or item 35 = 0.42), while a zero correlation appears for item 20: comes from countries I approve of politically. The latter result should be expected for the ‘‘ethical concern’’ dimension because of its relatively low reliability a and low item-total correlation r that affect its internal validity (see Table 2). Furthermore, many of the estimated correlations at the factor level are higher than 0.85, not satisfying CFA1’s discriminant validity (Kline, 2005). Based on the above outcome of CFA1, the most natural step forward is to focus on the problematic ‘‘ethical concern’’ motivational dimension. A hierarchical sequence of nested CFAs is then implemented, as follows: (a) by excluding item 20 due to its zero correlation with the ‘‘ethical concern’’ dimension (CFA2); (b) by separating the ethical dimension into the three items it is comprised of (i.e. items 20, 32 and 19; CFA3); (c) by combining a and b above (i.e. excluding item 20 and considering items 32 and 19 as separate ethical concern-related sub-dimensions; CFA4); and (d) by excluding the three items that from ‘‘ethical concern’’ all together (CFA5). Nevertheless, all model’s goodness-of-fit indices remain marginal (see Table 3A), whereas many of the estimated correlations at the factor level are still higher than 0.85, not satisfying discriminant validity (correlation matrices of CFAs’ 1–5 available upon request).

CFA5: 33 items (excl. it. 19, 20 32) 8 factors

Confirmatory factor analyses (CFAs)

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Fig. 1. Path diagram, adapted CFA8, 24-item FCQ with 7-point scales (N = 997).

As shown in Table 3B, CFA6 again shows modest statistical properties. However, the convergent validity of the model is satisfactory, with the large majority of the 1st-order standardized factor loadings being higher than 0.50. Moreover, discriminant validity can be assessed at the 2nd-order level, with the correlations among the 2nd-order factors being lower or slightly higher than 0.85; however, many of the correlations among the 1st-order factors result higher than 0.85, disconfirming discriminant validity of the model at the 1st-order level. This line of thought leads to the subsequent CFA7 model, which shows improved discriminant validity at both the 1st and the 2nd-order levels, even though its goodness-of-fit indices result slightly worse than those of CFA6 (correlation matrices of CFAs’ 6–7 available upon request).

underlying structures of the new factorial design (results available upon request), since EFA is appropriate for new or ad hoc measures (Conway & Huffcutt, 2003), as mentioned before. All EFAs are conducted with the Maximum Likelihood estimation method and the oblique rotation. Items with item-to-total correlation (r) lower than 0.40 and items that do not load clearly on one factor are discarded. As expected, excluding several items from each factor leads to improved reliability (e.g. ‘‘health’’: a = 0.78, ‘‘mood’’: a = 0.74, ‘‘convenience’’: a = 0.79, ‘‘sensory appeal’’: a = 0.71, and ‘‘familiarity’’: a = 0.63). The last step then is to confirm the robustness of the adapted factorial structure through CFA (hereafter called CFA8, Fig. 1). CFA8’s goodness-of-fit indices can be found in Table 3C, whereas the discriminant validity of the model can be assessed from Table 4.2

Computation of an adapted FCQ version In an effort to improve the statistical properties of the FCQ factorial structure, the possibility for the ad hoc existence of a shorter model is finally examined (i.e. fewer items loading on 8 of the original FCQ dimensions, excluding ‘‘ethical concern’’). Significant effort is also put into balancing convergent and discriminant validity of the shorter model. With this conditions in mind, a series of EFAs is conducted (SPSS 15.0) to explore the

2 A two-item ‘‘ethical concern’’ dimension (by excluding the clearly unrelated item 20) can be maintained in an adapted 26-item FCQ version, with equally satisfactory goodness-of-fit indices but worse discriminant validity (results available upon request). However, as postulated by the Lindeman and Vaananen (2000) work and confirmed by the present results, the ‘‘ethical concern’’ dimension as formulated in the original FCQ version remains problematic and its inclusion in a food choice motivation typology merits careful consideration.

C. Fotopoulos et al. / Appetite 52 (2009) 199–208 Table 4 Correlations of the adapted CFA model, 24-item FCQ with 7-point scales (N = 997). CFA8: 24-items in eight factors Factors

1. H

2. M

3. C

4. SA

5. NC

6. P

7. WC

8. F

1.H 2. M 3. C 4. SA 5. NC 6. P 7. WC 8. F

1.00 0.50 0.50 0.88* 0.95* 0.65 0.46 0.39

1.00 0.54 0.62 0.50 0.53 0.40 0.68

1.00 0.41 0.36 0.52 0.52 0.46

1.00 0.83 0.59 0.23 0.46

1.00 0.62 0.46 0.36

1.00 0.37 0.48

1.00 0.27

1.00

*: Represents correlations between FCQ factors larger than 0.85 that do not satisfy discriminant validity. Key: H: health; M: mood; C: convenience; SA: sensory appeal; NC: natural content; P: price; WC: weight control; F: familiarity.

Hierarchical cluster analysis (HCA) The final stage of the analysis is the implementation of a motivation-based segmentation task. Grouping criteria are the eight motivational factors emerging through CFA8. After the initial implementation of hierarchical cluster analysis with the

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option of identifying 3–7 clusters (SPSS 15.0), follows the k-means procedure on the hierarchical clusters’ centroids for each of the 3– 7 clusters’ scenario. The four-cluster scenario is finally selected as the one with the highest number of statistically significant food selection motives discriminating the four clusters in pairwised comparisons (Duncan and Scheffe post-hoc ANOVA tests, p < 0.05) and the highest correlation between the hierarchical and the k-means cluster membership variables (Pearson correlation 0.84, p < 0.01). In order to develop the socio-demographic profile of each cluster, a cross-tabulation process took place between the cluster membership variable and the socio-demographic variables (chi-square tests, p < 0.05). Nine out of 13 socio-demographic variables are found to clearly discriminate among the four clusters (p < 0.05). The profile of each cluster can be seen in Table 5. It is worth highlighting that the eight motivational factors exhibit very strong discriminating power also in the scenarios of 3, 5, 6 and 7 clusters (Table 6). Moreover, the socio-demographic profile of the clusters in each of those scenarios can also be clearly drawn, since the majority of the relevant variables exhibits statistically significant differences among the clusters in each scenario (chi-square tests, p < 0.05).

Table 5 Motivational cluster profiles, statistically significant differences (%, N = 997).

Key: Bold characters correspond to the higher figures and italicised figures correspond to the lower figures among clusters. 1: 7-point bipolar scale with end-points 1 = ‘‘extremely unimportant’’ and 7 = ‘‘extremely important’’. * Duncan and Scheffe post-hoc paired ANOVA tests, p < 0.05. ** Chi-square tests, p < 0.05. + Not statistically significant differences in cluster pair-wised comparisons, ANOVA tests, p < 0.05.

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Table 6 Process of the hierarchical cluster analysis, scenarios of 4–7 clusters (N = 997).

Key: H: health; M: mood; C: convenience; SA: sensory appeal; NC: natural content; P: price; WC: weight control; F: familiarity. 1: Duncan and Scheffe post-hoc ANOVA test between each pair of clusters in each scenario in terms of importance assigned to the eight motivational factors (p < 0.05). 2: Correlation between the hierarchical cluster membership and the k-means cluster membership variables (k-means cluster analysis implemented on hierarchical cluster centroids) (p < 0.01).

Discussion For the participants to the present research, the FCQ appears to represent a realistic typology of general food selection-related motives and a comprehensive and easily administrated research instrument, as concluded by the high mean importance scores assigned to individual motives, as well as to the overall instrument-usually higher than 5 in the 1–7 scale. Compared to the results from the normative Steptoe et al. (1995) sample, the Greek consumers seem to attach high importance to more or less the same motivational dimensions (i.e. ‘‘convenience’’, ‘‘health’’, ‘‘sensory appeal’’ and ‘‘price’’); however, the importance assigned to foods’ ‘‘natural content’’ is the highest among all motives. This result could possibly be the outcome of more than a decade of food scares since the original Steptoe et al. (1995) research was conducted, a time period that has shifted consumer attention closer to food safety-related issues internationally. Except for the ‘‘ethical concern’’ dimension, the FCQ also appears to be a quite reliable research instrument both in parts, as well as a whole. However, compared to the values reported by Steptoe et al. (1995) for their normative sample, in the present sample almost all motivational scales (except for ‘‘weight control’’) show different (lower) reliability indices.3 Overall, the findings of the present research confirm the observation that the reliability values for the UK sample reported by Steptoe et al. (1995) tend to be higher than those for other western populations, such as the Canadian, Italian and Belgian samples, as reported by Eertmans et al. (2006). Nevertheless, this deviation from the original does not

3 Equality of reliabilities of the FCQ scales is tested between the present sample and the normative Steptoe et al. (1995) sample with the method of Van de Vijver and Leung (in Eertmans et al., 2006), that is ‘‘. . .by computing (1  a1)/(1  a2), where a1 is the reliability obtained for the first sample and a2 the reliability of the second sample. When the resulting value exceeded the critical value of the F distribution with numerator df1 = N1  1 and denominator df2 = N2  1 (N1 being the size of the first sample and N2 the size of the second sample), the hypothesis of equal reliabilities was rejected . . . Equality was tested at the p < 0.01 significance level. . .’’ (Eertmans et al., 2006, p. 346). For example, regarding the internal consistency of ‘‘sensory appeal’’, the comparison yielded a value of (1  0.66)/(1  0.72) or 1.21, which exceeded the critical F-value at the p < 0.01 level (with df1 = 996 and df2 = 357).

alter the quite satisfactory reliability of the instrument for the Greek sample. At the individual item level, item-total correlations are lower than the threshold of r = 0.40 for more items in the present research (9/36) than it is the case for the Canadian (3/36), Belgian (4/36) and Italian (6/36) samples (see Eertmans et al., 2006, p. 347). However, no persisting pattern can be observed in these low correlations across countries. For instance, item 4: tastes good that appears weakly correlated with the ‘‘sensory appeal’’ dimension in the Canadian, Italian and Belgian samples does not follow this trend in the Greek sample. On the contrary, item 34: helps me cope with life that is weakly correlated with the ‘‘mood’’ dimension also shows very high mean and quite low standard deviation, suggesting a ceiling effect and skewed distribution of ratings, which is confirmed to a certain extent by additional statistics (skewness = 1.48, kurtosis = 2.37). Moreover, from the overall pattern of reliabilities and itemtotal correlations, it gets clear that the ‘‘ethical concern’’ dimension shows particularly low internal consistency, an observation that is unique to the present sample. This observation is confirmed through the models CFA1–CFA5: eventual exclusion of the ‘‘ethical concern’’ components individually or as a whole results to a noticeable improvement at an acceptable level for most of models’ fit indices and a considerable improvement of models’ discriminant validity. Although direct comparisons are not possible between the present and past efforts, it is plausible to hypothesise that the use of more ‘‘rich’’ in variance generation, bipolar scales might have revealed similar weaknesses in other FCQ applications. Furthermore, the above line of thought points out to the need for the development of a more robust motivational scale with – possibly – fewer dimensions or fewer items per dimension, validated across wider and more country-representative samples than the ones used so far in the literature. A further result that adds to the above suggestion is the lack of discriminant validity of the various FCQ models, even upon exclusion of the ‘‘ethical concern’’ dimension (i.e. model CFA5). The absence of discriminant validity among specific FCQ dimensions (e.g. ‘‘familiarity’’ with ‘‘mood’’; ‘‘natural content’’ with ‘‘health’’ and ‘‘sensory appeal’’; and ‘‘sensory appeal’’ with ‘‘health’’ and

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‘‘mood’’) is supported by the 2nd-order CFA models (CFA6 and CFA7), showing the existence of some higher-abstraction dimensions behind those originally confirmed by Steptoe et al. (1995). It thus seems that the initial FCQ dimensions could be reduced to as much as five – or maybe four. For instance, the five-dimension FCQ version could be formed by: (a) a 2nd-order dimension indicatively termed ‘‘health and safety concern’’ – composed by the FCQ dimensions ‘‘health’’ and ‘‘natural content’’; (b) an additional 2ndorder dimension indicatively termed ‘‘expected pleasure’’ – composed by the FCQ dimensions ‘‘sensory appeal’’, ‘‘mood’’ and ‘‘familiarity’’; and (c) the three remaining FCQ dimensions ‘‘convenience’’, ‘‘price’’ and ‘‘weight control’’. Even further collapsing of the 2nd-order motivations ‘‘health and safety concern’’ and ‘‘expected pleasure’’ could be considered, as indicated by CFA7, forming a four-dimension FCQ version, though such a conclusion might be hard to support from a theorydriven, psychometric point of view. The psychometric view can also stimulate some other ideas about the development of shorter FCQ versions, such as a three-dimension version formed by merging: (a) ‘‘weight control’’ with ‘‘health’’ and ‘‘natural content’’ (dimension 1); (b) ‘‘sensory appeal’’ with ‘‘mood’’ and ‘‘familiarity’’ (dimension 2); and (c) ‘‘price’’ with ‘‘convenience’’ (dimension 3). However, this line of thought is not supported by the correlations of the relevant constructs of CFA5. For instance, ‘‘weight control’’ does correlate significantly but not strongly with any other FCQ dimension; moreover, ‘‘convenience’’ and ‘‘price’’ are correlated with each other only moderately, not supporting their merging to a higher order dimension. Nevertheless, one has to keep in mind that all the above 1st or 2nd order approaches (i.e. CFA1–CFA7), either computational or theoretical/psychometrical, do not exhibit but only marginally accepted statistical properties in terms of main goodness-of-fit indices and discriminant validity. The adapted CFA8 model, however, definitely points out to the need for excluding redundant items from the original FCQ dimensions. This ad hoc FCQ version shows quite robust statistical properties and significantly improved discriminant validity, with only two factors showing correlations higher than 0.85. Again, this discrepancy could either point out to the existence of a higher order factor or-mainly-that ‘‘health’’ and ‘‘natural content’’ could be considered as one dimension in a revised FCQ. On the other hand, the results of HCA bring about an optimistic message for the FCQ’s ability to predict food selection behaviour at the subpopulation level. Four clusters with clearly discriminated socio-demographic profile (clusters that represent segments of unequal size and market prospects) assign different level of importance to different FCQ motivational factors. More specifically (see Table 5): More-than-average educated and better off consumers, who live far from very large urban centres more than the sample average (cluster 1) assign higher than average importance to all FCQ motivational factors (and the highest of all clusters, with the exception of ‘‘convenience’’), and especially to ‘‘nutritional content’’ and ‘‘health’’, and then to ‘‘sensory appeal’’, ‘‘price’’ and ‘‘convenience’’. Rather ‘‘average’’ consumers, but with lower-than-average income (cluster 2), assign little average importance to all FCQ motivational factors (and lower than that of the overall sample per motive). However, they assign quite high importance to ‘‘nutritional content’’, ‘‘health’’ and ‘‘sensory appeal’’, but they are indifferent towards ‘‘familiarity’’ and ‘‘mood’’. Low educated, somewhat older, more male and less into fulltime employee jobs consumers (cluster 3) also assign little average importance to all FCQ motivational factors. However, they assign the highest and the second higher of all clusters (and

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higher than sample’s average) importance to ‘‘sensory appeal’’ and ‘‘nutritional content’’, but the lowest and the second lower of all clusters importance to ‘‘convenience’’ and ‘‘weight control’’, respectively. Finally, younger, single, urban, low-than-average educated, more into full-time employee jobs consumers (cluster 4) are at best indifferent towards all FCQ motivational factors, while they find ‘‘mood’’, ‘‘weight control’’ and ‘‘familiarity’’ in particular to be rather unimportant food selection motives. From the above it gets apparent that a tailor-made marketing strategy based on different combinations of food selection motives can have a substantial impact on as much as 90% of the market (clusters 1–3). Moreover, a further encouraging message for the predictive ability of the FCQ at the subpopulation level constitutes the finding that relevant motivation-based strategies could be also successful in more fragmented markets, as the successful FCQbased segmentation into more than four clusters indicates (see Table 6). Conclusion Empirical data of the present research do not support the robustness of the original 36-item FCQ as such – even upon exclusion of the problematic ‘‘ethical concern’’ dimension; neither do they confirm the formation of higher-abstraction dimensions based on a more ‘‘logical’’ psychometric restructuring of the original FCQ constructs. This inherent controversy between empirical evidence and reasoned, theory-based assumptions about more concrete versions seems to lie in the heart of the ‘‘problematic’’ behaviour of the original FCQ, which is not altered by the adoption of 7-point bipolar scales either. Results at the subpopulation level, however, indicate that some of the basic concepts of the original FCQ (i.e. sensory appeal and mood, or natural content and health) can form the basis for a new motivational typology. It is, therefore, reasonable to claim that future research towards developing an enhanced, FCQ-originated general food motivation typology, which would incorporate fewer, of higher abstraction level dimensions and fewer items per dimension, is a right step forward. Such an enhanced motivational typology, however, must be supplemented by a number of food selection constructs that are apparently missing from the current FCQ conceptual framework. Such currently overlooked concepts can be various multidimensional constructs, such as: (a) general food safety perceptions, with emphasis put on the microbial dimension, not just the chemical component of safety; (b) ethical concerns, with emphasis put on the environmental protection and overall ‘‘green’’ consumption attitudes; (c) various personality traits similar to the FCQ’s ‘‘familiarity’’, such as ‘‘variety seeking’’, ‘‘innovativeness’’, ‘‘loyalty’’, ‘‘involvement’’ etc.; (d) food purchasing context-related constructs apart from ‘‘convenience’’, such as ‘‘satisfaction’’ in a specific retail outlet, etc.; and (e) ‘‘quality consciousness’’ and ‘‘use of label cues’’, such as brand name influences, search for quality assurances, etc. Nevertheless, such a step should be theory-driven-thus taken a priori – and not be the outcome of an ad hoc data-driven statistical process, as well as the outcome of rigorous cross-national statistical testing and validation. Acknowledgement The authors wish to express their gratitude to the Greek Ministry of Agricultural Development and Food for the financial support provided for the accomplishment of this survey.

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