A comparison of three laddering techniques applied to an example of a complex food choice

Food Quality and Preference 15 (2004) 569–583 www.elsevier.com/locate/foodqual

A comparison of three laddering techniques applied to an example of a complex food choice C.G. Russell a, A. Busson b, I. Flight a, J. Bryan a, J.A. van Lawick van Pabst c, D.N. Cox a,* a

Consumer Science Program CSIRO, Health Sciences and Nutrition, P.O. Box 10041 Adelaide BC, SA 5000, Australia b National Institute of Agri-Food industries and Management [INSFA], Rennes, France c Unilever Research Vlaardingen, P.O. Box 114 3133 AC, Vlaardingen, The Netherlands Received 8 June 2003; received in revised form 12 November 2003; accepted 30 November 2003 Available online 29 January 2004

Abstract Laddering techniques (means-end-chains) have become popular as a means of understanding consumers’ motivations for (food) product choice. Comparisons of the output of interview (soft) laddering (SL, n ¼ 49) were made with two forms of questionnairebased (hard) laddering, pencil-and-paper (PL, n ¼ 46) and computerised presentations (CL, n ¼ 45). Within the context of mothers choosing breakfast for their children, the aim was to assess whether the form of administration would have a differential effect upon results. The laddering methods produced different results. Hard laddering produced more ladders (CL > PL > SL; p < 0:01) when values were excluded whereas SL produced more linkages between levels of abstraction (SL > CL > PL; p < 0:01), though constructs were similar across all groups. Differences were attributable to administration, which in turn was interpreted to be attributable to differences in participants’ cognitive processing, specifically: memory recall (SL) versus recognition (PL and CL). The SL primary result, the hierarchical value map, was difficult to interpret and, contrary to previous literature, the results question the use of SL when a succinct understanding of complex food choices is the aim of the study.
2003 Elsevier Ltd. All rights reserved. Keywords: Means-end-chains; Interviews; Computerised laddering; Pencil-and-paper laddering; Hard laddering; Soft laddering

1. Introduction Means-end-chain theory, frequently operationalised as laddering (Reynolds & Gutman, 1988), or less often as the association pattern technique (APT) (Feunekes & den Hoed, 2001; ter Hofstede, Audenaert, Steenkamp, & Wedel, 1998) has been applied to assist in the understanding of consumers’ motivations for various product choices, including foods. Researchers have indicated that these methods are useful for understanding the meaning behind important product attributes, yet many methodological issues remain unanswered in relation to this approach of understanding (food) product choice (Grunert & Grunert, 1995). For example, it is unclear how adaptations of the method of administration, such *

Corresponding author. Tel.: +61-8-8303-8811; fax: +61-8-83038899. E-mail address: [email protected] (D.N. Cox). 0950-3293/$ - see front matter
2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2003.11.007

as using questionnaires (so-called ‘‘hard laddering’’) rather than interviews (so-called ‘‘soft’’ laddering), might influence results. In 1995 Grunert and colleagues (Grunert, Grunert, & Sorensen, 1995) suggested that comparisons should be made between hard and soft laddering techniques to determine whether hard laddering would lead to similar results to those obtained with soft laddering. Hard laddering holds advantages over soft laddering in that it is quicker, cheaper, and less prone to interviewer bias (Grunert & Grunert, 1995). Furthermore, it is possible that a computerised hard laddering methodology may provide these benefits to an even greater degree due to automation of data collection and the relative anonymity provided by interacting with a computer and minimal interaction with researchers. Therefore, it would be useful to understand under what circumstances hard laddering could be used in preference to soft laddering.

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Soft laddering, which utilises individual, face-to-face, semi-structured interviews to elicit consumers’ meansend-chains, is the original and to date, the most commonly used laddering method for researchers. In the context of a soft laddering interview consumers are prompted to ‘‘ladder’’ their way up means-end-chains (M-E-C) to reveal in-depth information about the connections between products, or product attributes and the consequences and values attributable to those products. Each M-E-C typically ends in a value (e.g. Kahle, Rose, & Shoham, 2000; Rokeach, 1973) which, the theory purports, is considered to be a primary driver of product choice (Audenaert & Steenkamp, 1997). Recently, hard laddering and the APT, using paper-and-pencil or computerised questionnaires, have been developed as alternative methods for uncovering M-E-C (Botschen & Thelen, 1998; Russell et al., 2004; ter Hofstede et al., 1998). However it is unclear how the output of these methods compares to those from soft laddering interviews. Therefore, in the present study our first research aim was to determine whether the method of administration influences results. Botschen and Thelen (1998) provide some insight into this matter when, in a study comparing soft and penciland-paper hard laddering, the researchers concluded that both methods produced similar results with regard to identifying relevant characteristics (attributes, consequences) of products. This particular form of hard laddering (adapted from Walker & Olson, 1991) required respondents to write their answers into a sequence of boxes preceded by the statement ‘‘. . .and this is important for me, because then. . .’’. However, respondents were not permitted to ‘‘fork’’ (provide more than one reason why something is important to them) their answers in the hard laddering as they were in the soft laddering. Our examination of the resultant hierarchical value maps (HVMs) from Botschen and Thelen’s (1998) study indicated that whilst there were some similarities between the two, soft laddering produced a more complex HVM, and there was little overlap in the nature of the links; that is, the pattern of links produced by hard laddering was not a subset of those produced by soft laddering. This result was surprising, as similarities were evident between the attributes, consequences and values chosen, which was possibly reinforced by the cross-over design of the study. It is unclear as to why this occurred. However it is possible that differences in the results of two methods were not only related to differences in forking capability, but also to interviewer effects which may have influenced the elicitation of the cognitive structures and which may also be related to the complexity of the topic. Indeed, it is thought that the complexity of the topic can influence the outcome of the laddering (Botschen & Thelen, 1998; Grunert & Grunert, 1995). For example, in soft laddering with a complex topic the interviewer

will have more influence on the participants’ responses than for a simple topic (Grunert & Grunert, 1995) due to the need for deciphering, separating and interpreting ladders. In a complex topic it is therefore possible that hard laddering may produce results that are less influenced by the interviewer. Previously, soft laddering has successfully been used to understand simple (i.e. with a less complex cognitive structure) or complicated (i.e. those with a more sophisticated cognitive structure) matters surrounding motivations for product choice. However, there is little research examining the effectiveness of different laddering methods at various levels of cognitive complexity. Botschen and Thelen (1998) considered the topics in their study (fashion stores and women’s clothing items) to be of ‘‘medium comprehension’’ and suggested that further research on hard laddering should consider examining laddering on topics of more or less cognitive complexity. Nevertheless, it is unclear when, why or how differences may emerge between hard and soft laddering methods. Therefore our second research aim was to examine the output of different forms of laddering using what was anticipated to be a complex topic. Recently we developed two hard laddering methods. The first was administered via a computer, and the second took the form of a paper-and-pencil questionnaire (see Russell et al., 2004). In the paper-and-pencil hard laddering (PL) method, respondents were required to complete branching ‘‘charts’’. The computerised form of hard laddering (CL) required consumers to create their ladders by selecting items from two-column tables on a computer screen using a mouse click. In both of these methods (PL and CL) consumers were able to skip levels of abstraction and ‘‘fork’’ their answers (i.e. give more than one response as to why an attribute or consequence was important to them). This was thought to be more akin to soft laddering where consumers fork and skip without restraint. Both of these hard laddering methods used a priori lists (Audenaert & Steenkamp, 1997; Fotopoulos, Krystalis, & Ness, 2003; ValetteFlorence, 1998; Valette-Florence, Sirieix, Grunert, & Nielsen, 2000) pertaining to four levels of abstraction: attributes; physical consequences; psychosocial consequences and values (see below), from which participants were required to choose appropriate constructs. PL participants were required to write in their answers from the corresponding lists and to put a dash in those boxes where they considered there to be no appropriate responses available on the a priori lists. These participants were permitted to fork their responses to a maximum of 3 attributes, 9 physical consequences, 27 psychosocial consequences and 27 values. This design allowed participants access to previous responses. The CL participants were permitted to fork their responses to a maximum of 3 attributes, 9 physical consequences, 27 psychosocial consequences and 81 values. In individual

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booths, respondents were presented with the a priori lists as a 2-column table with a check box next to each item. On each screen, the research question was visible, along with the progression of responses so that participants could see the series of answers that led them to the current screen. However, participants were not permitted access to previous ladders. Whilst hard laddering using a priori lists or the APT (which also uses a priori lists) are potentially more efficient methods to conduct a laddering study, they also cue the participant and may hinder (do not facilitate) free ranging responses. Specifically, a priori lists of potential choices serve to remind participants of options they may be likely to make. Therefore, rather than engage in a self-initiated retrieval search of memory (freerecall), participants select and recognise particular options that are true for them amongst others. The use of a priori lists may have one of two effects on the results: (1) it may remind participants of options they may fail to spontaneously recall themselves––awakening unconscious but important constructs, or (2) it may limit responses to those provided and fail to detect the wider variety of individual differences that emerge under soft laddering. Thus accuracy of output may be affected by the method used (Baddeley, 1997). As reported in the earlier paper, the computerised and paper-and-pencil forms of hard laddering produced similar results with regard to the strongest links; however differences have been observed among the weaker links on the resultant hierarchical value maps (HVMs) (Russell et al., 2004). It was also apparent that consumers responded differently to the two presentation methods (Russell et al., 2004). For instance, participants in PL were less likely to choose the same link more than once, whereas CL participants did, hence PL selected a greater range of responses than CL participants. However, what is still unclear is how the two hard laddering methods compare to soft laddering. The previous paper compared two of three groups discussed in this present paper (CL and PL). The current paper includes a third group, enabling a comparison of hard with soft laddering, with a focus on the differences in methods these two approaches involved. We hypothesized that both hard laddering techniques (PL and CL) would not give the richness or the complexity of information that would be provided by respondents in soft laddering, due to restrictions imposed by the a priori lists at each level of abstraction. However, we also hypothesized that the main ‘‘themes’’ or the strongest chains would be similar between the two methods as these would be considered most important to consumers and would therefore be more frequently chosen and hence exceed the cut off level necessary to appear on HVM. We also expected CL rather than PL would produce results more similar to SL, because in the administration of both CL and SL participants are focussed on one ladder at a time and did not have access to

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their previous responses. The topic, mothers’ beliefs about the influence of breakfast foods on their child’s mental and physical health and well-being, was argued to have a complex cognitive structure which in turn was thought to highlight or exaggerate differences between the methods more so than a less complex topic.

2. Material and methods The topic used for the methodological comparison concerned mothers’ perceptions of the perceived role for food in influencing a child’s cognitive performance. Aspects are described in more detail in a previous paper (Russell et al., 2004). Mothers were chosen to participate as they were assumed to be the major ‘‘gatekeepers’’ of their children’s food and well-being (Hursti & Sjoden, 1997). Only mothers of children (5–7 years) enrolled in Year 1 at higher socio-economic status State junior primary schools in Adelaide, South Australia were chosen to participate in the current comparative study. Recruitment details are described in a previous paper (Russell et al., 2004). Briefly, participants in the computerised (CL) and soft laddering groups (SL) were recruited and sequentially allocated to either CL or SL upon recruitment. The PL participants were selected from a separate study, however, recruitment protocols were similar across all groups. Demographic variables. For the purposes of controlling for socio-demographic characteristics, background information, including age range, education, mother’s occupation and the age and gender of the child, was collected using a questionnaire. 2.1. Hard laddering The methods are described in detail in the earlier paper (Russell et al., 2004). Briefly, because previous qualitative work (unpublished) had suggested that children’s mental well-being was perceived to be achieved through the physical consequences of ingestion, items for four a priori lists (representing 4 levels of abstraction) were created pertaining to (a) attributes of breakfast foods; (b) physical consequences of ingestion; (c) psychosocial consequences (mental well-being) and (d) human values. Attributes and physical consequences were both elicited from focus groups. The psychosocial consequences were constructed as lay interpretations of items derived from the literature and were considered to be constructs that were both emerging with increasing age during childhood as well as sensitive to possible nutritional intervention (Hughes & Bryan, in press). These constructs were extensively tested for lay comprehension (unpublished data) whereby 32 mothers were asked to provide synonyms for the psychological

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constructs. Content analysis of those synonyms was undertaken by two independent cognitive psychologists (who were unaware of the constructs each synonym had been assigned to) and the synonyms classified as the psychological terms. Results suggested broad similarities between the lay descriptions and the experts’ classifications thus providing evidence of an understanding of the constructs. Kahle et al’s List of Values (9) (Kahle et al., 2000) was chosen as a concise and pertinent set of values relevant to mothers’ wishes for their children. Lists were refined by internal and external pilot testing. Lists were presented in four randomised orders and participants were sequentially assigned to a list order. For CL, lists were presented on screen as a 2-column table with a check box next to each item. On each screen, the research question was visible, along with the progression of responses so that participants could see the series of answers that led them to the current screen. For the PL, printed lists of the attributes, consequences and values were provided to each respondent to accompany the ‘‘charts’’. For both versions of hard laddering, the initial question posed for the first list (attributes) was ‘‘If I were to choose a breakfast for my child, it is important that it. . .’’. The succeeding lists were preceded by the statement ‘‘. . .and this is important for me, because then my child. . .’’. Participants were asked to select up to three important attributes or consequences, with a minimum of one attribute to be chosen initially. Trained interviewers (including authors AB, IF, CGR) explained to the participants how to complete the tasks using a nonfood example.

were taped for subsequent transcription and textual analysis. 2.3. Data preparation

2.2. Soft laddering

2.3.1. Soft laddering The soft laddering (SL) data were analysed in the same manner as Reynolds and Gutman (1988). Interview tapes were transcribed and transformed into a series of hierarchical ladders containing the key elements, from attribute to highest level of abstraction. The two interviewers separately assigned a construct category code to the elements of each ladder produced by every participant. To create a meaningful comparison between SL and the two hard laddering methods elements were coded to the same construct categories as those presented in the hard laddering procedure. Otherwise, new categories were created. Differences in coding were resolved by discussion and consensus between the two interviewers. Interviewers then separately coded all elements and comparisons were made and differences resolved through discussion. Negative elements were reversed to become positive for example, phrases such as ‘‘not get sick’’, ‘‘not sick all the time’’ and ‘‘not be weak’’ were categorised into ‘‘good health’’. We recognise that such treatment may not precisely reflect recent observations that doing well at moving toward an incentive is not necessarily the same experience as doing well at moving away from a threat (Carver, 2001; Carver & Scheier, 1998). Within one methodology designed to elicit why product attributes are important to consumers, this issue would be important. However, our methodological study specifically addressed comparability of output across three methods necessitating consistent coding.

Soft laddering was conducted by two trained interviewers (authors AB and IF). Participants in the soft laddering group were presented with the same a priori concrete attributes list as presented to the hard laddering participants (four different randomisations, which were allocated in block fashion to mothers). In the same manner as the hard laddering formats, mothers were first asked to choose between 1 and 3 items from the attribute list which best described what they considered to be the most important attribute if they were to choose a breakfast for their child. The interviewer went through each attribute chosen, asking the mother ‘‘and why is that important for your child?’’. The answer to this question was followed by further similar probes. Where more than one reason was nominated as to why an attribute or consequence was important, each reason was probed by the interviewer, thus allowing Ôforking’ of answers. The procedure continued until the respondent could provide no further answers. The same procedure was repeated for all chosen attributes. The interviews

2.3.2. Hard laddering The hard laddering (HL) data were also analysed in the same manner as Reynolds and Gutman (1988) and this method is described in more detail in the earlier paper (Russell et al., 2004). The data collected from the (PL) required manual entry into the database and was analysed using the same procedures as for the (CL), where data were handled electronically and available immediately. Hierarchical value maps (HVMs) were created for each laddering method from an aggregate implication matrix using top-down cut off procedure (Leppard, Russell, & Cox, in press; Russell et al., 2004). Choosing a cut-off of the top 4 provides the four Ômost important’ (most frequently chosen) links between two levels of abstraction. Using the top 4 reported links from one level of abstraction to another, rather than an absolute value, standardises the results for the number of ladders in the data, hence the problems of too low, or having non-meaningful results are reduced and differences in

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frequencies of selecting between the three laddering methods would not be reflected on the maps. The possibility of ties was allowed, meaning that more than four items could be included in the HVM at one particular level of abstraction when the cut-off was 4. This cut-off method was chosen because the magnitude of the entries in the structured implication matrix (SIM) varies according to number of participants as well as the total number of links chosen by the group of participants. The top-down cut-off approach allows only the most frequently chosen links between two levels of abstraction to be included on the HVM (Leppard et al., in press). For a specific laddering study, the number of links between the different levels of abstraction is not necessarily of the same numerical size and so this procedure adjusts to the variations in the entries in the SIM. For instance, even if many more links are made between the attribute and physical consequence levels than between the psychosocial consequence and the value levels, a HVM derived from this procedure will reflect the most frequent (and therefore most important) links between any two levels relative to the total number of links made at that level by that particular group of participants. Conversely, links occurring at a frequency less than the cut-off value chosen by this procedure indicate associations considered to be less important (Ônoise’) and are therefore not included on the HVM. The advantage of this top-down method is that the researcher can be certain that the links on the HVM are the most important relative to the other links at that level of abstraction. This also makes the results more comparable between groups of participants when variations in the numbers of links are elicited. In addition, using the top down procedure to determine cut-off values, rather than an absolute value as a cut-off, standardises the results for the number of ladders in the data. Hence the problems of too low, or having nonmeaningful results are reduced. The entries in the cells of the SIM are the basis of extracting ladders, where entries higher than a selected cut-off value are considered evidence of a significant linkage between two items from two different levels of abstraction and thus important enough to be included on the HVM. In the present study the SIM was based on direct linkages only, with responses considered to be direct when a level of abstraction was ‘‘skipped’’. This allowed, for example, for both direct links between attributes and psychological consequences and also via the intermediary of physical consequences. A cut-off level which we refer to as the ‘‘top 4’’ was determined in the following way. In the first instance, cells containing entries representing associations between attributes and physical consequences were examined by the data handling program referred to above. By inspection of the cells in the SIM linking attributes to physical consequences, the fourth largest entry was

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chosen as the cut-off value. All entries greater than this value (i.e. the top 4 most frequently chosen links) were included as part of the resulting HVM. This top-down procedure allowed for the possibility of ties in the entries of the SIM. On the HVM, frequencies were ‘‘normed’’ by converting to percentages.

3. Results 3.1. Participants Forty nine participants completed the SL; 45 participants the CL and 46 participants the PL. No significant differences were observed between the groups for gender of child, country of birth, mother’s age, whether the mother and/or child was on a special diet, mother’s education and employment status, ðp > 0:05Þ. 3.2. Ladders and chains A Ôladder’ is defined as one participant’s sequence of responses from attribute to a higher level of abstraction. Such ladders are decomposed into their direct components and then reconstructed into aggregate data to form Ôchains’, the sequence of elements which emerge from the aggregate implication matrix and are graphically displayed in the HVM as representative of several individuals’ ladders (Reynolds & Gutman, 1988). Up to 8 elements in a chain appeared on the SL HVM (Fig. 1), with a range of 2–10 links being created by individuals in each ladder (Table 1). CL and PL respondents were restricted to a maximum of 4 levels of abstraction. That is, each individual participant could have a maximum of four elements in each chain (hence ranges 1–4 (CL) and 2–4 (PL) respectively); whereas soft laddering respondents were free to choose as many or as few as they liked, Table 1. 3.3. HVMs The HVMs represent the main output from the studies and are portrayed in Figs. 1–3. For added depth of information the HVMs follow the suggestions of Gengler, Klenosky, and Mulvey (1995) and are keyed in the following way: All three HVMs use a cut-off of the top 4. The size of each Ônode’ (circle) represents the percentage of occurrences of a particular node of all nodes within the particular level of abstraction (attribute, consequence, value). The actual percentage is reported in each node. The thickness of each line represents the strength of connection between nodes. The percentage reported next to each line represents the number of occurrences of a particular link out of the total number of links emanating from that node.

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Self respect 21.28 %

22%

8% Have fun & be happy 27.36 %

Sense of accomplishment 10.94 %

Better attitude to study 5.54 %

Same level as their peers 4.64%

8%

6%

14.7 %

Sense of belonging 10.03 %

16%

10%

Warm relationships 8.51 %

24%

Learn new things 14.64 %

10%

23% 19% 6%

Even tempered 12.14 % Comprehension skills 3.93%

Able to physically attend school 1.72 %

6%

6%

6%

27%

19%

20%

21.4% Does well at sport 6.03 %

5%

33% 20%

Not tired 5.93 %

Think quickly 5.36%

12% Good health 19.29 %

17%

Has had enough food 6.03% More energy 11.75 %

24% 25% Easy to digest Good metabolism 2.91 % 5.50 %

Grows well 9.05 % 51%

High in calcium 11.55 %

28% Rich in vitamins & minerals 11.37%

31%

16%

16%

43%

6%

11% 8%

16%

17%

5%

16%

Concentrate, stay focused 19.11 %

6%

Physically active 4.74 %

11%

7%

8%

16% Communicate 5.54%

Perform at optimum level 11.43 %

8%

27% Nutritious food 5.28 %

60%

Has a good appetite 8.41 %

31% 89% High in fibre 11.90 %

High in carbohydrate 9.77%

97% Familiar product 3.20%

Tastes good 6.04%

Fig. 1. Soft laddering hierarchical value map using a cut-off of the top 4.

Table 1 Mean (standard error, SE) number of ladders generated by participants and t-tests of differences by administration method (Soft, SL; Computerised, CL, and Pencil-and-Paper, PL) Number of ladders including values Number of ladders without values Number of links per ladder Links per ladder (range) a–h

SL ðn ¼ 49Þ

CL ðn ¼ 45Þ

PL ðn ¼ 46Þ

10.18 (0.65)a 8.45 (0.45)d 4.77 (0.08)g 2–10

36.40 (3.09)b 13.78 (0.96)e 3.45 (0.81)h 1–4

20.59 (0.75)c 18.35 (0.81)f 3.52 (0.02)h 2–4

Values not sharing the same superscript letter were significantly different ðp < 0:01Þ from each other within the variable.

3.4. Soft laddering HVM The coding process resulted in 1485 elements being coded into a total of 94 Attributes, Consequences or Values (ACVs). There were 17 extra constructs (additional to the items in the a priori lists utilised for the two hard laddering methods) created. Key to HVMs value psychosocial consequence physical consequence attribute

 

3.5. Frequency of selection, abstractness and centrality Analysis of the frequency of selection, the abstractness and the centrality of the constructs in SL, PL and CL showed many similarities (Tables 2–5). In particular,

the abstractness and centrality (Pieters, Baumgartner, & Allen, 1995) as well as the relative frequency of the selection of attributes, physical consequences, psychosocial consequences and values were similar across the three laddering methods (Tables 2–5). Pearson’s correlation coefficients for abstraction values across the three methods were SL–CL r ¼ 0:64; SL–PL r ¼ 0:64 and PL–CL r ¼ 0:94 (all p < 0:01) indicating high convergent validity. Similarly for centrality values the correlations across the three methods were also very high: SL–CL r ¼ 0:81; SL–PL r ¼ 0:80 and PL–CL r ¼ 0:93 (all p < 0:01). The relative frequency was calculated as how often a particular construct was selected out of the total number of selections made within that level of abstraction. Despite this, the frequency at which each element was chosen differed markedly between the methods. CL produced the largest frequencies, followed by PL, then SL. Some anomalies were present at each level; for example high in protein was not chosen in the

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Self - fulfilment

Accomplishment 24.98%

Self respect 20.61%

19.93%

21%

26%

575

17%

24%

Learn new things 19.45%

Concentrate 28.26%

18%

17%

21%

32%

21% Grows well 14.88%

Good health 27.79%

More energy 15.44%

Not hungry 8.98%

26% 30%

33% 31% Rich in vitamins & minerals 14.57%

Low in sugar 14.57%

High in fibre 15.60%

Fig. 2. Computerised ‘‘hard’’ laddering hierarchical value map using a cut-off of the top 4.

Relationships 8.37%

Excitement 9.15%

Self - respect 13.95%

23%

Self fulfilment 15.18%

Accomplishment 23.10%

36% 19%

55% Better attitude towards study 11.56% Communicate 4.83%

49%

Learn new things 10.8 0 %

11%

Concentrate 22.62%

16% 19%

Grows well 13.06%

Good health 21.11%

27% Not hungry 7.50%

19% 29% 27%

Rich in vitamins & minerals 17.23%

High fibre 14.04%

27%

Low sugar 13.31%

Fig. 3. Pencil-and-paper ‘‘hard’’ laddering hierarchical value map using a cut-off of the top 4.

PL, in contrast to the other two methods where it was chosen 33 times (SL) and 216 times (CL). Five extra

constructs were created for the SL at the physical consequence level. Eight new categories were created for the

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Table 2 Top ten ranked frequencies of chosen attributes for the three laddering techniques Computer laddering

Paper laddering

Construct

Abstractness

Centrality

Frequency (%)

Construct

Abstractness

Centrality

Frequency (%)

Construct

Abstractness

Centrality

Frequency (%)

High in fibre

0.07

0.04

67 (12%)

High in fibre

0.00

0.06

316 (16%)

0.00

0.07

189 (17%)

High in calcium Rich in vitamins and minerals High in carbohydrate Low in sugar

0.19

0.04

65 (12%)

Low in sugar

0.00

0.05

295 (15%)

Rich in vitamins and minerals High in fibre

0.00

0.05

154 (14%)

0.16

0.04

64 (11%)

0.00

0.06

295 (15%)

Tastes good

0.00

0.05

153 (14%)

0.02

0.03

55 (10%)

0.00

0.04

216 (11%)

Low in sugar

0.00

0.05

146 (13%)

0.11

0.03

51 (9%)

Rich in vitamins and minerals High in protein Tastes good

0.00

0.03

193 (10%)

0.00

0.05

141 (13%)

0.04

0.03

51 (9%)

0.00

0.04

181 (9%)

0.00

0.03

99 (9%)

0.08

0.02

34 (6%)

0.00

0.03

155 (8%)

0.00

0.03

66 (6%)

0.25

0.02

33 (6%)

0.00

0.02

99 (5%)

0.00

0.01

45 (4%)

0.06

0.02

32 (6%)

0.00

0.02

79 (4%)

Natural product High in carbohydrate Contains dairy food Contains fruit Low in fat

0.00

0.01

33 (3%)

0.05

0.01

20 (4%)

0.00

0.02

76 (4%)

0.00

0.01

33 (3%)

Contains dairy food Tastes good High in protein Natural product Contains fruit

High in carbohydrate Contains dairy food Natural product Low in fat High in calcium

High in calcium

C.G. Russell et al. / Food Quality and Preference 15 (2004) 569–583

Soft laddering

48 (4%) 0.03

4. Discussion

0.51

0.02

46 (3%)

0.51

51 (5%) 0.03 0.50

0.03

64 (4%)

0.51

54 (5%) 0.03 0.03 0.50

Consequences in italics are unique to the soft laddering. a

44 (5%) 0.04 0.57 Is physically activea

49 (5%) 0.05 0.53

577

soft laddering at the psychosocial consequence level. These extra categories accounted for 40% of psychosocial consequences chosen by the soft laddering participants. There was also evidence to suggest that whilst participants in the SL created extra categories, participants in PL and CL chose psychosocial consequences that participants in soft laddering did not, for example hold things in mind while working on them was chosen 30 times by CL participants, and 26 times by PL participants, but zero times by SL participants. With regard to the values, there was greater similarity in the values chosen by participants in the CL and PL compared to SL. Four extra value constructs were created for the SL.

Has a good metabolism Has had enough food Not become overweight 51 (6%) 0.05 0.55

Has a good metabolism Nutritious food a

55 (6%)

56 (6%)

69 (4%)

0.51

63 (6%) 0.04 0.06 0.51

0.50

0.03

70 (4%)

0.51

63 (6%) 0.05 0.06 0.51

0.50

0.04

95 (5%)

0.50

63 (6%) 0.04 0.50 56 (6%)

179 (19%) 109 (12%) 84 (9%) 78 (8%)

Mentally stimulated Sustains their blood sugar level Not tired 0.06 0.52

0.50

0.04

113 (6%)

Not become overweight Mentally stimulated Has a good metabolism Sustains their blood sugar level Has a good appetite Has had enough food

228 (21%) 141 (13%) 105 (10%) 81 (7%) 0.50 0.50 0.50 0.50 Good health Grows well More energy Not hungry

Centrality

Good health More energy Grows well Not hungry 0.17 0.11 0.08 0.08 0.56 0.53 0.54 0.51

Good health More energy Grows well Has a good appetite Has had enough food Does well at sports Not tired

0.50 0.50 0.50 0.50

0.18 0.10 0.11 0.05

495 (28%) 275 (15%) 265 (15%) 160 (9%)

0.17 0.09 0.07 0.05

Abstractness Construct Frequency (%) Centrality Abstractness Construct Abstractness Construct

Frequency (%)

Computer laddering Soft laddering

Table 3 Physical consequences top ten ranked frequencies of chosen physical consequences for the three laddering techniques

Paper laddering

Centrality

Frequency (%)

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No definitive conclusions can be drawn from the present research about which method of laddering provided the most accurate or unbiased responses; however the comparison of the three laddering methodologies provided insight into why such differences may emerge and suggests that researchers using adaptations of laddering will need to be aware of how the chosen elicitation method may produce differences in the linkages appearing on the resultant HVM. There were a number of similarities between the three methods, such as the relative frequency at which constructs were chosen, the abstractness of constructs and their centrality, however the results from the hard laddering methods (PL and CL) were more similar to each other than either one was to the result of the soft laddering method with regard to which items were frequently chosen and particularly the connections between the items as portrayed in the HVMs. Whilst the cut-off level of the top 4 was the same for all methods, the HVMs of the CL and PL were uncomplicated (Figs. 2 and 3) compared to the soft laddering HVM (Fig. 1). The soft laddering HVM was complex in that many lines crossed and links were evident in both directions between elements within the same and different levels of abstraction, especially at the psychosocial consequence level. This variation in complexity was expected and confirms previous research (Botschen & Thelen, 1998) as well as the complex nature of the topic revealed by the sophisticated cognitive structures on the SL HVM. Also, as expected, some of the more frequently chosen links were found on all three methods (e.g. high in carbohydrate fi more energy); and there were some instances when direct links in CL and PL were indirect in SL, as one might expect when consumers are allowed to incorporate more levels of abstraction into each ladder. For instance, the link high in fibre fi good health emerged as a direct relationship from the CL and PL methods, however in SL has a good metabolism was a mediator between these constructs. In addition, the main

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Table 4 Top ten ranked frequencies of chosen psychosocial consequences for the three laddering techniques Computer laddering

Paper laddering

Construct

Abstractness

Centrality

Frequency (%)

Construct

Abstractness

Centrality

Frequency (%)

Construct

Abstractness

Centrality

Frequency (%)

Concentrate, stay focussed Learns new things

0.53

0.11

107 (19%)

0.51

0.16

449 (28%)

0.13

178 (23%)

0.08

82 (15%)

0.50

0.12

309 (19%)

0.52

0.07

91 (12%)

Is even tempered a Perform at their optimum level a Communicate

0.55 0.58

0.07 0.06

68 (12%) 64 (11%)

0.50 0.50

0.04 0.04

110 (7%) 102 (6%)

Concentrate, stay focussed Better attitude to study Learns new things Think quickly

0.52

0.58

Concentrate, stay focussed Learns new things

0.50 0.52

0.06 0.04

85 (11%) 55 (7%)

0.52

0.03

31 (6%)

Better attitude to study Think quickly

0.55

0.03

0.53

Be at the same level as their peers a Comprehension skills Participate in class activities a a

0.51

0.03

93 (7%)

Communicate

0.51

0.03

38 (5%)

31 (6%)

Communicate Finds solutions to problems Better attitude to study Think quickly

0.51

0.04

85 (5%)

Common sense

0.51

0.03

37 (5%)

0.03

30 (5%)

Think of new ideas

0.50

0.03

75 (5%)

0.50

0.03

32 (4%)

0.55

0.03

26 (5%)

More initiative

0.50

0.03

72 (5%)

Finds solutions to problems More initiative

0.51

0.02

30 (4%)

0.67

0.02

22 (4%)

Better organised

0.50

0.02

51 (3%)

Better organised

0.53

0.03

29 (4%)

0.57

0.02

17 (3%)

Comprehension skills

0.51

0.03

46 (3%)

Hold things in mind while working on them

0.53

0.02

26 (3%)

Consequences in italics are unique to the soft laddering.

C.G. Russell et al. / Food Quality and Preference 15 (2004) 569–583

Soft laddering

Table 5 Top ranked frequencies of participants chosen values for the three laddering methods Computer laddering

Paper laddering

Construct

Abstractness

Centrality

Frequency

Construct

Abstractness

Centrality

Frequency

Construct

Abstractness

Centrality

Frequency

Have fun

0.83

0.06

90 (27%)

1.00

0.09

400 (25%)

0.08

207 (23%)

0.85 0.72

0.04 0.03

70 (21%) 36 (11%)

1.00 1.00

0.07 0.07

330 (21%) 319 (20%)

Sense of accomplishment Self-fulfilment Have fun

1.00

Have self respect Sense of accomplishment Sense of belonging Relationships Have security

Sense of accomplishment Have self respect Self-fulfilment

1.00 1.00

0.05 0.05

136 (15%) 129 (14%)

0.64

0.03

33 (10%)

Have fun

1.00

0.07

296 (18%)

Have self respect

1.00

0.05

125 (14%)

0.68 0.79

0.02 0.02

28 (9%) 23 (7%)

1.00 1.00

0.02 0.01

79 (5%) 62 (4%)

Excitement Relationships

1.00 1.00

0.03 0.03

82 (9%) 75 (8%)

Self-fulfilment

0.73

0.01

16 (5%)

1.00

0.01

51 (3%)

0.02

48 (5%)

0.69

0.01

9 (3%)

1.00

0.01

46 (3%)

1.00

0.02

47 (5%)

Mother wants the best for her child a Respect, be admired A role in society a Saving money a Mother’s happiness a

0.75

0.01

9 (3%)

Have security

1.00

0.00

18 (1%)

Respect be admired Sense of belonging Have security

1.00

Excitement

Excitement Respect, be admired Sense of belonging Relationships

1.00

0.02

47 (5%)

0.64

0.01

7 (2%)

0.80 1.00 1.00

0.00 0.00 0.00

4 (1%) 2 (1%) 2 (1%)

a

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Soft laddering

Consequences in italics are unique to the soft laddering.

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(strongest) links on the three HVMs were not the same between the three methods. This is similar to the results of the Botschen and Thelen’s (1998) comparative study, where participants chose similar constructs but the linkages between the constructs differed according to administration. It is unclear as to why this occurs, but the evidence suggests it could be attributable to the differing presentation methods and therefore differing response methods. Furthermore it suggests that the differential results are independent of the use of a priori lists (derived from focus groups and the extant literature) as in our study, or, the open responses allowed in Botschen and Thelen’s (1998) hard laddering method. The response differences between the three laddering techniques were highlighted in the frequencies of each element chosen and the number of ladders generated by each participant. The most frequently chosen elements at each level of abstraction were similar across the three laddering methods. However, there were differences in the individual frequencies of each construct chosen, respectively CL > PL > SL. Despite the fact that the CL participants chose more constructs, the respondents in the PL produced the greatest number of ladders (when values were excluded from analysis). This suggested that PL participants were choosing many different constructs and a more diverse range of responses, compared to SL or CL participants, with the frequency at which each element chosen being less than CL. This difference may be due to the ease of the CL presentation (i.e. participants were only required to mouse-click on boxes) and additionally the lack of awareness that CL respondents had of repetition because they were obliged to focus on only one link at a time and were therefore likely to repeat themselves, whereas in PL participants were required to write in their answers and had access to their previous responses (hence were able to compare and contrast their own responses). In SL participants were conversing with an interviewer where repetition may be perceived to be socially undesirable. In this respect, CL may be more useful for sensitive or potentially embarrassing subject matters where the absence of an interviewer may allow the participant more freedom to choose truly, however this needs further investigation. It was also apparent that participants in the SL created additional categories not available in the a priori lists in the PL and CL administrations. In the SL five new categories were created at the physical consequence level of abstraction, eight at the psychosocial consequence level and four at the values level. This suggested that the a priori lists did not provide all possible range of answers surrounding the topic. Having said this, there were many similarities in the relative frequency at which each construct was chosen as well as the abstractness and centrality of each construct, which suggests congruency across methods. SL participants were also less likely to choose some specific items which were available in the

a priori lists and were frequently chosen by both PL and CL participants. For instance hold things in mind while working on them was chosen 30 times by CL participants, and 26 times by PL participants, but not at all by SL. It is possible that when this working memory construct was presented as an option in the PL and CL, these participants recognised it as an important aspect of their child’s cognition. Participants may be unlikely to recall this construct spontaneously in SL, perhaps because mothers are more likely to be focussed on the practical outcomes of cognitive performance in their children like school performance and did not think about more purely cognitive constructs like working memory. Grunert and Grunert (1995) make a distinction between cognitive structures and cognitive processes surrounding product choice, and suggest that a laddering interview is an estimate of both cognitive structure and cognitive processes. They define a cognitive structure as ‘‘the organization of experience and other types of information in human memory’’ (p. 210) and cognitive processes as ‘‘the processes by which the cognitive structures are changed due to the information from the environment, and by which information is retrieved from the cognitive structures to direct behaviour’’ (p. 211). Because the memory processes required to respond are likely to be different in the CL and PL compared to the SL due to the different task demand characteristics (i.e. the presentation method), the excerpts of cognitive structures surrounding the current product choice which were elicited in the laddering interview were also different. In SL, the memory processes required to respond are thought to be those described by the spreading activation theory (Anderson, 1983; Grunert & Grunert, 1995). This theory suggests that semantic (general knowledge) and episodic (personally relevant) memory ‘‘events’’ are concepts that are stored as interconnected nodes. The links between nodes are strengthened with use. Once a node is activated (i.e. elevated into working memory) by the laddering prompt (‘‘and why is that important. . .?’’) this activation spreads to related nodes (in laddering studies are linked by subjective causality) making any related attributes and associates (consequences and values) more accessible. In hard laddering (PL and CL), participants responded to the cues provided to them in the lists. Thus their responses may reflect a decision based on familiarity, rather than via the spreading activation processes. In the current study differing administration effects on the cognitive processes were demonstrated by the differences portrayed in the HVMs. The present research suggests that cognitive processes influence the elicitation of the cognitive structures surrounding this product choice. That is, dissimilar connections were formulated between hard and soft laddering participants even when similar elements were selected as important. For instance, in both hard laddering methods (CL and PL) participants were likely to

C.G. Russell et al. / Food Quality and Preference 15 (2004) 569–583

form a connection between good health and concentrate, stay focussed and did so numerous times (105 times in CL, and 43 times for PL). However SL participants did not generate this link strongly (not enough times to appear on the HVM), despite the fact that both concepts concentrate, stay focussed and good health appeared on the SL HVM. This suggests that this particular link is likely to be deemed familiar or ‘‘true’’, and it is therefore selected, only when it is suggested to the participant by its inclusion in the a priori lists. If the participant is not prompted, he or she is not likely to make this connection. It is likely that the responses elicited by SL may be more representative of the actual range of a person’s cognitive structure and the topic of interest than those elicited by hard laddering. However, it is unclear as to which method of elicitation is a better reflection of the motivations behind product choice. Our study supports concerns expressed by Grunert and Grunert (1995) that the ‘‘accuracy’’ of a hard-laddering method is crucially dependent on the range of a priori choices provided. Whilst lists are usually (as in our study) derived from small groups of participants with similar characteristics to the population of interest there is no published evidence, that we are aware of, that has checked the validity of such lists through, for example, replication. Grunert and Grunert (1995) suggested that a M–E–C researcher’s goal should be to ‘‘aim for an estimate of cognitive structure and cognitive processes which will be the most useful in explaining/predicting the kind of behaviour we are interested in.’’ (p. 212). In the context of a hard laddering study using a priori lists, it is likely that by providing a specific list from which participants choose and which contains items in which a researcher may be interested increases the likelihood that those items will be selected, even though participants were given the option to select nothing. This can be considered both advantageous and disadvantageous, depending on the objectives of the study. When the topic is more subtle, less involving, or the interest is only in a specific subset of possible responses, then this can be a positive aspect of the technique. However, if the purpose of the study is to gain a fuller picture of the participants’ cognitive structures, generate new ideas, possibilities or intricacies of the beliefs surrounding a group of products, then it is likely that soft laddering would provide this information. The topic in this study was complex, in that mothers were not only asked to consider their own preferences but also to nominate (at the consequences and values levels of abstraction) what their child was likely to consider important and what values their child was likely to aim for and so differences in the response patterns of consumers may be exaggerated beyond that of a simple laddering topic. Consumers were free to provide complex and detailed responses in SL; indeed the resulting SL HVM would seem to be a reflection of what Grunert and Grunert (1995) termed ‘‘considerable

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strategic processing’’ (p 216). However, the complexity of the SL HVM may be partially attributable to interviewer effects. With a complex topic, interviewer effects are likely to be greater than with a simple topic as the interviewer is required to cognitively process the participants’ responses in terms of deciphering, separating and interpreting ladders (Grunert et al., 1995). With complex topics, participants are likely to move in a nonhierarchical way between levels of abstraction, as demonstrated in the SL HVMs in our study. Hence, the SL HVM is multidirectional. In addition to this, care had to be taken about the meaning of the extra categories created in the SL by the coding process. Categories that were too broad would lead to a loss of information whereas those that were too specific would pose a problem in the next steps. Because categories cannot be grouped together too widely, elements that may be meaningful without being really precise may not appear on the map. This should be considered in future soft laddering studies with complex topics. We had expected that CL rather than PL would produce results more similar to SL, as we considered the methods more comparable; that is participants were focussed on one ladder at a time and did not have access to their previous responses. It is difficult to say which hard laddering method was more comparable to soft laddering, however the results suggest that PL was more similar to SL than CL due to a lower frequency of responses and the production of a more complex HVM. Differences between the PL and CL have been discussed in an earlier paper (Russell et al., 2004). However, whilst it is evident that PL and CL share many similarities in outcome (Russell et al., 2004), no firm conclusions can be drawn about whether PL or CL produce results more analogous to SL at this stage. A possible limitation of our study and a confounder was that we adopted a between-subjects design. We could have attempted to adopt a within-subjects (crossover) design as undertaken by Botschen and Thelen (1998), however, a ‘‘learning effect’’ may have occurred in this study and we thought it important to avoid such an effect. Consequently, we adopted a between-subject design with subjects matched for socio-demographic characteristics. It should also be noted that dissimilarities occurred in the results of the Botschen and Thelen (1998) within-subjects study. Clearly there is a need for future studies to make comparisons between hard laddering methods that allow free expression of attributes, consequences and values (i.e do not rely on a priori lists) and soft (interview) laddering.

5. Conclusions The soft laddering method uncovered the idiosyncrasies of perceptions and beliefs surrounding food

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choice. The soft laddering HVM provided a complex pictorial representation of how the group of consumers perceived the links between foods and consequences and values. In the two hard laddering methods, in particular CL, participants chose the same elements a number of times, more so than in SL. The HVMs from CL and PL were less complicated than the SL and there were more direct links; however, they lacked the detail of the soft laddering HVM. The usefulness of the laddering output would depend on the researcher’s specific goals. If the aim of the study is to uncover an unprompted broader and more detailed picture of people’s perceptions and beliefs, then soft laddering would seem to be appropriate. If the purpose is to investigate strong links between certain pre-determined elements, then hard laddering with a priori lists would be more suitable. As the original manifestation of the means-end-chain theory, a theory apparently rooted in a robust theory of memory (spreading activation theory), soft laddering could be considered to be a ‘‘gold standard’’ and Grunert and Grunert (1995) have previously argued that complex subjects require soft laddering methods. However, as our study has demonstrated, soft laddering may not be so useful when the purpose of research is to explain complex phenomena in a succinct and useful way. It remains to be seen, however, whether soft or hard laddering is more predictive of behaviour. 5.1. Future research The research presented here has generated a number of questions. The subject matter was complex and it is unknown how the response differences observed here would be reflected with a simple product choice. The generation of the a priori lists is of utmost importance and it is unclear how different methods for generation (e.g. a limited number of soft laddering interviews, triadic sorting, or focus groups) would influence results. It is also unclear which type of laddering is more predictive of behaviour, or provides information more applicable to intervention or advertising strategies. The influence of interviewer effects and the tendency of respondents to provide what are perceived to be socially desirable responses needs further investigation. Additionally, comparisons between soft laddering and hard laddering (allowing forking and skipping), but without a priori lists are necessary.

Acknowledgements The computerised and soft laddering data collection was supported by CSIRO Health Sciences and Nutrition strategic funding. Data collection of the Pen and Paper administration was supported by funding from Unilever

Research, Vlaardingen, The Netherlands. We would also like to acknowledge Julie Syrette for developing the computerised laddering program and Phil Leppard for statistical analysis.

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