Online measurement of mental representations of complex spatial decision problems: Comparison of CNET and hard laddering

Transportation Research Part F 22 (2014) 170–183

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Online measurement of mental representations of complex spatial decision problems: Comparison of CNET and hard laddering Oliver Horeni a,⇑, Theo A. Arentze a, Benedict G.C. Dellaert b,1, Harry J.P. Timmermans a a

Eindhoven University of Technology, Urban Planning Group, PO Box 513, 5600 MB Eindhoven, The Netherlands Erasmus University Rotterdam, Erasmus School of Economics, Department of Business, Economics, Marketing Section, PO Box 1738, 3000 DR Rotterdam, The Netherlands b

a r t i c l e

i n f o

Article history: Received 20 December 2011 Received in revised form 2 July 2013 Accepted 5 December 2013

Keywords: Mental representations Online qualitative data collection Causal Network Elicitation Hard laddering Activity-travel scheduling

a b s t r a c t This paper introduces the online Causal Network Elicitation Technique (CNET), as a technique for measuring components of mental representations of choice tasks and compares it with the more common technique of online ‘hard’ laddering (HL). While CNET works in basically two phases, one in open question format and one as guided linking of attributes and benefits, HL works completely structured with revealed attributes and benefits. Mental representations of two activity travel tasks were collected with both techniques among members of a nationwide Dutch household panel. The results confirm the hypothesis that the revealed format of variables in HL has an effect on the indication of variables as the elicited mental representations are almost twice as big for HL than for CNET. Furthermore, it turned out that CNET is more sensitive in measuring shifts among attributes in the mental representations for situational changes of the activity-travel task. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction With recent developments in information and communication technology, it is becoming ever more important to develop a better understanding of why and how people make their spatial decisions in everyday life such as trip planning or activity scheduling. For example, research has shown that travellers differ in their needs for travel information according to behavioral and situational aspects (Chorus, Arentze, Timmermans, Molin, & van Wee, 2007; Kenyon & Lyons, 2003). Great potential is hence seen in providing personalized and hence relevant travel information to travellers by means of Advanced Traveler Information Services (Chorus, Molin, & van Wee, 2006). The fluency of information processing is then believed to increase with a match between the traveller’s mental representation (MR) and the type of information that is provided as Labroo and Lee (2006) argue for the case of consumer shopping behavior. This in turn increases travellers’ evaluation of the information provided and the probability that they decide to act on the information. Traditionally, laddering has been a successful approach to investigate why and how consumers shop (Gutman, 1982; Reynolds & Gutman, 1988). In particular relatively recent advances such as the association pattern technique (APT) (Ter Hofstede, Audenaert, Steenkamp, & Wedel, 1998) and (online) ‘‘hard’’ laddering (HL) (Russell, Flight, et al., 2004) have allowed for large scale data collection and segmentation based on shoppers’ cognitive means-end representations of shopping alternatives. These techniques allow for the detection of causal links with which shoppers mentally connect different ⇑ Corresponding author. Tel.: +31 40 247 3315; fax: +31 40 243 8488. 1

E-mail addresses: [email protected] (O. Horeni), [email protected] (B.G.C. Dellaert). Tel.: +31 10 408 1301; fax: +31 10 408 9169.

1369-8478/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trf.2013.12.002

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benefits such as convenience and pleasure (i.e. why they shop) to shopping attributes such as parking spaces, decoration and personal service (i.e. how they shop). Although preliminarily used to investigate shopping related choices these techniques can also be applied to any other discrete choice situation such as for transport mode and time of shopping. Recently research has emphasized that depending on the context with which decision makers are faced different aspects in such attribute-benefit chains may be more or less prominently activated (Dellaert, Arentze, & Timmermans, 2008; Rathneshwar, Warlop, Mick, & Seeger, 1997; Srivastava, Leone, & Shocker, 1981). For example, researchers have shown that the relative emphasis on benefits vs. attributes may vary based on psychological distance (Trope & Liberman, 2003) and that depending on the consequences of the activity task, the number of aspects may vary between contexts (Dellaert et al., 2008). These types of variations may be difficult to capture with current large scale survey-based approaches to laddering because these methods strongly rely on aided recall (i.e. the full set of relevant components is presented directly to respondents) which is likely to dampen the method’s sensitivity to context effects since all attributes and benefits are presented to respondents up front. Traditional face-to-face laddering may be a viable alternative for small samples and in depth understanding of certain individuals. It is however very hard to scale this approach to the level of larger samples due to the intensive respondent-interviewer interaction it requires. The objective of this paper therefore is to propose and test an online method for measuring mental representations for large-scale applications in a flexible and open-ended elicitation process that maintains the scalability of the known approaches for measuring mental representations and similar mental constructions. A new online technique for measuring MRs is introduced based on the recently developed face-to-face CNET approach (Arentze, Dellaert, & Timmermans, 2008). Its properties are compared to the more commonly applied online HL (Russell, Flight, et al., 2004). The method is applied in an online survey conducted to test these expectations. The experimental settings of the survey and the outcomes are presented and discussed in sections four and five. The paper closes with conclusions. 2. Theoretical background Choice behavior is grounded on a more or less conscious decision making process. On the one hand, there are impulsive or habitualized decisions which happen almost without any deliberation process at the moment they are performed. On the other hand, there are conscious decisions for active choices which we naturally feel as though we are in control of it. Kahneman (2011) distinguishes both types of decisions as thinking fast (system 1) and slow (system 2). Without calling it system 2 psychologists agreed, however, much earlier that human decision makers have a simplified image of reality in mind which allows them to evaluate their available actions and oversee the potential consequences in case of deliberate decision making. Craik (1943) postulated that the human mind constructs ‘small-scale models’ of reality that are used to anticipate events, to reason, and to provide explanation. The concept of mental representations (MR) has then explicitly been introduced in mental model theory (Johnson-Laird, 1983). MRs are the result of individual perception being stored in working memory each time a choice situation is being considered. Due to the limited capacity of the working memory, individuals will experience limitations on the amount of information that can be represented (Anderson, 1983). Consequently, MRs will generally involve a significant simplification of reality capturing only relevant attributes of the decision alternatives, benefit requirements of the decision maker, situational variables and the causal relations between them (Arentze et al., 2008; Dellaert et al., 2008). While attributes relate to physically observable states of the considered choice options, benefits describe outcomes in terms of dimensions of more fundamental needs. Situational variables describe states of the system which are beyond reach for the decision maker or they result from a far-reaching decision in the past. In other words, attributes describe very concrete characteristics of the choice set (e.g. the price of a commercial product) which changes its value depending on the choice of the consumer. A situational variable is also a concrete characteristic in the frame of the choice which however cannot be influenced by the acting person in the moment of decision making (e.g. crowdedness in the shopping location). Benefits in turn are abstract concepts which cannot be measured in a physical way. They rather conceptualize a person’s needs related to the decision such as feelings of safety or pleasure. As MRs represent causal knowledge of the environment, i.e. complex IF-THEN relations under different circumstances, they can be mapped as causal networks with nodes as variables and unidirectional graphs as causal links (Arentze et al., 2008). Fig. 1 shows such an example of a mental representation for a shopping trip scheduling task represented as a causal network. It is worth noting that means-end chains for personal values are different than mental representations for a decision task. The former ones serve in marketing research to understand consumers’ positioning of products free of any decision context and emphasize differences in personal values between consumers. The latter are situation dependent representations of a choice task. Nevertheless, the commonality between both concepts is their mapping as a causal network. Means and attributes are rather concrete components which are causally linked to the more abstract level of benefits and ends. Hence, the methods for eliciting means-end-chains should also be applicable for measuring mental representations. The probably most prominent elicitation technique for means-end-chains is laddering (Reynolds & Gutman, 1988) where the usually subconscious components are enquired in a chain style. Components of the higher level of abstractness (benefits) are elicited by interviewer questions of the form ‘‘Why is that important to you?’’. This questioning proceeds until the highest desired level of abstracted has been accomplished. Components of the lower more concrete level (attributes) are elicited by questions that focus on ‘‘How is this [benefit] achieved by this product?’’.

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Fig. 1. Mental representation for a shopping trip scheduling task.

Russell, Flight, et al. (2004) and Russell, Busson, et al. (2004) applied laddering in an experiment on consumers´ motivations for food product choice as face-to-face interview, paper-and-pencil technique and computerized laddering. The results showed that the computerized laddering variant and the paper-and-pencil version which both worked with a priori listed variables (hard laddering – HL) yielded significantly more ladders than face-to-face-laddering which, in turn, worked with open questions (soft laddering – SL). SL yielded significantly more linkages between levels of abstractness. According to Russell et al., the differences between the variants of laddering are caused by the different administration and are, thus, attributable to differences in participants´ mental processing of the task. While respondents would use recall to retrieve responses from memory in soft laddering, they use recognition in hard laddering which leads to answers that respondents may fail to recall spontaneously. On the other hand, the a priori listing of responses might hamper respondents in considering other options than the ones provided. Earlier, Ter Hofstede et al. (1998) suggested APT as a more scalable alternative to laddering for measuring means-end chains. Similar to the hard laddering variants respondents are faced with revealed attributes, consequences and values. The difference is that the variables are not shown in list format and that the ladders are not elicited one-by-one. Rather, APT consists of two matrices (one for attributes and consequences and one for consequences and values) where respondents can indicate causal relations by ticking off the corresponding cells. Hence, all ladders are elicited simultaneously which makes this technique quite difficult for respondents. Accordingly, Ter Hofstede found in a food product study with both laddering and APT that the content of the measured networks differs; a fact which he attributes to the different task formats. Nevertheless, significant differences in the structures could not be found between the techniques. Due to its simple matrix format, the advantage of APT is the convenience it brings for the researcher and the large-scale applicability. Thanks to the pre-defined labelling of attributes and benefits no post-processing of responses is necessary, thus, making MRs conveniently comparable. Yet, the downside of this convenience is, that respondents are limited in their response freedom and – like in HL – possibly influenced by the revealed presentation of attributes and benefits which might rather evoke cognitions that actually do not play a role in decision making. Arentze et al. (2008) and Dellaert et al. (2008) detected the need to develop a technique for measuring mental representations of decision problems in line with the previous work on laddering but more specifically tailored to the context of situationally dependent decision problems. The result is a semi-structured interview protocol, the so-called Causal Network Elicitation Technique (CNET). This technique has been applied in an experiment on measuring mental representations for a complex shopping trip decision task by the developers of CNET and in a slightly modified version for leisure-shopping travel decisions by Kusumastuti, Hannes, Janssens, Wets, and Dellaert (2010). Both support the applicability of CNET. Nevertheless, the drawbacks of the above mentioned (soft) laddering interview remain also for CNET. It is especially the long interview duration (Dellaert et al. (2008) report 55 min) and the repetitive asking for underlying concepts of higher levels which leads to fatigue among respondents. Kusumastuti et al. (2010) report that some respondents might have given answers due to feelings of humiliation or in order to satisfy the interviewer. For the researcher CNET is a very time-consuming and thus costly method. Interviewees need to be trained, interview locations need to be organized and the collected data need to be coded afterwards for electronic data processing. Large-scale applications of CNET interviews are hence out of the question. Furthermore, as for all face-to-face interviews there might also be a risk for CNET in influencing the interviewee by the reviewer. With their findings on intercoder reliability De Ceunynck, Kusumastuti, Hannes, Janssens, and Wets (2013) however showed that this does not need to occur in a CNET study. Nevertheless, a high reliability across coders or interviewees, respectively, requires well-trained personal, which is costly. All these shortcomings were the authors’ motivation for the development of online CNET. With a web application of the CNET protocol interviewer impacts can be excluded to a large extent, large-scale applications become easily feasible, difficult-to-reach groups become better accessible, and the collected

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data are immediately available in electronic format. In sum, mental representations become better quantifiable and comparable and the data collection more economical. Although computerized data collection methods using the label CNET exist already (Kusumastuti et al., 2011), they do not have the open format character as we propose it. This makes that these earlier methods more similar to HL. Hence, the online application of CNET, as we understand it, will be described in the next section. In order to be able to draw comparative conclusions on the technique’s sensitivity we compared its results to those obtained in online HL (i.e. a technique with a priori presented variables). 3. Network elicitation 3.1. Online CNET Before outlining the course of an online CNET interview some basics need to be explained. The semi-structured CNET interview protocol has been translated into a PHP-based algorithm working closely together with a MySQL database. The latter is not only necessary to store the MRs being elicited. It also serves as a substitute for the memory of the human interviewer, i.e. together with a string recognition algorithm the program compares the given responses with the passive vocabulary stored in the database in order to interpret the responses. Nevertheless, like for face-to-face CNET the respondent will be asked to confirm whether the interpretation is correct. The aim of the interpretation is to identify underlying concepts in individuals’ responses which enable statistical analyses of mental representations across individuals. Furthermore, it is essential for the process of a CNET interview to categorize the response as attribute or benefit. Situational variables are operationally treated like attributes as there is no need to distinguish between these two categories during the elicitation process. It is merely important to notice in the modeling stage that no causal relationship exists between decision and situational variables. In the remainder of this paper for simplicity, when we speak about attributes we also refer to situational variables. Anyhow, whether an interpreted response belongs to the category of benefits or the extended category of attributes needs to be retrieved from the database as well. The need of so much pre-stored information requires an extensive data collection of likely responses in advance of the actual survey. For the underlying research project working material from a previous face-to-face CNET project served as origin of the data base. A first round of brainstorming was then hold among six employees of TU Eindhoven in order to come up with more attributes and benefits for the activity-travel tasks under investigation. A second round of variable exploration has been performed by a native Dutch speaking student assistance who interviewed her friends, family members and student colleagues. In any case, it was aimed at approaching people from different educational levels and age groups to cover differences in the social variety of language. Furthermore, dictionaries in online and printed version were consulted to find synonymous expressions for the brainstormed considerations. Finally, test rounds of online CNET among five native Dutch speaking students who were instructed to think aloud served as final source for discovering missing variables and linguistic expressions. All pre-defined variables from the database are listed in the Appendix A to this paper. The string recognition algorithm, which is applied in online CNET, works in a stepwise procedure. Firstly, the typed response is parsed into words. Subsequently, small words without information content are disregarded. The remaining words are then considered as keywords for which the Soundex value is calculated, which is a phonetic index encoding similar consonants with the same value. Vowels are not considered at all. Hence, this algorithm allows already for the recognition of slightly deviating spellings. Although based on the English pronunciation, it can be applied to other languages too. Here its primary purpose is to minimize computation effort. The string search algorithm queries then all words from the database which have the same Soundex value as the keywords from the input. This procedure minimizes the possible result set. In a next step the Levenshtein-distance is computed between the keywords from the input string and the words with matching Soundex values from the database. This distance measure counts the steps which would be necessary to transform a keyword into a pre-stored word. These transformations can be deletions, substitutions or completions of single letters. The keyword with the least Levenshtein-distance is likely to deliver a match. When all least Levenshtein matches are computed the algorithm checks to which variable(s) they refer. The respondents are then confronted with the pre-defined label(s) of the matching variable(s) for which it was pre-tested in the pilot that wordings are self-explanatory in order to ensure their correct comprehension. The course of a CNET interview is represented by Fig. 2. It starts with instructions (line 1) and sorting of decision variables. When the applied decision task consists of more than one decision variable the respondent is asked to rank them in the order in which s/he prefers to make decisions (line 2). The main part of the interview will proceed separately for each decision variable according to the indicated rank order. The first part of MR elicitation is the open question phase which can also be analyzed separately. The respondent is informed about the choice alternatives for the decision variable at hand and prompted to type his considerations into edit fields by the question: ‘‘What are your considerations when making a choice between these alternatives?’’ (line 3). The part of the MR which is elicited in this phase is thus completely based on spontaneous recall. At the same time it serves as starting point for the next interview phase which aims at interpreting and structuring the first responses (line 4). For each consideration the string recognition algorithm is applied as described earlier. From the presented matches the respondent is asked to select the one which comes closest to his consideration. In case no match is found

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Fig. 2. Nassi-Shneiderman-diagram for the CNET interview protocol.

the respondent can either retype his consideration or continue the interview with the unidentified input which is then treated as an attribute. This definition has been set as respondents are more likely to come up with attributes as pilot tests had shown. Nevertheless, a correction by hand can be done by the analyst in a post-experimental step. In case the selected label stands for a benefit, the interview continues with the interpretation of further typed considerations. In case the selected label stands for an attribute the interview proceeds with the elicitation of underlying benefit (line 5). In contrast to the protocol from face-to-face CNET this step is done by means of an a priori list of benefits tailored to the attribute at hand as it turned out during pilot testing that respondents are not likely to come up with benefits for open questions. Thus, they are prompted to indicate the underlying benefits from the list by asking ‘‘Why is this [considered attribute] important for you?’’. Missing benefits can be added to that list as well. Having completed the indication of benefits the last two described steps are repeated until all considerations from the open question step are processed. If so, the whole procedure is repeated for the remaining decision variables. When this is finished, the final summary step (line 6) takes place. With it the respondents get an overview of all elicited benefits and their linked attributes. Missing links or even missing attributes can be added. At the very end of the online CNET interview respondents are asked to state their preferred choices for each decision variable (line 7) and to evaluate different aspects of the survey by means of rating scales. 3.2. Online HL Besides the application of CNET to an online agent also a structured technique with revealed variables has been brought online to allow for a substantial and procedural evaluation of CNET and the mental representations elicited by it. The underlying ideas of it stem basically from computerized hard laddering (Russell, Flight, et al., 2004 and Russell, Busson, et al., 2004) and Ter Hofstede’s association pattern matrix. The attributes and benefits are presented in a priori list format tailored to the underlying elicitation background. For example, when eliciting attributes underlying a specific decision variable only the meaningful attributes are listed. This way, the lists include not more than 16 attributes keeping the readability for respondents up. The same applies to the lists of benefits which are again pre-determined depending on the attribute at hand. Nonetheless, if respondents miss any attribute or benefit which is yet part of their MR they can always add it to the lists. An online HL interview starts exactly like online CNET with instructions and sorting of the decision variables. The elicitation of attributes and benefits happens then separately for each decision variable in the indicated order. Again, the respondent is informed about the choice alternatives for the decision variable at hand and prompted to indicate his considered attributes by the question: ‘‘What are your considerations when making a choice between these alternatives?’’. Accordingly, for each of the indicated attributes the underlying benefits are elicited by asking ‘‘Why is this [considered attribute]

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important for you?’’ as for online CNET. This procedure is repeated for all decision variables and at the end the respondents are asked to state their choices. A summary is not given in online HL as due to the structured character of the technique it is not likely that links and variables remain unmentioned. 3.3. Theoretical expectations The above described methods are compared methodologically on the applicability to large-scale based surveys and measurement sensitivity to (situational shifts in the) mental representations. There are clear expectations about the technical ability (Table 1) and the measured outcomes in terms of MRs (Table 2) of HL and CNET. In addition, a third column labeled Open elicitation refers to the first part of CNET only up to the structuring phase – in this stage respondents are only asked to list the variables that come to mind as relevant to make a choice when considering the decision presented to them. Including this partially elicited MR in the analysis allows for an estimation of the partial mental representations which respondents are able to recall before being led through the guided structuring phase in CNET. Mental representations are stored in working memory which has only limited capacity. Hence, decision makers will focus on important and decisive attributes and benefits only in their considerations. As also the importance of attributes and benefits is likely to shift between decision situations we expect also to measure this shift for mental representations. Taking however into account the findings from Russell, Flight, et al. (2004) and Russell, Busson, et al. (2004) in that respondents are influenced by revealed variables and given that the number of listed a priori variables in HL is limited we expect the elicited MRs in case of HL to be rather similar between individuals and across situations. Hence, we anticipate that HL is a relatively unsensitive method for measuring situational shifts in MRs. Open elicitation, in turn, does not give respondents any other opportunity than recalling attributes and benefits. Thus, this technique is expected to reflect inter-individual and intercontextual shifts most strongly as it is expected to be measured in a lower number of elicited attributes and benefits. Owing to its freedom in phrasing responses even very subtle shifts can be registered. Yet, due to the generalization in the structuring phase some degree of inter-individual variation might get lost for CNET. The structuring phase is nevertheless justified as the variables from the open elicitation are not linked to each other. Due to the degree of individuality remaining from the open elicitation also CNET is deemed highly sensitive to capture shifts in MRs. A technical advantage CNET has over HL is the ability to link benefits directly to the decision variable. According to Mental Model Theory decision makers may under some circumstances not consider attributes when their focus is on a more abstract level (benefits). Yet, HL forces respondents to indicate attributes while CNET does not. Hence, we expect to find associations between decision variables and benefits without interconnected attributes in CNET. In terms of the elicited response we expect the MRs to differ between the techniques. In light of the revealed handling of variables in HL a higher number of elicited attributes and benefits and consequently also for causal associations than for CNET is to be expected. But also the revealed benefits in the CNET structuring phase are likely to lead to a significant difference between the spontaneously recalled benefits and the number of total benefits for the CNET mental representations. An auxiliary for the sensitivity analysis is the implication matrix which comprises all links for all respondents of a scenario. By means of its row and column sums it allows us to calculate two indexes, prestige and centrality, for any variable elicited. The prestige of a certain variable is the ratio of its incoming links over the sum of all links in the implication matrix and stands for the attractiveness of the variable as goal variable. The centrality of a variable expresses the share of all causal links which have the respective variable as origin or end point. It is calculated by dividing the sum of all in and out degrees of a certain node (variable) over the sum of all links in the implication matrix (Knoke & Burr, 1982). The higher the prestige or centrality of a variable on a range from 0 to 1, the higher is the number of links ending in or running through it, respectively. If MRs change over situations, also the prestige and centrality of their variables should change. For instance, by the existence

Table 1 Elicitation techniques’ operational ability to measure mental representations.

Attributes and benefits in the network (nodes) Elicits links between attributes and benefits Direct links allowed between decision variables and benefits

Hard laddering – HL

Causal Network Elicitation Technique – CNET

Open elicitation

Low sensitivity Yes No

High sensitivity Yes Yes

High sensitivity No Yes

Table 2 Elicitation techniques’ expected measurement outcomes. Elicited response

HL

CNET

Open elicitation

Number of variables Number of associations Prestige and centrality of variables Interview duration

High Higher Stable over scenarios Short

Medium Lower Variation between scenarios Medium

Low Not applicable Not applicable Short

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of some uncertainty for an attribute which is essential for fulfilling the decision task, we would expect this attribute having a higher centrality value than under situations where this uncertainty is less relevant. Given that CNET is more sensitive in measuring situational shifts, we expect that this technique will be able to measure these shifts at least for the top prestigious and central variables. However, we do not expect the same sensitivity for HL. Interest appertains also to the respondent-computer interaction which we expect to be less problematic for HL. Due to fewer interview steps and probably less pauses for reflection we expect HL to yield a shorter average interview duration than CNET. Nonetheless, also for CNET we expect the interview duration to be significantly shorter than for comparable face-toface interviews. (Online) questionnaires in tick-off manner are nothing outstanding. Yet, open questions with an interpreter function are rather unusual in suchlike queries. Hence, we expect respondents to be more critical in their comments about CNET than about HL. 4. Experiment 4.1. The experimental decision task For testing HL and CNET on the above mentioned dimensions an activity-travel experiment was developed which represented a familiar decision problem for respondents (Dellaert et al., 2008). It consisted of a scheduling task for a usual workday in a fictive environment. More precisely, respondents were shown a map of a random city with some locations of interest like home, work place, shopping locations, etc. Accordingly, respondents were instructed about their activities (working fulltime and grocery shopping) and the alternatives for the decisions they faced. These were in detail: time of grocery shopping (during lunch break, after work, in the evening), shopping location (week market, corner shop, supermarket) and transport mode (car, bicycle, bus). The written description of alternatives was supported by small images on the map. Extra information on certain situational circumstances like traffic conditions and available product assortment was varied to test the sensitivity of the applied techniques. This variation led to the setup of two experimental scenarios: a basic scenario without any additional information on the situational circumstances and a scenario where some fuzzy information about these circumstances was given. In this so-called uncertainty scenario the chance for congestion with a travel time delay of 30 min was reported to be 25%. Furthermore, respondents were informed that some products which they needed (e.g. bread) were possibly sold out. Both scenarios were applied to the online HL and CNET interview resulting in a two (techniques) by two (experimental scenario) design. 4.2. Data preparation Before experiments could be performed a database had to be developed with likely response attributes and benefits as it has been outlined above. This required an extensive pre-survey qualitative effort. A literature review was conducted to uncover similar decision problems in the field and the variables that were found to be of influence in individuals’ decisions in previous research. Furthermore, records from an earlier face-to-face application of CNET with and open question surveys were used. Finally, a number of separate interview sessions with a mix of individuals from different socio-economic backgrounds we used as a source for the linguistic and substantive variety of responses that we could expect for the underlying decision tasks and scenarios. These raw data were grouped by the research team into conceptually identical attributes and benefits, respectively, and for each of grouping one preferred keyword label was selected. This was done through coding by one of the researchers, which was then checked independently by two other researchers. This resulted in a database consisting of 1344 words which were grouped into 74 attributes and 21 benefits. This database was pilot tested in a final survey round with several respondents and their feedback was used to make minor adjustments in the content and classification of the variables. 4.3. Respondents and sample Participants were recruited from the LISS panel, a nationwide Dutch household panel maintained by CentERdata, Tilburg. Panel members were selected according to three criteria: possession of a driving licence, aged between 18 and 60 years and having Dutch language skills. The former two characteristics were setup to ensure a real world reference for the experimental situations. The latter condition resulted from the interview language. The invitation by email and the compensation of respondents were carried out by CentERdata according to their practices. Both the technique and the scenario were assigned randomly to respondents after they started the interview. In total, 934 respondents could be surveyed of which 754 finished the interview successfully (80.7%). Table 3 presents the sample descriptors of all finishers. It shows that there are only little differences between the sub-samples which did not result in significant Chi-Square values. 4.4. Structural results Firstly, the elicited MRs were analyzed in terms of their structural components. To test our expectations (see Table 2) the means for all experimental groups are presented in Table 4 for the following dependent variables:

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O. Horeni et al. / Transportation Research Part F 22 (2014) 170–183 Table 3 Socio-demographics of the sample. Variable

HL basic

HL uncertain

CNET basic

CNET uncertain

N Gender (% men) Age (years) (M/SD) Status Single (%) Childless Couple (%) Couple with child (%) Single parent (%) Other (%)

213 46.0 43.3/11.1

189 45.0 42.8/11.4

185 43.8 42.8/11.3

167 46.1 43.2/10.5

13.1 25.4 55.4 4.2 1.9

12.2 24.3 58.2 3.7 1.6

13.5 31.4 49.7 4.3 1.1

16.8 26.9 48.5 6.0 1.8

4.7 24.4 9.4 29.6 24.9 7.0

4.8 20.2 9.6 29.8 23.4 12.2

2.7 21.1 11.9 33.0 22.7 8.6

6.6 21.6 10.8 28.7 28.7 3.6

Education Primary school (%) Practical professional training (%) Secondary education only (%) Higher level professional training (%) Bachelors degree (%) Masters degree (%)

Table 4 Means of the dependent variables.

Number of attributes Number of benefits Number of associations

HL basic

HL uncertain

CNET basic

CNET uncertain

F

df

p

8.70 8.10 18.77

8.91 8.29 20.93

3.77 (3.38) 5.78 (0.54) 10.05

3.69 (3.27) 6.31 (0.57) 9.79

143.191 21.608 29.356

3 3 3

<.001 <.001 <.001

(1) Number of attributes (including situational variables) being part of the MR. (2) Number of benefits being part of the mental representation. (3) Number of associations: link chains of the form: Decision Variable – (Attribute-) Benefit. The numbers in parentheses represent the spontaneously recalled attributes and benefits in CNET. The average number of attributes elicited is more than twice as high for the HL groups than for the CNET groups. Bonferroni corrected t-tests showed significant (p < .001) differences between techniques but not between scenarios. This finding supports the hypothesis that the a priori attribute lists influence responses. The numbers in parentheses for the spontaneous recalls do not differ significantly from the final CNET numbers indicating that only a few attributes were subsequently added during the structuring phase. This finding speaks to the hypothesis that respondents did not forget recalling important attributes. However, this has to be taken with caution as the content analysis will show. The fact that scenario had no influence on number of attributes speaks to the fixed cognitive capacity respondents can spend on MRs. Post hoc tests showed a significant (p < .001) difference for number of benefits between techniques (8.10 and 8.29 in HL vs. 5.78 and 6.31 in CNET) but not between scenarios. The low numbers of benefits in the open elicitation procedure (0.54 and 0.57) shows that only few respondents mentioned benefits spontaneously. This finding justifies the inclusion of the structuring phase in CNET which completes the mental representation structure by connecting attributes to corresponding benefits. The same applies to number of associations which is almost twice as high for HL (18.77 and 20.93) than for CNET (10.05 and 9.79). This highly significant effect (p < .001) points to an induction effect possibly due to the revealed handling of variables in HL. It is conceivable that HL respondents indicated causal links between variables which they recognized as plausible reasons but which were not necessarily part of their MR. One aim of developing CNET was to allow for more flexibility in the elicitation process. For HL respondents it is not possible to link benefits directly with decision variables. Hence, it is of interest to see whether respondents indicate to consider benefits for decision variables and if so, whether CNET is able to measure it. The analysis showed indeed that 54.5% of CNET respondents mentioned a benefit without linking it to an attribute. 4.5. Substantive results In a further analysis step the mental representations were examined in terms of the frequency of elicited variables and the salience of their links. The response matrices were setup for each experimental group connecting decision variables, attributes and benefits. Accordingly, prestige and centrality values were computed for all variables. The first measure is the ratio of incoming links over the total sum of causal links in the matrix. Centrality in turn is the ratio of incoming and outgoing links of a variable over the total sum of links in the matrix. Thus, dividing the centrality value by two tells us

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the percentage of causal links going through a variable. While Table 5 lists the overall top ten prestigious variables per experimental group, Tables 6 ranks the top ten central attributes and benefits per group. When looking at Table 5 we see that, as expected, the most prestigious variables are benefits. Prestige captures the attractiveness of a variable as an end variable in the MR and each causal association has to conclude with a benefit by definition. Hence, the link frequency of benefits is inherently higher than for attributes because range of available benefits is smaller than the range of attributes in the database. In all four experimental groups, time savings, ease of shopping and ease of travelling are the top three prestigious benefits, although in a varying order. This shows that the strongest underlying benefits are stable for mental representations across the different situations. Furthermore, it speaks for CNET as a valid method for eliciting benefits. Nevertheless, also some attributes are among the top prestigious variables. While there are one to two attributes among the top ten variables in both HL scenarios, CNET yields only one prestigious attribute for the uncertain scenario. Interestingly, this attribute is available product choice for which uncertainty was implied by the situational task. The prestigious attributes in HL are not related to the implied situation. When summing up the prestige values in the experimental groups it turns out that the top ten variables in CNET (0.533 and 0.579) represent between 2.4% and 6.5% more of the prestige than in HL (0.509 and 0.514). This finding indicates a somewhat stronger concentration of causal links in CNET, whereas the links in HL are more uniformly distributed among the variables. It is interesting to see that the prestige shift between the scenarios in CNET. The top ten variables in the uncertain scenario comprise 4.6% more of the prestige than in the basic scenario indicating a concentration of causal links when the decision becomes uncertain. This finding suggests a stronger emphasis on the key variables in the uncertain scenario. The centrality values presented in Table 6 are very insightful regarding the sensitivity of the techniques. CNET was able to measure a shift in mental representations for the implied uncertainty as the top central attribute becomes available product choice with 7.6% of all links going through it evidences. This finding is supported by the likely underlying benefits shopping

Table 5 The top ten prestigious variables.a HL basic

a

HL uncertain

CNET basic

CNET uncertain

TIME SAVINGS EASE OF SHOPPING EASE OF TRAVELLING SHOPPING SUCCESS RELAXATION FINANCIAL SAVINGS

0.094 0.085 0.069 0.055 0.043 0.040

TIME SAVINGS EASE OF SHOPPING EASE OF TRAVELLING SHOPPING SUCCESS FINANCIAL SAVINGS RELAXATION

0.089 0.087 0.077 0.063 0.038 0.034

TIME SAVINGS EASE OF TRAVELLING EASE OF SHOPPING RELAXATION MENTAL EASE SHOPPING SUCCESS

0.097 0.095 0.066 0.056 0.044 0.043

0.104 0.103 0.068 0.064 0.055 0.043

0.035 0.035

EASE OF TRAVELLING TIME SAVINGS EASE OF SHOPPING SHOPPING SUCCESS MENTAL EASE DIVERSITY IN PRODUCT CHOICE RELAXATION available product choice

SHOPPING PLEASURE TRAVEL COMFORT

0.033 0.032

TRAVEL COMFORT SHOPPING PLEASURE

0.034 0.033

SHOPPING PLEASURE FINANCIAL SAVINGS

available time for shopping number of bags to carry

0.031

opening hours

0.031

TRAVEL COMFORT

0.032

SHOPPING PLEASURE

0.032

0.029

DIVERSITY IN PRODUCT CHOICE

0.030

COURSE OF PERSONAL FITNESS

0.030

SHOPPING COMFORT

0.030

0.041 0.040

Benefit variables in capital letters, attribute variables in regular font.

Table 6 The top ten central variables.a HL basic

a

HL uncertain

CNET basic

CNET uncertain

TIME SAVINGS EASE OF SHOPPING crowdedness in the store number of bags to carry

0.094 0.085 0.077

TIME SAVINGS EASE OF SHOPPING EASE OF TRAVELLING

0.088 0.086 0.077

TIME SAVINGS EASE OF TRAVELLING available product choice

0.097 0.095 0.087

available product choice EASE OF TRAVELLING TIME SAVINGS

0.152 0.104 0.103

0.070

number of bags to carry

0.072

0.069

EASE OF SHOPPING

0.068

EASE OF TRAVELLING available time to shop

0.069 0.065

0.068 0.066

0.066 0.061

SHOPPING SUCCESS accessibility of the store

0.064 0.060

weather

0.063

0.064

RELAXATION/ RECREATION

0.056

MENTAL EASE

0.055

travel time

0.059

0.063

accessibility of the store

0.049

number of bags to carry

0.052

opening hours

0.057

travel time crowdedness in the store available product choice accessibility of the store SHOPPING SUCCESS

distance from current location EASE OF SHOPPING number of bags to carry

0.063

parking opportunities

0.048

0.043

available product choice

0.056

available time to shop

0.060

MENTAL EASE

0.044

DIVERSITY IN PRODUCT CHOICE distance from current location

Benefit variables in capital letters, attribute variables in regular font.

0.043

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success and diversity in product choice that also get higher centrality values in CNET uncertain than for the basic scenario. This inter-situational difference was not measured very well by HL. Most likely HL respondents were more inclined to indicate other more generally important attributes and benefits in both conditions because these were presented to them directly. This resulted in a fewer changes in centrality values for the HL condition. We also investigated the difference in centrality between scenarios for both measurement approaches. In line with our expectations (see Table 2) the overall sensitivity to the situational conditions is higher in CNET than in HL. In order to test the significance of the difference in sensitivity, a multivariate analysis of variance (MANOVA) was performed separately for HL and CNET with centrality scores of the 10 most central attributes and the 10 most central benefits (Table 7) as dependent variables and scenario as factor. For this, the centrality scores of attributes and benefits were calculated on the basis of individual MRs (as opposed to the aggregated implication matrix). The results in Table 7 show that the centrality of attributes differs significantly between the basic and the uncertainty scenario in CNET (p = .048) but not in HL (p = .164). This finding supports the hypothesis that CNET is a more sensitive method for measuring situational shifts in MRs. For benefits neither method finds significant differences between scenarios. This finding suggests a greater stability at the benefit than at the attribute level in respondents’ mental representations as generated by the two elicitation methods. 4.6. Response burden results In order to evaluate the techniques in terms of placing a response burden on participants, we report three dimensions as worthwhile indicators. First, the completed vs. dropout rate will give insight about respondents’ willingness to perform and finish the interviews. Second, If completed, the interview duration indicates the time respondents were willing to take or the time they needed to complete the interview, respectively. Third, respondents’ post-experimental comments can deliver precious information about the techniques’ user friendliness and understandability. 934 respondents could be surveyed of which 754 finished the interview successfully. This results in a finisher rate of 80.7%. Table 8 presents numbers for dropouts and finishers per experimental group based on the 934 interview trials. The higher number of dropouts in CNET is noteworthy and significant (Chi-Square test – v2 = 7.157, p = .007). A Chi-Square test for scenario differences between certain and uncertain did not show significant differences. Thus the CNET approach does appear to place a somewhat greater burden on respondents than HL. A deeper look into the cases revealed that 70% of the HL dropouts and 48% of the CNET dropouts happened still in the introduction phase of the interview. Besides low interest in the experimental subject it might especially be the technically demanding drag-and-drop task for ranking the decision variables which caused so many early dropouts. The rest of the dropouts were distributed roughly equally over all interview steps in a declining pattern with a higher occurrence for CNET. The interview duration of about 10 and 12 min is short compared to conventional face-to-face interviews. Dellaert et al. (2008) report an average interview duration of 55 min. Yet, is has to be added that their interview included an additional set of questions to reveal parameters of the causal network (i.e., conditional probabilities and utilities). Although the interview duration in HL (9 min 54 s) proved to be significantly shorter than in CNET (11 min 57 s – p < .001), this difference is rather negligible from a practical point of view, which suggests that CNET is not more taxing to respondents than HL. Finally, besides these rather objective measures it is worthwhile to analyze respondent’s evaluations of the survey. After completing the interview, respondents of both techniques were asked to rate four statements on a scale from 1 (dissent) to 5 (consent). Table 9 summarizes the means and the outcomes of a t-test.

Table 7 Results of the MANOVAs on the centrality for attributes and benefits between scenarios (basic vs. uncertain). HL

Attributes Benefits *

CNET

F

df

p

F

df

p

1.431 1.420

10 10

.164 .169

1.872 1.489

10 10

.048* .142

Significant at p < .05.

Table 8 Respondent completion and dropout percentage per version. Technique

Scenario

Completed

Dropouts

HL

Basic Uncertain

213 (83%) 189 (86%)

45 (17%) 31 (14%)

CNET

Basic Uncertain

185 (77%) 167 (77%)

54 (23%) 50 (23%)

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Table 9 Survey evaluation questions. HL

CNET

Did you find it difficult to answer the questions? 2.92 3.32

t

df

P

3.94

752

<.001

Were the questions clear to you? 3.21 2.68

5.607

721

<.001

Did the questionnaire trigger your thoughts? 2.75 2.52

2.724

726

.007

Did you have an interest in the research subject? 3.05 2.93

1.386

713

0.166

Did you enjoy answering the questions? 3.11 2.91

2.328

706

0.02

Except for the question addressing the interest in the research subject, all results are significantly different between HL and CNET. Thereby, HL was evaluated less difficult, clearer, more motivational and more pleasant than CNET as expected (see Table 2). Paradoxically, the clarity of the interview questions was rated differently between the techniques although the questions did not differ at all. Surprising is also the low rating for the techniques’ motivational effect on thinking about the choice problem. Especially striking is the higher value for the recognition-based HL whereas one would expect recallbased techniques evoke deliberation processes much stronger. 5. Discussion In this section the results will be discussed from three different perspectives: from the techniques’ prospect, the researchers’ interest and from respondents’ side. From a structural point of view the results showed that number of elicited attributes, benefits and their causal interlinks are significantly higher in HL. Hence, this study confirms an effect of a priori listing variables as it has already been reported by Russell, Flight, et al. (2004) and Russell, Busson, et al. (2004) or Kusumastuti et al. (2009) who compared the non-computerized CNET interview with an HL-like card game version of CNET. The rare observations of spontaneously recalled benefits in an initial stage of CNET indicates that respondents have difficulties, at least in the present choice task, in recalling rather abstract variables from higher levels of their mental representations (benefits) than concrete attributes, which justifies the provided support in eliciting benefits. Still, this might differ for different decision tasks. While techniques with revealed variables such as HL are deemed as appropriate methods for examining the causal knowledge a person has, less structured techniques are believed as yielding the more genuine individual image of reality with the risk of not mentioning highly abstract concepts. By combining the open elicitation which keep the foundation stone of the MR unbiased and a subsequent structuring which brings the former one into a good structure and checks for gaps, we believe to come closer to individually tailored but structurally complete mental representations. The detection of causal links between decision variables and benefits without interlinked attributes and the significantly slimmer MRs in CNET support this assumption. From the researcher’s perspective CNET rules out HL in the sensitivity for measuring situational shifts as the differences for the listed top ten prestigious and central variables between the scenarios show. The MANOVA confirmed the higher sensitivity for measuring attributes. Quite stable for all experimental groups was the finding that time savings, ease of shopping and ease of travelling are the three most prestigious and central benefits. Surprisingly, financial savings does not have a top prestige value. In CNET uncertain scenario it does not even appear among the top ten prestigious variables which again confirms the sensitivity CNET has in measuring shifts. With regard to centrality, financial savings appears in no group within the top ten variables even though the choice alternatives are known to differ largely in price level. Nevertheless, in many models it is assumed that costs saving is a major drive human decision making. Especially in transport mode choice models, travel cost serves often as an explanatory variable. Variables derived from ease of shopping or ease of travelling, in turn, are less frequently incorporated in decision modeling which might not only be caused by their arduous quantification. From the view of the respondents HL seems to be in favor. Especially, the lower number of dropouts after deducting the respondents who gave up before sorting decision variables speaks for a higher user friendliness of HL. Confirming the findings from the upper mentioned technical comparison from Kusumastuti et al. (2009) the ratings for the post-experimental questions regarding difficulty, clarity and pleasure support that assumption, although their mean values are rather in a neutral area. This shows that also HL is a cognitively demanding technique. In contrast to these rating stands the interview duration which is rather short with 10 min (HL) and 12 min (CNET). One point which might cause criticism is the fact that all respondents were panel members who might feel more committed to participate in and finish a suchlike survey than respondents who are addressed anonymously. The feedback from those respondents showed that especially CNET respondents were more critical about the open character of the questions which has often been commented on. Yet, in light of the fact that the open format in CNET was an unknown survey technique for the panel members, the rating of CNET attunes us positively.

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Any learning effects for the elicitation of attributes and benefits between the three decision variables or any possible influence of the provided images of the choice alternatives cannot be excluded and might be subject to future research. Furthermore, the interview procedure might be extended to allow also for the elicitation of conditional probabilities and utility values as it was part of the semi-structured interview protocol from Arentze et al. (2008) and Dellaert et al. (2008). The centrality values are merely a computed characteristic of the variables basing on their structural position in the causal network. The utility values could however provide insight on the importance respondents attach to the considered benefits. Thus, a little central benefit might still weigh hard on respondents’ utility scale. This issue is of central interest in formal choice modeling, the scientific domain the measurement techniques are designed for. 6. Conclusions This paper introduced a new online method to measure mental representations of choice tasks and compared the technique (CNET) to an online version of an existing technique (HL), in measuring MRs of 754 respondents for a fictive activitytravel task. This task consisted of interrelated choices for time of grocery shopping, shopping location and transport mode. The results of the study clearly show that MRs elicited by CNET are slimmer than the MRs elicited by HL. Number of associations, number of attributes and number of benefits are all significantly higher in HL than in CNET. This finding is in line with our conjecture that presenting variables to respondents may induce responses. A basic finding of this study is that the most essential underlying benefits in this activity-travel task are ease of shopping, ease of travelling and time savings. Although in a varying order, they are the top three prestigious variables in each scenario. Furthermore, they are also always among the top ten central variables. CNET is able to measure shifts in the mental representation concerning the centrality of attributes. When looking at the top ten central variables the shifts between scenarios could clearly be seen for CNET. The implied uncertainty about the product availability yielded the highest centrality value of all variables and its underlying benefit shopping success was also among the top ten central variables in the uncertain scenario. HL is not able to show this effect. A Manova test confirmed this increased sensitivity. In conclusion then the objective of this paper was to propose and test an online method for measuring mental representations for large-scale applications in a flexible and open-ended elicitation process. We conclude that CNET seems to be more advantageous than HL. Its ability to link benefits directly with decision variables and the break up into an open elicitation phase and a structuring phase make it clearer for respondents and better comprehensible for researchers. The higher sensitivity of CNET for measuring shifts speaks to a higher validity of its elicitations. It might hence also very applicable when the research focus is on the dynamics of MRs such as situational impacts on decision making or adaptations due to learning effects. It depends however on the research aim and the scale of the survey whether the flexibility and sensitivity of CNET compensates for the higher effort in preparation such as collecting and phrasing possible responses. The elicitation of benefits turned out to be more difficult than for attributes. Support in terms of a priori lists is hence necessary. When the research

Table A1 Attributes and situational variables presented in HL. Attributes

TMa

SLa

Simplicity of the route Travel time Number of bags to carry Opening hours Weather Parking costs Available product assortment Crowdedness in the store Time to find a parking lot Flexibility of departure time Size of the shopping location Available time to shop Crowdedness on the road Price level of the assortment Accessibility of the store Durability of products Capacity of the transport mode Sportiness Atmosphere in the shopping location Costs for petrol Travel costs Familiarity with the shopping location Leisure time Required time to shop

x x x

x x x x x x x x x

x x

x

x

x x x x x

TSa x x x x x x x x x x

x x x x

x

x x x x x

a The columns TM (Transport Mode), SL (Shopping Location), and TS (Time of Shopping) indicate for which decision variables the listed attributes were presented.

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O. Horeni et al. / Transportation Research Part F 22 (2014) 170–183 Table A2 Hidden attributes and situational variables understood by CNET. Attributes (1–39)

Attributes (40–75)

distance from current location available time to shop time pressure Weather outside temperature number of bags to carry durability of products required time to shop familiarity with the TM physical condition stress at work chance of unexpected events service level of the store canopy at the SL with or without company amount of greenspace layout of the SL state of maintenance of the SL flexibility of departure time accessibility of the store presence of gastronomy in the SL accessibility for the disabled signage in the SL possibilities to rest atmosphere in the SL crowdedness in the store size of the SL availability of the TM special offers presence of special events familiarity with the SL quality of products price level of assortment available product assortment type of shop (chain) time to find a parking lot reliability of the TM waiting time expected delay

preparation time capacity of the TM privacy in the TM chance of a seat in the TM presence of a bus shelter habituation to the TM travel costs costs for petrol parking costs possibility to store shoppings route flexibility of the TM safety of the TM diversity of branches opening hours accessibility of public transport noise pollution authorisation to use the TM simplicity of the travel route time to transport shoppings recreation time at work necessity leisure time sportiness travel time parking possibilities (car/bicycle) time of the day conflict with planned agreements combination with other activities physical effort crowdedness on the road illumination working hours type of shoppings opinion of friends amount of exhaust gases support middle class

Table A3 Benefits presented in HL and CNET. Benefits time savings ease of shopping shopping comfort shopping pleasure travel pleasure diversity in product choice shopping success travel comfort ease of travelling safety of travelling health mental ease relaxation/recreation financial savings safety in the shopping location attractivity of the shopping location culinary pleasure social acceptance environmental pollution personal care course of fitness/wellbeing

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