The Decision Plan Nets Method
3.3 DPN As an Alternative
Bettman (1979) describes a DPN as “another alternative for the representation of consumer choice heuristics. In the branching structure attributes [price] and situa-tional factors can be used to predict the acceptance or rejection of an alternative.”
Bettman perceives the housing consumer as a processor of information. In that vein, the key concepts in the search process are the motivation, information acquisition, and decision-making. His approach is therefore also focused on the motivational, situational and procedural aspects of housing choice!
The DPN is based on a structured (computerized) interview that shows people’s choice processes. It reveals a set of imaginary houses that the housing consumer would consider acceptable based on a mix of compensatory and non-compensatory choice rules that respondents are free to mention. It has the structure of a tree or net. We use the tree. Like a tree, we make a distinction between the main branch and the lateral branches (Fig. 3.1). The DPN is literally depicted and reveals the attributes (rooms, garden, garage, attic), the operators (>, =, etc.), the levels (4, 5, YES, NO), and the choice rules.
The interview has two steps: revealing the main branch and the lateral branches.
1. The interview starts by asking a consumer which attributes will be considered. It is vital that a saturated set of attributes is known since this is required for the second step. Every time an attribute is mentioned, we depict it as a node on the main branch. In Fig. 3.1 the main branch consists of three nodes: “Rooms > 5 AND Garden = yes AND Garage = yes.” The sum depicts an imaginary house and the most preferred.
2. The interviewer triggers the consumer to reveal the choice rules that are used if a vacancy that fails to satisfy only one of the attributes of the main branch is offered. Since we have three nodes, we have three-pronged questions, for
example: “Would you consider a vacancy that has a garden and a garage, but the number of rooms is five or more?” We stress that it is vital that all other main branch attributes remain constant! A deviation from this rule invokes measurement errors that can only partially be corrected afterwards (Van Zwetselaar and Goetgeluk (1994). The answer reveals the choice rules. They can only be threefold. Park et al. (1981) link the rules to the attributes and call them dimensions: rejection-inducing dimensions, relative-preference dimen-sions and trade-off dimendimen-sions. We will use an adapted set of abbreviations in the remainder of this chapter, and we will use the commonly used word “pref-erence” instead of “dimension” – rejection-inducing preference (RIP), rela-tive-preference (RP), and trade-off preference (TOP):
An RIP is a preferred attribute, which, if not present, will lead to immediate
•
rejection of the offered dwelling. An RIP is rigid or a claim, like the avail-ability of a garden. If an RIP results in a “reject” no branching will occur at that point.
The RP leads to the ranking of alternatives. The housing consumer will give
•
a higher score to an alternative that has a preferred attribute than one that lacks it. This resembles the example presented above. The Garage is an RP in Fig. 3.1.
A TOP emerges when a preferred attribute is not satisfied, but other attributes
•
can compensate for it. The preference Rooms is a TOP. Fewer rooms may be compensated for by, for example, an attic. An attic is often mentioned as a less valuable kind of room since it is often less usable than a normal room. The TOP leads to lateral branching and must end with a “reject.” “Would you consider a vacancy that has a garden and a garage, but the number of rooms is five or
Rooms ≥ 5 Reject
Reject
Accept Garden= YES
Garage= YES
−Garden=NO
−Garage=NO
−Room ≥ 5 +Rooms ≥ 4
+Attic=YES
Fig. 3.1 A DPN (After Van Zwetselaar and Goetgeluk 1994)
more?” has to be followed by a new question: “Would you consider a vacancy that has a garden and a garage, but the number of rooms is four or more and an attic?” In this case, the consumer rejects the offer. We stress again that this compensation may never be one of the remaining attributes on the main branch.
The lateral branch can be very long since the attic may be a TOP as well and might be compensated by, for instance, another attribute like “storage room in garden.” Important to know is that the last node of the lateral branch by defini-tion is an RIP. In noncomputerized versions of the DPN violating this rule results in measurement errors (Van Zwetselaar and Goetgeluk 1994).
We conclude that this method (using the DPN) differs from those methods where the number of attributes, their levels, and the combination of attribute levels are predetermined, such as in conjoint models. The DPN also allows for a mix of com-pensatory and non-comcom-pensatory rules to be used.
3.4 Applications
3.4.1 Explorative Research
Often the DPN is used in the explanatory phases of research to determine which preferences are important to people. Based on frequency distribution the commonly mentioned preferences and their values are selected.
Two examples illustrate the strength of the DPN in explorative research. The studies by Heins et al. (2003) and Boumeester et al. (2008) show how the DPN reveals unknown attributes. The study by Boumeester et al. (2008) also showed the importance of checking assumptions with regard to attribute levels.
Heins et al. (2003) used the simplified DPN to analyze the preferences for rural housing in the Netherlands. Corresponding to the conceptual framework described in Sect. 3.2, they analyzed the preferences of respondents in two cities and two suburban municipalities in a housing market region in the Randstad-Holland (Utrecht) and a southern province Noord-Brabant (Den Bosch). Both regions differ with respect to the pressure on the housing market and in the amount of private and public green space. They wanted to test whether “rural housing” refers to being housed in the countryside or to an image or brand that can also be located in urban areas. Table 3.1 shows one of the results of this study.
Table 3.2 shows the preferred attributes that were mentioned by at least 68% of the respondents (n = 112). They used two steps to unravel all the information in the table. First, they defined the preferred attribute by type. They combined house (H), location (L), function (F) and esthetics (A). For instance, HF refers to a functionally preferred attribute of a house like Garden/Balcony (column type). In the second step, they calculated the share of each preferred attribute (column % total). For instance, “Garden/balcony” is mentioned by 98 of each 100 respondents. The mini-mum in the table was “presence of animals,” which was shared by 68 of each 100 respondents. Heins et al. (2003) argued that the RP is less important if we want to
gain insight into what they define as the “rural” profile or housing bundle. The table shows that in 94 of the 100 respondents the preferred attribute “Garden/Balcony”
is an RIP or a TOP. However, the preferred attribute “Presence of animals” has a significantly lower percentage (31). If we use this percentage as a weight factor, the ranking of Table 3.2 becomes: ((RIP + TOP)/100)* Total.
It turns out that most preferred attributes are linked to functionality except for many of the preferred attributes with respect to rural housing (HA and LA). The preferred attributes linked to the location are more often defined by the researchers as esthetics (Heins 2002). Other aspects relate to the way people act in a neighbor-hood (type of population), the maintenance of public space (safety) or a “feeling”
that was attached to a preferred attribute. We can also define these as the social aspects. This would result in the subdivision of the location features into function (amenities), social (people), and design (esthetics).
The weighted ranking shows that housing and location-related preferred attributes were mixed in the upper part of the ranking, while lower down location dominates.
Table 3.1 Most frequently stated preferred attributes by type and ranked by weighted share (Heins et al. 2003)
Type of buildings LA 92 56 33 11 82
Plot size HF 84 75 15 11 76
Type of landscape LA 89 47 37 16 75
Road safety LF 83 76 10 14 71
Outbuildings HF 89 58 22 20 71
Architecture – surroundings
LA 84 71 12 17 70
Atmosphere LA 90 69 7 24 68
Open space LF/LA 86 71 8 21 68
Proximity to nature LF/LA 87 47 23 30 61
Architecture – dwelling
HA 71 71 13 17 60
Type of population LA 77 62 14 24 59
Age of building LA 62 48 26 26 46
Proximity to water LF/LA 77 27 24 49 39
Presence of animals LF/LA 68 38 8 53 31
H house, L location, F function, A esthetics
Table 3.2 The original classes for the conjoint model and the mean preferred class of the DPN (Boumeester et al. 2008)
Attribute
Predefined conjoint
model levels DPN levels Ratio DPN/CM Size of living room
Minimum 140,000 € 222,727 € 1.6
Maximum 300,000 € 395,109 € 1.3
This is in concordance with other DPN studies (Goetgeluk 1997; Daalhuizen 2004;
Boumeester et al. 2008). What is more interesting is that location-preferred attributes like “green space,” “peace and quiet” and “safety” score high. However, preferred attributes like “open space”, “proximity to nature,” or even “presence of animals”
score low. This suggests that “rural” housing does not necessarily have to be associ-ated with agriculture, the agribusiness, and its effects on the landscape.
Based on many more different analyzes with different datasets, Heins (2002) concluded that the countryside is a construct. This implies that the construct or image can be used as a brand in marketing. Furthermore, they argued that “pseudo-countrysides” can be designed and developed within the urban fringe. And indeed, currently, “rural” neighborhoods are developed and marketing uses “buzz words”
that appeal to the best that both the countryside and the city have to offer.
Boumeester et al. (2008) used the same procedure as Heins to test whether the attributes and their levels of a conjoint model were justified. Since these profiles were used in a nationwide large-scale representative sample in the Netherlands, any misspecification could lead to enormous financial losses and of course useless find-ings. We will only show an example of how the DPN is used to test the appropriate-ness of the category levels used.
The study showed that prior defined attribute levels were not always justified.
Table 3.2 shows the results of the predefined minimum and maximum levels of the attributes related to space and tenure/price, and the levels according to the DPNs;
in other words, the minimum and maximum preferred size, number of rooms and price, as indicated by the respondents. If the ratio between the DPN and the con-joint model levels fluctuates around 1, then we can conclude that the predefined levels are justified. However, Table 3.2 shows that most minimum levels were wrongly defined. The DPN levels reflected consumers’ perceptions of how quality is related to price. This result also improved the conjoint model since the realistic levels of the attribute define the outcome of the conjoint model. This can be
illustrated by the size of the living room. Therefore, the result of the conjoint models depends on a valid set of attributes and a valid set of attribute levels.
Both examples show the value of the DPN as a fairly simple method to analyze at an aggregate level the character of preferences or to determine which attributes and their levels should be used in other more restricted preference models.