Durable goods will be the fastest growing sector of the consumer market in the. 1980's. Through Guttman scalogram analysis, consumer acquisition patterns.
Consumer Acquisition Patterns for Durable Goods JACK J. KASULIS ROBERT F. LUSCH EDWARD F. STAFFORD, JR.* Durable goods will be the fastest growing sector of the consumer market in the 1980's. Through Guttman scalogram analysis, consumer acquisition patterns for twelve heterogeneous durables are examined. Ownership patterns are compared through split-half analyses, across data collected in two consecutive years, and between types of dwelling units.
T
hree families are in the process of purchasing a major consumer durable. What will each purchase-another car, a microwave oven, a freezer, or some other item? Is there some underlying priority schema that consumers use in determining which durables to buy? Ifa schema exists, does it differ for those who own a home, rent one, or live in an apartment? In this article we examine the extent to which there is a common utility structure for major durables across the population. Priority patterns are assessed for twelve major household durables, and the reliability of the findings are examined through an analysis of split-half samples and data collected from two separate calendar years. For a variety of reasons, including the increased size of the 18- to 34-year-old age group and the rise of affluence in young adults, the fastest growing sector of the consumer market in the early 1980's will be durable goods (Reynolds and Wells 1977). This situation will attract a great deal of attention by practitioners and academics alike. This subject is also of interest to researchers who are attempting to understand the consumer behavior process, e.g., home economists, marketers, and socialpsychologists. In the classical sense, the acquisition of a durable is a discretionary purchase. But in today's society, there is sufficient discretionary income for everyone to be in the market for a durable at one time or another. The examination of priority patterns of acqui-
sition may be viewed as an attempt to determine which durables are perceived more as necessities and which are considered frills, bought only as additional discretionary dollars become available. Beyond the focus on consumption behavior regarding durables are the issues of adoption of innovations, the acquisition patterns of time-saving appliances, and others, which warrant study. The goal ofthis research is primarily descriptive, and to a lesser extent, explanatory. Although attempts should be made to explain how consumers accumulate durable goods, it is also important to describe the order in which they are acquired. In science, there is no single correct "logic of discovery." Descriptive research is likely to be just as useful' in the discovery process as other available modes (Hunt 1976). However, although our research goal is primarily descriptive, our approach is not totally lacking in explanatory orientation. In fact, the analytical technique used to describe acquisition patterns assumes that all consumers have similar utility structures. If the analytical technique uncovers a common acquisition pattern, then we have an indication that our assumption was reasonable. This being the case, we have the beginning of an explanatory model. Subsequent research can then focus on the explanation of the observed behavior.
THEORETICAL BACKGROUND Few people in our society are able to acquire all the goods desired at the same time. Income is received over time, and income and credit are limited. Thus, consumers need to prioritize, or order, their acquisition of goods, particularly regarding those that are high priced, such as household durables. In a real sense, particular household durables are competing for a fixed set of consumer dollars. The order in which durables are acquired is an indication of the utility structure for a
*Jack J. Kasulis is MBA Program Director and Assistant Professor, Division of Marketing, Robert F. Lusch is Director of the Division of Marketing and Associate Professor, and Edward F. Stafford, Jr., is Assistant Professor, Division of Management, all at the University of Oklahoma, Norman, OK 73019. The authors gratefully acknowledge the role of Phil Stout and Jim Williams of the Oklahoma Publishing Company in the collection and use of the Continuing Consumer Audit data base.
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© JOURNAL OF CONSUMER RESEARCH. Vol. 6. June 1979
48
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particular family. Although classical economic theory does not deal with the consumption of durables over time, one could reasonably hypothesize that consumers acquire goods to maximize the present value of their utility function. Given knowledge offuture prices, consumer incomes, and the utility function, one could theoretically determine the order in which consumers will acquire durables in the future. Furthermore, if all consumers had similar utility structures, one could predict the particular order in which all consumers acquire goods. Consider, for example, the existence of k durable goods. Mathematically there would be k! possible patterns of acquisition. However, if one tentatively accepts the notion that there is a common order of acquisition for all consumers, then only one of the possible k! patterns represents the underlying hierarchy for the population. Such a pattern could be represented as: D 1 , D 2 , D 3 , • • • D k • Dl would be acquired first, then D 2 , and so on, until Dk is acquired. Second or third units of a particular durable may be treated as separate products and designated with their own Dks. The necessity of prioritizing the acquisition of durabIes and the potential value of finding similar utility structures has led to several studies examining whether there is a common order of acquisition for consumer durables (Pyatt 1964; Paroush 1965; McFall 1969; Hebden and Pickering 1974; Lusch, Stafford, and Kasulis 1978). The outcome of this research is somewhat encouraging, and can be summarized as follows: 1. All the preceding research concluded that the populations under study could be characterized as having a
common order of acquisition for the durables examined.
2. The pattern of acquisition in some cases varied by population group and in other cases remained consistent across groups. For example, Hebden and Pickering (1974) found that the acquisition pattern for five leisure goods varied by social class, and the pattern for five diverse goods varied by family life cycle. McFall (1969) demonstrated that the pattern for 17 consumer durables varied by income and urban/rural consumers. On the other hand, Paroush (1965) found that for families in Israel, the order of acquiring a set of four durables was identical regardless of the continent of birth and duration of residence. 3. Acquisition patterns have been observed for both homogeneous goods (McFall 1969; Paroush 1965; and Lusch et al. 1978) and heterogeneous goods (Hebden and Pickering 1974). A homogeneous set of goods consists of goods capable of performing similar or highly related functions (e.g., kitchen appliances), whereas a heterogeneous set of goods consists of goods capable of performing quite different and unrelated functions (e.g., TV, car, dryer, stereo, freezer). 4. The underlying priority patterns have been revealed by a variety of analytical techniques. They have included the Guttman coefficient of reproducibility
(Paroush 1965; McFall 1969), the point correlation matrix (Paroush 1965), a matrix of conditional probabilities (Hebden and Pickering 1974), a dynamic Bayesian differential equation model (Pyatt 1964), and a multiple criteria evaluation procedure combining the work of Guttman, Loevinger, KuderRichardson, and Green (Lusch et al. 1978).
While the contributions of the previously mentioned research are recognized, there is need for more study of consumer durable acquisition patterns. The focus of this article is directed to two broad research hypotheses: HI: There is an underlying order of acquisition for a large set of heterogeneous durables. H2: There is a difference in the order of acquisition for a large set of heterogeneous durables across dwelling units. In examining these hypotheses, this article contributes to the literature in the following ways: 1. The specific set of durables examined have not
previously been explored.
2. The data analyzed are a large representative sample of a major metropolitan market not previously examined, except in the Lusch et aI. (1978) study of five kitchen durables. 3. Recognizing that prioritization patterns may change over time, the more current data of this study provide additional insight. 4. The impact of a recent innovation on the pattern of acquisition is examined. 5. The impact of second purchase durables (e.g., second vehicles) on the pattern of acquisition is examined. 6. Only one other study examined such a large set of durables, and that study dealt with a more homogeneous set of items (McFall 1969). 7. The relationship of dwelling unit on the priority patterns is examined. 8. The internal validity of the results is examined through an analysis of split-half samples, and the longitudinal reliability is tested across two years of data.
METHODOLOGY The empirical analysis focuses on the ownership of twelve household durables: (1) clothes dryer, (2) dishwasher, (3) freezer, (4) microwave oven, (5) range, (6) refrigerator, (7) stereo or tape player, (8) first television, (9) second television, (10) first vehicle, (11) second vehicle, and (12) washer. These are common durables acquired by American consumers, and their substantive costs require household prioritization. Several other factors make this set of items especially interesting. The durables represent three major
49
CONSUMER ACQUISITION PATTERNS FOR DURABLE GOODS
categories-kitchen durables, entertainment items, and transportation vehicles. The selection of such a diverse group of items probably decreases the likelihood of finding a common priority pattern. However, inasmuch as a consumer's budget is not realistically confined to a subset of the items, it is important to include all of them. One major exclusion is a house. It was excluded because one's dwelling is thought to be a potential determinant of the priority patterns. Therefore, type of dwelling is used as a market segmentation variable wherein prioritization patterns are analyzed. An additional point of interest is that the set of durabIes includes a microwave oven. By examining this innovation, one can see the extent to which an innovation cuts across established acquisition patterns or is acquired only by those who own the other eleven items. Finally, multiple ownership of televisions and vehicles was studied. This is the first time multiple ownership has been examined in the literature. It was thought to be appropriate, because second purchases of these items compete for the same scarce consumer resources as the first purchases of other items.
Subjects The data were obtained from the DRP/OPUBCO Continuing Consumer Audit. The Distribution Research Program (DRP) at the University of Oklahoma and the Oklahoma Publishing Company (OPUBCO) collaborate in the collection of data on the purchasing behavior of individuals in the Oklahoma City Standard Metropolitan Statistical Area (SMSA). Reported in this article are some findings from the 1975 audit, which included 1,747 respondents, and the 1976 audit, which included 2,025 respondents. The sample is a stratified, random cluster, representative of the populations in geographic regions in the Oklahoma City SMSA. Subjects from urban, suburban, and rural areas are included. All types of dwelling units-houses, apartments, condominiums, trailers-are included. New samples are drawn each year with the distribution of the sample reflecting population changes in the strata.
Procedure The Consumer Audit questionnaire is administered in a personal interview with both the male and female heads of the household responding. The interviewer is directed to a specific address as a starting point in a cluster of three or four residences. The starting point is a randomly selected location within the geographical strata. The interviewer is instructed to obtain a designated number of completed interviews from adjacent residences. Each visit is a "cold" call with no pre-visit contact made to request cooperation; the first contact is when the doorbell is rung. The respondents are told the study is being conducted by the University of Oklahoma and the interview will last at least one hour.
If necessary, the interviewer will arrange an appointment for a more convenient time. "Not-at-home" families are revisited at different hours of the day (three times) before a substitute respondent is designated for the interview. After three not-at-home visits or a refusal, a neighbor's house in the cluster is assigned as a substitute. Approximately 50 percent of the households selected complete the interview. Of the other 50 percent, approximately one-half are refusals and one-half are not-at-home. The data are collected continuously throughout the year by professional interviewers, under close supervision. Each week completed questionnaires are returned for processing. Telephone callbacks are made within three days of the return to verify the data collection. In addition, subjects may be telephoned again to obtain clarification of responses, if needed.
Analysis The stock of durables that a household possesses can be characterized by a multivariate distribution of O's and 1'so A value of "one" would depict possession of the durable and a "zero" nonpossession. If consumers have relatively similar utility structures for a large set of heterogeneous goods, the data on durable ownership can be described in terms of a unidimensional scaling model. For example, if one considers five durables, a logically consistent conceptual model would be theoretically characterized by the pattern exhibited in Table 1. The data in this table indicate the order of acquisition to be D 1 , D 2, D 3 , D 4 , D 5 • If it is observed that each consumer fits anyone of the patterns (rows), then one could transform the multivariate data into a unidimensional scale. Thus, by only knowing the last durable acquired, one can perfectly predict a consumer's total stock of durables and the durable to be purchased next. In other words, if the last durable added to one's stock of durabIes was D 3 , then one would know that Dl and D2 were also owned, but not D4 and D 5 • Furthermore, the next durable to be acquired would be D 4 • Clearly, not all consumers will acquire a set of durabIes in the same pattern. Deviations will inevitably exist. The task is to determine whether this divergence TABLE 1 SIX OWNERSHIP SITUATIONS REPRESENTING A PERFECT SCALE PATTERN FOR FIVE DURABLES Durables Scale score
05
D.
03
D.
0,
S5 S. S3 S. S, So
1 0 0 0 0 0
1 1 0 0 0 0
1 1 1 0 0 0
1 1 1 1 0 0
1 1 1 1 1 0
50
is sufficiently large for the perfect model to be considered unrealistic for the real world. Thus, some measure of scalability is needed to empirically test the appropriateness of the model. Guttman (1971) developed a scaling model called scalogram analysis, which may be applied to this task, even though it was originally devised for a different purpose (attitude measurement). In the discussion of attitudinal dispositions and the role of scalogram analysis, Guttman stated that" ... the universe is said to be scalable for the population if it is possible to rank the people from high to low in such a fashion that from a person's rank alone we can reproduce his response to each of the items in a simple fashion" (Guttman 1971, p. 188). In this article, Guttman's ranking procedure is used to examine durable priority patterns. Such an approach has been used previously by Paroush (1965), McFall (1969), and Lusch et al. (1978). Guttman scaling has been traditionally used with cross-sectional data to rank an individual's attitude toward an object. In this study, Guttman scaling is used to model the temporal phenomenon of consumers acquiring discretionary durable goods. Although the data used are cross-sectional, it is possible to scale the underlying temporal phenomenon. As the sample is large and the interviewing procedure is tightly controlled, it is reasonable to assume that the sample represents a true cross section of the entire population of the Oklahoma City SMSA. Therefore, individuals at all stages in the order of the acquisition process will be represented in the sample. And, because this cross section of individuals is at various stages in the acquisition process, conclusions can be drawn about the order of acquisition over time. The thesis of this paper is that the twelve durables mentioned earlier tend to be acquired in a designated priority pattern, with the "more difficult" durables being acquired only after the "less difficult" items. In this context, a lesser degree of difficulty is synonymous with higher levels of expected utility derived from the ownership of the durables. Among the various scaling techniques available, the Guttman approach is almost unique in its possession of this cumulative property (Nie, Hau, Jenkins, Steinbrenner, and Bent 1975). The results of the Guttman analysis are tested for statistical significance by using the Green (1956) technique for assessing the quality of Guttman scaling. The ownership patterns are compared for 1975 and 1976, and the reliability of the results are further analyzed by using split-halves. The analyses of priority patterns are also extended to include an assessment of the importance of a respondent's dwelling unit. Priority patterns are examined separately for those who own their dwelling and for those who rent their dwelling. Renters are further analyzed by looking at whether the respondent rents a house or an apartment/duplex.
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RESULTS AND ANALYSIS With twelve durables, there are 4,096 (212) possible ownership combinations. In the case of2k, the k depicts the number of durables and the 2 indicates the class of ownership-own or not own. Of these 4,096 combinations, k + 1 or 13 situations would characterize the perfect Guttman scale. If we take anyone of these 13 ownership combinations in which i items are owned, these items could have been obtained in any of i! different permutations. Nevertheless, only one of these i! permutations matches the perfect acquisition scale. The Guttman coefficient tests whether the frequency distribution of ownership on the i items reflects a perfect scale when applied to all 13 ownership combinations. Table 2 presents the underlying priority patterns for each of the analyses performed. The durables are ranked from one to twelve, with a one indicating the first item acquired, a two the second, and so on. The numbers in parentheses below the asset rankings depict the percentage of the sample owning each durable. Table 2 summarizes the 14 separate scalogram analyses performed for this study. (Each row comes from a separate scalogram table.)1 The Figure represents a tree diagram ofthe different analyses performed herein. Thus, one can see that, first, an examination was made of the total sample. The second stage of analysis was to divide the total sample into dwelling owners and renters, as renters may depend on the landlord or furniture rentals for many durabIes, and therefore their priority patterns may be different. Stage three further segments the study of renters into those who rent a house and those who rent an apartment or duplex. People who rent a house may perceive themselves as being more transient than those who rent an apartment, and the landlord of a house may provide a different set of appliances than a landlord of an apartment. The analysis represented by the tree is duplicated for the 1975 data and the 1976 data. Below each of the nodes of the tree are the measures of scalability. The first number is Green's index ofreproducibility.2 Note that in every case the index of reproducibility is significant at the 0.001 level. The bottom number is Green's index of consistency. 'This summary was done for reasons of simplicity. The separate scalogram tables may be obtained from the authors. "Green's approach to establishing the reliability of a scale depends not on the scale item and test variances, as do most other reliability measures, but rather on the summary statistics of the sampling results obtained when a scale is designated. Green's measures, along with other scale reliability measures (Green, Kuder-Richardson and Loevinger), and the associated statistical equations are described and compared by Lusch, Stafford, and Kasulis (1978). Additionally, a technical appendix that details the calculations necessary to derive these reliability measures is available from these authors,
CONSUMER ACQUISITION PATTERNS FOR DURABLE GOODS
51
TABLE 2 OWNERSHIP RANK ORDERS AND FREQUENCIES FOR TWELVE DURABLES BY ANALYSIS GROUP
Stage analyses
Freezer
Second TV
Dishwasher
Second vehicle
Dryer
Stereo or tape player
Range
Refrigerator
First vehicle
Washer
First TV
12" (3)b
11 (34)
10 (30)
9 (40)
8 (59)
7 (67)
6 (73)
5 (77)
4 (88)
3 (90)
2 (92)
1 (97)
12 (3)
11 (34)
10 (33)
9 (42)
8 (59)
7 (69)
6 (73)
5 (79)
4 (89)
3 (90)
2 (93)
1 (97)
12 (3)
11 (35)
9 (39)
10 (39)
8 (60)
7 (66)
6 (73)
5 (76)
4 (87)
3 (90)
2 (92)
1 (97)
12 (5)
11 (38)
9 (49)
10 (40)
8 (60)
7 (63)
6 (74)
5 (77)
4 (88)
3 (90)
2 (92)
1 (97)
12 (4)
11 (38)
9 (49)
10 (39)
8 (61)
7 (67)
6 (75)
5 (76)
4 (88)
2 (91 )
3 (91 )
1 (97)
12 (6)
11 (38)
9 (48)
10 (41 )
8 (60)
7 (68)
6 (73)
5 (77)
4 (88)
3 (90)
2 (92)
1 (97)
12 (4)
11 (42)
10 (45)
9 (50)
8 (66)
6 (78)
7 (75)
5 (88)
1 (99)
2 (99)
4 (95)
3 (98)
12 (5)
11 (45)
9 (55)
10 (49)
8 (67)
6 (78)
7 (75)
5 (87)
1 (99)
2 (98)
4 (94)
3 (98)
12 (0)
10 (11 )
9 (20)
11 (10)
7 (38)
8 (33)
3 (68)
6 (44)
5 (50)
4 (60)
2 (83)
1 (92)
12 (2)
10 (13)
9 (27)
11 (7)
7 (38)
8 (31 )
3 (70)
6 (38)
5 (51 )
4 (61 )
2 (83)
1 (92)
12 (0)
10 (16)
9 (23)
11 (14)
8 (43)
7 (48)
5 (69)
6 (63)
4 (74)
2 (87)
3 (86)
1 (94)
12 (2)
10 (19)
9 (30)
11 (11)
8 (41 )
7 (43)
5 (70)
6 (55)
4 (73)
2 (86)
3 (83)
1 (94)
12 (1 )
11 (4)
6 (17)
10 (4)
4 (33)
9 (9)
3 (67)
7 (15)
8 (15)
5 (22)
2 (80)
1 (91 )
11 (2)
10 (5)
6 (22)
12 (1 )
4 (33)
9 (12)
3 (72)
8 (12)
7 (16)
5 (23)
2 (81 )
1 (90)
Microwave oven
Stage One (1) Full Sample (1975) (n = 1747) (2) Split Half I (1975) (n = 867) (3) Split Half II (1975) (n = 880) (4) Full Sample (1976) (n = 2025) (5) Split Half I (1976) (n = 1009) (6) Split Half II (1976) (n=1016) Stage Two (7) Owners (1975) (n = 1323) (8) Owners (1976) (n = 1586) (9) Renters (1975) (n = 414) (10) Renters (1976) (n = 439) Stage Three (11) Rent House (1975) (n = 244) (12) Rent House (1976) (n = 264) (13) Rent Apt.! Duplex (1975) (n = 162) (14) Rent Apt.! Duplex (1976) (n = 167) For each item:
a
= ownership rank order. b = percentage
owning durable.
Whether the rankings in Table 2 represent priority patterns or merely frequency of ownership without any underlying schema is assessed through the methodology developed by Green (1956). The index of reproducibility measures how successful the Guttman scale
reproducing an individual's ownership of the twelve durables given knowledge only of an individual's scale score. A value of "one" depicts perfect reproducibility (i.e., all the respondents conformed perfectly to the scale), and a "zero" characterizes perfect
IS In
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52 FIGURE
TREE DIAGRAM OF THE STAGES OF ANALYSIS AND THE GREEN STATISTICAL MEASURES OF SCALABILITY STAGE TWO
STAGE THREE
OWNERS OF DWELLING
TOTAL SAMPLE
..!2Z? .924 .421
1976
.936 .373
.930 .352
~ .918 .394
SPLIT HALF I 1975 1976 .926 .421
1975
.913 .356
1975
1976
.914 .383
.908 .352
SPLIT HALF II 1976
.!..!2. .921 .407
;:r A';~6USE
1 RENTERS OF DWElLlNc
