Copyright MCB University Press Limited
1993
INTRODUCTION
The firm's demand curve has always played an
important role in microeconomic theory, particularly in terms of its function in
the theory of the firm. The demand curve acts as an informational input
describing market conditions. Combined with information on cost conditions, it
allows selection of the optimal price-output combination necessary for profit
maximization. A substantial part of microeconomic analysis has traditionally
been concerned with the characteristics of the firm's demand curve (i.e.
position, shape, elasticity). The arrival of the theory of imperfect
competition[1] and the kinked-demand curve theory of oligopoly[2,3] stimulated
further interest in the specific nature of the firm's demand curve, not least
because of its strategic implications under conditions of oligopoly. These
implications are reflected in the incorporation of possible competitive
reactions in the decision-making process of the firm. Focus then shifted away
from atomistic market situations to markets supplied by interdependent
firms.
More recently, Gutenberg's[4,5] doubly-kinked demand
curve provided additional theoretical insights into the shape of the demand
curve in imperfect markets. He highlighted the role of switching costs and
supplier loyalty in influencing customer's reactions to price changes. Moreover,
he drew attention to the potential existence of a "retaliation free"
decision-making range. Within certain bounds, an oligopoly firm can manipulate
price without incurring competitive retaliation[6].
In marketing, theoretical discussions of demand
curves are typically found in the context of the price element of the marketing
mix. Marketing contributions have drawn particular attention to additional
variants of demand curves. These include the "backward sloping" demand curve,
reflecting price-quality perceptions, the "jagged" curve associated with
odd-pricing and the "stepped" curve related to price-lining (for a review and
discussion, see[22,23,24]. In addition, a range of concepts borrowed from the
economic literature have been elaborated and expanded (e.g. tracing the
implications of different price elasticities for market segmentation).
Empirical analysis of the firm's demand curve has
paralleled the more recent theoretical developments. There have been many
econometric investigations of ex-Post demand curves at the firm level, based on
historical data (see[25] for a review and relevant examples). An alternative
strand concerns attempts to construct ex-ante demand curves from data obtained
directly from decisionmakers. Such attempts have generally formed part of
broader investigations of the pricing practices of firms. Estimation of demand
curves through direct questioning of decision makers reflect perceptions of the
firm's demand curve rather than the "real" or "true" shape of the curve.
The present article contributes to the second stream
of the empirical literature by reporting on an in-depth investigation of demand
curve perceptions within a multiproduct industrial firm. Specifically, the study
aims to:
(1) develop a typology of demand curves based on
product managers' perceptions; and
(2) link the derived typology to descriptors of the
market environment.
Before proceeding to the empirical analysis, it is
necessary to set in context the role of the perceived demand curve and point out
methodological issues in previous studies.
BACKGROUND TO THE PERCEIVED DEMAND CURVE
Attention to the perceived demand curve is warranted
for several reasons. First, it can be argued that, for decision making, the
relevant demand curve is subjective in nature. Pricing decisions depend on
executives' assessments of the likely impact of price changes on demand. As
Horowitz points out, "from the standpoint of decision making, the relevant
demand curve is the one on which management bases its pricing and production
decisions. This need not be the actual demand curve. From the decision-making
standpoint, it suffices that management behaves if it were the demand
curve"[26].
A second reason for studying perceived demand curves
is that they may be the only kind available for assessing the optimality of
pricing decisions. This is because objective estimation of actual or "true"
demand curves is either difficult, or simply not undertaken in practice. It has
been argued that pricing decision are often very difficult to evaluate because
"the required information involves detailed knowledge of the firm's demand curve
and its price elasticity. Such information has proved to be beyond the ability
of most present marketing information systems except through subjective
estimation"[27, p. 426, emphasis added].
Empirical research shows that formal/quantitative
methods to demand estimation are much rarer than judgemental methods. A recent
study concluded that "companies do not approach demand elasticity in a
systematic strategic fashion. Qualitative approaches are relied on in demand
measurement rather than quantitative approaches. The majority of the firms do
not maintain detailed pricing data bases, nor do they regularly make efforts to
estimate demand sensitivity"[28, p. 175].
A third reason for examining executive's demand
perceptions is that even in cases where data is available and costs are not a
barrier to using formal methods, managerial judgement may still be preferable.
This is because the confidence internal of statistical estimates of price
sensitivity "is frequently wider than the interval that a well-informed manager
could specify with equal confidence simply from managerial judgement"[29, p.
288].
A final reason for concentrating on perceived demand
curves is that, in contrast to econometric and experimental methodologies, their
construction requires managerial involvement. Because of this, the curves are
much more likely to be accepted and used by executives in the pricing
process[30].
Over the years, several pricing studies have inquired
into the demand curve perceptions of business executives (see, for example,
[31-35]). While this research has provided important insights into the nature of
the demand curve as seen in practice, most relevant contributions have been
limited in one or more of the following respects:
(1) UNIT OF ANALYSIS/LEVEL OF AGGREGATION. The demand
curve issue has usually been approached at a company-wide level as part of the
broader examinations of pricing practices. While this presents no problems for a
producing a single product, it is not true if a multiproduct or multimarket firm
is involved (as are most firms today). In this latter case, "the typical firm
usually faces a variety of potentially distinct environments for its various
product markets and has a different set of pricing objectives and/or pricing
methods for each"[36, p. 166]. This implies that the firm has a series of demand
curves associated with different product markets and a lower level of
aggregation is appropriate for detailed examination.
(2) RANGE OF PRICE CHANGES CONSIDERED. Typically, the
data used to construct perceived demand curves are estimates of volume changes
associated with different price changes (with both magnitudes expressed in
percentage terms). However, these estimates have typically been restricted to
only one or two increases and decreases in price. Also, the departures from the
present level have been small (e.g. 2 per cent and 5 per cent in either
direction)[37 The information provided covers only a very small part of the
demand curve and inferences about its overall shape are difficult to
corroborate. Clearly, it is not feasible to obtain estimates of the expected
volume effects of all possible price changes. Nevertheless, it is important to
cover a reasonable range of upward and downward price changes to generate
sufficient data points to give an accurate representation of the perceived
demand curve.
(3) ANALYTICAL APPROACH. Once the shape of the demand
curve has been obtained from the responses given by executives, there is a
tendency to continue the analysis on a purely subjective basis. Usually, this
consists of "eyeballing" the extent to which the empirical curve obtained
conforms to those described in theory. While this is a useful exercise, what
appears to be missing is systematic and quantitative investigation of the link
between the market environment on the one hand and the shape of the demand curve
on the other. The present study provides further insights into the nature of the
demand curve as perceived by an industrial firm's decision makers. It builds on
previous empirical efforts and attempts to improve on the three problem a areas
above. Rather than focusing on the firm as a whole, the research design takes
the product group as the unit of analysis. Following Little[30], the expert
informants used are the executives responsible for managing the particular
product groups. In addition, a wide range of price changes is considered when
obtaining expert judgement of the volume consequences of price increases and
decreases. Lastly, statistical techniques are employed both to derive
homogeneous groupings of perceived demand curves and to link these to market
environment descriptors.
EMPIRICAL SETTING AND RESEARCH METHODOLOGY
DATA
The data used in this investigation were obtained
from a large manufacturing company operating in the UK medical supplies
industry. The firm concerned produces a wide variety of repeat-purchase
industrial products (over 900 in all). Products are organized in 21 product
groups, each of which is managed by a product manager. While their precise
nature cannot be disclosed, the products in question are used in the operating
theatre and fall under the "single use hospital supplies" (or "disposables")
product category.
The markets for all 21 product groups are
oligopolistic in nature, the maximum number of competitors being eight firms and
the lowest three-firm concentration ratio is high at 0.85. The main customers
are institutional buyers (mainly hospitals) located throughout the UK. Sales are
effected through distributors who are given a discount off list prices.
Responsibility for setting prices rests with the product management team and the
marketing manager.
Information regarding perceived demand curves was
collected using a self-administered questionnaire which was distributed to
product managers. The research instrument also elicited information on major
environmental variables describing the market context of individual product
groups. Thus a total of 21 questionnaires, one per product group, provided the
raw data for analysis. The questionnaire was pre-tested prior to distribution,
by the protocol method (see [38]). Subjects for this pre-testing comprised the
marketing manager and a former product manager (now occupying a different
position in the company).
VARIABLES
Demand curve perceptions for each product group were
obtained by asking the relevant product manager to estimate (in per cent terms)
the likely increase (decrease) in volume sold over a 12-month period, which
would result if current prices were decreased (increased) by 5 per cent, 10 per
cent, 20 per cent and 50 per cent, respectively[39].
The range of price changes considered was, in
consultation with the marketing manager, deemed to be sufficiently wide without
becoming unrealistic for product managers to give a reasonable response on the
likely changes in sales volume. The focus on a "realistic" range is important.
If managers are asked to estimate paramaters relating to outcomes outside their
range of experience, substantial estimation errors may occur[40].
Regarding the specific increments chosen, these
reflect the fact decision makers cannot be expected to visualize the volume
impact of very fine variations in price. As an expert in economic psychology
observes, "it is possible for businessmen to think of increasing their sales or
their prices by 5 or 10 per cent, but it is hardly possible for them to have in
mind quantity-price relationships for every possible change between 5 and 10 per
cent. In other words, businessmen may have an idea about the position of certain
points of a demand schedule, but it will be hardly ever justified to speak of
subjective sales curves"[41, p. 218].
Respondents were also asked to supply information on
a range of market descriptors. These included: market growth, number and size of
competitors, product substitutability, market price sensitivity, intensity of
price and nonprice competition, and frequency of temporary (promotional)
price-cutting by competitors. Details on the operationalization of these
variables can be found in the Appendix.
ANALYSIS
Product managers' perceptions of the expected volume
effects of alternative price changes are summarized in Table I. (Table I
omitted) In addition to the descriptive data, the results of a Wilcoxon
signed-ranks matched-pairs test are also shown. This test compares the mean
absolute volume changes between a price increase and a price decrease of a given
magnitude.
Looking initially at the mean values, demand is
perceived to be inelastic with respect to both upward and downward price
changes. In no case is the average percentage change in volume greater than the
associated percentage change in price. However, the relatively high standard
deviations suggest considerable variation between different product groups.
Also, responses to price increases and price decreases are not symmetrical.
A wide range of responsiveness is shown by the
maximum and minimum percentage volume changes resulting from a given price
change. This suggests material variations between product groups. Moreover, the
variations become progressively greater with increases in the magnitude of price
changes. This is demonstrated by the increases in the range and standard
deviation as one moves from 5 per cent change in price to a 50 per cent
change.
The difference in demand responsiveness to price
increases vs. decreases is demonstrated by the significant Wilcoxon test
results. For changes of 20 per cent or more, demand elasticity is greater for
upward rather than downward price changes. In other words, the volume effects of
a given change in price are not symmetrical when a substantial departure from
the current price is involved.
From a visual inspection of the demand curves a wide
diversity of demand curve shapes is evident. Some product groups have demand
curves that are roughly linear, others are reminiscent of the kinked demand
concept and still others resemble Gutenberg's doubly-kinked demand curves.
Figure 1 shows some of the typical shapes encountered. (Figure 1 omitted) The
calibration of the price and quantity axes highlights departures from the
current price and output levels (both indexed at 100). This form of demand curve
representation is similar to that used by Tull et al. 2]. It enables direct
comparisons across the various product groups, in spite of differences which
exist in terms of absolute price and volume levels.
Interestingly, for most product groups, there appears
to be a price interval about the current price where price changes have no
effect on volume. Thus, a vertical band (i.e. a completely inelastic section)
appears in the demand curve. Specifically, a vertical band extending both above
and below the current price is observed for 15 product groups. For another two
groups the vertical band extends above the current price only; the remaining
four product groups do not exhibit a completely inelastic section in their
demand curves.
Given the diversity in demand curve shapes, it can be
argued that this reflects differences in the market and competitive conditions
facing the various product groups. Indeed, it seems logical to expect that the
perceptions of product managers regarding the likely volume impact of price
changes, would be based on knowledge about the specific market environment
within which such price changes are contemplated. To investigate this issue
further, a two-stage strategy was followed. First, cluster analysis was used to
construct an empirical demand curve typology based on executive perceptions.
Second, a series of one-way
Kruskal-Wallis analyses of variance were conducted to
compare the derived clusters and uncover differences in terms of market
characteristics.
The cluster analysis was performed using Ward's[43]
hierarchical clustering algorithm. The expected volume changes associated with
the various price increases/decreases (see Table I earlier) were used as
clustering variables. A four-cluster solution was selected, based on an
examination of the fusion coefficient[44] to which Mojena's[45] "stopping rule
#1" was applied. In addition, a visual inspection of the similarities in demand
curve shapes within each cluster was undertaken, to check that the solution
obtained was indeed meaningful from a Practical point of view. The latter
exercise was felt necessary since, ultimately, "the appropriate number of
clusters should be chosen on the basis of managerial utility rather than
intrinsic mathematical relationships"[46, p. 426].
The appropriateness of the cluster solution was
further tested by examining the discriminating power of the clustering variables
in differentiating among clusters. A series of one-way Kruskal-Wallis analyses
of variance were used to this effect. Further, the relevant F-ratio for
within-cluster homogeneity was calculated for each clustering variable[47]. As
Table II shows, there is strong support for the four-cluster solution selected.
(Table II omitted) There are significant differences between the four clusters
with respect to all clustering variables and all but two F-ratios (out of a
total of 32) are smaller than unity.
Clusters I and IV contain product groups for which
the responsiveness of sales volume to price variations is perceived to be the
lowest and highest respectively. In cluster I, the average reduction (increase)
in volume is proportionately always much smaller than the corresponding price
increase (decrease), which points to a situation of highly inelastic demand. In
contrast, in cluster IV, demand is elastic in both directions once price changes
of more than 5 per cent are contemplated. The two remaining clusters (i.e. II
and III), appear to be characterized by inelastic demand conditions, but they
differ from one another in terms of their responsiveness to upward and downward
price changes. Cluster II product groups have demand curves that appear to be
roughly symmetrical about the current price. The demand curves in cluster III,
in contrast, have asymmetric shapes where demand is more elastic above the
current price level than below it.
Having derived broadly homogeneous groupings of
perceived demand curves, attention is now shifted to the identification of
differences amongst them in terms of market characteristics. This throws light
on the factors and market conditions which help to form product managers'
perceptions of the demand curve. Equally importantly, it also provides an
external validation test on the empirical demand curve typology developed. It
has been pointed out that "the user of cluster analysis should provide a
demonstration that clusters are related to variables other than those used to
generate the solution"[49, p. 335]. Relying solely on the same set of variables
to both derive and validate a cluster solution may well exaggerate the quality
of the solution. Table III displays the relevant results. (Table III
omitted)
The significant results obtained with respect to five
variables, show that market conditions vary materially between the four demand
curve clusters. Cluster I has the highest market concentration and lowest market
price sensitivity. Competition takes place primarily along non-price dimensions
(both the intensity of price competition and the frequency of competitive
price-cutting are low). These features are entirely consistent with the
inelastic demand situation characterizing the product groups belonging to
cluster I (see Table II earlier). On the other hand, the price-elastic demand of
cluster IV is consistent with the low market concentration and intense
competitive conditions characterizing that cluster. It is also reflected in the
high degree of price sensitivity, the importance of price and non-price
competition and the frequency of competitive price cutting.
Market conditions associated with clusters II and III
fall somewhere between the two "extreme" positions held by clusters I and IV.
They differ from one another primarily in terms of price-related variables, with
cluster III being characterized by a higher degree of market price sensitivity
and greater intensity of price competition than cluster II. Somewhat
surprisingly, the frequency of competitive price-cutting is higher in the latter
cluster than the former. While this may reflect the lower degree of market
concentration in cluster II, the mean ratings in both clusters are so low that
the substantive importance of the observed difference is negligible. A summary
picture of product managers' demand curve perceptions, based on the findings in
Tables II and III, is given in Table IV. (Table IV omitted)
DISCUSSION AND CONCLUSIONS
In a recent book on pricing, it is stated that the
assessment of the impact of price changes on demand "is invariably a matter of
managerial judgement"[29, p. 58]. The present study has attempted to utilize
such judgement to construct a typology of demand curves and relate it to
pertinent market characteristics.
In this study, the perceptions of executives point to
a situation of inelastic demand for most product groups. In the empirical
setting under investigation, this can largely be attributed to the end-benefit
effect. This states that "customers -- particularly industrial buyers purchasing
raw materials or component parts -- are less price sensitive when the
expenditure is a relatively small proportion of the total cost of the end
product"[50, p. 468]. As already mentioned, the products examined are operating
theatre supplies and represent only a small proportion of the total cost of an
operation. Consequently, moderate departures from existing prices would not
normally result in major purchase adjustments by organizational buyers. Having
said that, there are instances where demand is perceived to be price elastic
(demand curves in cluster IV) and these reflect a market situation characterized
by intense competition, particularly on price dimensions.
Consistent with the oligopolistic nature of the
product markets concerned, the shapes of the demand curves are often (but not
always) asymmetric about the current price, resembling the kinked and
doubly-kinked variants discussed in microeconomic theory. This tallies with the
findings of previous empirical studies that reported similar shapes (e.g.
[19,20,33,42]). The asymmetric shapes suggest that product managers appreciate
that "customers may have different elasticities for a price increase compared to
a decrease over the same range"[24, pp. 41-2].
A second feature of most of the demand curves
identified, is the existence of a vertical band, i.e. a range within which a
price modification appears to have zero impact on sales volume. This
characteristic was also observed in Hankinson's study, which revealed that
"while a significant raising of price by the firms would certainly contract
demand, a marginal increase would not. Similarly, a marked lowering of price
would extend demand but a nominal decrease would not. Thus the demand curve
clearly possessed a perfectly inelastic section at the relevant output
level"[34, p. 37]. Vertical bands were also reported by Fog[31], Skinner[32] and
Wied-Nebbeling[19,20],
Although a detailed analysis of the vertical band is
outside the scope of this paper, there are various managerially relevant aspects
that influence the size of the band. Evidence is fragmented but the greater the
size of the band the greater the pricing discretion of an organization. Buyer
loyalty has been cited as a contributory factor[10], suggesting that strong
buyer-supplier relationships building inertia into the system, comes into play.
A large vertical band may be perceived by the supplier if he/she feels that
his/her relationship with customers is good and service and other non-price
aspects are important.
In addition, supplier switching costs may also
contribute to the creation of a vertical band[51]. There are always some
switching costs involved and the current supplier can use these to enhance the
size of the vertical band (which may be thought of as a barrier to entry).
Product differentiation as a strategy used to create price premiums, can also
lead to the development of a vertical band[20]. Given the repeated observance of
vertical bands in empirical studies and the associated managerial implications
(e.g. potential for increasing profitability without sacrificing market share),
this is clearly an area in which further research is needed.
From a purely profit maximizing point of view an
organization would be foolish to price at any point lower than the top of a
vertical band. In practice, it seems that managers are willing to sacrifice
potential profits. In the organization under study there was a strong ethic
against overcharging the customer as well as a long-run orientation to profit
generation. While it is difficult to generalize, these factors may explain the
apparently suboptimal pricing behaviour exhibited. Another explanation is that
managers may lack the confidence to price according to their stated perceptions,
being willing to sacrifice some potential profits for a less risky decision.
The four demand curve clusters identified in the
present study, serve to emphasize the complexity of the demand environment
facing a multiproduct industrial firm. The differences between the clusters
identified in terms of market characteristics, reiterate the textbook
recommendation of "tailoring to the market" and the dangers of writing "one's
pricing policy on "tablets of stone"[52, p. 137 It is important to bear in mind
that the demand curve typology developed in the present investigation reflects
executives' perceptions on the likely short-term impact of price changes on
sales volume (i.e. over a 12-month period).
In the industry concerned, annual revisions of price
lists are the norm and a year is considered as a short-run time-horizon. The
long-run consequences of price changes on demand are also of major significance
in the formulation of pricing strategy, since "a price set today may have
considerable implications for future demand, cost and competition"[53, p. 2]. It
should be stressed that long-run effects are not captured by the demand curves
presented in this article.
A methodological issue concerns the need for
considering a sufficiently wide range of price changes when calibrating demand
curves based on executive judgement. To illustrate this point, had the range of
price variation been restricted within +/- 10 per cent of the current level, the
demand curves for five product groups would have appeared to be perfectly
inelastic (i.e. depicted as vertical lines). Had the range been restricted to
+/- 5 per cent, then three-quarters of the groups concerned would have displayed
total insensitivity to price! At the same time, however, one must be careful not
to elicit executive judgements on "unreasonable" price variations (e.g. two or
three times the current price) since these would be well outside their range of
experience. Needless to say, what is a "reasonable" range varies from industry
to industry and, thus, measurement instruments have to be adapted
appropriately.
Finally, the authors readily recognize that, although
the present study avoids many problems associated with previous work in this
area, it is itself by no means perfect. There is a need for replicating the
present analysis in diverse empirical contexts to overcome the inherent
limitations of single-industry company data. However, access to the relevant
organizational subunits (e.g. product groups) for data gathering, may be
difficult because of confidentiality. Since company co-operation is essential to
tap into the expertise of appropriate decision-makers, sample size may
inevitably have to be sacrificed.
The role of other market variables in differentiating
between demand curve types is also worthy of future attention. As a recent
literature review on pricing shows, there are many contextual variables that
have been found to impinge on pricing decisions[35]. Certainly, some of them
(e.g. buyers' future expectations and the price "spread" of competitive
offerings) could be expected to influence demand responsiveness to price changes
and, therefore, the shape of the demand curve and the size and nature of the
vertical band.
NOTES AND REFERENCES
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2. Hall, R.L. and Hitch, C.J., "Price Theory and
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3. Sweezy, P.A., "Demand under Conditions of
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4. Gutenberg, E., "Zur Diskussion der
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5. Gutenberg, E., Grundlagen der
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6. For an exhaustive literature review on the kinked
demand curve, see Reid[7]. The doubly-kinked demand curve is discussed almost
exclusively in the German economic pricing literature, the main contributions
being Kilger[8], Albach[9,10,11], Willeke[12,13], Ott[14,15],
Piekenbrock[16,17], Helmedag[18] and Wied-Nebbeling[19,20,21].
7. Reid, G.C., The Kinked Demand Curve Analysis of
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36. Diamantopoulos, A., "Pricing: Theory and Evidence
-- A Literature Review": in Baker, MJ. (Ed.), Perspectives on Marketing
Management, Vol. I, Wiley, London, 1991, pp. 63-192.
37. Another limitation of previous studies is that
they often fail to specify the time-frame within which volume gdins (losses)
resulting from price cuts (rises) are to be considered. This is a rather serious
omission since "quality demanded" is a flow (rather than a "stock") concept,
i.e. quantity demanded at a particular price over a particular period.
38. Hunt, S.D., Sparkman, R.D. and Wilcox, S.B., "The
Pretest in Survey Research: Issues and Preliminary Findings": Journal of
Marketing Research. Vol. 19, May 1982, pp. 269-73.
39. Strictly speaking, when estimating the volume
effects of price changes, one should specify whether possible reaction of
competitors should be ignored. In the present setting, however, a lack of
reaction is hardly realistic, given the oligopolistic nature of the markets
involved. Previous empirical attempts at estimating the effect of price changes
on demand have proved rather troublesome when non-reaction has been assumed.
Fog, for ewmple, found that "the interviewee could not accept the ceteris parius
clause and tended to answer on a set of facts he himself considered relevant,
e.g. that competitors would react to his changes of prices"[31, p. 11].
Similarly, Wied-Nebbeling states that "from seve comments on questions 2/IV and
3/V it appears that these questions were perceived to be purely hypothetical in
that price changes without competitons reacting were unthinkable"[19, p.
174].
40. McIntyre, S.H. and Currim, I.S., "Evaluating
Judgement-based Decision Models: Multiple Measures, Comparisons and Findings",
in Zoltners, A. (Ed.), TIMS Studies in the Management Sciences, Special Issue on
Marketing Models, North Holland, Amsterdam, 1982.
41. Katona, G., Psychological Analysis of Economic
Behaviour. McGraw-Hill, New York, NY, 1951.
42. Tull, D.S., KBhler, R. and Silver, M.S.,
"Nachfrageerwartungen und Preisverhalten deutscher Unternehmen: eine empirische
Studie": Marketing, Vol. 7, November, 1986, pp. 225-32.
43. Ward, J., "Hierarchical Grouping to Optimize an
Objective Function", Journal of the American Statistical Association, Vol. 58,
1963, pp. 236-44.
44. Friedman, H.P. and Rubin, J., "On Some Invariant
Criteria for Grouping Data", Journal of the American Statistical Association.
Vol. 62, 1967, pp. 1159-78.
45. Mojena, R., "Hierarchical Grouping Methods and
Stopping Rules -- An Evaluation", computer Journal, Vol. 20 No. 4, 1977, pp.
359-63.
46. Saunders, J.A., "Cluster Analysis for Market
Segmentation", Euroean Journal of Marketing, Vol. 14 No. 7, 1979, pp.
422-35.
47. The F-ratio within-cluster homogeneity is defined
as s sub ij /S sub i where s sub ij stands for the variance of variable i in
cluster j and S sub i is the variance of variable i across the total set of
entities; the lower this F-ratio, the smaller the variation of the variable
within a cluster compared to the collection of entities as a whole[48].
48. Backhaus, K., Erichson, B., Plinke, W.,
Schuchard-Fischer, C. and Weiber, R., Multivatiate Analysenmethoden, Springer,
Berlin, 1989.
49. Punj, G. and Stewart, D.W., "Cluster Analysis in
Marketing Research: Review and Suggestions for Application", Journal of
Marketing Research, Vol. 20, May 1983, pp. 134-48; reprinted in Hair, J.E.,
Anderson, R.E. and Tatham, R.L., Multivariate Data Analysis, Macmillan, New
York, NY, 1987.
50. Boyd, H.W. and Wallrer, O.C., Marketing
Management: A Strategic Approach, Irwin, Homewood, IL, 1990.
51. Buckner, H., How British Industry Buys,
Industrial Market Research, London, 1967.
52. McDonald, M.B., Marketing PEans, Heinemann,
London, 1984.
53. Dorward, N., The Pricing Decision: Economic
Theory and Business Practice, Harper & Row, London, 1987.
FURTHER READING
Oxenfeldt, A.R., "A Decision-making Structure for
Price Decisions", Journal of Marketing, Vol. 37, January 1973, pp. 48-53.
Simon, H., Preismanagement, Gabler, Wiesbaden,
1982.
Wasson, C.R., Dynamic Competitive Strategy and
Product Life Cycle, Challenge Books, St Charles, IL, 1974.
APPENDIX. MARKET VARIABLES
Market growth -- Two-year growth rate in sales volume
(units).
Number of competitors -- Number of rival firms
operating in the market.
Market concentration -- Herfindahl index of volume
shares (i.e. sum of squared market shares of all firms supplying the particular
product market).
Product substitutability -- Percentage of all
products in a given product group for which direct equivalents are offered by
competitors.
Market price sensitivity -- Product managers'
assessment of customer price sensitivity; scored on five-point scale, ranging
from 5="extremely price sensitive", to 1="not at all price sensitive".
Price competition -- Two items describing the
intensity of:
* list price competition; and
* size of discounts given to distributors.
Scored on five-point scales, ranging from
5="extremely important" to 1= "not at all important"; subsequently combined in a
single index (Cronbach's alpha=O.58).
Non-price cometition -- Eight items describing the
intensity of non-price forms of competition, notably:
(1) product quality;
(2) sales support;
(3) technical support;
(4) packaging quality:
(5) promotion;
(6) product innovation;
(7) speed of delivery; and
(8) product availability.
Scored on five-point scales raging from 5="extremely
important" to 1= "not at all important" and subsequently summed to form a single
index (Cronbach's alpha = 0.80).
Cometitive Price-cutting -- Frequency of promotional
(i.e. temporary) price-cutting by competitors, assessed on a four-point scale,
ranging from 4="very frequently" to 1="never".
My Research