A discretionary model of industrial buying

A Discretionary Model of Industrial Buying

SAMUEL CAMERON

and DAVID D. SHIPLEY

Lecturer in Economics, Department of Economics, North Staffordshire Polytechnic, UK

Senior lecturer in Economics and Marketing, Department of Management Studies, North Staffordshire Polytechnic, UK

This paper demonstrates that product differentials exert an important influence on the demand for industrial inputs. It presents a utility-maximizing model of industrial buying which integrates the empirical findings of marketing scholars with orthodox economic theory. The model receives statistical support in the form of partial correlations of industrial sourcing determinants and a factor-analytical extraction of hypothetical constructs corresponding to features which in the model influence sourcing decisions.

INTRODUCTION

It is generally accepted that product-differentiation influences the preferences of the ultimate buyer of consumer goods in such a way that short- or long-run brand loyalty may emerge. In the case of capital goods, there is more scope for controversy about the role of product-differentials. Andrews (1 964) asserts that profit-maximizing industrial buyers will ignore non-price factors and simply seek to minimize pecuniary costs so that opportunity costs and non-pecuniary income will not influence the purchasing decision. Koutsoyiannis (1975) describes the less extreme view that the above argument is valid for intermediate goods, especially raw materials, but does not apply to the market for investment goods. Purchases of machinery, etc. are subject to a high risk factor on account of the large financial commitment and lengthy period of use involved. We can explain this phenomenon as follows: these factors will make any ‘mis- takes’ in supplier selection extremely burdensome as the competitive advantage foregone is written into the technology of the firm for a considerable time to come. The avoidance of such errors requires that the potential purchaser looks at some proxies for productivity. This only applies if the buyer is penalized for his ineptitude or, in the absence of strict profit- maximizing-based monitoring of his performance, is unable to consume some of the firms proceeds in the form of ‘on-the-job consumption’.

This paper seeks to demonstrate that product- differentiation influences the sourcing decisions of industrial buyers irrespective of the broad category of manufactured goods being purchased. Our demonstra-

tion takes the form of a model which predicts that all categories of manufactured inputs are selected on the basis of non-price, as well as price, factors. Support for the model is found in a factor-analytic investigation of the reasons given for choosing a particular supplier which reveals important non-price factors in the purchase of machinery, components and manufactured materials.

The data used in this study were previously presented in tabular form in Shipley (1982). Table 1, amended from Shipley, provides some simple pre-

Table 1. Percentage Frequencies of Cited Source Selection Determinants

Determinant

Speed of sales quote Packaging Advertising Personal selling Sales promotions Public relations Price After-sales service Ease of servicing Credit terms Product quality Product range Labelling Trade marks Willing to produce

to Specifications Speed of delivery Reliability of delivery Product is patented In-group purchasing Other

Type of capital goods Manufactured

materials Components Machinery

43 47 37 6 4 1 3 5 7 21 17 19 3 3 4 3 3 4

94 93 88 42 49 79 17 33 71 30 25 30 85 91 92 20 18 16

1 1 0 2 2 2

46 46 32 70 66 56 81 78 64

2 3 3 16 15 9 2 1 1

N = 461 401 426

CCC-0143-6570/85/ooO6-0102$05.00

102 MANAGERIAL AND DECISION ECONOMICS, VOL. 6, NO. 2,1985 (0 Wiley Heyden Ltd, 1985

liminary support for the contention that vendor-selec- Expected Product . ..

tion does not hinge on monetary price but rather on a ‘shadow price’. The shadow price is envisaged in the model, developed below, as emerging from buyers’ attempts to maximize their own total utility from salary, non-pecuniary remuneration and psychic income accruing from activities at work and the con- geniality of the work environment. It is assumed that buyers are permitted to behave in this way, by the ultimate owners of the enterprise, so long as they satisfy some prescribed level of disbursable revenue to shareholders.

The present work, as outlined above, is congruent with the view of marketing scholars and businessmen on the industrial buying decision. This view is expres- sed in a growing volume of literature which emphasizes the powerful influence of non-price factors on the purchasing decisions of industrial buyers.’ A re- presentative and interesting view comes from Levitt (1980), one of the foremost marketing scholars. Levitt argues that all goods are differentiable, even com- modities like primary metals, grains, chemicals and pork bellies. What matters is what the supplier sells not the generic product. This ‘Offered Product’ differs from the generic product partly because sellers differ in the services they offer with the product. For example, two otherwise identical goods might be sold with differences in credit terms, delivery and quotation efficiency, after-sales services and so on. In the case of heterogeneous products the degree of non-price differentiation is extended due to variations in product reliability, durability, style, ease of repair, etc.

The offered product for industrial goods may include personal benefits to the buyer@). The chance of personal gain ensures that product-differentiation will function as a market signal through responses on the demand side. This will in turn ensure that sellers will contrive to emit the appropriate signals about their offered product.

~~

THE MODEL

We propose a simple managerial model in which purchasing executives pursue their own self-interest in a world of uncertainty, imperfect information and imperfect competition. Fundamentally, the model is one where purchasing executives maximize their own utility subject to providing some security level of proceeds to those above them. In the event of failure to pass this threshold the executives will be replaced by rival or newly promoted purchasing executives. We assume that ownership is divorced from control with the requirement that reported profits equal or exceed their opportunity costs so that new shareholders will be attracted and current ones will not withdraw.

In the remainder of the text the term ‘Product’ will refer to offered product. We introduce two additional concepts-the ‘Expected Product’ and the ‘Aug- mented Product’.*

Denoted X , this is the minimum that a buyer must get from a deal before he will contemplate a transaction with the particular supplier. It is a cluster of characteristics from which the absence of one would make the product unsuitable even if emoluments were lavished on the purchaser. On the basis of prior research3 we expect that X will include product quality and price plus, in particular cases, delivery conditions or after-sales services and product-maintenance conditions. The importance of the elements of the cluster that comprise X will differ over time and amongst firms and types of product. This implies that some form of disaggregation be used in empirical work. Due to data-limitations we investigate three types of product but treat all firms as having similar functions. Temporal variation is controlled for as we use cross- section data.

Augmented Product

Denoted A, this comprises X plus additional benefits ‘thrown in’ by the seller to increase the likelihood of first-time patronage and/or loyalty. These benefits may accrue directly to the buyer who is the point of contact (see ‘Buyer’s Utility Function’ below) or to the owners or controllers as proceeds from improved productivity of the factors of production. Examples of the latter type of benefit are better than expected quality of the product, safer packaging, prompter quotations, improved credit terms, etc. The benefits existing in the margin between the expected and augmented products in some senses fall into the hands of the purchasing executives. They will be faced with the problem of dividing these between themselves and their superiors. If their superiors do not consume some of this margin but direct it into reinvestment then the status and long-term job security of the buyer will be improved through the growth and prosperity of the firm. Irrespective of the mode of dividing the margin amongst claimants it is clearly in the interests of buyers to attempt to secure an augmented product.

Buyers’ Job-security

We now look more closely at the buyer’s job- security. The divorce of ownership and control means that managers can satisfice rather than profit- maximize. Nonetheless, managers’ jobs will be at risk if shareholders’ returns are insufficient or if minimal ploughback investment requirements are not met.

We assume that senior management, which might include purchasing executives, specify a target level of purchasing achievement ( T ) that ensures the achievement of minimum acceptable profits. Failure to satisfy T leads to the dismissal of the purchaser; if he can not be replaced with a purchaser who can achieve T then the firm will perish. In order to guarantee the con- tinued existence of his job, the buyer must specify X in such a way that T will be achieved with certainty, ignoring the vicissitudes of the final product-market.

MANAGERIAL AND DECISION ECONOMICS. VOL. 6, NO. 2, 1985 103

Hence, we assume that the achievement of X , such that X satisfies T a t least, has no influence on the final product-market but abolishes the risk of total failure of the firm.4 Other risks will remain but their effect will fall in determining the size of the expectedfaug- mented margin.

A buyer who achieves T, as specified above, is paid what we call ‘Normal Salary and Perks’ ( N S P ) , that is, normal pecuniary and non-pecuniary income from his employers for doing his job as specified by them.

Through his initial choice of an employer, the buyer will maximize N S P . This gives him the utility level he would get from goods and leisure choices if he simply procured X , we will denote this U , . If he goes beyond the achievement of X to procedure an A then, given the actual A acquired, there will be a maximum potential utility to him which we denote U , . The magnitude U , - U , will be an estimate of the maximum possible value to him of procuring the augmented rather than just the expected product. Depending on how we view it, this is a type of consumer’s surplus or rent on a factor of production. The buyer has a number of sellers to choose from, with it being obvious, from the ra- tionality postulate, that he picks the one who will enable him to maximize U , - U , . Some of this rent will be appropriated by those on a higher level of the firm. Even if this is the case, it may be demonstrable that the buyer is responsible for the surplus, in which event the firm will have to compensate him adequately for the rental on his unique buying abilities or he will transfer to another firm.5 This compensation is desig- nated as ASP, being ‘Augmented Salary and Perks’.

If we denote the surplus or rent from U , - U , as AB, being ‘Augmented Benefit’ then it would seem that A B - ASP accrues to the firm. This is not conclusive; although he is monitored after a fashion by the security constrained,the buyer still has a large area of discretion through his choice of supplier. The supplier being aware of this can provide the buyer with ‘on the job consumption benefits’ ( A J ) , which vanish at the point of sale so that the AB which the firm perceives is smaller than the total AB. AJ is an imperfect substitute for take-home salary but there are strong incentives to take it up if salary is constraint by the attitudes and power of alternative interest groups within the organization, such as shareholders, top management and workers.

From casual empiricism it seems that seller-provision of meals and outings, Christmas presents, other gifts, prizes and actual monetary bribes are common- place in industry. Bribery is a difficult concept to observe scientifically, but several reports bear testimony to its prevalence.‘j

Buyers’ Utility Function

The buyers’ utility function is the basis for the inter- pretation of the statistical results given in a later section.

Formally, the buyer maximizes U = U , (Si,. . . , S,) subject to two constraints: ( 1 ) the provision of X such

that T is met; (2) E = Esi + ..., Esn. The s’s are the sources of utility with E being the total amount of ‘effort units’ available to the buyer, this being a function of his ability and how hard he works. The subscripts i to n denotes effort devoted to procuring a utility source.

We assume that leisure, away from the workplace, is fixed by institutional considerations such as a fixed total of working hours. If we call this HL and assume it can be measured in effort units, then:

n

H L = E - C E i i = 1

The above model has its equilibrium where:

M U s J E s i = M U s j I E s j = . . . , MUs,/Es,

A comprises A J , X , augmented characteristics of X and additional services such as safer packaging or more precise match to order specification than the buyer anticipated. This last group of benefits operates as what we call a ‘sellers services income effect’, denoted K . For example, prompter quotation than expected is equivalent to augmenting E to E* = E + E m s , where Ems is the magnitude of the income effect in effort units.

If we stipulate the assumption of homotheticity of the buyer’s utility function, then the income effect, K , raises the demand by the buyer for all S’s in pro- portion to the initial equilibrium.

The utility function itself is neither directly nor in- directly susceptible to estimation from our data. Any demand function which could be derived from the utility function is likewise unobservable. Given the above considerations, our empirical work restricts itself to looking for the manifestations of the arguments of the utility function and the seller’s services income effect in the data available to us.

The data are factor-analysed to reduce twenty features of influence in the purchasing decision into factors which capture aspects of ASP, N S P , AJ and K in the form of hypothetical constructs. N S P will be revealed in the clustering together of variables associated with X , chiefly price and quality. AJ will be revealed in the clustering together of sales promotion, personal selling and public relations. K and ASP will probably be more difficult to observe as both are reflected in obtaining an A . In addition to this, A contains X to a varying extent; if those above the buyer in the hierarchy became more exacting in their requirements, the specification of A would converge on X , with this convergence increasing with the homogeneity of buying abilities and the homogeneity of the offered product. Problems of the difficulty of observa- tion are taken up in the penultimate section of this paper, which could be skipped to now without loss of continuity.

SURVEY AND DATA

The data used here were collected in 1980 by means of a postal sample survey conducted among ‘The

104 MANAGERIAL A N 0 DECISION ECONOMICS, VOL. 6 , NO. 2, 1985

Senior Purchasing Executive’(s) of manufacturing organizations listed by product group in the KOM PASS register. Information about the survey and its possible deficiencies is available in Shipley (1 982). Business surveys have been much maligned as a vehicle for empirical research, and this encourages us to explain the steps taken to avoid the problems which arise when there has been a failure to achieve meaningful communication between researcher and respondent.

The survey was preceded by a verbally checked 25- firm pilot study to identify ambiguity and other problems in the questions. The amended question- naire, used in the survey proper, was restricted to two sides of A4 paper, comprising a brief introductory letter and five questions which called for 172 pieces of information. To encourage a high response rate, the respondents were each offered anonymity and a copy of the findings. To help foster a precise understanding, technical terminology was, wherever possible, omitted from the questions, which were generally composed of words in common use. Overall, the returns were completed in a satisfactory manner, although 28 were discarded as faulty. This left a usable response rate of 16.6%, representing 497 manufacturing firms. These seemed to be well dispersed geographically, and together their leading outputs accounted for over 150 SIC product groups and all manufacturing orders.

The questions asked the respondents to consider the variables listed in Table 1 and to indicate those which ‘help you to decide to buy the products of one manufacturer rather than those of other manu- facturers’. The respondents were invited to cite as many variables as they wished, the mean being 5.9 for manufactured materials, 6 for components and 6.2 for machinery. As Table 1 illustrates, not all of the respondents answered the questions in relation to all three

types of input. This may be because some firms buy some input types rarely or never or because the relevant decision is not taken by the buyer asked or by him alone.

The answers obtained were the respondents’ public statements about their own perceptions of the determinants of their patronage decisions. Both statements and perceptions raise questions over reliability. The former may be false and the latter may be misjudged. Arguments presented in Shipley (1982) suggest that the data are valid material for social scientific investigation. This position is strengthened by the conformity of the results in the penultimate section of this paper with the model of the second section. Overall, both authors feel justified in accepting the respondents’ statements as reliable in view of the absence of any clear counter-evidence from other sources. Ultimately it does not matter if perceptions do not adequately mirror reality as it is buyers’ perceptions which influence their decisions, irrespective of whether their perceptions are objectively valid or not (Hirsch, 1971).

STATISTICAL METHOD

The propositions of the second section of this paper are investigated by a type of Factor Analysis which is explained briefly here. Factor Analysis is a data-reduction technique,’ best known to economists in the form of Principal Components, which is offered as a ‘solution’ to the multicollinearity problem in most econometrics texts. Unlike a regression analysis, a factor analysis has no dependent variable-all variables in the data are looked upon as having their variation explained by a set of ‘factors’ or ‘components’ (depending on which technique we use) which are

Table 2. Partial Correlations of Other Source-selection Determinants with Price Type of capital goods

Determinant Manufactured materials Components Machinery

Product quality (1) 0.423 (1) 0.682 (1) 0.594 Reliability of delivery (2) 0.399 (2) 0.546 (3) 0.427 Speed of delivery (3) 0.382 (3) 0.521 (4) 0.419 Speed of sales quote (4) 0.235 (4) 0.368 (7) 0.277 Willing to produce to specifications (5) 0.23 (5) 0.353 (8) 0.249 Credit terms (6) 0.208 (8) 0.267 (6) 0.297

(2) 0.483 After-sales service (7) 0.2 (6) 0.338 Personal selling (8) 0.12 (9) 0.223 (9) 0.16 Ease of service (9) 0.094 (7) 0.312 (5) 0.417 In group purchasing (10) 0.093 (10) 0.176 (10) 0.135 Advertising (11) 0.062 (12) 0.125 (12) 0.071 Product range (12) 0.056 (11) 0.192 (11) 0.085 Other (13) 0.045 (14) 0.059 (15) 0.054 Packaging (14) 0.042 (13) 0.106 (14) 0.063

Sales promotion (16) 0.02 (18) 0.037 (13) 0.066 Trade marks (17) 0.01 (17) 0.044 (17) 0.029 Product is patented (18) -0.001 (15) 0.056 (18) 0.09 Public relations (19) -0.009 (19) 0.015 (16) 0.052

Labelling (15) 0.042 (16) 0.052 i

Notes: 1. Figures on right are correlations; figures on left in parentheses are rankings of correlations. 2. ‘Labelling’ is not cited in Machinery therefore the matrix is prepared from the remaining 19

variables.

MANAGERIAL AND DECISION ECONOMICS, VOL. 6, NO. 2, 1985 105

~

Table 3. Partial Correlations of Other Source-selection Determinants with Quality

Determinant

Price Reliability of delivery Willing to produce to specifications Speed of delivery After-sales service Personal selling Credit terms Speed of sales quote Sales promotion Product is patented Other La belling Ease of servicing Advertising Packaging Product range In-group purchasing Trade marks Public relations

Type of capital goods Manufactured materials Components

(1) 0.423 (1) 0.682 (2) 0.298 (2) 0.503 (3) 0.204 (4) 0.371 (4) 0.182 (3) 0.455 (5) 0.151 (5) 0.348 (6) 0.122 (8) 0.179 (7) 0.199 (7) 0.199 (8) 0.095 (6) 0.331 (9) 0.08 (14) 0.07

(10) 0.065 (13) 0.091 (1 1) 0.061 (15) 0.061 (12) 0.056 (19) 0.004 (13) 0.054 (9) 0.255 (14) 0.053 (12) 0.13 (15) 0.043 (17) 0.033 (16) 0.042 (11) 0.15 (17) 0.038 (10) 0.157 (18) 0.038 (16) 0.048 (19) -0.033 (18) 0.02

Machinery

(1) 0.594 (4) 0.393 (6) 0.209 (5) 0.289 (2) 0.487 (9) 0.157 (8) 0.171 (7) 0.244

(13) 0.077 (12) 0.081 (18) -0.014

(3) 0.402 (1 5) 0.052 (17) 0.012 (10) 0.153

(16) 0.02 (14) 0.066

(11) 0.099

Notes: 1 . Figures on right are correlations; figures on left in parentheses are rankings of correlations. 2. 'Labelling' is not cited in Machinery therefore the matrix is prepared from the remaining 19

variables

artificial constructs extracted from the original data. The difference between principal components and factor analysis resides in the assumptions made about the causes of variation in the data. In classical factor analysis it is posited that some of the determinants of a variable are shared by other variables, called the common part, while others are unique to the variable and hence are called the unique part.

Classical factor analysis may result in a solution in which there are fewer factors than variables whilst principal components necessarily produces a number of factors (i.e. components) equal to the number of variables. Both methods start from a correlation matrix of the original data. This matrix is the source for Tables 2 and 3, which show correlations by rank order of the two most conventionally, and heavily cited in Table I , regarded determinants of demand with other determinants.

The difference in the methods, computationally viewed, is that in principal components the initial factors or components are extracted from a correlation matrix with unity along the main diagonal, while in factor analysis these are replaced with estimated communalities. We used an iterative method of 'principal factoring', which determines the number of factors to be extracted and replaces the main diagonals with the squared multiple correlation coefficient (R') between a variable and the rest in a set. The factors are extracted from this matrix and the variation accounted for by these forms the new communality estimate. This process is repeated until the estimated communality converges. The final communality estimates are given in Table 4.

Following the initial factoring, further simplification was achieved through rotation into a terminal solution via the oblique method. This aids the discovery of patterns of clusters amongst the features cited in the survey. The oblique method allows factors to be cor-

related' and is used here on account of the greater realism of this assumption than that of orthogonality. Computation using the oblique method yields two matrices of variable X factor--the factor pattern matrix gives the direct contribution which a factor makes to explaining a variable whilst the factor structure matrix gives the total contribution. Other methods will provide only a pattern matrix. Tables 5-7 repro- duce the factor pattern matrix.

In Tables 5-7 each row can be regarded as analog- ous to a regression to explain the variance in the variable where the R 2 will necessarily equal the

~~ ~ ~~~ ~~

Table 4. Communalities of Determinants of Source- selection

Manufactured Determinant materials Components Machinery

Speed of sales quote 0.46 0.423 0.399 Packaging 0.088 0.1 23 0.1 87 Advertising 0.31 7 0.33 0.235 Personal selling 0.287 0.283 0.352 Sales promotions 0.287 0.326 0.357 Public relations 0.077 0.226 0.71 2 Price 0.572 0.657 0.566 After-sales service 0.355 0.347 0.498 Ease of service and

repair of product 0.583 0.608 0.384 Credit terms 0.1 37 0.1 99 0.223 Product quality 0.372 0.852 0.523 Product range 0.5 0.253 0.1 62

Trade marks 0.363 0.674 0.729 Willing to produce

Speed of delivery 0.352 0.52 0.464 Reliability of delivery 0.328 0.52 0.436 Patented product 0.484 0.381 0.224 In-group purchasing 0.283 0.31 0.21 6 Other 0.1 43 0.01 1 0.066

La belling 0.049 0.329 I

to specifications 0.1 8 0.326 0.21

Note: 'Labelling' is not cited at all for Machinery therefore it has to be omitted in order that factorization can be carried out.

106 MANAGERIAL A N 0 DECISION ECONOMICS, VOL. 6. NO. 2. 1985

Table 5. Factor Analysis for Machinery

Variable

Speed of sales quote Packaging Advertising Personal selling Sales promotions Public relations Price After-sales service Ease of service Credit terms Quality Product range Trade marks Produce to specifications Speed of delivery Reliability of delivery Patented product In-group purchasing Other

Factoi

1 2 3 4 5 6

1 2

-0.101 0.051 -0,010 0.385

0.052 0.102

0.075 0.176 0.134 0.492

0.677 0.002 0.676 0.019 0.567 0.003 0.157 0.055 0.73 0.037

-0.024 0.874

0.032 -0.012

0.101 -0.012 0.173 -0.063 0.297 -0.003 0.027 -0.047

-0.065 0.08

0.043 0.08 0.082 -0.037

Percentage variation

48.6 17.9 13.6 8.6 6.1 5.0

Factor 3 4 5

- 0.044 - 0.024 - 0.1 18 0.303 0.205 0.025 0.214 -0.024 -0.046

-0.039 0.336 -0.324 -0.092 0.089 -0.226 -0.038 0.336 -0.324

0.018 0.013 0.067 0.025 -0.053 0.03 0.039 0.024 -0.069 0.082 0.108 0.159

-0,014 0.069 -0.007 -0.006 0.35 -0.039

0.85 0.015 -0.043 0.057 0.115 0.193 0.014 0.017 0.084

-0.089 -0.016 0.083 -0.012 0.458 0.137 -0.017 0.135 0.406

0.233 -0.05 0.008

Cumulative percentage variation

48.6 66.5 80.1 88.7 95.0

100.0

6

- 0.58 - 0.1 39 - 0.099 - 0.1 64

0.093 0.1 64

- 0.1 08 - 0.061 - 0.084 - 0.253

0.05 - 0.1 01 - 0.01 - 0.261 - 0.566 - 0.46

0.1 15 - 0.043

0.063

Table 6. Factor Analysis for Components Factor

Variable 1 2 3 4 5 6 7 8

Speed of sales quote 0.52 -0.047 0.218 -0,099 0.033 0.005 0.021 0.036 Packaging 0.035 0.053 -0.027 -0.014 0.007 -0.058 0.341 -0.075 Advertising 0.1 9 0.352 0.154 0.092 -0.269 0.075 -0,009 0.098 Personal selling 0.22 -0.036 0.306 0.02 -0.188 0.122 0.063 0.107 Sales promotions -0.009 0.007 0.536 0.02 0.102 0.1 0.064 0.012 Public relations 0.051 0.023 0.472 -0.03 0.007 -0.091 -0.008 -0.016 Price 0.271 0.04 -0.127 -0.198 0.028 0.046 0.092 0.497 After-sales service 0.054 -0.027 0.1 -0,475 0.034 0.01 0,019 0.148 Ease of service 0.006 0.02 -0.05 -0,788 -0.037 0.019 0.023 0.034 Credit terms 0.284 -0.071 -0.087 0.007 0.156 0.042 0.183 -0.004 Quality -0,022 0.043 -0.027 -0.128 0.023 -0.045 -0.048 0.903 Product range -0.024 -0.003 0.061 -0.031 -0.114 0.056 0.465 0.067 Labelling -0.033 0.053 -0.027 -0.022 0.049 0.576 -0.011 -0.033 Trade marks 0.03 0.82 -0.105 - 0.054 -0.021 0.141 -0.034 -0.046 Produce to specifications 0.012 -0.052 0.07 -0,017 0.078 0.099 0.376 0.256 Speed of delivery 0.571 0.094 -0,033 -0,179 0.051 -0,088 0.006 0.127 Reliability of delivery 0.401 -0.023 -0.001 -0.046 0.142 0.08 0.1 8 0.24 Patented product - 0.1 4 0.49 0.121 0.041 0.173 -0.108 0.187 0.068 In-group purchasing 0.089 0.032 0.074 0.011 0.538 0.054 -0.063 0.049 Other 0.034 -0.007 -0.068 0.023 0.017 -0.016 0,011 0.046

Factor Percentage of variation explained Cumulative percentage variation

1 46.0 46.0 2 16.4 62.4 3 9.4 71.8 4 8.7 80.4 5 5.7 86.1 6 5.1 91.2 7 4.5 95.7 8 4.3 100.0

MANAGERIAL AND DECISION ECONOMICS. VOL. 6. NO. 2. 1985 107

Table 7. Factor Analysis for Manufactured Materials

Variable

Speed of sales quote Packaging Advertising Personal selling Sales promotions Public relations Price After-sales service Ease of service Credit terms Quality Product range La belling Trade marks Produce to specifications Speed of delivery Reliability of delivery Patented product In-group purchasing Other

Factor

1 2 3 4 5 6 7 8

1

- 0.031 0.01 7 0.055 0.066 0.05

0.65 0.1 23

- 0.063 0.1 99 0.596

- 0.1 97 0.088 0.006 0.1 61 0.209 0.426 0.01 5 0.01 0.004

- 0.098

2 0.007

0.1 25 0.089 0.061

- 0.1 32

- 0.065 - 0.046 - 0.05

0.033 - 0.1 11

0.061 0.085

- 0.01 9 0.561 0.074

- 0.044 - 0.009

0.622 0.097 0.035

3

0.024 - 0.036 - 0.096

0.071 0.03 0.041 0.068 0.556 0.77 0.005 0.062 0.075

- 0.025 0.1 12 0.005 0.055 0.034

- 0.042 0.01 8 0.01 7

Percentage of Variation explained

35.0 15.8 13.2 10.5 8.6 7.0

4.1 5.8

Factor 4 5

0.077 -0.014 -0.102 0.059

0.125 -0.116 0.101 0.188 0.03 -0.035

- 0.05 0.05 -0.036 -0.05 -0.019 0.023

0.027 -0.06 - 0.21 0.1 02

0.018 0.033 -0,088 0.636

0.053 0.188 0.071 0.069

-0,143 0.177 -0.13 -0.024 -0.078 0.031 - 0.324 - 0.006 - 0.522 - 0.056

0.02 0.035

6

- 0.698 - 0.028 - 0.01 9 - 0.1 06

0.077 - 0.07 - 0.201

0.03 - 0.024

0.002 0.051

- 0.063 0.023

- 0.01 9 - 0.1 75 - 0.431 - 0.2

0.026 - 0.01 4

0.01 2

7

- 0.01 6 0.1 49 0.385 0.21 2 0.541 0.1 9

- 0.008 0.091

- 0.09 0.001 0.088 0.033

- 0.031 0.023

- 0.032 - 0.032 - 0.059

0.086 - 0.049 - 0.055

Cumulative percentage variation

35.0

64.0 74.5

90.1 95.9

100.0

50.8

83.1

8

- 0.034 0.1 57 0.271

- 0.06 - 0.043 - 0.1 08

0.1 02 0.033 0.01 1 0.01 2 0.025 0.237

0.1 44 - 0.069 - 0.099 -~ 0.1 28 - 0.086 - 0.021

0.372

- 0.037

estimated communality (c) in the structure matrix; in the pattern matrix the figure will fall short of c due to the neglect of the indirect contribution made through the correlation of a factor with other factors. The total variance explained is calculated by summing the squares of the coefficients. The correlation between two variables is obtained by multiplying together their respective correlations with the factor. More crudely, the existence of clustered variables can be gauged by looking at the size of their respective coefficients on a common factor.

RESULTS

The generation of the output in Tables 2-7 has been explained above; in order not to confuse the reader a recap is given of which stages of the analysis yielded which table. Tables 2 and 3 are obtained from the correlation matrix which is prepared prior to factoring. Table 4 is obtained from the first step of the factor analysis, the results of using iterative methods to deter- mine communalities, not the second stage-rotation using oblique methods into a terminal solution. The pattern matrices of Tables 5-7 are obtained from the oblique rotation results.

In the variation explained portions of Tables 5-7, factors enter in descending order of importance, with the cumulative explanation equalling the summed individual explanations on account of strict orthogonality.

Table 2, and Table 3, reveal a very high correlation of price and quality which could in any case be inferred from the frequency of their citation in Table 1. The other features which show some signs of correlation with price are mostly those which reflect risk and uncertainty considerations. The relationships with price are all strongest in components and their ranking is extremely stable; for example, the same features appear in the first nine for all three groups. In Table 3 much the same is revealed for correlations with quality except that features associated with on-the-job consumption rank slightly higher in the quality table than the price table. A notable feature of both tables is the higher rank of after-sales service and ease of service and repair in machinery than in the other two groups. This is to be expected from the nature of the product.

Table 4 shows the variance of a variable that is shared by at least one other variable in the set. One minus these figures gives the unique variation. Generally, the unique variation of most variables, other than price and quality, is greater than 50%. As with Tables 2 and 3, relationships appear to be much stronger in components.

A major problem with factor analysis is the naming of the factors. Being hypothetical constructs, the definition of the factors is in danger of being the product of the whim of the researchers. This problem can be mitigated by the development of a model, as in the second section of this paper, which is specifically related to the variables observed in the data.

We name our factors on the basis of combinations

108 MANAGERIAL AND DECISION ECONOMICS, VOL. 6, NO. 2. 1985

of variables which load heavily on the same factor. At most, there are only four factors to be named on the grounds of the second section of this paper. Some heavily loading combinations will represent the con- sequences of product-differentiation rather than the determinants of product-differentiation via demand- side signals. For example, if personal selling, trade marks and advertising loaded together it could reflect loyalty generated from the procurement of ASP and AJ by the buyer.

The number of factors emerging from a factor analysis depends on the rank of the matrix (in principal components, the number of factors equals the number of variables as all communalities are assumed to be zero). The ranks of the matrices are such that we have eight factors for components and manufactured materials and six for machinery. This means we have an excess over the specifications of the model and must treat the residual factors as being replications of some of those in the model or as unidentifiable.

Support for the model is found in the existence of four factors comprising X and A variables. It is difficult to isolate a pure X factor, as the critical features of both X and A are represented by the same variables. Accordingly, one factor is entitled X A on the grounds that it contains the crucial characteristics of X , plus some probable augmentation of its characteristics through the exercise of buying power.

The other three factors pertain to the procurement of A , One of these is A J , represented by the clustering of personal contact variables like personal selling, sales promotion and public relations. The remaining factors are facets of K . One is K l , shown in the clustering of variables which denote the provision of information by sellers thereby reducing the number of units of E, which must be devoted to search activity. The relevant variables are advertising, personal selling, trade marks, etc. It follows that KZ might be correlated with AJ to some extent so that they will be difficult to tell apart except where personal contact variables and the advertising, trade marks, etc. group of variables load heavily as groups but not together. The fourth factor, K P , comprises variables which increase buyer productivity above the level resulting from the augmentation of pure X variables and the provision of KZ. These will be such things as meeting specifications, prompt quotation and secure packaging. Finally, it is demonstrable that both KZ and K P can help to reduce buyers’ risk and uncertainty. In this regard, earlier findings show that uncertainty reduction is an important motivation in decision-making generally, (Cox, 1967; Cyert and March, 1963) and in industrial purchasing specifically (Cardozo and Cagley, 1971 ; Lehmann and O’Shaughnessy, 1974).

As Lehmann and O’Shaughnessy found in their factor analysis of the determinants of industrial source

selection, the results here vary by type of goods purchased. In our study, materials and components yield an X factor predominantly composed of price, quality and delivery, whilst machinery yields an X factor comprising price, quality, after-sales service and ease of service. In machinery, it seems that the delivery variables form part of the K P factor. In materials and components K P includes after-sales service and ease of servicing.

Our model suggests that X would load as the first factor in all cases given its paramount importance in industrial sourcing. X A is the first factor in machinery and materials. In components, X A appears as the first and eighth factors with a correlation of 0.533 between them (the order of factors in the rotated solution is not of the same importance as in the initial solution). It is notable that, apart from in machinery where the correlation between the first factor ( X A ) and the sixth factor ( K P ) is 0.48, all other factor correlations are extremely low.

The most unambiguous AJ factor is factor three in components. Others which display signs of being AJ are two, four and five in machinery and seven in materials.

The clearest K l factors are three in machinery and two in components. These have light loadings on advertising but heavier loadings on trade marks, which may be important information-carriers. KZ influences can also be seen in the following factors; three in components, two in machinery and seven in materials.

K P factors are revealed as three and seven in materials, four and seven in components and six in machinery.

SUMMARY AND CONCLUSIONS

The briefest possible way to summarize this paper is to say that product-differentiation does matter in the sale of industrial goods. This has often been demon- strated in the marketing literatureg which, however, fails to provide the kind of theoretical rationale that is satisfactory to economists.

The present paper breaks new ground in integrating the empirical insights of marketing scholars with the utility-maximizing paradigm of orthodox economic theory. This integration is performed via a model which is a simple twist on managerial models of the firm. The model generates specific predictions con- cerning the influence of product-differentiation on industrial buyers. It is given statistical support through partial correlation of patronage determinants and the extraction of hypothetical constructs coresponding to the features in the model which influence a sourcing decision.


NOTES

A X /

/ /

1.

2.

3. 4.

5.

6.

7.

8.

See, for example, Dempsey (1978), Evans (1980). Lehmann and O'Shaughnessy (1974), Michell (1979). Pass (1971), Samiee (1982), Shipley (1982), Turnbull and Cunningham (1981). Udell (1964) and Wood and Easton (not dated, released 1981 ) . These terms are concepts developed by Levitt (1980), although our treatment of augmented product is considerably broader than his. See note 1. Given the existing personnel of the firm and that the firm will not perish via product-market uncertainty. ThisisunlikeWilliamson (1 963),whoregards'emoluments'as being salary and perks in excess of those required to prevent a manager withdrawing from his current employment. See, for example, Dempsey et al. (1 980). Deschampsneufs (1979), Kaikati (1977), Kaikati and Label (1980). Levy (1 977), Nossiter (1 976). The Financial Times (1 979) and The Times (1 979). See, for example, Catell (1965a). Catell (1956b), Harman (1963) and Lawley and Maxwell (1963). In factor analysis, a partial correlation matrix is prepared from data normalized to have mean zero and variance equal to one. It is assumed that data are measured on a continuous scale; the dichotomous data used here violates some of the basic statistical assumptions of the model. This can be overcome by the use of a partial correlation matrix estimated by the analysis of association, rather than correlation. However, violations of assumptions such as ours are common in social science applications and ought not to lead to drastic alterations in the broad outline of the results. In factor analysis, the number offactorsextracted isdetermined by the rank of the matrix and these are rotated; in principal components only factors contributing above some minimal explanation of variance are retained for rotation as the number of initial factors equals the number of variables. Rotation is illustrated in Fig. 1; take any pair of factors and locate al l the variables in these co-ordinates according to their weighting coefficients on each factor, then rotate the axes as shown until the fit through the observed points is improved as much as

-I

Rotated 'factor Y

--* FactorX

X'\ \

Rotated -I l J factor X

X

Figure 1.

possible. There are a number of methods for doing this according to the criterion chosen for improvement of variance explained; it is intuitively obvious that any method will result in weighting coefficients which differ substantially from the initial ones.

The diagram illustrates an orthogonal rotation. Oblique rotation is carried out here by the 'oblimin' criterion of minimizing the cross-products of the factor loadings on the reference axes according to the following formula:

i (i A X ) (1) p < q = l ,=1

where the A'sfactor loadings and m is the number of common factors.

9. See note 1.

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