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Land and Environment : Agribusiness Assoc. of Australia
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Agribusiness Review - Vol. 10 - 2002

Paper 1
ISSN 1442-6951


Influential Factors of Agricultural Chemical Purchase

Paula Tidwell, Kim Watson and Daniel Pedersen

Abstract

The purpose of this research was to identify the key factors in the decision making process that motivate end users to purchase a particular brand of agricultural chemical within a designated product category. The Australian cotton industry was selected as a suitable candidate due to both the high purchase frequency and total volume of agricultural chemicals utilised in the production of the crop. It is anticipated that the results from the research project will enable manufacturers to more accurately target advertising and promotional strategies to the needs of the end user. Maximising the effectiveness of advertising and promotion will generate demand for branded products from the field and minimise the ever increasing influence of agricultural distributors on buyer behaviour.

Introduction

Buyer behaviour in the agricultural chemical industry is an aspect of marketing that relies almost exclusively on extrapolation from a range of other industries. The purchase of agricultural chemicals involves a high level of consumer involvement and easily qualifies as a complex decision making process. Lawson et al (1996) indicate that the process incorporates five phases which include need arousal, consumer information processing, brand evaluation, purchase and post purchase evaluation. These phases are not distinct but are continuously interacting at various levels. This results in a dynamic selection process.

Buyer behaviour

In the context of consumer decision making, the consumer's psychological set is directed to brand, product, or distributor evaluations. The psychological set is comprised of two components choice (benefit) criteria, and brand attitudes. 

Choice criteria

One definition of choice criteria is a cognitive state of the buyer which reflects those attributes of the brands in the product class that are salient in the buyer's evaluation of a brand and are related to the buyer's motives that are relevant to this product class in the sense that the brands in the product class have the potential for satisfying those motives. Thus choice criteria links motives to brands via attitudes (Howard and Sheth 1969).

Choice criteria are associated with a particular product class and represent latent dimensions on which the buyer evaluates each brand in order to form an attitude toward that brand. These criteria represent dimensions of anticipated means of achieving reward, and means of avoiding punishment, that will be available to a buyer if purchasing any brand in a given product category. These brands are closely substitutable in terms of the motives served.

In the purchase of agricultural chemicals, the choice or benefit criteria will vary in accordance with the dominant external variables operating at the time the purchase decision is undertaken. Consistently, the most important criteria in selecting a brand of product from a designated category is efficacy the combination of both knockdown and residual performance of the product on the target pest. Other product attributes considered relevant in choosing between competing brands include spectrum of activity, relative toxicity, odour, price, formulation, means of application, compatibility, application rate and volume, pack size, and availability.

Brand attitudes

Howard and Sheth (1969) state that attitude refers to the buyer's relative preferences of brands in an evolved set based on evaluative beliefs about these brands as goal objects. Connotative meanings of the brands in the buyer's evolved set are compared with choice criteria to yield a judgement on the relative contribution of the brands toward satisfaction of the buyer's motives. This judgement includes not only a personal estimate of the value of the brand, but also an estimate of the confidence with which that position is held. This uncertainty aspect of attitude is referred to as brand ambiguity, since the more confidently it is held, the less ambiguous is the connotative meaning of the brand to the buyer and the more likely it is to be purchased.

Attitude is a cognitive state that on a number of dimensions reflects the extent to
which the buyer prefers, in terms of motives, each brand in his evoked set in relation to other brands in the set (Howard and Sheth 1969). It has only a directive effect, and not an arousal or energising effect, and contains only evaluative information regarding a brand.

Assael (1992) believes that brand attitude is a reflection of a consumer's overall preference for a brand, and is the result of a sequential process involving cognition, connation, and action. This sequence of events is commonly referred to as the 'hierarchy of effects' model of consumer decision making and is an important basis for defining the factors that influence consumer behaviour.

Bass and Wilke (1973) indicate that attitudinal measures provide useful predictions of brand preference and choice. Marketing research on attitudinal variables has focused on multi-attribute models that transform consumer cognition of brand offerings on several dimensions to uni-dimensional measures of brand effect. These models are typically based on expectancy theories which assert that an individual's predisposition to given behaviours are governed by the set of satisfactions presumed to flow from such behaviour, together with the probabilities of obtaining these satisfactions through such behaviour.

Bass and Talarzyk (1972) state that attitude theory offers considerable potential as a basis for studies of consumer choice behaviour. This theory incorporates a computational model developed by Fishbein, which claims that a consumer's attitude toward a particular brand is hypothesised to be a function of the relative importance of each of the product attributes and the beliefs about the brand on each attribute.

Stimulus exposure

Stimulus exposure is the sum of all the social influences and of the marketing effort to which the buyer is exposed. Selective attention is a response to the exposure of complex stimuli that is directed by the need to reinforce existing brand attitudes and perceptions in addition to seeking further relevant information.

Need recognition

Motives are defined by Howard and Sheth (1969) as biogenic or psychogenic needs, wants, or desires of the buyer in purchasing and consuming an item in a product class. They include the consciously sought goal, which is considered to determine behaviour.

Assael (1992) states that when a need has been recognised, a state of tension occurs that causes the consumer to search for information that will assist in decision making. Active information search is maximised when the consumer is unaware of well defined choice criteria or lacks awareness of the various brands and their potential.

In contrast, Howard and Sheth (1969) indicate that motives, in association with brand comprehension and choice criteria, are the three elements in the decision making process which combine to yield attitude. Motives are believed to be the most important element in the process due to their impact on learning, behaviour and regulating the input of information.

Consumer information processing

Consumer information processing involves the exposure to, organisation of, and search for information (Assael, 1992). This process is generally more cumbersome in situations involving industrial products due to the complexity of the technologies involved. This is particularly relevant for the agricultural chemical industry which utilises complex technologies in researching, manufacturing and in the effective application of product.

Attention

Attention is a continuous variable that is measured at discrete times. It is a psychophysical measure that describes the opening and closing of the buyer's sensory receptors, thus indicating the level of concentration on a specified stimulus.
Therefore, it is the magnitude of the flow of information into the buyer's nervous system, as contrasted with the much greater amount of information to which the buyer is exposed.

Brand comprehension

Brand comprehension is the completeness of the buyer's verbal description of a brand on a set of bipolar objectival scales, which serve to communicate the denotative meaning of the brand to others. It includes all brands in the buyer's evoked set, which refers to the set of brands considered when contemplating the purchase of a product. Without an effective strategy in obtaining the attention of potential customers, the brand will not appear in the evoked set and will fail to be comprehended.

Search for additional information

In situations where further information is required to effectively evaluate competing brands, the consumer may obtain information from either the commercial or social environment. With respect to the agricultural chemical industry, the commercial environment will include significative and symbolic information from both manufacturers and distributors.

Settle (1972) indicates that in order for commercial information to be effectively utilised by potential consumers, it needs to identify with relevant choice criteria. Although these criteria will often be similar between brands in the same product category, it is essential that for the purpose of differentiation, the integrity of all attributes be maintained. The test of integrity includes distinctiveness, consistency over time, consistency over modality, and consensus. Even though commercial information may contain all the desired characteristics, the incremental costs to the consumer in terms of time and energy may surpass the incremental gains resulting from the search.

The social environment is comprised of consultants, associates and state Departments of Agriculture staff who are all considered independent advisers to the grower. Information obtained via the social environment is extensive with significant interaction between all parties at field days, social events and through personal contact. The reliability of the information received from the social environment is relatively high due to the majority of information being obtained directly from experience within the local area.

Objectives

The primary research objective is to identify the key factors that influence buyer behaviour in the purchase of agricultural chemicals. Although numerous factors will account for the variability associated with buyer behaviour, in any industrial market segment a number of core factors will undoubtedly account for a significant proportion of the variation. The literature indicates that no specific studies have been previously conducted in this field.

A comprehensive survey of growers, within the major market segment of the insecticide market in Australia, will provide valuable information on two components involved in the process of complex decision making. These components include need arousal and consumer information processing. A number of factors suspected to be important in both these components of the decision making process include brand awareness, brand attitude, product positioning, performance, availability, user friendliness and urgency.

Once the key factors have been identified, the information would provide agricultural chemical manufacturers with a unique profile of the end user. This information would be used to develop advertising and promotional strategies to more effectively target the end user. The information would enable the manufacturers to differentiate between strategies that operated with the support of distributors and strategies that operated independently of distributors.

Method

The scope of this project was restricted to evaluating buyer behaviour in the purchase of insecticides, for the specific purpose of controlling heliothis caterpillars in Australian cotton crops. As the cotton industry utilises a number of particular groups of insecticides in controlling these major pests, the evaluation of buyer behaviour was only concerned with brand choices from within homogenous groups. These groups refer to the different categories of insecticides which are classified according to their chemical structure and include the organophosphates, carbonates, synthetic pyrethroids, biologicals and insect growth regulators.

The decision makers responsible for the purchase of insecticides for use on individual crops can be categorised demographically into four distinct segments. These segments include cotton growers, crop consultants employed by cotton growers, agricultural chemical distributors entrusted by cotton growers, and aerial operators contracted to cotton growers. A purchase decision will on average be required on twelve separate occasions over the course of the six month production cycle. Total expenditure on heliothis insecticides will account for approximately twenty per cent of the cost of growing the crop, ensuring a high level of involvement in a process of complex decision making.

Primary data collection

The primary research component of the project was conducted in two distinct stages. The first stage involved personal interviews with four cotton growers randomly selected from one of the major production areas. The purpose of the in-depth interviews was to develop an understanding of buyer behaviour peculiar to the cotton industry and identify attributes important in the selection of specific brands of insecticides. This was achieved by encouraging growers to discuss in considerable detail a number of recent insecticide purchases, comment upon the nature of the decision process, and to describe the level of complexity associated with each purchase experience.

Linking new information gleaned from the personal interviews with background industry information, a written questionnaire was developed for the purpose of interviewing, by mail, a representative sample of Australian cotton growers. The four page survey contained a combination of thirteen open and closed questions. The questions were designed to identify the relevant decision makers and determine the relative importance of a wide range of product attributes. These attributes were: price, knockdown performance, residual performance, formulation type, product odour, phytotoxic potential, ease of container disposal, pack size, performance on resistant insects, spectrum of insect activity, compatibility with other pesticides, name of manufacturer, reputation of the brand, and environmental hazard. Growers were asked about the role of non-growers in the decision making process for example, regarding the role which consultants played in the purchase decision process.

A total of 250 questionnaires were mailed to a random selection of New South Wales cotton growers. The sample size represented approximately fourteen per cent of the total grower population and included growers from all of the four major cotton production areas within the State. Growers were allowed three weeks from the time of posting to the date of return, in order for their survey to qualify as a legitimate response. Ninety completed questionnaires were received within the specified return period which equated to a response rate of 36 per cent. Considering that the survey was conducted over the course of the busy harvest period for the crop, the rate of response was above average for this data collection method. 

Results and discussion

The frequency of the purchase process, and the economic ramifications of an inappropriate decision, predispose growers to seek a range of informed opinions. The results set out in Tables 1 and 2 show that it was more common for growers to develop a nucleus of primary decision makers than to rely exclusively on the actions of one individual. The decision making nucleus was most commonly comprised of the grower and the grower's crop consultant. However, agricultural chemical distributors, and to a lesser extent aerial operators, would occasionally be entrusted to make decisions considered in the best interest of the grower without fear of the intervention of pecuniary interest.

In situations where one individual was responsible for all purchase decisions throughout the production cycle, the results indicate that crop consultants or growers were the only two categories of decision makers likely to perform this role. With respect to choosing branded products within specific chemical groups, 31 per cent of crop consultants and 12 per cent of growers always made the decision themselves. Based on the in-depth interviews, it was established that the crop consultants that fell into this category were generally more established and had significant industry experience developed over many years of practice. The relationships developed over time between consultants and their grower clients were integral and provided the foundation for generating trust which was associated with progressively higher levels of responsibility for purchase decisions.

Similarly, the in-depth interviews revealed that growers that made all their own purchase decisions were commonly well established with many years of experience. These growers may or may not have utilised the services of a crop consultant to provide regular information on insect activity within the crop as well as recommendations of what control measures to utilise and when to adopt them. The grower would then evaluate the available information and make the spray and subsequent purchase decision, based on the consultant's recommendation in conjunction with the grower's personal experience.

Table 1: Contribution of potential decision makers on choice of chemical groups

Decision

Maker

All of the time

(%)

Most of the time

(%)

Some of the time

(%)

Very rarely

(%)

Never

(%)

Cotton Grower

Consultant

Distributor

Aerial Operator

14

25

1

0

20

33

14

0

27

17

24

2

8

10

6

7

28

13

53

90

Table 2: Contribution of potential decision makers on choice of brands within chemical groups

Decision

Maker

All of the time

(%)

Most of the time

(%)

Some of the time

(%)

Very rarely

(%)

Never

(%)

Cotton Grower

Consultant

Distributor

Aerial Operator

12

31

0

0

17

45

1

0

36

15

10

2

7

0

16

12

25

7

72

85

The results in table 3 show that the relative importance of a comprehensive range of product attributes varied between the different groups of decision makers. When selecting between product groups, cotton growers that made all their own purchase decisions were concerned with the entire list of attributes but were ultimately influenced by the availability of product from particular manufacturers. A positive correlation significant at the 1 per cent level of probability was identified between the cotton grower decision maker, and the chemical manufacturer.

Table 3: Correlation coefficients for attributes in insecticide group choice for the major decision makers in the Australian cotton industry

Decision makers choice of insecticide group

Product

Attribute

Cotton grower

Crop consultant

Chemical distributor

Aerial operator

Price

Knockdown performance

Residual performance

Type of formulation

Product odor

Phytotoxic potential

Ease of container disposal

Pack size

Control of resistant insects

Biological spectrum

Product compatibility

Manufacturer

Product reputation

Environmental effect

.1250(.240)

-.1420(.182)

-.0688(.519)

.0935(.381)

-.0632(.554)

.0551(.606)

.1608(.130)

.1082(.310)

.1086(.308)

.0720(.500)

-.0440(.680)

.3342 (.001)

-.0223(.835)

.0980(.358)

-.1090(.306)

.0388(.716)

.2344 (.026)

-.2142 (.043)

-.6084(.522)

-.0168(.875)

-.0467(.662)

.0522(.625)

-.1313(.217)

.0373(.727)

.1085(.309)

-.2283 (.030)

.0324(.762)

-.0475(.657)

2.1437(.177)

-.0062(.954)

-.1721(.105)

.0600(.575)

-.0562(.599)

.0387(.717)

.2750 (.009)

.2187 (.038)

.1207(.257)

-.1543(.146)

.0538(.614)

.0000(1.00)

-.0066(.951)

-.1367(.199)

.1726(.104)

.1286(.227)

.0000(1.00)

.2104(.046)

-.0130(.903)

.0982(.357)

.2191 (.038)

.2424 (.021)

.1216(.254)

-.0740(.488)

.0949(.374)

-.0061(.955)

-.0138(.898)

-.1242(.243)

The survey results indicated that the largest group of decision makers were the crop consultants. When selecting between product groups, crop consultants were motivated to evaluate three specific choice criteria. Above all other product attributes, these included residual performance of the insecticide, formulation type, and the name of the manufacturer. A positive correlation was evident for residual performance, and a negative correlation was evident for both formulation type and for name of the manufacturer. All these three correlation coefficients were significant at the 5 per cent level of probability.

Utilising step-wise linear regression and incorporating two of the three product attributes identified in the correlation matrix residual performance and name of the manufacturer the coefficient of determination was 0.13 and was significant at the 1 per cent level of probability (F (2,87) = 6.26). The corresponding regression equation was

Y' = 1.5218 + 0.6037 (Residual Performance) - 0.4086 (Manufacturer)

This would indicate that crop consultants were indeed influenced by the residual performance of the specific chemical groups and also by the manufacturers of products from within those groups. However, selecting one of a range of chemical groups is a complex issue and is influenced on separate occasions throughout the season by an array of attributes with fluctuating priorities.

Although chemical distributors and aerial operators are not individually responsible for any spray decisions, they commonly influence growers with their recommendations. When selecting between product groups, both chemical distributors and aerial applicators are motivated to evaluate, as a priority, two specific choice criteria. These include ease of container disposal and product pack size. The product attributes for both chemical distributors and aerial operators demonstrated a positive correlation, with the correlation co-efficients significant at the 5 per cent level of probability.

Utilising step-wise linear regression and incorporating one of the two product attributes identified in the correlation matrix ease of container disposal the coefficient of determination for chemical distributors was 0.08 and for aerial operators 0.05. The amount of explained variance was meaningful for both regression equations with chemical distributors significant at the 1 per cent level of probability (F = 7.20) and aerial operators significant at the 5 per cent level of probability (F = 4.43) . The corresponding regression equations were:

Y' = 0.0647 + 0.2156 (Ease of container disposal) Chemical Distributor

Y' = 0.0060 + 0.1033 (Ease of container disposal) Aerial Operator

The results in table 4 indicate that when choosing a specific brand of product from within a predetermined chemical group, cotton growers, crop consultants, and chemical distributors are ultimately influenced by a single product attribute. These data indicate that aerial operators collectively are not influenced by any particular product attribute. However, due to the low level of representation of aerial operators in the decision making nucleus, the sample size of the survey may have prevented the detection of a statistically meaningful set of product attribute(s).

Cotton growers, when choosing between both chemical groups and branded products, are significantly influenced by the manufacturer of those products. A positive correlation effective at the 1 per cent level of probability was identified between the cotton grower and chemical manufacturer. This would suggest that when provided with a number of alternative products, cotton growers are both brand and manufacturer loyal.

For chemical manufacturers, the implication of this research is to highlight the benefit of advertising and promotional strategies that target the individual cotton grower. These strategies should clearly communicate brand name and incorporate the name of the manufacturer. Increasingly, cotton growers are assuming responsibility for their own decisions and when supplied with relevant information they will more readily make the transition from utilising the services of a crop consultant.

Table 4: Correlation coefficients for attributes in insecticide group choice for the major decision makers in the Australian cotton industry

Decision makers choice of insecticide group

Product

Attribute

Cotton grower

Crop consultant

Chemical distributor

Aerial operator

Price

Knockdown performance

Residual performance

Type of formulation

Product odor

Phytotoxic potential

Ease of container disposal

Pack size

Control of resistant insects

Biological spectrum

Product compatibility

Manufacturer

Product reputation

Environmental effect

.1705(.108)

-.1218(.253)

-.0043(.968)

.1289(.226)

.0257(.810)

-.0177(.868)

.0944(.376)

.0490(.646)

.0437(.683)

.0479(.654)

-.0505(.637)

.3191(.002)

-.0328(.759)

.0833(.435)

-.2070 (.050)

.1643(.122)

.1618(.128)

-.1477(.165)

.0338(.752)

.1381(.194)

.0010(.992)

-.0033(.976)

.0242(.821)

-.0494(.644)

.0852(.425)

-.1566(.141)

.1121(.293)

-.0118(.912)

-.1461(.169)

.0164(.878)

-.1166(.274)

.0669(.531)

-.1225(.250)

-.2136 (.043)

.0947(.375)

.0538(.615)

.1705(.108)

.1209(.256)

-.1117(.295)

.0151(.887)

-.1240(.244)

-.0592(.579)

.0924(.386)

.0735(.491)

.0543(.611)

.1380(.195)

.0732(.493)

.0478(.655)

.1536(.148)

.1777(.094)

.0980(.358)

.0163(.879)

.0586(.583)

.0513(.631)

.0232(.828)

-.1001(.348)

 

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