The pre-decisional factors and information research ... · PDF fileThe person who is using a smaller car like Maruti 800 Std - Bharat III, Maruti 800 AC - Bharat III, Alto Std

AARJSH VOLUME 1, ISSUE 1 (JULY 2012) ISSN : 2278 – 859X

Asian Academic Research Journal of Social Sciences & Humanities

www.asianacademicresearch.org

A Peer

Reviewed International Journal o f Asian Academic Research Associates

AARJSH A S I A N ACADEMIC RESEARCH

J O U R N A L O F S O C I A L S C I E N C E & H U M A N I T I E S

The pre-decisional factors and information research related to the proposal for

buying ‘C- Segment’ cars in Chennai

J.VICTOR CHARLES *

* Research Scholar,

Manonmaniam Sundaranar University, Tirunelveli

Lecturer,

Bell Institute of Hotel Management,

2/242, Srivillipudur Road, Bell Nagar, Malli post, Sivakasi - 626 141.

Abstract

This article discusses the pre-decisional restraints that are found in the purchase of C- segment car

purchasers in and around Chennai city. Various customers in the C- segment car industry sector are

various strategies in selecting cars. Our study shows an insight of the different strategies adopted and the

search behaviour that are related in the pre- decisional process that are involved in the buying process of

„C‟ segment car industry. Our hypotheses are evolved as a result of the survey based on the C- Segment

car buyers in Chennai. Long – linear, logistic regression and linear regression analyses were used to test

the hypotheses. The various aspects that influence the buying decision process and the pre-decisional

restraints that are found behind this process are herewith highlighted for our study.

Keywords: C-Segment car; Consumer behavior; Purchasing Behavior; Decision Processes

Introduction:

Today transportation plays a vital role in our business economy. Mostly successful businessmen

are use cars as a main mode of transport for their business activity. In cities like Chennai most

businessmen prefer to use „C-Segment‟ cars. This helps them to the following ways.

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After the Globalization people for more aware of the different segment of the automobile

industries like, A-Segment, B-Segment, C-Segment, D-Segment, and so on. During my stay at Chennai in

the year 2010, I was able meet lot of business people who were interested in using the „C-Segment‟ cars.

So this prompted me to investigate the reason behind the purchase of „C-Segment‟ cars.

Consumer Behaviour

The person who is using a smaller car like Maruti 800 Std - Bharat III, Maruti 800 AC - Bharat

III, Alto Std, Alto Lx, Tata indica GL BS III, Hyundai Santro Euro III - XK NonA/C. Hyundai Santro

Euro III XL, Alto Lxi, Tata indica V2, Tata indica V2 Turbo, Tata indica Xeta, is interested to improvise

the utility space of the cars. He decides to buy a car in the higher segment. Here the income of the

consumer plays a vital role in making decision for purchase of the car. This may involve individual

decision and also group decision among the members of the family. When a person decides to buy a car

he need not be the one who pays for it in the buying. This is met by his family members or he is able to

procure loan facilities from the bank or financial institution.

So consumer durable is a product that must be durable in use and must be expensive relative to

income. An item may be durable for a working class family and at the same time may not necessarily be

durable for upper middle class consumer. Durable purchases by and large are group decisions for the

three reasons: one it involves the considerable outlay of the family; second the user of the person may not

necessarily be the one who actually pays for it; and third it may be bought for the use of several members

of the family. However, in certain cases unilateral decisions for the buying of durable item are taken by

one member of the household, and are not common. The buying decisions of such items are generally

unique and irrevocable. These decisions are not taken frequently, rather taken very rarely, perhaps once

and twice in one‟s life. The buying decisions of durables are by and large group decisions; complex ones;

and more concentrated amongst the upper-income groups. The durable goods are mass-produced in

anticipation to consumers‟ demand and involve huge capital cost (Downham and Treasure, 1956).

Decision Process

Variations in the reported frequency of use of information sources by decision makers were

investigated. Although the perceived quality of information available for decision making was related to

the rated importance of information sources, reported frequency of use was found to be primarily a

function of the rated accessibility of the sources. While purchasing products, consumers are commonly




assumed to progress through the decision phase of problem recognition, information retrieval from

memory, external search, alternative (or brand) evaluation, choice and post – purchase evaluation (Engel,

Blackwell, & Miniard, 1993; Howard, 1989.

There are noticeable differences in purchase decision times for new C-Segment cars. The study

was conducted on 1300 C-Segment car users who had purchased one or more products of study before

September 2011. The decision times were found to vary widely. About half of the buyers took two weeks

or less while a third took six months or more. The study reveals that the purchasers satisfied with their

old products were found engaged in less information seeking than those who either were not fully

satisfied with their old products or did not have regular use of the product. Moreover the satisfied users

were able to gather required information in less time than other types of buyers. The satisfied users,

whose products had already expired their life, took less time than those satisfied users with their products

in working conditions. Similarly the buyers who had extensive purchase experience in the past took less

time than those who had not much experience. Even the highest income households lacking buying

experience took more time than any other income group. Also the increased information seeking activity

was associated with longer decision times (Newman and Staelin, 1972).

The stages in the life-cycle also play a considerable role. As families grow, the size and

characteristics of the product that was last purchased, change. The average satisfied user of his old

product who was giving considerably high importance of out-of store information seeking took greater

time than the average buyer who was either dissatisfied with his earlier purchases or did not have regular

use of that kind product. There had been contrasting result to Ferber‟s hypothesis that „larger the size of

planned purchase, the longer the purchasing horizon is likely to be‟ as the same was not observed for

cars, the average duration of which was not much longer than that for appliances. The study concludes

that the decision times are not affected by traditional demographic variables, rather these depend upon

condition of old product, ability to judge the product well, and prior experience (Newman and Staelin,

1971). Stages in the life-cycle also play a significant role as with the growth in the family, needs change

and therefore, family may have to buy a different appliance than they earlier bought (Newman and

Staelin, 1970)

Consumers have considerable product-related information stored in memory, prompting them to

base these purchase decisions on simplifying purchase heuristics such as high brand name recognition,

low price, last brand purchased etc. (Hoyer, 1984; Dickson & Sawyer, 1990). One possibility is that




consumers make some product –related decision in advance and store them in memory for later use

(Thelen & Woodside, 1997)

To understand the notion of “Pre-decisional restrains”, consider a person contemplating a new car

purchase. Several decisions need to be made to complete such a purchase. For example, the prospective

buyer has to decide what make a car to buy (e.g. mid-size cars like the Suzuki, Honda, Ford, Logan, and

Hyundai or some other make), what model to buy (e.g. Suzuki SX4, Honda City, Ford Fiesta (or Ikon),

Logan, Hyundai Accent or Verna), what dealer to buy from (e.g. CAR WORLD- Ambattur, Sundaram

Honda, Olympia Honda, Kun Hyundai, or some other dealer), and what body style to buy (e.g. sedan or a

SUV – Sports Utility Vehicle, or coupe) what engine size to buy (e.g. 1.4 or 1.6 or 1.9 or 2.4 or 2.0 or 3.8

Liter capacity), what price range to buy within Rs 4,83000 or 4,99,903 or Rs 5,50,000 to Rs 6,06,000 or

some other price range) and so on. In other words, the “decision to purchase a car” may be disintegrated

into several “Sub-decisions” as illustrated above.

An examination of the “sub-decision” reveals that some of them directly related to the brand

choices offered by marketers (e.g. should I buy a Ford or a Hyundai? Or Should I buy from Car world or

Kun Hyundai?). These decisions are linked to the specific “brands” that are available in the marketplace.

We refer to these type of sub-decisions (when made in advance) as marketer-related pre-decisional

restraints because they all involve choices among different marketers (i.e. between makes and / or models

and / or dealers). On the other hand, some of the “sub-decisions” directly relate to household preferences

or restraints that are not directly related to the brand choices offered by marketers (e.g. should I buy a

ford or Honda? Should I buy in the Rs 4,83000 or 4,99,903 or Rs 5,50,000* to Rs 6,06,000 to price

ranges). In other words, these decisions are not brand dependent. We refer to these type of choices (when

made in advance) as household – related pre-decisional restraints.

Several studies on consumer decision-making have speculated on the potential dependencies that

may exist between the earlier and later phases of the choice process (Bettman & Park, 1980; park,

Hughes, Thunkral, & friedman, 1981). Research on how „„pre-decisional restraints‟‟ influence the rest

of the decision process represents an opportunity to understand the interaction between memory and

subsequent information processing. Reviews of consumer decision-making have identified this link

as a significant area for additional research (Bettman, 1986; Jacoby, Johar & Morrin, 1998).

The research is important for at least three reasons. First, it attempts to understand the decision

heuristics consumers use for making consumer durable purchases. As mentioned earlier, not




enough is known about the strategies consumers may use to simplify major purchase decisions.

Second, it provides a way to understand the organization of product-related knowledge in memory by

focusing on the pre-decisional restraints that are formed from the information that is accumulated

there. Third, it attempts to answer the key question: how do these pre-decisional restraints and the

problem recognition event which activates them determine the „„route‟‟ consumers follow for the

remainder of the decision process?

The study is also important from a managerial perspective because the constructs used in

the study have high external validity. Consumers with many pre-decisional restraints represent a

very lucrative segment to the marketer because, in a sense, these consumers are „„ready to buy.‟‟

Information on both the nature and extent of a consumer‟s pre-decisional restraints can be used by

the marketing manager to favor the company‟s brands and also accelerate the purchase (Lapersonne,

Laurent, & Le Goff, 1995). Finally, descriptive taxonomies of how consumer segments differ in

terms of both problem recognition and pre-decisional restraints can be used for the purpose of

positioning a company‟s brands in relation to its competition (Rossiter, 1997).

1. Review of Literature:

1.1 C-segment car:

It was a tough year for C-segment cars in 2008-2009, when there was a fall in the price range of Rs. 8.5

lakh to 5.5 lakh. This included mid-size cars like the Suzuki SX4, Honda City, Ford Fiesta (and Ikon),

Logan, Hyundai Accent and Verna, etc.

It was bad enough for them coping up with a differential excise duty structure for two years when

the finance minister passed a decree in 2006 that small cars be levied a lower 16%. The latest Budget has

only increased the gap and today large cars continue to attract 24% which is twice as much as small cars

whose levels are further down to 12%.

More than the traditional A and B-segment offerings like the Maruti 800, Alto, Santro and Indica

or the more recent Zen Estilo and i-10, it is the B-plus products (Swift, Getz and Palio) that are the real

threats to the C-segment range. This is because they have emerged the best option for the Santro and

Indica customers are keen on graduating to the next category of products.




Till a couple of years ago, this meant they preferred to move to mid-size cars like the Honda City,

Ford Ikon or Hyundai Accent. More have been added to this list in the form of the Fiesta, Verna, Logan

and SX4 and all face a bigger threat from the B plus range now, especially after the excise duty cut.

Of course, thanks to the inane definition of a small car, the petrol-driven Swift does not qualify as

one thinks to its 1.3 litre engine (the cap is 1.2 litres) while its diesel sibling passes muster since its

engine at 1.3 litres is under the stipulated 1.5 litres. It won‟t be a surprise, therefore, if Suzuki brings in a

smaller petrol engine for the Swift on the lines of what Hyundai did with the Getz.

For the record, small cars are those that are up to four metres long which explains why the mid-

size Tata Indigo was trimmed recently to create the Indigo CS.

Moving on, C-segment makers will have to think out of the box if they do not want to lose their

customer base in a hurry. In all fairness, the Honda City and Suzuki SX4 are established products though

they will also be impacted, though only marginally. The Logan could be in quite a spot because potential

buyers would much rather go in for a cheaper diesel Swift or Getz which offer similar features even

though they do not have a boot.

This could also be true for the Ford Fiesta whose diesel version has been doing reasonably well

but could end up losing customers to B plus cars. The fact that the Honda City and Suzuki SX4 do not

have diesel options and are doing well is tribute to the strength of both brands. One option for C-segment

makers is to either go in for price cuts to stay on in the race or offer stripped-down versions. Either move

would be suicidal in terms of brand. The pragmatic way out is to qualify as a small car by going the

Indigo way and have the length clipped to four metres.

Year 2008-2009 saw a greater pace in dieselisation, thanks largely again to the B plus segment.

The Fiat 1.3 diesel engine will be a critical component of this growth as it will be part of the Swift, Palio

and the new Indica scheduled to be launched in the latter half of 2008-09.

1.2 Statement of the problem: Actual State versus Desired State

Problem recognition, or need recognition, is the first of the five steps consumers take when

moving through the decision process. In very simple terms, it is only when we recognize that we need

something that we consider starting the process to find the product or service that will deliver the benefits




to fill the need or solve the problem. Exhibit 2-1 contains a flowchart that shows the various influences

on problem/need recognition

Problem recognition is a psychological process through which we evaluate the difference between

our actual state and our desired state. This is a comparison of our current need or benefits state with what

we would like it to be. The greater the „„perceived distance‟‟ between these two, the more clearly the

consumer recognizes potential need. Remember, however, that need recognition depends on the

perception of the individual consumer. Problem recognition can be influenced by a variety of situational,

consumer, and marketing factors. These may operate singly or in combination to trigger problem

recognition. These „„triggers‟‟ are of interest to marketers of goods and services and are often part of the

copy points included in promotional communications.

The conceptualization of problem recognition used here attempts to combine these two

perspectives of the construct by viewing it as a behavioral event (i.e. the „„actual‟‟ vs. „„desired‟‟

difference being exceeded) that has distinct underlying motivations (i.e. the „„purchase motives‟‟

taxonomy). The advantage of combining these two approaches is that the problem recognition

construct can then be regarded as having both behavioral and cognitive underpinnings, instead of only the

latter (Bruner, 1986; Sirgy, 1983).

Based on our approach, there are at least five distinct conditions that may lead to problem

recognition. The first factor corresponds to the „„normal depletion‟‟ of the current product. The second

factor relates to product acquisition corresponding to a „„new need‟‟. The third factor corresponds to an

„„urgent purchase‟‟, which is caused by a product breakdown or failure. The fourth factor relates to

„„current dissatisfaction‟‟ with the product. The fifth factor relates to „„anticipated higher satisfaction‟‟

with the next purchase. Note that the two satisfaction/dissatisfaction categories are conceptually distinct

since one is anchored to a „„desired state‟‟ and is merely an abstraction, while the other is anchored by the

„„actual state‟‟ and represents the current condition. The first four factors correspond to purchase motives

that have previously been identified to be important (Rossiter, 1997) and are relevant for consumer

durables. The fifth factor attempts to capture the distinction between the „„actual‟‟ vs. „„desired‟‟

states (Bruner & Pomazal, 1988) with regard to satisfaction.

1.3 Pre-decisional product and purchase related information

Our memories define who we are and what we do. Aside from a few preferences hardwired by

evolution, they also define what we like and how we choose. The increasingly more hierarchical coding




of experience in memory makes a gourmet out of a gourmand. Grieving and the process of letting-go of

our desire for departed love ones involves the extinction of associations between them and a host of daily

encountered stimuli, which serve as constant reminders of their absence. Memory processes in the

construction of preference even have a utility of their own. We savor the memory and anticipation of

pleasant choice options and may delay choice to lengthen the pleasurable experience (Loewenstein,

1988). Yet despite this wealth of evidence of the involvement of memory processes in preference and

choice, their role in preference construction has largely been ignored in existing models of judgment and

decision making (Johnson & Weber, 2000; Weber, Goldstein, & Barlas, 1995).

Pre-decisional restraints can be viewed from various theoretical perspectives. First and foremost

they can be regarded as an indicator of how product knowledge is organized in memory. The organization

of product knowledge in memory has received relatively little research attention in spite of having been

recognized as an important determinant of decision behavior (Bettman, 1986; Johnson & Russo, 1984).

An alternative viewpoint on pre-decisional constraints is offered by the declarative and

procedural knowledge dichotomy. Under this perspective, procedural knowledge takes the form of

production rules and heuristics that operate on these facts (Anderson,1983). Over time, various

„„sequences of productions‟‟ that comprise aspects of procedural knowledge are combined into a

single production, through the process of automatization (Anderson, 1983). Quite literally, this is

what pre-decisional product and purchase related information represent.

Yet another conceptual view of pre-decisional product and purchase related information is provided

by Johnson-Laird (1980) in terms of his description of mental models. He suggests two levels of

knowledge representation: mental models and propositional knowledge. Mental models are analog

representations of knowledge that are used during the information retrieval stage. On the other hand,

propositional knowledge is assumed to be employed during the evaluation process (Linde,1986).

Pre-decisional product and purchase related information are de facto mental models because they

decrease the burden on memory. In drawing this equivalence, we adopt a more limited view of the

concept of mental models by assuming they are embedded in knowledge schemas.

The making of product-related decisions outside the time boundaries of the decision process

(when the normal pressures of decision-making are absent) and inventorying them for later use is a

viable simplification strategy, and therefore consistent with the „„satisfying‟‟ model of consumer




decision-making (Simon,1955). It is also consistent with the notion of information being stored in

memory in the form of „„chunks‟‟ (Miller, 1956).

2. Hypothesis:

As argued in earlier sections, the main concept behind all the hypotheses: the manner in

which the purchase problem is initially recognized and the consequent retrieval and use of stored

product-related pre- decisional restraints, significantly influences external search and circumstance stage

set formation. There is considerable evidence to suggest that the consumer purchase decision process

contains distinct „„screening‟‟ and „„evaluation‟‟ phases (Kahneman, Slovic, & Tver- sky, 1982; Klein,

1983). We hypothesize that a good portion of the initial screening occurs before the decision process is

activated, and that the output of such initial screening is stored in memory in the form of pre-decisional

restraints. Thus, we can expect a high frequency of both marketer-related and household- related

pre-decisional restraints. Since the incidence of these restraints cannot be precisely quantified, we express

this prediction as an (empirically untestable) proposition.

P1: Consumers will have a significant amount of marketer- and household-related pre-decisional

restraints at the onset of the problem recognition event.

During the initial stages of the purchase process many, consumers are likely to focus on

needs and preferences that are independent of marketer influence. They are going to be more

concerned about whether the C-segment car will be appropriate for the household, given the restraints

and needs relating to family size, household income, etc. For example, a family with many small

children may only consider station wagons, or a young single business executive may only examine

high-priced coupes. In other words, most consumers are going to more concerned about household-

related restraints such as body style, engine size (which is a surrogate for car size) and price range.

Only after they have narrowed down their choices using these criteria are they likely to turn

their attention to the „„brands‟‟ that meet these criteria.

The formation of household-related restraints prior to start of the decision process is consistent

with the „„top-down‟‟ perspective of decision-making. Under this perspective, choice criteria are

assumed to originate in higher order goals such as personal values and household needs, which are then

„„translated‟‟ into relevant product attributes and features through means – ends relationships

(Pieters, Baumgartner, & Allen, 1995). In contrast, the development of marketer- related restraints is

consistent with the „„bottom-up‟‟ view of decision-making. Under this perspective, choice criteria




are assumed to directly originate in the product attributes and features of specific alternatives (Olshavsky

& Kumar, 1997). Hence, as a baseline hypothesis, we propose:

H1: The incidence of pre-decisional restraints that are household-related will be higher than those

that are marketer-related at the onset of the problem recognition event.

Despite the expected higher incidence of household-related restraints overall, there will

be many consumers who will use marketer-related restraints as a means to simplify their purchase

decisions. Fig. 1 provides a schematic view of the role of pre-decisional restraints in the purchase

decision process. The remainder of the hypotheses attempts to link household-related and marketer-

related pre-decisional restraints to both their antecedent and consequent variables.

Recall that the problem recognition event serves as the „„trigger‟‟ that activates pre-decisional

restraints. Consequently, we can expect unique interrelationships between the problem recognition event

and the type of the pre-decisional restraints that are activated. For instance, the „„normal depletion‟‟

problem recognition category is more likely to be associated with marketer- related pre-decisional

restraints. These consumers are generally making a routine replacement purchase and can be expected to

be quite knowledgeable about the various C-segment car choices available in the marketplace. In

many instances, they are likely to purchase the same make and/or from the same dealer. Also, these

consumers are not under any particular time pressure and therefore have had adequate time to evaluate

the product offerings of different manufacturers. Hence, these consumers are likely to have made

several marketer-related „„sub-decisions‟‟ and stored them for later use.

For consumers in the „„anticipated higher satisfaction‟‟ group, we can expect marketer activities

such as TV advertising to have played an important role in forming their initial restraints. These

consumers are likely to be attracted by a new feature or attribute that they have learned about through

such advertising, and would like their next car to possess it. For example, someone may decide that

their next car will be an Honda because they are attracted by the new styling of these models. Or

someone else may decide to only consider Ford cars because of their superior acceleration ability.

Hence, a greater association with marketer-related restraints may be expected for these consumers.

For the „„current dissatisfaction‟‟ group, there may be an initial propensity to exclude the

current brand from consideration. However, the dissatisfaction will eventually be traced back to the

product attribute or




PROBLEM PRE-DECISIONAL DECISION

RECOGNITION RESTRAINTS PROCESS

Urgent Purchase------- (H2E) -- Market-related ------------- (H3A) ---- Amount of

Information search

Normal Depletion------- (H2A) -- Market-related

Anticipated Higher ----- (H2B) -- Market-related------------- (H3B) ---- Amount of

Satisfaction Information

Search

Current ------------------- (H2C) -- Household-related -------- (H4A) ---- Circumstance stage

Dissatisfaction set Size

New Need ---------------(H2D) - Household – related --------(H4B) ---- Circumstance stage

set Breadth

Fig. 1. Hypothesized roles of pre-decisional product related information search restraints in the decision process

feature that has caused it. Consumers will then seek to address that source of dissatisfaction by

forming household-related restraints to prevent its occurrence in the future. For example, someone may

be dissatisfied with the size of their car because it no longer serves their requirements. Or

someone else may be unhappy about not having the most recent safety equipment in his or her

car.

Finally, for the „„new need‟‟ problem recognition category, there is also likely to be a greater

association with household-related pre-decisional restraints. These consumers are making a purchase in

response to a new need and hence are likely to be focused on satisfying that need, without paying much

attention to specific brands. For example, a family with small children may only want to consider

station wagons and form a constraint to reflect that need. Or there may be a family which is nearing

retirement and therefore only considers cars that are economical to run.

Consumers in the „„urgent purchase‟‟ problem recognition category are probably making a

decision in response to a product failure. For example, their car may have been in an accident or

is stolen. These consumers are not likely to have had adequate advance warning to contemplate about

the household-related restraints to use for the next purchase. On the other hand, they may be able to recall

a brand to which they are favorably disposed. Also, they may exhibit a tendency to simply

repurchase the same make. While it is possible that an urgent purchase could also be caused by a




change in household circumstances, we expect a stronger association between this problem

recognition category and the occurrence of marketer- related restraints.

Hence, the manner in which the problem recognition event is „„triggered‟‟ will influence the type of pre-

decisional restraints that are activated, leading to:

H2A: Consumers making a purchase because of normal depletion will have more marketer-related

pre- decisional restraints.

H2B: Consumers making a purchase because of anticipated higher satisfaction will have more

marketer-related pre-decisional restraints.

H2C: Consumers making a purchase because of current dissatisfaction will have more

household- related pre-decisional restraints.

H2D: Consumers making a purchase in response to a new need will have more household-related

pre- decisional restraints.

H2E: Consumers making an urgent purchase will have more marketer-related pre-decisional

restraints

The amount and type of prior decision restraints is likely to effect both the degree and nature of external

information search (Johnson & Russo, 1984).

The nature of the above relationship may depend on the type of prior decision restraints

rather than their amount. Consumers with household-related prior decision restraints may increase search

because they would need to identify a suitable brand and retailer that meets these restraints, which

could be potentially time consuming. For example, if someone is looking for station wagons with a

2.0-l engine size, it may require an extended search effort that involves visiting dealers of different

makes. On the other hand, those with marketer-related restraints may actually reduce their external

information search because often the nature of these restraints may somewhat limit the purchase options

available to them. For example, if someone has decided to purchase a Ford, they only have to

evaluate the various models offered by Ford and only visit dealers that carry that make.

For those with household-related prior decision restraints, we may also find evidence of the

search process becoming more „„diffused‟‟ as they shift their attention from satisfying all pertinent

household criteria to the realities of products available in the market. One reason why the search

effort may become more diffused is because of the difficulties in forming „„means – ends‟‟

connections between what they want and what is available in the market (Pieters etal 1995) In other




words, the „„matching‟‟ of household needs to car features may need to be done iteratively. On

the other hand, for those with marketer-related pre-decisional restraints, the search process may be

more focused because of the ability of these consumers to use conjunctive (or other non- iterative

heuristics) to quickly eliminate manufacturers or brands that do not satisfy their restraints, leading

to:

H3A: Consumers with marketer-related pre-decisional restraints will exhibit a greater tendency

to limit external information search, compared to those with household-related pre-decisional

restraints. H3B: Consumers with marketer-related pre-decisional restraints will exhibit a greater

tendency to engage in „„focussed‟‟ search, as opposed to „„diffused‟‟ search, compared to those

with household- related pre-decisional restraints.

We can expect the type of pre-decisional restraints to effect the nature of processing used

by consumers during the alternative evaluation and choice phase of the consumer decision process.

Also, it is likely that consumers with household- related pre-decisional restraints are

willing to consider a variety of brands for purchase, causing them to have „„broader‟‟ (i.e. more

heterogeneous) circumstance stage sets. A heterogeneous circumstance stage set implies that the

consumer is willing to consider brands that are less similar as a „„hedge‟‟ against accidentally

eliminating a potentially attractive brand. On the contrary, those with marketer-related pre-decisional

restraints may have „„narrower‟‟ circumstance stage sets because they may not be inclined to experiment

with many brand (some of which they have already eliminated), indicating that:

H4A: Consumers with household-related pre-decisional restraints will exhibit a greater tendency

to have larger circumstance stage sets, compared to those with marketer-related pre-decisional restraints.

H4B: Consumers with household-related pre-decisional restraints will exhibit a greater tendency

to have „„broader‟‟ (i.e. more heterogeneous) circumstance stage sets, compared to those with

marketer-related pre-decisional restraints.

Taken together, the hypotheses predict the relative incidence of household-related and

marketer- related pre-decisional restraints and the subsequent impact these two types of

restraints have on external search and circumstance stage set formation. In reality, various

consumer segments are likely to exist in terms of the „„mix‟‟ between household- related and

marketer-related restraints.




Some consumers will have few pre-decisional restraints, while others will have many pre-

decisional restraints. Similarly, for some consumers, a greater proportion of their pre-decisional

restraints will be marketer-related, while for others they will be household-related. Cross-classifying

these two dimensions enables us to define four „„a priori‟‟ segments in terms of the amount (few vs.

many) and type (more marketer- related vs. more household-related) restraints. We examine these four

segments by seeking to profile them in terms of psychological and demographic factors. The

purpose of the segmentation analyses is to provide additional insights about the role of pre-

decisional restraints in purchase decisions.

3. Research Design

We use a naturally occurring purchase environment to test the above hypotheses. The data were

generated from a probability sample of actual consumers in the product-market used in the study.

Thus, the study is descriptive and cross-sectional rather than experimental and longitudinal. Because of

this, we exercise extra caution when drawing cause – effect inferences from correlational or

associational data. However, the hypotheses tested in the study are mainly derived from related

theories constructed under controlled experimental conditions. By measuring both problem recognition

and pre-decisional restraints in a natural external setting, we provide a real-world context to

previously generated lab findings that exist in this area. Pre-decisional restraints denote purchase goal-

dependent information in memory after it has been substantially processed. It is difficult to

measure or manipulate such information in memory through experimental methods. In fact,

research on schemas and script elicitation has been found to consistently under predict the role of

memory in consumer purchase decisions (Bettman, 1986). Thus, the study provides a complimentary

approach to experimental methodologies for examining the significance of memory-based information

on consumer decision-making. While examining the external validity of lab information integration

studies, Several researchers have called for additional research of this type (Levin, Louviere,

Schepanski, & Norman, 1983; Wells, 1993).

4. Data

The data used in this research were collected from various dealers in Chennai city. Various

segment of people like C-segment car owners, dealers, drivers, and automobile association actively

participated with enthusiasm in collection of the required data. We contacted about 20 dealers in and




around Chennai who were dealing in the C-segment car industry[ like Suzuki, Honda, Hyundai, Ford etc.,

primarily we collected the database of their clients ( like email ids, and mobile contact numbers) who had

purchased cars from their show room with in a time span of 4 to 6 months time. Respondents were pre –

contacted by telephone to solicit their participation in the study. The research instrument (questionnaire)

was administered using professionally trained research scholars and students volunteers who helped me

in my study.

Out of the 2000 prospects we tried to conduct through email, phone calls and direct survey. We

had 500 respondents who replied our questionnaire through email. For the other 931 respondents the

questionnaire were collected from various person like car drivers, car owners who came to the respective

show room during the time of car service and others in their respondents business places like their offices

and other premises. To control the potential decision process variations across C-segment car types, the

overall sample was split into smaller samples based on the car category. Only data for the mid size cars

category was made available to us for this research. A majority of the cars sold in this market are mid size

cars. We had accrued a total of 1431 respondents using the above method. At the end of the survey the

respondent rate was about 78%. The final data sample consisted of 1041 respondents. I thanks to all

dealers, car owners, drivers, research scholars and students for helped me to collect the relevant data.




5. Results

5.1. Explorative analyses Table 1 Descriptive sample information

Frequency Percentage Mean Range

Nature of Problem recognition

Urgent purchase

107

(11.4)

Normal depletion

170

(18.1)

Current dissatisfaction

208

(22.2)

New need

227

(24.2)

Anticipated higher Satisfaction

99

(10.5)

Other

128

(13.6)

Pre-decisional Restraints

Make

461

(44.3)

Model

235

(22.6)

Dealer

193

(18.5)

Price range

517

(49.7)

Engine size

485

(46.6)

Body style

591

(56.8)

None

75

(07.2)

Number of dealer visits

Make bought

1.33

1-5

Other makes

1.65

0-12

Total

2.98

1-13

Size of circumstance stage set

2.48

1-6

Circumstance stage set composition

“Breadth” index (rescaled)

2.34

1-5

Decision confidence

8.71

1-10

Final Satisfaction

8.98

1-10




A exploratory set of analyses along with descriptive sample information on all the

study variables is reported in Table 1. The distribution across the problem recognition categories

showed that, for 18.1% of the respondents, the purchase was triggered as a consequence of normal

depletion, while 11.4% made an urgent purchase. Moreover, 22.2% and 10.5% of our sample

mentioned current dissatisfaction and anticipated higher satisfaction, respectively, as the primary

motivation for the purchase. Finally, 24.2% of the respondents made the purchase in response to a new

need. The remaining 13.6% respondents could not be classified because of ambiguity in their responses

(e.g. „„got a good deal‟‟, „„time was right‟‟, etc.).

The incidence of pre-decisional restraints ranged from a high of 56.8% for the body style, to

44.3% for make, to a low of 18.5% for dealer. Only 7.2% of the sample reported having made no pre-

decisional restraints whatsoever. Hence, as expected, there is significant variation across pre-decisional

constraint categories. The sample distributions of the external information search and circumstance

stage set variables also revealed significant variation across their respective scale categories. For

instance, the external information search measure ranged from 1 to 13 dealer visits with a mean of 2.98

visits, which is comparable to other automobile market studies. The range of the circumstance

stage set size variable was from 1 to 6 makes, with a mean of 2.48 makes. The „„breadth‟‟ of

circumstance stage set measure showed that for 60% of the sample, more than half the circumstance stage

set consisted of car models corresponding to the make finally purchased

5.2. Hypotheses tests

5.2.1. Hypothesis 1

A substantial portion of our sample reported having various product-related decision restraints in

advance of the problem recognition event, as depicted in Table1. In terms of the type of pre-

decisional restraints , 44% of the respondents reported having at least one (out of a maximum of

three) marketer-related pre - decisional restraints . The corresponding percentage for household-related

pre-decisional restraints was significantly higher at 79%. A test for the difference in the number of

marketer-related vs. household-related restraints , assuming dependent samples, was significant (t =

16.41; df = 1040; p < 0.01). Similarly, for those with at least one pre-decisional constraint,

the relative proportions for respondents with household- related vs. marketer-related restraints was

70% and 30%, respectively. A test for the difference in proportions between marketer-related and




household-related, assuming dependent samples, was significant (t = 17.80; df = 898; p < 0.01). Thus,

there appears to be strong support for H1.

5.2.2. Hypotheses 2A and 2B

A cross-classification of the problem recognition categories with the pre-decisional constraint

variables revealed several distinctive relationships, as shown in Table 2 (Part A). Five of the six

individual pre-decisional restraints (make, model, dealer, price range and body style) were associated

with the problem recognition categories, providing preliminary evidence of the expected link

between the two constructs. As an illustration, the pre-decisional constraint relating to make had the

highest (percentage) incidence for the normal depletion problem recognition category, while

having the lowest (percentage) incidence for the urgent purchase group (v5 = 22.53; df =5; p < 0.01).

The strength of the remaining relationships along with significance levels are depicted in Table 2 (Part

A).

In terms of the type of pre-decisional restraints , it appeared that the normal depletion category

had the strongest association with marketer-related restraints , while the urgent purchase category

had the weakest (v5 = 38.21; df = 15; p < 0.01), as shown in Table 2 (Part B). Similarly, the current

dissatisfaction category seemed to have the strongest association with household-related restraints ,

while (once again) the urgent purchase category had the weakest (v5 = 42.71; df = 15; p < 0.01), as

also depicted in Table 2 (Part B).

Log-linear analysis was used to further examine the interrelationships between the problem

recognition categories and individual pre-decisional restraints (i.e. with each constraint considered

separately). As expected, the normal depletion category was associated with each of the three

marketer-related pre- decisional restraints (i.e. make, model and dealer). The standardized logistic

coefficients for these restraints were b = 0.64 for make; b = 0.66 for model; and b = 0.47 for

dealer, respectively. The magnitudes of the logistic coefficients suggest the relative influence of

each constraint. Also, as expected, the new need category was associated with two of the three

household-related pre-decisional restraints (i.e. engine and body). The standardized logistic

coefficients for these restraints were b = 0.23 for engine and b = 0.28 for body style. Once again,

the magnitudes of the logistic coefficients suggest the relative influence of each restraints.




Table 2 (Part A)

Cross-classification of problem recognition and pre-decisional restraints

Type of pre-decisional restraints

Problem recognition Make Model Dir. Price Eng. Body

Urgent Purchase 41 (38.3) 15 (14.0) 16 (15.0) 41 (38.3) 41 (38.3) 53 (49.5)

Normal depletion 102 (60.0) 59 (34.7) 44 (25.9) 87 (51.2) 84 (49.4) 103

(60.6)

Current

dissatisfaction

81 (38.9) 38 (18.3) 35 (16.8) 134

(64.4)

104

(50.0)

121

(58.2)

New Need 107 (47.1) 55 (24.2) 40 (17.6) 110

(48.5)

118

(52.0)

141

(62.1)

Ant. higher

satisfaction

39 (39.4) 20 (20.2) 13 (13.1) 39 (39.4) 44 (44.4) 58 (58.6)

Entries are raw frequencies and (row percentages) for affirmative responses for each type of pre-

decisional restraints.

v5 = 22.53; df =5; p < 0.01 for make pre-decisional restraints.

v 5 = 21.67; df =5; p < 0.01 for model.

v 5 = 09.94; df =5; p < 0.10 for dealer.

v5 = 31.54; df =5; p < 0.01 for price.

v5 = 07.64; df = 5; not significant for engine size.

v5 = 16.13; df =5; p < 0.01 for body style.




Table 2 (Part – B)

Cross-classification of problem recognition and pre-decisional restraints

Market – related pre-

decisional restraints

0 1 2 3

Urgent Purchase 41 (38.3) 15 (14.0) 16 (15.0) 41 (38.3)

Normal depletion 102 (60.0) 59 (34.7) 44 (25.9) 87 (51.2)

Current

dissatisfaction

81 (38.9) 38 (18.3) 35 (16.8) 134 (64.4)

New Need 107 (47.1) 55 (24.2) 40 (17.6) 110 (48.5)

Ant. higher

satisfaction

39 (39.4) 20 (20.2) 13 (13.1) 39 (39.4)

Household – related

pre-decisional

restraints

Urgent Purchase 30 (28.0) 27 (25.2) 33 (30.8) 17 (15.9)

Normal depletion 30 (17.6) 42 (24.7) 62 (36.5) 36 (21.2)

Current

dissatisfaction

38 (18.3) 40 (19.2) 71 (34.1) 59 (28.4)

New Need 36 (15.9) 70 (30.8) 64 (28.2) 57 (25.1)

Ant. higher

satisfaction

20 (20.2) 36 (36.4) 24 (24.2) 19 (19.2)

Entries are raw frequencies and (row percentages).

v5 = 38.21; df = 15; p < 0.01 for marketer-related pre-decisional restraints.

v5 = 42.71; df = 15; p < 0.01 for household-related pre-decisional restraints.

Logistic regression analysis was used to formally test the relationships between the various

categories of the problem recognition „„event‟‟ and the type of pre- decisional restraints . Two

dichotomous dependent variables based on the absolute incidence of pre- decisional restraints that

were marketer-related vs. household-related (i.e. „„high‟‟ marketer-related or „„high‟‟ household-

related) were constructed. The categorical independent variable was defined by the various categories

of the problem recognition event. Using the „„high‟‟ marketer-related restraints as a dependent variable, a

significant positive relationship was observed for the normal depletion problem recognition category

(b = 0.64; Wald‟s statistic = 19.02; p < 0.01) and a marginally significant inverse relationship was

indicated for the current dissatisfaction category (b = 0.22; Wald‟s statistic = 2.62; p < 0.10).

The overall 2 log likelihood difference for the fitted logistic model indicated a significant fit (v5

= 22.54; df =5; p < 0.01). Thus, H2A appears to be strongly supported by the data, while H2C is only

weakly supported. Analogously, using the „„high‟‟ household-related restraints as a dependent




variable, a significant positive relationship was observed for the new need problem recognition category

(b = 0.39; Wald‟s statistic = 5.26; p < 0.05) and a marginally significant inverse relationship was

indicated for the urgent purchase category (b =0.34; Wald‟s statistic = 2.98; p < 0.10). The overall

2 log likelihood difference for the fitted logistic model indicated a significant fit (v5 = 21.25;

df =5; p < 0.01). Thus, H2D appears to be supported by the data, while H2E is only weakly supported

For consumers in the anticipated higher satisfaction category, no significant relationship was

observed. Hence, H2B is not supported by the data.

5.2.3. Hypotheses 3A and 3B

The interrelationships between pre-decisional restraints and external search are depicted in

Table 3. There appear to be several significant relationships between both the amount and type of

pre-decisional restraints , and the amount and nature of external search. Linear regression models

were used to test the relationships between the amount and type of information search and the type

of pre-decisional restraints. The dependent variables used were the amount of external search and

the amount of „„diffused‟‟ search, respectively. The two independent variables were the number

of marketer-related and household-related restraints .

Table 3

Interrelationships between pre-decisional restraints and external search

Means

External information search

(number of dealer visits)

Total Diffused

Pre-decisional restraints

Market - related

0 3.63 2.32

1 2.26 0.92

2 2.22 0.82

3 1.93 0.67

Household - related

0 2.40 1.11

1 2.88 1.57

2 3.30 1.94

3 3.22 1.89

Standardized regression coefficients (b)


Market - related 0.36 (12.24) 0.39 (13.32)

Household – related 0.22 (07.48) 0.22 (07.59)

Goodness – of-fit statistics

Adjusted R square 0.14 0.16

F – value 87.19 100.42

Significance p < 0.01 P < 0.01




Entries for means are significantly different at the p < 0.05 level. Entries are significant for the standardized regression

coefficients at the p < 0.01 level; t-values are shown in parentheses.

As predicted, there was a significant negative relationship between marketer-related pre-

decisional constraints and the amount of external search (b =0.36; t =12.24; p < 0.01) and a significant

positive relationship between household-related restraints and the amount of external search (b = 0.22; t

= 7.48; p < 0.01). It appears that household-related restraints tend to increase the extent of external

search conducted, while marketer-related restraints tend to limit it. Hence, H3A is supported by the

data.

In terms of the relationship between the type of pre- decisional restraints and the nature of external

search, there is a significant negative relationship between marketer-related pre-decisional restraints

and the extent of „„diffused‟‟ search (b = 0.39; t =13.32; p < 0.01), as well as a significant positive

relationship between household-related restraints and the amount of „„diffused‟‟ search (b = 0.22; t =

7.59; p < 0.01). It seems that household-related restraints tend to make search more „„diffused‟‟, while

marketer-related restraints tend to make search more „„focussed‟‟. Hence, H3B is supported by the data.

5.2.4. Hypotheses

4A and 4B The interrelationships between the amount and type of pre-decisional restraints and

the circumstance stage set variables are shown in Table 4. Several distinct relationships among the

variables are apparent. Once again, linear regression models were used to test the relationships

between the size and „„breadth‟‟ of the circumstance stage set and the type of pre-decisional

restraints . The dependent variables used were the size and „„breadth‟‟ of the circumstance stage

set, respectively. The two independent variables were the number of marketer-related and

household-related restraints . As expected, there is a significant positive relationship between household-

related restraints and the size of the circumstance stage set (b = 0.26; t = 9.05; p < 0.01). Also, the

circumstance stage set seems to become more heterogeneous (as indicated by the circumstance

stage set „„breadth‟‟ index) with increases in the number of household-related restraints (b = 0.27;

t = 9.49; p < 0.01). In contrast, there are significant negative relationships between marketer-related

restraints and the size of the circumstance stage set (b =0.44; t =15.41; p < 0.01), as well as the

circumstance stage set „„breadth‟‟ index (b = 0.46; t =16.36; p < 0.01). Thus, it appears that

those with household-related restraints




Table 4

Interrelationships between pre-decisional restraints and circumstance stage set

Size

Means

“Breadth” index


Market - related

0 3.02 2.85

1 1.97 1.82

2 1.77 1.68

3 1.72 1.60

Household - related

0 1.96 1.85

1 2.48 2.34

2 2.76 2.60

3 2.61 2.46

Standardized regression coefficients (b)


Market - related 0.44 (15.41) 0.46 ( 16.36)

Household – related 0.26 (09.05) 0.27 (09.49)

Goodness – of-fit statistics

Adjusted R square 0.21 0.23

F – value 135.91 152.44

Significance p < 0.01 P < 0.01

Entries for means are significantly different at the p < 0.05 level. Entries are significant for the standardized regression

coefficients at the p < 0.01 level; t-values are shown in parentheses.

tend to have larger and more heterogeneous circumstance stage sets, while those with more

marketer-related decision restraints tend to have smaller and more homogeneous circumstance stage

sets. Hence, both H4A and H4B are supported by the data.

5.2.5. Segmentation analysis

For some consumers, a greater proportion of their pre-decisional restraints will be marketer-

related, while for others, they will be household-related. Recall that cross-classifying these two

dimensions enabled us to define four „„a priori‟‟ segments in terms of the amount (few vs. many) and

type (more marketer-related vs. more household-related) restraints . The segments were analyzed in

terms of selected psychological and demographic variables. The purpose of the analysis was to

provide some insights into how differences in the amount and type of pre- decisional restraints may

be profiled.




The results of the segmentation analyses are reported in Table 5. They show that consumers with

greater numbers of both marketer-related and household-related restraints (Segment D) tend to

be the most confident about their decisions, while those with less marketer-related and more household-

related restraints (Segment A) tend to be least confident ( F = 9.78; p < 0.01). Thus, it appears that

the relative effect of having marketer-related restraints is to increase decision confidence.

Similarly, consumers with more marketer-related and less household-related restraints (Segment C)

tend to be most satisfied, while those with less marketer-related and more household-related

restraints (Segment B) tend to be least satisfied ( F = 3.11; p < 0.05). Taken in isolation, this

result indicates that the relative effect of having more marketer-related restraints is to increase

final satisfaction with the purchase. However, these cause – effect inferences can only be made with due

caution because other potential causal influences

(e.g. the problem recognition construct) are not being captured through the univariate tests performed.

Future research should consider more comprehensive segmentation schemes that can

simultaneously capture the effect of multiple decision process variables. Interestingly, consumers with

more marketer- related restraints , while being less thorough in their decisions (as indicated by the

information search and circumstance stage set variables), are nevertheless confident about their

decisions, and quite satisfied with their purchases! On the other hand, consumers with more

household-related restraints , while being more thorough, seem to be less confident and satisfied in

comparison. Maybe the high standard they set for themselves in terms of making a wise purchase

leaves them unhappy because they are not able to meet their own goals.




Table 5

Pre-decisional restraints segment profiles

Psychological and demographic profile

Confid.

(Median)

Satisfact.

(Mean)

Education

(Mean)

Age

(Median)

Income

(Median)

Segment A

(n=337)

0-1 Mktr and 0-1 Hhld

8.64 8.95 School Cert. 60-64 years INR.50 – 70 K

Segment B

(n=332)


8.48 8.84 Univ.Entr. 55-59 years INR.50 – 70 K

Segment C

(n=118)



Segment D

(n=214)



F-Ratio 9.78 3.11 na na na

Significance p < 0.01 p < 0.05

Kruskal – Wallis V5 na na 17.04 20.52 4.54

Significance p < 0.10 p < 0.01 ns

Mktr = Marketer-related pre-decisional restraints.

Hhld = Household-related predecisional restraints.

na = Not applicable. ns = Not significant

There are some significant differences across the segments in terms of the distribution of

demographic variables. For example, consumers with few pre-decisional restraints (Segment A) tend

to be somewhat older (Kruskal – Wallis v5 = 20.52; p < 0.01), while those with less marketer-related

and more household- related restraints (Segment B) tend to be somewhat more educated (Kruskal –

Wallis v5 = 17.04; p < 0.10). However, for the most part, these differences are relatively minor (at

least in terms of median values). Thus, demographic variables appear to be of little value in

distinguishing between the pre-decisional segments in a managerially actionable manner. Although

this result is somewhat disappointing, the finding is consistent with the results of most studies that

have attempted to use demographic variables to model the decision process for new automobile buyers

(Kiel & Layton, 1981; Punj & Staelin, 1983).




6. Conclusions

Moreover our study proposes that pre-decisional restraints have a major effect on the purchase

behavior. They play a more critical role in the purchase decision process. Most of the hypothesis is

supported by valid data. Our analysis shows clearly that problem recognition events and pre-decisional

restraints that are subsequently activated play a major role in influencing the rest of the purchase decision

process.

From our hypothesis we can conclude that consumers with marketer – related pre-decisional

restraints like (make, model etc.) tend to simplify their purchase decision. A major part of the purchase

decision it appears to be completed even before the problem recognition. By using heuristic processing

strategies consumers were more focused on searches using heuristic processing strategies. These

consumers tend to have smaller and narrower circumstance stages in the decision making process.

Our analysis suggests that consumers have both marketer and house hold related decision

restraints prior to onset of problem recognition. Depending on specific circumstances pre-decisional

restraints arise during problem recognition phase. This problem recognition phase plays a crucial role in

consumer decision making process and purchase process.

From a theoretical standpoint, the findings provide a detailed picture of the internal dynamics

of the consumer decision process for consumer durables. Clearly, there is evidence of decision

simplification but only under limited and specific conditions. For others, the process appears to

become more convoluted than previously believed. Specifically, it appears that consumers whose

purchase decision is anchored to household-related decision restraints adopt a more „„constructive‟‟

view of the decision process, while those with marketer-related decision restraints tend to use

more of a „„preprocessed‟‟ approach to their purchase decisions. Previous research has also found

evidence of both approaches to consumer decision- making (Bettman et al., 1998). The current

study provides both the circumstances that lead to these different processing strategies and the

specific manner in which they are executed.

Another theory suggests that consumers are cognitive misers. These consumers are limited

information processing before buy the product. First, there are those who make most of the purchase

decision prior to the onset of problem recognition, perhaps so as not to unduly strain their mental

abilities later. These consumers then expend the „„cognitive savings‟‟ so realized in making the

rest of the purchase process more efficient. However, there is a second group of consumers who




act differently and as a consequence encounter serious „„cognitive deficits‟‟ that they are unable to

overcome for the remainder of the purchase process.

In business point of view the marketer must be more aware of the pre-decisional restraints

involved in the purchase processes of the consumer. They must identify the key points that are involved

in the pre-decisional processes. Different strategies must be executed to ensure the circumstances stage of

the marketer brand. Marketer can influence their customers by understanding preferences of the

customers beforehand. By doing proper analysis and research the customers and marketers can influence

each other in the buying and selling process successfully. But our study is limited to the C- Segment cars

industry only. Hence our inference of our survey pertaining to limited segment of our car industry only.

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