0

SYSTEM EFFECTS ON INNOVATNENESS

AMONG TNDTAN FARMERS

Thesis for the Degree of Ph. D.
MICHIGAN STATE UNNERSTTY
ANANT P. SAXENA

1968

LI 8 72 -14 n "
Michigan ante
Um E‘s/f. £sz ty

      
   

1m mquLTITLTITTTT TMTLTTTT '

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This is to certify that the

thesis entitled

SYSTEM EFFEC'I‘S ONI'LINNOVATIVENESS
AMONG INDIAN FARMERS

presented by

0 .
Anant' P . Saxena

has been accepted towards fulfillment
of the requirements for

Eh.D degree in Jammication

I
Major professor |

Date Novwber 2O . 1968

0-169

 

 

 

 

.//\

ABSTRACT

SYSTEM EFFECTS ON INNOVATIVENESS
AMONG INDIAN FARMERS

by Anant P. Saxena

The present study focused on the simultaneous and

systematic consideration of individual variables and system

 

variables in accounting for more variance in individual
innovativeness than previously. Individual variables are
operations of the communication, social and psychological
behavior of the individual. System variables are the
aggregative measures of individual variables for each sys-
tem. Innovativeness was operationalized as having ever used
(or tried) lO innovations, regardless of when it was adOpted
and whether its use was continued.

The present data were part of the Diffusion Project,
conducted in India by the Department of Communication at
Michigan State University. The social systems in the study
weré’eight Indian villages selected randomly to represent
a range in village modernization. The sample numbered 680
farmers in these eight systems.

The major objectives of the study were threefold:

(l) to ascertain the degree to which system variables affect
the innovativeness of individual members of a system, (2) to
determine the extent to which system independent variables
affect individual innovativeness when the effects of indivi-
dual independent variables are controlled, and (3) tol

understand the way in which system variables affect individual

Anant P. Saxena
ixunrvativeness under Specified situations when interactions
among the independent variables are controlled.

The data were analyzed, first, by means of zero-order
correlations between each system variable and individual
innovativeness. Our analysis produced significant correla-
tions for 14 of the 15 system variables. In most cases,
both individual and system-level measures of an independent
variable were related to innovativeness. Thus, we encountered

system effects on individual innovativeness. §ystem effects

 

are the influence of systemic structure and/or composition
on the behavior of the members of a social system.

Not only did we find system effects, but also that
system effects made a unique contribution beyond individual
effects in explaining innovativeness, i.e., the system effects
occurred even when the corresponding individual-level
variables were controlled. All of the 15 partial correlations
for the system variables showed a significant relationship
with innovativeness, except two. Even clearer support of
system effects beyond individual effects was found when eight
independent variables (both individual and system measures)
were combined in a series of multiple correlations. The
simultaneous consideration of both individual and system
variables explained 62 percent of the variance in innovative-
ness, an increase in eXplained variance of 1a and 21 percent
over that eXplained by individual and system variables,
respectively.

In general, the relationship of all the individual

variables with innovativeness is linear. Contrarily, all

Anant P. Saxena
the System variables were found to be curvilinearly related
with innovativeness with "take-off" occurring at different
points, depending on the variable.

System effects tend to predominate somewhat over
individual-level effects. Predominance of system effects
was visualized in the sequential interaction analysis, and
also in a series of two-way analyses of variance. In the
sequential interaction analysis, we observed that the total
sample initially split on a system variable. Also, it
provided us with a configuration of variables organized in
such a way as to demonstrate how variables combine to max-
imally explain variation in innovativeness. As a result of
the configurational analysis, three typologies (most, moderate,
and least innovative) of innovativeness emerged.

A simultaneous consideration of bgth individual and sys-
tem variables in the configurational analysis yielded a
greater range in means, and a much reduced standard devia-
tion around the means, for all the typologies, than when
either individual or system variables were considered
separately. A substantial degree of interaction between
individual and system variables was also evident in the
configurational analysis, eSpecially in the case of the
less innovative reSpondents.

For an analysis within a balance theory framework, the
dichotomization of individual and system variables into
high and low levels were used to construct four typologies
of reSpondents. These were (1) modern individuals living

in modern systems, (2) modern individuals living in

Anant P. Saxena
traditional systems, (3) traditional individuals living in
modern systems, and (4) traditional individuals living in
traditional systems. Within each of these typologies, three.
types of pressure (internal, individual and external) on
individuals were assumed to be Operative.

The results of the two-way analysis of variance
illustrated our concern with the process by which social
systems generate dissonance in individuals. We found that
farmers high on both individual and system variables were
more innovative than when they were high on one type of
variable and low on the other, or when they were low on
both individual and system variables. In the case of im-
balanced situations, system effects seemed to predominate
over individual effects, and the dominance was greater when
individual effects were lower.

Our results document the existence of system effects
on individual innovativeness, and warrant further considera-
tion of system effects to building a more adequate theory.
The study augurs the beginning of research designs which
consider simultaneously and systematically both individual

zxnd system variables in predicting individual innovativeness.

Accepted by the faculty of the Department of
Communication, College of Communication Arts, Michigan
State University, in partial fulfillment of the

requirements for the Doctor of Philosophy degree.

I
Director of Thgsigé

Guidance Committee: 4’ “IL: , Chairman

Ari/am

 

SYSTEM EFFECTS ON INNOVATIVENESS
AMONG INDIAN FARMERS

By

(5“‘
Anant P. Saxena

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements

for the degree of
DOCTOR OF PHILOSOPHY
Department of Communication

1968

ACKNOWLEDGEMENTS

The author wishes to eXpress his appreciation to his
advisor, Dr. Everett M. Rogers for his considerable
assistance and support throughout the writer's disserta-
tion and entire graduate program.

The author also extends his appreciation to Drs.
Duane Gibson, Vincent Farace, and Larry Sarbaugh, who served
as members on the doctoral guidance committee and who
offered valuable suggestions and comments in regard to the
present thesis.

Data for this thesis were gathered as a part of a
larger research project, the Diffusion of Innovations in
Rural Societies. The author is indebted to the United
States Agency for International Development for the finan-
cial support which made the present project possible.

Many persons have substantially assisted the author
during the course of the dissertation. The author is
jparticularly indebted to James P. Bebermeyer and Joseph
.Ascroft for the helpful suggestions they contributed to-
Imard the improvement of the present thesis.

To Mrs. Irene Ascroft, the typist, for her many hours

cxf work, especially at the "eleventh hour," the author

eXpresses his thanks .

ii

TABLE OF CONTENTS

Page
ACKNOWLEDGMENTS . . . . . . . ii
LIST OF TABLES . . . . . . . iv
LIST OF FIGURES . . . . . . . v
CHAPTER
I INTRODUCTION ‘2. . . . . . 1
II REVIEW OF LITERATURE . . . . 16
III METHODOLOGY . . . . . . 30
IV FINDINGS . . . . . . 52
V SUMMARY AND DISCUSSION . . . . 81
BIBLIOGRAPHY . . . . . . . 103
APPENDIX . . . . . . . 108

111

Table
l.

9.

10.

11”

:12.

JLB.

LIST OF TABLES

Correlations of Individual and System
Variables with Innovativeness . .

Multiple Correlations of the Eight
Individual-level and System-level
Variables with Innovativeness . .

Testing for the Linearity of Relationships
between the Dependent Variable and the
Independent Variables . . . .

Mean Levels of Innovativeness and
Independent System Variables by Villages

Configurational Typologies of Innovative-

ness . o 0

Mean Innovativeness
and System Measures

Mean Innovativeness
and System Measures

Scores Across Individual
of Education . .

Scores Across Individual
of the Value of Agri—

cultural Products sold . . . .

Mean Innovativeness
and System Measures

Mean Innovativeness
and System Measures

Mean Innovativeness
and System Measures
tion . . .

Mean Innovativeness
and System Measures

Mean Innovativeness
and System Measures

Mean Innovativeness
and System Measures

Scores Across Individual
of Credit Orientation

Scores Across Individual
of Urban Pull .

Scores Across Individual
of Deferred Gratifica-

Scores Across Individual
of Extension Contact

Scores Across Individual
of Level of Living

Scores Across Individual
of Mass Media Exposure

iv

Page

56

59

61

62

71

75

75

76

76

77

77

78

78

Figure
1.

LIST OF FIGURES

Normative and Deviant Behavior as Explained
by Individual and Social System Modernization

Types of Balanced and Imbalanced Situations

Involving the Individual and His Social
syStem o o o o o o o o 0

An Illustration of System Effects . .

Hypothetical Example of the Blau Technique

in which the Dependent Variable (W) is
Related to the Individual Variable Z and
the System Variable ng . . . .

Typologies of Analyses on the Bases of Unit

of Response and Unit of Analysis . .
Typologies of System Effects

Configurational Analysis of Prediction of
Individual's Innovativeness with Individ-
ual Variables . . . . . . .

Configurational Analysis of Prediction of
Individual's Innovativeness with System
Variables . . . . . . .

Configurational Analysis of Prediction of
Individual's Innovativeness when both
Individual and System Variables are
Combined . . . . . .

Page

10
17

24

44
49

65

66

67

CHAPTER I
INTRODUCTION

... Any system not only bears in itself the seeds
of its change, but generates the change inces-
santly, with every act, every reaction, every
activity it discharges (Sorokin, 1961, p. 1312).

The Problem

The basic problem of the present thesis is focussed on
the simultaneous and systematic consideration of a set of
concepts, 2222 in individual and aggregate (system) forms, in
accounting for more variance in individual innovativeness*
than previously. Past work considered a set of independent
variables either as properties of the individual or
properties of the system of which he is a part. None attended
to both conceptual forms simultaneously.

It is hoped that this approach will provide a more
precise picture of the relationships among the dependent
'variable of innovativeness and certain independent variables
.in.both individual and system forms. Such a set of independent
”variables relate to the communication, social, and psycho-
Jmogical behavior of the individual and of the system of which

tie is a member.

‘

*Innovativeness is "the degree to which an individual is
relatively earlier in adopting new ideas than other members
or his social system" (Rogers, 1962, p. 20).

1

In approaching the present problem, one must attend to
the question of whether or not the properties of a social
system have influence over the behavior of its members.
Further, given such influence, what is its nature and direc-
tion? Such influence may be conceived as springing either
from aSpects of systemic structure* such as system norms, or

from system composition** with reSpect to members' attri-

 

butes, or from both.

Similarly, the prOperties of a system may be classified .
as (l) aggregate properties, which are based on character- I
istics of smaller units within the system being described,
and (2) integral pgoperties, which are not based on smaller

units.*** Thus, an aggregate property of a system is its

 

*Blau (1957 and 1960) and Campbell and Alexander (1965)
refer to such effects as "structural effects."

**Davis and others (1961) call these phenomena "composi-
tional" rather than "structural" because they think there is
only a partial overlap between these relationships and what
sociologists consider to be social structure. Our pretensions
here are not so much that of ending the semantic debate but
rather of striving to search for the existence of such
effects relative to systems under the label of "system
effects." So we subsume both structural and compositional
variables under system variables.

***Lazarsfeld and Menzel (1961) used the term "analytic"
and "global" in place of "aggregate" and "integral", to
describe the properties of a system as used by Selvin and
Hagstrom (1963). The latter authors do not agree with the
terms used by the former authors and feel that "global"
falsely suggests an overall description of the system, and
"analytic" emphasizes the decomposition of system properties
into individual data, rather than the combination of
individual data into system properties. Cattell (1951)
Provides a threefold classification of system variables:
Szntality variables which describe the performance of the
System.acting as a whole (e.g., some kind of social program

mean on some attribute (e.g., X on education), which is an
aggregation of the behavior of the individual members. In
contrast, whether or not the system has an educational
institution to impart education is an integral character-
istic which is not derived directly from the behavior of
the individual members or of any subsystem.

Another example differentiating the aggregate and
integral properties of a system is found in the Diffusion
Project;* the Phase I variables are measured at the village
level and describe the "integral" prOperties of the village.
The Phase II data were gathered from farming heads in from

8 to 20 villages per country, and thus the village mean

 

that the system undertakes); structure variables are based
on.particu1ars of internal structure and interaction (e.g.,
average number of friends chosen from within the system);
20pmlation variables are characteristics of the distribution
(If personality, status, and attitude-interest variables
ennong the members of the system (e.g., prOportion interested
111 campus politics). Cattell h0pes to explain variations in
Syntality as functions of pOpulation and structure variable.

 

*The Diffusion of Innovations in Rural Societies Research
szaject, a three-phase study conducted since 1964 by the
Department of Communication, Michigan State University, under
contract with the U.S. Agency for International Development,
1n3ed.survey research and multivariate analysis to explore
Tale diffusion of agricultural innovations in India, Brazil.
and Nigeria. Phase I of the study used the village as the
Inxit of analysis in order to explore the system effects of
‘Willage environments on villagers' behavior. Phase II used
the individual as the unit of analysis to explain
variability in innovativeness of individual farmers. In
Phase III, controlled field experiments were designed to
compare the effectiveness of such inputs as adult literacy
PI‘Ogram, animation (leadership clinics for informal leaders),
zuui radio forums in diffusing information about techno-
logical innovations.

values of variables represent the "aggregate" properties of
the system.*

Our assumption is that the_prgperties of a system will
exert influence over an individual member's behavior. This
assumption is made because the value system and normative
milieu of the system typically influence the behavior of
individual members by means of rewards and sanctions. Also,
other possible constraints of a system limit alternatives
that are open to their members. Thus, if there is no
electricity in a village the question of adOpting electrical
equipment by the individuals of that village does not arise.

We are also assuming that individual behavior (on some
dependent variable such as innovativeness) depends, or is in
part a function of, the individual's position on a number of
independent variables.

Based on these assumptions, the main thesis we advance
is that more variance in individual behavior can be
explained by utilizing 39m individual and system variables
inman.by using only individual variables.** Thus, an individ-
Iual's innovativeness may well depend in part on his literacy,
kar example, but also in part on the percent literate in the
Village in which he lives. Why would we expect these

SYStem effects?

_

*In the present study we intend to use only "aggregate"
SYStem variables. Details of our limitation to use only
aggregate system variables may be found in Chapter V.

**Because very few studies, as we shall show later,
utilized system variables.

Normative and Deviant Behavior

Through past interactions, individuals have organized
themselves into social systems and, through ongoing inter-
action, they maintain and adapt that organization. The
system includes norms which affect the behavior of individ-
uals. Norms have such effect when they become embedded
into the life patterns of individuals through the life-
long process of socialization. Socialization, the teaching
of norms and their later enforcement, is done in part by\
certain members of the system who transmit messages of
approval or disapproval to other members. Thus, socializa-
tion is accomplished through communication, the transmission
of messages with the intent to affect individual behavior.
This kind of socialization may be regarded as within-system
socialization.

To the extent that a social system enters into inter-
raction.with other systems, all of which, when put together,
turn be considered to form one larger social system (e.g.,
regional communities comprising a nation state), a similar
type of "socialization" for each individual social system
lmay occur. This type of socialization may be regarded as
lxstween-system socialization. Facilitating this kind of
Socialization is communication, just as in the case of

ixuiividual socialization within a system.*

k

*Direct measures of interaction among individuals within
a System and between systems would have been desirable and
likely would have been highly related to our dependent
variable. However, lacking such direct measurement, we

The research question generated by these observations
is: What are the effects of between-system socialization
upon within-system socialization?* What happens to an
individual who is already socialized in a particular social
system when that social system enters the process of being
socialized to norms of yet a larger social system? In
broad outline, we can conceive of two opposing forces acting
upon such an individual: (1) an internal source of influence
to maintain an existing normative structure (within-system
socialization), and (2) an external source of influence act-
ing upon the system to either reinforce or change its norm-
ative structure (between-system socialization). To inves—
tigate the manner in which these two forces interact with
each other, let us arbitrarily create dichotomies of social
systems and individuals as to whether they are modern or

traditional, and cast these in a two-by-two table as in

liigure l.

(ubnceive our individual and system variables as indirect
Imeasures of interaction in that individual behavior and
Systemic norms may well be considered as products of human

communication.

*Our usage of the word "socialization" is slightly
<iifferent from the usual sociological usage. Herein, we shall
Inse the word "modernization" to include both within-system
axul between-system socialization. Modernization parallels
art the individual level what development represents at the
Tuitional or societal level. Modernization is the process by
Whicklindividuals change from a traditional way of life to
a more complex, technologically-advanced, rapidly-changing
stifle of life (Rogers and Svenning, 1968).

 

 

 

 

 

 

 

Individual Social System
__ Modern Traditional
I II
< <
Modern Majority membership Minority membership
(Normative behaVior) (Deviant behavior)
A T' S 4
_L l _l__1
1: l I
l I l
\ III ;¢ IV ¢ . ,4;_
Traditional Minority membership Majority membership
(Deviant behavior) (Normative behavior)

 

Figure l. Normative and Deviant Behavior as EXplained by
Individual and Social System Modernization.

Legend: ..... Direction of Internal pressure to change (weak)
Direction of Internal pressure to change (strong)

Direction of External pressure to change

 

If the majority of the members of a social system are
in.cell I, then there will be strong pressure exerted
‘within the system on the minority membership in cell III to
Imove to I. Members in cell III may resist this pressure
arui in so doing may be considered as deviants from the
Enacial system's norms. In addition to the pressure from
Within, there is pressure from outside the social system
“such as from change agents) which is also brought to bear
upon the minority members in cell III to move to I. Thus,
the minority members in a modern social system eXperience
a double pressure to change, which is counterforced only

by its own internal pressure not to change.

If the majority of the members of a social system are in
cell IV, there will be strong pressure from within the social
system exerted upon the minority membership in cell II to
move back to IV. The members in cell II may have at one
time been like the members of cell IV in terms of normative
behavior, and have since broken away. In this sense, they
too may be considered as deviants. There is counter
pressure upon the minority membership to continue to accept
change. This pressure emanates from an external source.

The same external source also brings pressure to bear upon
the majority members in cell IV to accept change and move
into cell 1. A subsidiary source of pressure in cell IV to
accept change also comes from the minority membership in cell
II. Thus, the minority members in a traditional social
system eXperience a major pressure not to accept change
(internal pressure) and this pressure is counterforced by
enaother which encourages change (external pressure). In the
cmase of the majority members in a traditional social system,
tune major influence to change emanates primarily from an
external source and is counterforced primarily only by its
own resolve not to change.

Given this conceptual frame, we can hypothesize not
curly the direction of eXpected change but also the rate of
Change of the membership of each cell relative to the members
of all other cells.

The direction of change is for the normative behavior

<IF the members of cell IV to change into the normative

behavior of the members of cell I. This occurs by individ-
ual members of cell IV migrating to cell II until cell II

has the majority membership of the social system, i.e., until
cell II looks like cell I, i.e., the deviant behavior of cell
II becomes normative behavior when the majority of the social
system members have moved into cell II.

The rate of change is likely to be greatest in cell III
because of the double pressure upon it to change. The next
greatest rate of change is likely to occur in cell IV,
provided the external pressure to change is strong and
enduring. Cell II members, who have already accepted some
change, will continue to accept more change, but probably

at a slower rate than the members of cells III and IV.

Interaction Between the Individual and His Social System

To prOpose the probable manner in which the suggested
changes in the different cell types are impelled, let us
Shook at these cell types in terms of Newcomb's (1953, 1959)
IL-B-X model, or rather, in terms of an extended version of
inie model which we might call the ABC-X model, where A refers
tc» internal socialization (within social system), B refers
ta) external (between social system) socialization, C refers
ix) the individual attitudinal diSposition to socialization,
Emmi X refers to the new norms to which the social system is
‘bein@;socialized. The manner in which changes in the
different cell types of Figure 1 are impelled is depicted in

Figure 2.

lO

 

 

 

 

TYPE III TYPE IV
Figure 2. Types of Balanced and Imbalanced Situations
Involving the Individual and His Social System.
.Legend: ---- Relationships among systemic orientation
towards internal pressure (A), external pressure
(B), change (X) and individual attitudinal
disposition to change (C).
Relationships among an individual's orientation
towards internal pressure (A), external pressure
(B), change (X), and individual attitudinal
diSposition to change (C).
+ Positive orientation to each other.

- Negative orientation to each other.

The relationships between A, B, C, and X can be eXplained
iri terms of Newcomb's model. Basic to his theory is the
Inition that individuals will tend to maintain minimal dis—
crepancy between their own attitude towards the change (X)
Euui those of systemic orientation, depending upon the valence

that is jointly attributed to change (X) by the individual

11

and the system.

Why would we expect differences in the rate of change in
these four types of balanced and imbalanced situations?
Type I and Type IV are balanced, in that the overt behavior
of the individuals demanded by the system is in line with
their attitude. Types II and III are imbalanced systems.
Balance theory suggests that over time there will be a

1.- ...-«w

tendency for the individuals in types II and III to either

 

“~4- ......-_..

change their attitudes to make them consonant with the

behavior demanded by the system, or to give up their

existing behavior. From the viewpoint of balance theory,

we can describe these types as follows:

Type I: Modern individuals living in a modern system.
The situation is balanced in that there is
pressure only to maintain change and to eXpend it.

Type II: Modern individuals living in a traditional system.
The situation is imbalanced in that there is
external pressure to change but also an internal
counter force to resist change.

(type III: Traditional individuals living in a modern system.
The situation is imbalanced in that both pressures
(internal and external) demand change on the part
of the individual, and there is no counter-force
against change except that which comes from
themselves.

Type IV: Traditional individuals living in a traditional

system. The situation is balanced until the

12
external pressure to change is felt.

Our model also provides a basis to predict how the rela-
tionship between individual and system variables can be changed
and with what effect. Thus, taking Type IV as an example,
changing an individual's orientation (C) either toward change
(X) or toward external pressure (B) will make the system
imbalanced, which will lead to changed behavior. Similarly,
changing the relationship between change (X) and internal
pressure (A), or the relationship between internal pressure
(A) and external pressure (B) may perform the same function.

How can these relationships be altered? The crucial
variables that may change an individual's orientation towards
external pressure may be variables like source credibility,
extension contact, etc. An individual's orientation towards
change can be accomplished by means of mass media eXposure,
urban contact, education, literacy, etc. These variables all
have one common link; they are all communication variables.*

Testing the aforementioned propositions fully would
:require data gathered over time. Our data will allow testing
:some of the propositions with the data collected at one point
in.time and thus, possibly provide answers as to which of
the systems (balanced or imbalanced) will be more innovative
at one point in time. To determine which of the systems
‘will undergo more change over time is beyond the scOpe of
“this inquiry. However, a few Speculations on this matter

—_

'LBy'communication.variables we mean those transactions by
Fflmichfmessages are transmitted between and among human
beings. Literacy and education are mainly facilitators of

these transactions.

13

are made for the purpose of further eXploration.
Objectives

The present research is designed to test the existence
of system effects in a setting widely different from the U.S.
in culture and level of economic deveIOpment. Using natural
systems based on territory (such as village or community),
the present study attempts to predict individuals' agri-
cultural innovativeness.

Earlier studies demonstrated that some characteristics
of the system have an effect on the individual's innovative-
ness, but they do not show exactly hgw much influence the
system exerts over individuals' behavior. One of the concerns
of the present study, therefore, is to ascertain the degree
to which system variables affect the innovativeness of indivi-
dual members of a system. Our intent here is to answer such
questions as: Are there system effects? Do the properties
of systems affect individual innovativeness? How much
influence does the system exert over the individuals' behavior?

Another concern of the present research is to determine
the extent to which system variables affect individual
innovativeness when individual effects (differences among
individuals) are controlled. Such consideration allows us to
gauge if there are system effects beyond individual effects.
We also will examine the nature of relationships (linear or
otherwise) of innovativeness with individual and system

variables.

it

Still another concern of the present research is to
understand the way in which system variables affect individual
innovativeness under specified situations when interactions
among independent variables are controlled. Here, our
efforts will be to answer such questions as: Does the
strength of system effects vary with different combinations
and levels of independent individual and system variables?

Do system effects tend to predominate in these combinations?
Blau (1960) and Davis and others (1961) identified a
typology of system effects on the basis of (1) their linearity
or non-linearity, and (2) whether such effects have a direct

or indirect relationship to the individual-level dependent
variable. These authors do not provide a rationale for pre-
dicting the nature and direction of effects, nor Specify when
and under what conditions the strength of system effects will
vary. With the help of balance theory, the present study is
the first attempt to provide a rationale for predicting the
nature and direction of system effects and the specification
of situations under which such effects are likely to vary.

We also propose to establish certain typologies of
innovativeness, taking into account both individual and system
'variables, i.e., how the system and individual stand in rela-
tionship to each other.

Specifically, the major objectives of the present research
are three-fold:

1. To ascertain the degree to which system variables

affect the innovativeness of individual members of

a system.

3.

15
To determine the extent to which system independent
variables affect individual innovativeness when the
effects of individual independent variables are
controlled.
To understand the way in which system variables
affect individual innovativeness under Specified
situations when interactions among independent

variables are controlled.

CHAPTER II

REVIEW OF LITERATURE

In this chapter we propose to define system effects and
present a brief review of literature dealing with system
effects. We shall review the literature in two sections.

In the first section, a few major findings with reSpect to
each study will be listed, describing the nature of the
social system and the kind of behavior studied. In the
second section, the methodology used by past researchers in
determining system effects will be described. The descrip-
tion of these methodologies will be followed by a discussion
of their limitations. Finally, an outline of the procedure
that will be used in the present study in determining system

effects will be mentioned.

What Are System Effects?

System effects are the influence of systemic structure
and/or composition on the behavior of the members of a social
system.

Blau (1957) defined "structural effects", a term similar
to system effects, as follows:

If ego's X affects not only ego's Y but also

alters Y, a structural effect will be observed,

which means that the distribution of X in a

group is related to Y even though the individ-

ual's X is held constant.

The definition by Davis and others (1961) of system
effects is somewhat similar to that of Blau (1957). Davis and

16

17

others (1961) refer to such effects as "compositional."
Compositional effects, according to these authors, are the
effects of the composition of the system in which one is a
member, on his behavior (Figure 3). These authors state:
A compositional effect exists when the absolute
value of either (a) the within group difference
and/or (b) the between group difference for A's

and/or A can be described as a function of P,
where A refers to an individual attribute

present, A refers to an individual attribute

absent, and P refers to the per cent of A in a

group.

Thus, variation in the group composition (P) produces
an effect on the dependent variable (D) even when the

individual attribute (A) is controlled.

(D)

O H N CA) (7 U't
\ :>

 

 

cl .2 .3 on 05
(P)
iFigure 3. An Illustration of System Effects

Past Studies on System Effects

The phenomenon of system effects is not new. Its origin
may be traced to Durkheim (1897) who noted not only that

suicide rates varied censiderably among different religions,

18
but also that suicide rates for a given religion are much
lower when its adherents are in a distinct minority in the
society. Similarly, Groves and Ogburn (1928) showed that
the marriage rates for men and women vary in opposite
directions with the sex ratios of the communities in which
they live. Studies by Faris and Dunham (1939) on psychosis
rates, and Stouffer and others (1949) on U.S. soldiers,
were similarly concerned with system effects.

A number of more recent studies also demonstrates the
presence of system effects:

1. Berelson and others (1954) demonstrated the effects
of community composition in terms of party affiliation on
voting behavior.

2. Lipset and others (1956) found system effects in
their study of a labor union.

3. Wilson (1959) showed systemic influences on the
aspirations of high school students.

4. Blau (1960) observed that prevailing values in work
groups had system effects (in a public assistance agency) on
the conduct of the individual. In some cases, the group
‘values and the individual's orientation had similar but
independent effects; in other cases, they had opposite
effects; and in still other cases, the effects of the
individual's orientation were contingent on the prevalence
of this orientation in the group, a pattern which identifies
characteristics associated with deviancy.

5. Davis and others (1961), in a study of the Great

Books reading program, encountered system effects.

19

A number of research investigations have examined system
effects in formal organizations. Becker and Stafford (1967)
conducted a mail survey of 140 saving and loan associations
in Illinois to explain variation in organizational efficiency
and innovativeness. Five independent variables explained 40
percent variance in organizational efficiency and innovative-
ness. The independent variables included the size of the
organization in terms of assets, the growth rate of the sur-
rounding community, the adoption of innovations, size of the
administrative component, and the management's leadership
style.

Sapolsky (1967) studied nine retail organizations in six
department stores. He found that three major innovations
suggested by store executives were not implemented because
of the nature of the stores' organization and reward systems.
Similarly, in a study of factors associated with the success
or failure of various innovative staff proposals, Evans and
Black (1967) found that the nature of the staff-line relation-
ships affected the innovation acceptance.

Further evidence of the existence of system effects may
be found in a number of studies dealing with the diffusion of
innovations. Marsh and Coleman (1954) indicated that both
socio-economic characteristics of farmers and their neighbor-
hood of residence are significantly associated with the
individual farmer's innovativeness score. Even when the
socio-economic characteristics of the farmers are held con-

stant, the differences attributable to differences in

20

neighborhoods still exist. In their Kentucky restudy, Young
and Coleman (1959) also found that farmers in modern neighbor-
hoods had a more scientific orientation toward farming matters
than those in traditional neighborhoods.

Duncan and Kreitlow (1954) matched and compared 19 pairs
of rural neighborhoods on the adoption of 30 school practices,
using an index of 25 farming practices and four elements of
organizational participation. They used neighborhood as the
unit of analysis, and the mean score of 10 respondents in
each neighborhood as the acceptance level of the entire
neighborhood. They found that heterogeneous neighborhoods
were consistently more favorable toward a majority of the
innovations than were homogeneous neighborhoods.

In a study of 47 Wisconsin townships, van deanan (1960) A
classified the townships into four categories according to
the average adoption scores of the farmers. He observed a
significant difference among the four categories in the pro-
;portion of high adOpters, after controlling the effects of
:such variables as education, 4-H Club membership, size of
faxmn and net worth. He then made case studies of two town-
:ships, one modern and one traditional, and concluded:
'UDifferences 1n the adoption of new farm practices between
the townships studied can be only partly eXplained by
clifferences in individual characteristics or by values
directly affecting farming. Differences in social structure
sauem.to be more important."

Faced with the problem of low prediction level, allegedly

21

due to exclusive emphasis on the individual, Rogers (1961) 7;
included a community variable, "norms on innovativeness," in
his analysis. He found that the prediction of the innovative-
ness of truck farmers much improved because of the inclusion
of this previously-unused variable.

Coughenour (1964) analyzed the data on the diffusion of
five farm practices in 12 Kentucky localities. He found that
speed of diffusion is related to socio-economic and
attitudinal resources of each locality and also to the nature
of social relationships with information sources and media
contacts.

Qadir's analysis (1966) of data from some 600 villagers \’
in 26 Philippine neighborhoods, on the other hand, revealed
that compositional system variables (e.g., mean neighborhood
education, mass media eXposure) are about as effective as
jpredictors of individual innovativeness as are individual
'variables like education, mass media exposure, etc. Opera-
tionalizing "differentiation" as the adoption of modern
jpractices, he concludes:

The composition of the more differentiated

localities shows a concentration of individual

households with high education, modern orienta-

tion, media contact, material possession, and

communication facilities, which together generate

a social climate in favor of adoption of modern

practices [like our cell I in Figure l].* Under

such a climate, even individual households lacking

high education, modern orientation, etc., act like

adopters [like our cell 111 in Figure 11‘.“ On the
other hand, the less differentiated locality

 

*Parenthetical comments are provided by the present author.

22

has a generally low level of education, modern

orientation, media contact, material possessions,

and communication facilities, which together

account for the rigidity of the social structure

[like our cell IV in Figure 1]. As a result,

individual households even with a high level of

education, modern orientation, are found to fall

behind those with low level of education, modern

orientation, etc., in the more differentiated

locality in the adoption of change[:like our

cell 11 in Figure l].

The foregoing account of past research on system effects
illustrates that the bulk of such studies have been completed
(1) in more developed countries, eSpecially in the U.S.,

(2) in formal organizations or small group settings, and
(3) with the objective of identifying only the presence or

absence of system effects.

How to Determine System Effects

Some 60 years ago, Durkheim (1951) demonstrated the
method of isolating system effects by ascertaining the
relationship between the distribution of a given independent
variable in various systems and a dependent variable, while
holding the independent variable constant for individuals.
If a system effect is found, it will provide evidence that
differences in the system variable are responsible for the
variation in the dependent variable, since individual dif-
ferences on the independent variable have been controlled.

The strategy of Blau (1957) is similar; he
characterized an individual in terms of his score on
variable Z, and his systemic score on variable ng. As

shown in Figure 4, this strategy involves (l) determining

23

an empirical measure, Z, relative to some characteristic

of the individual member of a system, (2) combining the scores
for measure Z, into an index for each system (ng) to refer
to the characteristics of the system, and (3) determining
the relationship between the systemic attribute (ng) and
some dependent variable, W, while the corresponding
characteristic of the individual (Z) is held constant.
Thus, the effect of ng on W will be a "pure" system
effect, with the effect of the individual level of the
independent variable removed. This strategy has two

severe limitations: (1) the problem of contaminating
individual differences (within-column in Figure 4) with
system effects, and (2) the problem of contaminating system
effects with individual effects.

While Blau dichotomizes both the individual and the
system variables (Z and ng), the strategy employed by Davis
and others (1961) dichotomizes only the Z variable. Thus,
the systems are Spread out along a horizontal axis accord-
ing to their ng scores. This procedure eliminates the
problem of contaminating individual differences with system
effects, but the problem of eliminating individual effects

in the systemic remains.

24

 

 

 

Individual System

Low ng High ng
High 2 *fi *fi
Low 2 *W *W

 

Figure 4. Hypothetical Example of the Blau Technique
in which the Dependent Variable (W) is
Related to the Individual Variable Z and the
System Variable ng.
*Cell entries indicate the mean W for all individuals in
that cell.

To the extent that individual variables involved are
truly dichotomous, the problem of contamination does not
arise. But this is not true in the case of most social
science variables which are continuous. Thus, Tannenbaum
and Bachman (1964) propose several modifications of Blau's
(1957) or Davis and others' (1961) method. One modification
is to achieve less within-category variance on the individual
variable (Z). This objective can be accomplished by using
a larger number of categories for the individual variable
(Z). Then, the technique of Blau may be used except that it
will have an Nx2 rather than Blau's 2x2 form, in which N is
the number of categories used for the individual variable
(Z). This technique will culminate in holding individual
'level effects more or less constant. This technique can
further be modified to cover a borader range of group

'variables (ng), as suggested by Davis and others, in order

to (1) hold systemic characteristics strictly constant and

25

thus avoid the problem of Spurious individual effects, and
(2) achieve statistical efficiency.

Another method proposed by Tannenbaum and Bachman
consists of correlating the system variable (ng) with the
dependent variable (W) at each of the N levels of the
individual variable (Z). Such procedure requires that each
individual be assigned a ng score according to the system
in which he is located as well as his own individual W score.
These correlations do not provide information about
individual level effects. The individual effects might be
detected through the use of intersystem correlations (i.e.,
by correlating Z and W separately within each system, and
thus holding system effects constant), or by the technique
of partial correlation. Thus, a system effect can be
measured by correlating ng and W with Z partialled. Like-
wise, an individual level effect can be determined by the,
correlation of Z and W with ng partialled.

Multiple regression techniques may also be used. Thus,
the change in W eXpected with a unit change in ng and Z,
provides a measure of the system and individual effects,
reSpectively.

The effects of variables correlated with each other
:may pose the problem of confounding. For example, Davis and
cnfliers (1961) classified his systems according to the pro-
portion of members who were new to the system, the
proportion having contact with other members outside of the

system, and the proportion who were active in discussions.

26

These three characteristics are confounded in that the
individuals who are new to the systems are likely to have
few outside contacts and to be inactive in discussions.
Thus, the characteristics of the system formed from these
individual variables are probably associated, and if so, the
effects attributed to one of these variables are really,
at least to some extent, the effects of all three variables.
Selvin and Hagstrom (1963) therefore prOpose two
solutions to solve the problem of confounding. One is to
make the univariate description adequate by removing the
unwanted variables eXperimentally or statistically. The
other solution is to abandon entirely the effort to describe
the systems according to a single characteristic and to
construct a multivariate description. These authors use
factor analysis to solve the problem of confounding.‘
Systems are classified by means of factor scores, which
are then used in the analysis of system effects. Valkonen
(1966) suggests use of the factor scores of systems as the
properties of the system in correlationsal techniques. A
variable which has a high loading on the factor may be
chosen to represent each factor. If an orthogonal rotation
is applied, the factor scores will be relatively uncorrelated
'with each other. Instead of factor scores, a set of the
cxriginal variables could also be used to characterize the

systems.

27

Procedure To Be Used in the Present Study

We propose to use all the aforementioned techniques of
matching, correlation, and multiple regression in the
present study.

1. Multiple correlation will be used to predict
individual innovativeness by assigning two values to the
same attribute for every individual (one to represent his
individual score, and the other to represent the score of
the system on the same independent variable) for each of
the independent variables.

2. First-order partial correlations will be used to
hold constant either individual or system level variables
and to assess the relative contribution of each in eXplain-
ing the dependent variable.

3. Multiple correlation analyses with all the
independent variables will be computed to determine the
variance in innovativeness that can be eXplained by
individual and system variables in their separate and com-
bined form. Thus, there will be three multiple correlation
analyses: one with independent individual variables, the
other with the independent system variables, and one with
both independent individual and system variables.

4. Individual and system variables will be matched on
each of the independent variables to form the four possible
situations (balanced and imbalanced) discussed in Chapter I:
(1) modern individuals living in a modern system; (2) modern

individuals living in a traditional system; (3) traditional

28

individuals living in a modern system; and (4) traditional
individuals living in a traditional system. A two-way
analysis of variance design will be used to test the signi-
ficance of differences in innovativeness attributable to
differences in individuals, systems, and their inter-
action. Then, hypotheses will be tested in respect to the
innovativeness for each of the four situations.

In order to provide a meaningful interpretation of the
results of our correlational analyses, two assumptions must
be satisfied. One assumption is that the relationship
between the dependent variable and the independent variables
is linear. A second assumption, which is a.m3§£ for the
comparison of partial correlations, is that there is no
interaction. That is, the various levels of the independ-
ent variables do not interact with the dependent variable.

It is necessary, therefore, to test whether or not
these assumptions are met. To test the linearity of the
relationship between the dependent variable and the
independent variables, the zero-order correlations will be
compared with the corresponding eta. If the difference
'between the two is small, a linear relationship may be
assumed. For testing the presence of interaction, a two-
way analysis of variance design will be followed wherein
differences among individuals and systems will correspond
to row and column differences reSpectively.

If the two assumptions are not met, the technique of

sequential interaction analysis (Sonquist and Morgan, 1964)

29

will be followed. This technique is the only multivariate
analysis that does not impose the assumption of additivity

(linearity) and allows one to observe interaction effects.

CHAPTER III
METHODOLOGY*

The Data

Data gathered from eight Indian villages in Phase II
of the Diffusion Project will be used in the present
analysis. The two phases of the Diffusion Project differ
mainly in respect to the unit of analysis.

In Phase I, the village is the unit of analysis; data
from 108 villages were collected from the states of
Maharashtra, Andhra Pradesh, and West Bengal. These states
were selected to represent different modes of involvement
of local self-government in development administration: (1)
Andhra Pradesh, to represent locally elected people at the
block level, (2) Maharashtra, to represent locally elected
people involved at the district level, and (3) West Bengal,
to represent the control over development administration
coming mostly from the state level (as the emphasis on
local self-government has only recently been introduced in
this state). Two or three villages were randomly selected
111 each district and certain restrictions were imposed to
:represent a more or less normal distribution of villages

:nanging from least to most successful village in terms of

 

*The earlier part of this section was drawn heavily
from Roy and others (1968) .

30

31

the adoption of agricultural innovations. The emphasis in
Phase I was to investigate the integral properties of the
villages as related to their innovativeness.

Of the 108 Indian villages, eight were selected in
Phase II. The unit of analysis in Phase II is the individ-
ual farmer, 680 of whom constitute the sample. The
emphasis in Phase II was to eXplain the variance in

innovativeness of individual farmers.

Questionnaire Construction

A questionnaire was designed to use in personal inter-
views with the farm Operators. It had both structured and
Open-ended questions. The questionnaire was first trans-
lated into Telugu and the format was given a substantial
pretesting in the state of Andhra Pradesh. Questions which
obviously were not understood were revised.

After the first revision, the questionnaire was also
translated into Marathi and Bangali, the languages of the
other two states. The questions were then pretested again
in.all three languages and revisions were made. Final
:reproduction of the questionnaire resulted in three sets of
'bilingual instruments, corresponding to the three regional
languages, with English as the common language. We paid
jparticular attention to the translation in order to use
«expressions familiar to the farmer and to maintain identity

of‘ineaning across the different languages.

The questionnaire so designed was used by teams of four

32

interviewers led by a supervisor in each of the three states.
All team members had prior field interviewing eXperience

and had participated in Phase I interviewing.

Field Work

Personal interviews were conducted during March and
April of 1967 by teams of four interviewers led by a
supervisor in each of the three states. The teams worked
from a temporary residence in a sample village. They
prepared lists of eligible respondents by consulting
registration lists and knowledgeable people in the village.
On the completion of the lists, they interviewed eligible
reSpondents.

In most cases, the interview was conducted in private
and lasted about one hour and fifteen minutes. The general
purpose of the study was known to the interviewee from the
earlier visits made during Phase I of the study.

Interview schedules were checked by the supervisor in
the field, making it possible to return to the respondent
if one or more questions had been ommitted. Two weeks were

expent in each location obtaining the individual reSpondent

data.

Sample
As mentioned earlier, three states were selected to
represent different modes of involvement of local self-

government in development administration. Two or three

33

villages were selected in each state from the 108 villages
which had been included in the first phase of the Project.
Selection of the villages was restricted to a single
development block in each state to minimize travel costs.
In selecting villages, we imposed the same restrictions as
in Phase I of the study to select villages that represented
a distribution of villages ranging from least to most

successful village in terms of adoption of agricultural
innovations.

We selected only farm operators, those who actually
made the day-to-day decisions on the farm and who were
farming at least 2.5 acres (one hectare) of land at the
time of the data-gathering. ReSpondents could own or rent
the land they farmed. We excluded the smallest farmers
and landless laborers, because either many of the innova-
tions are not applicable to them or they are not involved
in making decisions regarding the adoption of farm innova-
tions.* By doing so, we were dealing with the farmers who
utilize most of India's agricultural innovations.

We selected only those farm operators who were heads
of farm households and were 50 years of age or younger at

the time of the data-gathering. This restriction was

 

*The India Census of Agriculture (1965) states that
about 24 percent of the village pOpulation in the nation
consists of landless laborers. The figures are 42, 34,
euui 28 percent for Andhra Pradesh, Maharashtra, and West
jBengal, reSpectively. Of the farmers who own some land,
60 percent own 2.5 acres or more and cultivate 93 percent
of the total arable area in the country.

34

imposed to avoid the ambiguous decision-making situation

in which the older generation is gradually transferring
responsibility for farming decisions to the younger, making
it difficult to determine who in fact makes farm decisions.

From each state we interviewed 200 to 250 farmers who
fitted the size of holding and age specifications. Three
villages from each state were selected, except Maharashtra
in which case we felt two villages will be sufficient to
provide enough cases.

Since we imposed a number of restrictions on our
sample, it is not "representative"* in a statistical sense.
However, it does permit the kind of statistical analyses
we want to make. Our analyses are mostly correlational and
hence we purposively included farmers covering a wide

range in agricultural modernization.

Operationalization of Variables

We turn now to consideration of the manner in which
the dependent and independent variables were indexed. The
'valdous techniques of scoring, weighting, and scale analysis
sure documented here. Appendix contains the scale items
insed in the present analyses, and Specific questions asked

ix; secure responses to these items.

Innovativeness

The dependent variable in the present study is

iruuyvativeness. The conventional definition of

 

*Of all of village India, at least.

35

innovativeness is the degree to which an individual is
earlier than others in his social system in adopting new
ideas.

Though problems of weighting, validity, reliability,
and internal consistency were considered in general, more
Specific considerations were given to (1) include items
that were applicable to the farmers in all three states,
(2) the unidimensionality of the items, and (3) examine the
distribution of the final measures to ensure a somewhat
normal distribution.

The final interview schedule obtained after two pre-
tests contained ten innovations that were equally app-
licable to all the farmers in the sample and were related
‘to fertilizers and manures, new seed varieties, insect-
icides and pesticides, and the breeding and protection of
cattle. All ten items were used and scored as a simple
unit-weighted index. This procedure of unit-weighting was
felt to be simpler (and not much different) than either
determining scale types for each farmer or factor weighting
of items for each farmer.

For each innovation, the questions "Do you know any-
‘thing about ...?" "Have you ever used ...?" and "Are you
:rtill using ...?" were asked to elicit responses at three
stages in the innovation-decision process which are
ccurventionally referred to as knowledge, trial and adoption.

One of the techniques used to test the scalability of

time ten items in terms of the three variables (knowledge,

36

trial, and adoption) was Guttman scaling. The Guttman scale
for the knowledge measure showed the highest degree of uni-
dimensionality (the coefficient of reproducibility is .94),
but in order to meet the second criterion of marginal
frequencies being more than 10 percent, a number of items
would have to be dropped. The trial measure showed an
acceptable level of scalability (the coefficient of
reproducibility is .90), and on the second criterion of the
marginal frequencies only one or two items were borderline
cases. The adoption measure was below the acceptable level
(the coefficient of reproducibility is .88), and about
three items were rejected to meet the criterion of marginal
frequencies. Thus, among the three measures of innovative-
ness, the trial measure* was regarded as the best measure.

Factor analysis was another method used to test the
unidimensionality of the ten items. The three inter-
correlation matrices of ten items for knowledge, trial and
adoption were subjected to factor analysis to determine the
amount of variance that any single dimension would eXplain
and to extract the principal component factor. The results
of the factor analysis were well in accord with those of
the Guttman scaling and hence the trial measure was
finally selected as the best measure of innovativeness.

In addition to unidimensionality, certain other

considerations were given some attention in determining

 

*We term this a "trial" dimension in that the
respondent was asked if he had ever tried the innovation.

37

the best measure of innovativeness. One of these was the
distribution of scores for all the three measures. The
knowledge and adoption curves were skewed to the left and
right, respectively, while the trial curve had a more nearly
normal, though a somewhat flattened, distribution. In terms
of variation in the scores, the knowledge scores varied
from a high of 9.85 (out of a possible 10) to a low of
6.03. The adoption scores varied from 5.41 to 1.62. The
trial scores had a wider variation ranging from 2.56 to
7.33.

Another consideration was more of the way in which
questions were phrased. The question "Are you still
using ...?" often unjustly penalized farmers who had
essentially used and had adOpted an innovation, but for
reasons of non-availability or crop rotation, or for some
other reasons, were not using the innovation currently.
Thus, "Have you ever used ...?" might be a more reliable
indicator of innovativeness than "Are you still using ...?"

Thus, for the purpose of the present investigation, we
Operationalized innovativeness as the trial of an innova-
tion regardless of when it was adOpted, and whether its use

was continued.

Independent Variables

A large number of variables were selected as possible
correlates of innovativeness. While selecting these

variables, a number of criteria were employed. One was

38

previous research findings relative to individual innovative-
ness, eSpecially in less developed countries. The other
criterion was more intuitive and intellectual, which was
felt necessary because of the paucity of research on system
effects in the research tradition on the diffusion of
innovations.

Inclusion of system variables was mainly guided by
the consideration that any type of human behavior can be
partitioned in terms of "within" and "between" variance.
One can visualize more homogeneity in human behavior within
social systems than between social systems. Besides eco-
logical reasons (such as the similarity of climate, soil,
heredity, and so forth), it is interpersonal communication,
the informal exchange of information and ideas, that brings
greater homogeneity among system members over time. Thus,
if a system has a greater proportion of individuals who are
literate, eXposure to print mass media is facilitated, and
one would expect a substantial amount of information
exchange in the system as compared to the system in which
there are very few individuals who are literate. Trans-
actions of messages about innovation decision and re-
inforcement of systemic norm will undoubtedly form an
important part of this information exchange.

On the basis of the aforementioned criteria, the
following independent variables were selected and included
in the data analysis.

1. Education of the Respondent. Education can enable

39

farmers to perceive the relative advantages of innovations
more readily and can assist in breaking traditionalism.

It is expected that education of the respondent will be
positively associated with innovativeness. In a recent
compilation of studies found in the Diffusion Document
Center of Michigan State University, Rogers and Stanfield
(1968) found that more than three-fourth of 193 publica-
tions indicated a positive relationship of education with
innovativeness.

2. Value of Agricultural Products Sold. This index
is a measure of farm Operation size, which takes into
account differences in the value of crOps. These ranged
widely in our sample from a very low return per acre of
pulses to a high return of sugar cane and cotton. Roy and
others (1968) computed six different measures of farm size
and found that this index of value of agricultural products
was the most direct and reliable measure, and that it was
highly related to innovativeness.

3. Credit Orientation. Borrowing credit for
commercial purposes presupposes an ability to have con-
fidence in the future. This orientation becomes more
important in a traditional subsistence system where deci-
sions for agricultural alternatives are based not on
monetary gains, but rather on the protection of one's
livelihood. We, therefore, eXpect a positive relationship
between credit orientation and innovativeness. This index

was measured by responses given to the questions "Did you

40

use any credit for farm purposes last year?" and "Would you
have used some more had it been available at reasonable
interest?"

4. Social Participation. Individuals who more actively
participate in the activities of the social system are more
likely to be innovative. We expected that membership and
office-holding in formal organizations would relate
positively with innovativeness. Roy and others (1968)
found that holding office in a formal organization was
conducive to higher levels of innovativeness.

5. Urban Contact. This variable is an operational
measure of one's cosmopoliteness, defined as one's orienta-
tion to the larger society which lies beyond one's
immediate surroundings. To measure this concept reSpondents
were asked whether they had previously lived in another
place, and also how frequently they visited any town or
city in the past year. Ryan and Gross (1943) found that
hybrid corn innovators travelled more often to urban
centers such as Des Moines than did average farmers.

Menzel and Katz (1955) confirmed this finding among the
more innovative medical doctors. Thus, we eXpect that
urban contact will be positively associated with
innovativeness.

6. Urban Pull. One's motivation to migrate to a city
indicates that one's reference group is no longer only his
village. We call this motivation to migrate to a city

"urban pull." We measured this concept by responses given

41

to the question, "If you were Offered a job in a city with
double your present income, would you go?" The economic
incentive mentioned in the question was deliberately used
in order to balance the higher cost of living in cities.

7. Educational Aspiration. Educational aSpirations
are defined as the level of education desired by parents
for their children. In the Indian settings, education is
a dubious venture as it cuts down on the family labor and
is most often associated with out-migration to cities.
However, it reflects a more modern outlook, and hence, it
is believed to be positively related to innovativeness.

8. Deferred Gratification. Deferred gratification is
defined as the postponement of immediate satisfaction in
anticipation of future rewards (Rogers, 1965). We eXpect,
the greater postponement of immediate satisfaction
accompanies greater innovativeness. This concept was
measured by an open-end question, "Suppose that your cash
returns from the farm last year had been twice your actual
income, what would you do with the extra money?" The
responses were scored depending on the nature of the grati-
fication exhibited in the response.

9. Extension Contact. Contact with change agencies
has been found to be positively related to innovativeness.
Rogers and Stanfield (1968) found that over 90 percent of
the 136 studies dealing with the relationship between
extension contact and innovativeness was positive. We used

four measures Of extension contact. They are: (1) the

42

number of times talked with the block development officer,
(2) times talked with village level worker, (3) times seen
a block film, and (4) times seen a demonstration. The
codes for these measures were summed to form an index of
extension contact.

10. Level of Living. As indirect measures of

 

wealth, we constructed indices of material possessions and
housing, and then summed these two into what we call a
level of living index. We eXpect a positive relationship
between level of living and innovativeness.

11. Political Knowledge. Political knowledge was
measured by an informal knowledge test asking the respondent
to identify by name (1) the prime minister of India; (2)
the chief minister of the state; (3) the elected
representative to the state legislature from that area.
Since political knowledge is one manifestation of the
respondent's participation in the body politic of the larger
society, we eXpect a positive relationship between political
knowledge and innovativeness.

12. Secular Orientation. Secular orientation was
measured by a set of questions with paired alternative
answers, one favoring tradition and the other non-tradition.
Of ten such questions, only eight were retained in the
final index. The items retained refer to two most important
elements of the village society, the caste system and norms
surrounding the cow.

13. Empathy. Empathy was defined by Lerner (1958) by

43

various descriptive terms such as ability to take others'
roles, the capacity for rearranging the self-system on short
notice, psychic mobility, etc. We measured it by a set of
questions in the form, "If you were ... then what would

you do ...?" The roles suggested were those of the district
administrative officer, the block develOpment officer,
village president, and a day laborer.

l4. Caste Rank. Caste rankings were obtained by ask-
ing knowledgeable reSpondents in each village to rank
photographs of people at work in caste occupations in terms
of ritual status for that village. Ritual status is defined
on the basis of interdining and sharing of water. It is
expected that higher caste status would be related to higher
innovativeness.

15. Mass Media Exposure. Four separate measures of
mass media exposure were used. Two of them related to radio
listening; one for respondent listening, and the other for
family listening. The third measure was the number of
commercial films seen in the past year. The fourth measure
was whether neWSpapers were either read by the reSpondent
or were read to him. We combined these four measures into
a mass media eXposure index. We eXpected a positive

relationship between mass media eXposure and innovativeness.

The Present Plan of Analysis

In the foregoing discussion it was pointed out that

past analyses of the diffusion of innovations lacked

44

attention to social system variables as explainers of
differences in individuals' innovativeness behavior. We,
therefore, conceptualized a farmer's innovative behavior,

the dependent variable, as explained by two types of indepen-
dent variables: (1) the individual's social, psychological
and personality variables; and (2) the characteristics of

the system, or village properties, in which the individual

lives. The first class of variables are individual, the

 

second are system. Thus, there may be four possible typo-

logies of analyses, as depicted in Figure 5.

 

 

 

Unit of Unit of Response
Analysis Individual System
Variable Variable
Individual
Variable l 2
System
Variable 3 h

 

Figure 5. Typologies of Analyses on the Basis of Unit of
ReSponse and Unit of Analysis.

1. 'Individual-Individual -- When data are gathered from

individuals as the units of reSponse, and the unit of

analysis is the individual also.

2. System-Individual -- When data are gathered from the

social system as the unit of response, and the individual

is treated as the unit of analysis.

3. Individual-System -- When data are gathered from the

45

individual as the unit of response, and the social system
is used as the unit of analysis.

4. Syppem-System -- When both the unit of response and the
unit of analysis are systems.

Type 1 is the most frequently-used approach in diffusion
research. The Phase II study of the Diffusion Project
represents this typology. Over 95 percent of the diffusion
studies found in the Diffusion Document Center* used this
type of analysis. Type 2 and 3 are neither very common nor
encouraging because of the possible fallacies associated
with them. When using system variables (aggregate data) in
the Type 2 approach, and if one infers about individuals,
he commits the "ecological fallacy" by assuming the
individual regression slope and the aggregate regression
slope (or their analogies) are equal (Robinson, 1950).

If Type 2 is subject to the "ecological fallacy," Type 3
is eXposed to the "system fallacy" in that the individual
relationship is incorrectly assumed to hold up for all
social systems (e.g., modern and traditional systems).
There are not very many studies that fall in Type 4. Most
of the studies in the anthropology diffusion research

‘tradition and the Phase I study of the Diffusion Research

ZProject represent this typology.

 

i"The Diffusion Documents Center, located in the
IDepartment of Communication, Michigan State University,
(contains studies, both empirical and non-empirical,
cievoted to the diffusion of innovations. At present
'the DDC contains over 1,500 such studies.

46

In the present study we propose a combination of Types
1 and 2, i.e., the independent variables will include both
individual-level variables and system-level variables. Thus,
the plan of analysis of the present study proceeds in four
stages.
1. Use of zero-order and first-order partial
correlations.
2. Use of multiple correlation techniques.
3. Formulation of typologies of innovativeness based
on both individual and system variables.
4. Hypothesis testing concerning the innovativeness

of balanced and imbalanced conditions.

1. Use of zero-order and first-orderjpartial correla-
Elgpg: First of all, zero-order correlations will be
computed between innovativeness and all other independent
variables (both individual and system). Then, first-order
partial correlations between the dependent variable
(innovativeness) and each Of the individual variables will
be computed, keeping constant the effects of their
respective system variables, and vice-versa. The partial
correlations thus obtained for individual-level and System-
.1evel measures will be compared for each of the variables
to assess the relative contribution of each in eXplaining
innovativeness.

2. Use of multiple correlation techniques: Only those
independent variables that have been found to be the best

jpredictors of innovativeness will be included in a multiple

47

correlation analysis. An aggregate measure of individual
scores by using the village mean on that variable will be
used to represent the system variable. Thus, each of the
independent variables will be included in the multiple

correlation analysis as follows:

I13 = a + bl X13 + r1213

where Yij is the innovative behavior of ith individual in
jth community, X13 is the score of the ith individual in
the jth community on individual variable X, X13 is the score
of the ith individual in the jth community on community
variable X, a is constant, and b1 and r1 are coefficients.

Thus, the amount of variance in Y (the dependent
variable, innovativeness), eXplained by X and X (the
individual and system independent variables,respectively),
will be attributable to individual and system effects,
respectively. The variance explained jointly by both the
individual and system variables will be the combined
contribution of both (X + X), plus their interaction effect,
if any.

3. Formulation Of the typolggies Of innovativeness:
This approach is similar to the graphic presentation used
by Davis and others (1961) in demonstrating the typology of
compositional effects (as shown in Figure 5). These
authors classified different types of effects on the basis
of: (1) their linearity or non-linearity, (2) whether such

effects have a direct or indirect relationship with the

48

individual-level dependent variable, and (3) their positive
or negative direction as indicated by the sign of the
regression coefficients.

Thus, Type III A (in Figure 6) and our example of the
regression equation (cited just previously) indicate that
Y can be eXplained by an individual level variable X
(b1 s 0) and, additionally by x, the system variable
(r1 # O) and also that these variables affect the dependent
variable in the same direction, since b1 and r1 have the
same positive signs. If these signs would have been
different, the individual-level and system-level variables
would affect the dependent variable in an opposite direction
éither Type III B, or any other relationship in Type IV).
Similarly, if bi = 0 and r1 # 0, only the system variable
will affect the dependent variable (Type II); if b1 # O and
r1 = 0, only individual-level (Type I) will affect the
dependent variable.

However, our typologies of innovativeness are different
from the typologies of system effects presented by Davis
and others (1961) in that these authors classified variables
while we propose to classify individuals considering
system effects on them.

We propose to use sequential interaction analysis for
establishing typologies of innovativeness. In these typo-
logies, we will take into account both individual and

system variables, i.e., how the system and individuals stand

in relation to each other.

49

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

Individual InterL System Level Effect
Level Action
EffeCt No Yes
Type 0 [ Type II
‘(A
NO NO [A a;
‘2:
Type I Type IIIA Type IIIB
Yes NO A
”"‘ A
Type IVA Type IVB
» I
I, [z
/ I I /’ A
Logically //
impossible
Yes Yes \ Type IVC
\ A
\
\
\
\
\ .—
AA

 

 

 

 

 

 

 

 

Figure 6. Typologies of System Effects.*

*AdOpted from Davis and others (1961).

50

4. Hypothesis testing. Hypothesis testing regarding

 

variation in system effects will be accomplished by a two-
way analysis of variance design, wherein differences among
individuals and systems correspond to the differences in
rows and columns. Further tests for the significance of the
difference in innovativeness in different systems will be
accomplished by use of the test for difference in means.

In drawing conclusion from the results of these proced-
ures, multiple correlation techniques will be used to
explain variance in the dependent variable (innovativeness)
attributable to a set of linearly-related independent
variables, both individual and system. Partial correlation
will be used to explain variance in the dependent variable
attributable to system variables, controlling the effects
of individual variables, and vice-versa.

From the results obtained from sequential interaction
analysis, certain typologies of innovativeness will be
formulated considering both individual and system variables
and their interaction. It is through the analysis of
variance design that hypothesis testing concerning the
variation in system effects on differing systems (balanced
and.imbalanced) will be put to test.

The unique contribution of the present research is in
advancing a more adequate and refined conceptualization
and methodology to predict system effects on individual's
innovativeness in adopting farm innovations. This research

is not primarily a methodological study but refinement in

51

the methodology is unavoidable in providing answers to the

research problem at hand.

 

CHAPTER IV

FINDINGS

As stated earlier, the dependent variable in the pre-
sent study is innovativeness, defined as "the degree to
which an individual is relatively earlier in adopting new
ideas than the other members of his social system" (Rogers,
1962, p. 20). An individual's innovativeness score is the
total Of his responses regarding time Of first use of ten
agricultural innovations investigated in the present research.

Fifteen independent variables were selected as possible
correlates of innovativeness. Two measures Of each in-
dependent variable are used to predict individuals'
innovativeness; one involves individual-level measurement
of variables based on the communication, social, and psycho-
logical behavior of the individual; the other involves
system-level measurement of the same independent variables
which are meant to represent the characteristics of the
systems. The former are termed individual variables, the
latter as system variables. The system-level measures are
designated as the norms of the systems and are computed as
the central tendency for each system on the individual-level
measures. Accordingly, every individual in a social system
is assigned the same score for the system variables, but

these scores differ from system to system depending upon the

52

53

central tendency of these systems on individual-level
measures. The relationship between the independent variables
and the dependent variable will be examined in this chapter.
It should be noted that while stating the problem of
this thesis, we assumed that the eight social systems under
considerations are marked by different norms which will
exert varying amounts of influence on the individual's
innovativeness.* It is on the basis of this assumption that

we eXpect system effects on the individual's innovativeness.

Objective 1
Presence of System Effects

Our first Specific objective is to ascertain the degree
to which system variables affect the innovativeness of
individual members of a system. Evidence bearing on this
objective is develOped by means of a series of zero-order
correlations.

The correlation coefficients between 15 independent
variables and the dependent variable are presented in
Table 1. These coefficients are Pearsonian product-moment
correlations, which measure the association between two
variables. Inspection of Table 1 indicates that the zero-
order correlations of all individual variables with
innovativeness are significantly different from zero at the

5 percent level except those with credit orientation and of

 

*There is variation in the aggregate means on our eight
independent variables, although the variance is quite
restricted in the case of education.

54

deferred gratification. Similarly, the zero-order correla-
tions of all system variables with innovativeness are
significantly different from zero at the 5 percent level
except that with caste rank.

In general, both individual and system variables are
related to innovativeness as shown in Table 1. In the case
of mass media exposure, secular orientation and social
participation, the t-values for the differences between the
correlations Of individual variables and of system variables
with innovativeness are not significant at the 5 percent
level. Thus, the individual and system measures of these
three variables are about equally related to innovativeness.
The system-level measures of value of agricultural products,
credit orientation, urban pull, educational aspiration,
deferred gratification, and empathy explain more var-
iability in innovativeness than their individual-level
measures.* However, individual measures of such variables
as education, urban contact, extension contact, level of
living, political knowledge, and caste rank explain more
variance in innovativeness than the corresponding system-

level measures.**

 

*The t values for all of these six variables are
significant at the 5 percent level.

**The t values for all of these six variables are
significant at the 5 percent level.

55

The pattern of significant, zero-order correlations
of independent variables with innovativeness suggests a
three-fold categorization of independent variables:
1. Those whose individual and system levels are both
related to innovativeness, i.e., all except the

three named below (in #2 and #3).

2. Those whose individual levels are so related
i.e., caste rank.

3. Those whose system levels are so related, i.e.,
credit orientation and deferred gratification.

Objective 2

System Effects Beyond Individual Effects

Our second objective is to determine the extent to
which system independent variables affect individual
innovativeness when the effects of individual independent
variables are controlled.

Partial correlation: We expect, within the second
objective, that system effects make a unique contribution
to explaining individual innovativeness. This notion is
examined by comparing the first-order partial correlations
with the zero-order correlations. Such comparisons indi-
cate the extent to which each independent system variable
exerts influence on the dependent variable, independent of
the correSponding individual variable. The partial cor—
relations are given in Column 5 of Table l. The difference
between Columns 3 and 5 indicates how much of the relation-
Ship>between each system independent variable and the

dependent variable is due to the influence of variance in

56

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57

the individual independent variable.

Obviously, if the control variable (whose effect is
partialled out) is unrelated to the two variables being
correlated, the partial correlation will equal the zero-
order correlations; if either of these two variables is
negatively correlated with the control variable but
positively related to the other variable, partialling out
will raise the zero-order correlation.

After partialling out individual-level effects, all
partial correlations are significantly different from zero
except those with systemic urban contact and caste rank.

In general, the partial correlations of the systemic
independent variables with innovativeness, controlling on
the corresponding individual independent variables, support
the notion of a unique contribution of system effects.

Multiple Correlation: We also expect, within the second

 

objective, that more variance in individual innovativeness
can be explained by simultaneous consideration of individual
and system variables than by considering only individual
variables alone.

Multiple correlations of the individual and systemic
measures for each of the 15 variables were computed.*
These multiple correlations are presented in Column 6 of

'Table l. A comparison of the multiple correlation with

*In every instance, these multiple correlations were
Irun.with two independent variables, the individual and
SPatenmeasures of the same concept. They are not to be
(Nanfused with the eight-variable multiple correlations
to be discussed later.

58

the correSponding zero-order correlation of the individual
and systemic variables with the dependent variable (Columns
2 and 3) indicates that the multiple correlation of each
independent variable is larger than the zero-order correla-
tion of either its individual or system variable. Thus,
more variance in the dependent variable can be explained by
considering simultaneously both the individual and system
level measures of each variable. This effect occurs because
the individual-level effects are not entirely independent

of system effects.

Even clearer evidence of system effects beyond
individual effects is provided when several independent
variables (both individual and system variables) are combined
in the multiple correlation. The selection of variables
for the multiple correlational analysis was necessarily
limited to eight variables.* While selecting these
variables, two main criteria were considered: only those
independent variables (both individual and system) were
included (1) that are highly related with the dependent
variable and less related among themselves on the basis of

their zero-order correlations, and (2) whose highest-order

 

*The restriction on the number of system variables that
can go in a multiple correlation equation was guided by the
number of systems under study. Since we studied only eight
Villages, there could be only eight meaningful observations
and.hence the number of variables could not exceed eight.
Mathematically, if more than eight system variables are used,
the system will be over-defined. We therefore selected 8 Of
“the 15 independent variables and concentrated mostly on
'these variables throughout the study. These variables are:
Education, value of agricultural products sold, credit
<Irientation, urban pull, deferred gratification, extension
Contact, level of living, and mass media exposure.

59

partial correlations (between each of the 15 independent
variables and innovativeness, controlling on the other

14 independent variables) were higher. The results of the
multiple correlational analyses are presented in Table 2.

TABLE 2. Multiple Correlations of the Eight Individual-
Level and System-Level Variables with

 

 

Innovativeness
Inde endent ariabl 8 Multiple Percent Variance
p V e Correlation EXplained in
Innovativeness
I Individual variables .69 48
II System variables .64 41
III Combined Individual
and system variables .79 62

The eight individual variables included in the multiple
correlational analysis account for 48 percent of the
variance in innovativeness (r = .69). Similarly, the
system-level measures of the same eight variables explain
41 percent of the variance (r = .64). But combining both
individual and system-level measures of the eight variables
in a multiple correlational analysis accounts for 62 percent
of the variance (r = .79). Thus, simultaneous consideration
of 222g individual and system variables is marked by an
increase in explained variance of 14 and 21 percent
reSpectively over that explained by individual and system

variables.

6O

Linearipy_of Relationshgps

Two major factors that affect a multiple correlation
are (l) linearity of relationships between the dependent
variables and the independent variables, and (2) the inter-
action of the dependent variable with various levels of the
independent variables. The linearity of relationships be-
tween the dependent variable and the independent variables
was tested by a procedure that provides a rough estimation.
This procedure calls for the comparison of the squares of
zero-order correlations of each independent variable with
its eta2.* If the difference between the zero-order cor-
relation and eta is small, a linear relationship may be
assumed. Table 3 shows the results of such comparisons.

Perusal of Table 3 reveals that the relationship of all
the individual variables with the dependent variable may
safely be assumed to be linear except for credit orienta-
tion and deferred gratification. Those two variables show
no significant relationship with innovativeness insofar as
their zero-order correlations are concerned.

Contrarily, all the system variables seem to have curvi-
linear relationships with the dependent variable.

*Although there is an appropriate statistical procedure
to test linearity, for the reason of simplicity we used
this method of comparison. The end-products of the two
methods are comparable.

61

TABLE 3. Testing for the Linearity of Relationships Between
the Dependent Variable and the Independent
Variables.

 

Independent Variables r2** eta2 Conclusion

 

I Individual Variables

1. Education .13 .13 Linear
2. Value Agricultural
Product .15 .16 Linear
3. Credit Orientation .02* .02* No Relation-
ship
4. Urban Pull .03 .03 Linear
5. Deferred Gratification .OO* .02* No Relationship
6. Extension Contact .25 .27 Linear
7. Level of Living .35 .37 Linear
8. Mass Media Index .25 .26 Linear

II System.Variables

9. Education .06 .40 Curvilinear
10. Value of Agricultural

Product .30 .41 Curvilinear
11. Credit Orientation .17 .40 Curvilinear
12. Urban Pull .30 .41 Curvilinear
13. Deferred Gratification .32 .41 Curvilinear
14. Extension Contact .18 .41 Curvilinear
15. Level of Living .29 .41 Curvilinear
16. Mass Media Index .21 .41 Curvilinear

*Not significant at the 5 percent level

**These correlation coefficients are computed by regarding
the independent variable as a category variable (rather than
as a continuous variable), and hence they are slightly
different from those reported in Table l.

62

TABLE 4: Mean Levels of Innovativeness and Independent
System Variables by Villages.

 

Poss—
Variables ible V1* V2 V3 V4 V5 V6 V7 V8
range
Innovative-
ness 1-10 6.5 7.3 6.7 3.2 2.6 4.1 3.2 4.3
Education 0-3 1.3 1.8 1.7 0.9 1.6 1.1 1.3 1.3
Value of Agri-
cultural
products 0-20 9.4 10.9 7.6 2.5 3.2 1.7 1.2 1.9
Credit

Orientation 0—2 1.4 1.6 1.4 1.0 0.9 1.5 1.5 1.1
Urban Pull 0-2 0.7 .1.0 ,0.8 .0.4 -0.2‘;0.7 .0.3 "0.2
Deferred

Gratifica-
tion 0-9 3.2 3.5 2.7 4.1 4.4 4.2 3.7 4.0

Extension

Contact 0-10 5.3 6.0 5.3 3.5 3.3 0.9 0.9 3.0

Level of
Living 0-16 10.1 11.6 9.4 4.2 6.4 6.1 8.1 7.6

Mass Media
Exposure 0-8 4.7 5.5 5.3 3.3 3.7 2.3 2.2 2.7

 

*V stands for village. The selected villages are:
Manchili (V1), Kanchumarru (V2), Polamuru (V3), all in
Andhra Pradesh; POphali (V4), and Mulawa (V5), in
Maharashtra; Amdole (V6), Laxmi-Danga (V8), and Harishpur
(V7), in West Bengal.

From.Tab1e 4 it appears that the relationships of all
the independent system variables with innovativeness is
non-linear with a "take-off" occurring at different points

depending on the variable in question. There is one

63
exception, however, with respect to education.*
Objective 3

System Effects and Interaction Among Variables

 

Our third objective is to understand the way in which
system variables affect individual innovativeness under
Specified situations when interactions among independent
variables are controlled. This objective is examined by
means of sequential analysis and two-way analysis of
variance.

Sequential Interaction Analysis: In an attempt to
determine the conjunctive effects of the individual and
system variables upon the dependent variable, the data
were analyzed, using the technique of sequential inter-
action analysis (Sonquist and Morgan, 1964). This technique
provides a configuration of variables organized in such a
way as to demonstrate how variables combine to maximally
eXplain variation in the dependent variable. In addition,
it is the only multivariate analysis that does not impose
the assumption Of additivity (linearity) and is free of
confounding interaction effects. Further, it allows for

more than one stage in the causal process, i.e., a set of

 

*The range of variation in mean educational levels for
the eight systems under consideration (Table 4) is very
restricted. Further, these levels of education are far
less than the "take-off" point found by Tumin and Feldman
(1956) in Puerto Rico and Briones and Waisanen (1966) in
Chile (at about five years of schooling).

64

variables is introduced first, and whatever variation they
do not explain is analyzed against a second set of variables.

Three such analyses were done. In the first run only
individual variables were subjected to configurational
analysis (Figure 7). In the second run only system variables
were included (Figure 8). The final analysis (Figure 9)
combined the individual and system variables to provide
a configuration that will eXplain maximum variation in the
dependent variable and will also demonstrate the inter-
action between the individual and system variables.

It is apparent from Figure 7 that the sample initially
splits on the value of agricultural products (VAP) and
secondarily on the level of living (LOL) in attempting to
eXplain variation in individual's innovativeness with the
help of individual variables only. At each Split, the
higher levels of the independent variable are associated with
higher level of innovativeness. This result indicates that
the greater the VAP and the higher LOL of an individual,
the greater is his innovativeness. Among those who had
high VAP but low LOL, however, credit orientation (CR) was
another strong indicator of innovativeness. Similarly,
among those who had lOW'VAP but high LOL, extension contact
(EXT) was a strong predictor of innovativeness.

Among the system variables (Figure 8), the first Split
again occurs on the VAP. For those with higher VAP, no
further splitting occurs, but the second Split of lower VAP

produces a further split on VAP. The third split concerns

65

 

 

 

 

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68

only the higher VAP'S from Split 2, this time on deferred
gratification (DF). Thus, in systems with high value of
agricultural products, the higher the VAP, the higher the
innovativeness. However, among systems with low VAP, the
lower the VAP and the higher the DF, the higher the
innovativeness.

Why are only two System variables important as
explanatory variables? Unlike multiple correlational
analysis, the interaction-detecting process is, in most
cases, not affected by intercorrelations among the independ-
ent variables. The process operates sequentially and uses
only the predictor which is the most powerful, and drOps the
others. Only two system variables, namely VAP and education
of the system (ED), enter into the configuration because
they are highly correlated with the remaining system
variables and thus account for most of their variance.

When pgpp individual and system variables are considered
(Figure 9), the sample initially splits on the system
variable VAP in attempting to explain variation in individ-
uals' innovativeness. In general, the greater the VAP of
the system, the greater the innovativeness of the individuals.
‘Further conjunctive effects of the individual and system
variables are presented as follows.

1. Besides the value of agricultural product of the
system, the individual value of agricultural products is
also associated with innovativeness. Thus, the greater the

“VAP'Of the individual and also of the system, the greater

69

the innovativeness of the individual. Among the individuals
with lower VAP who live in the system of higher VAP, those
with frequent contact with extension personnel and higher
mass media eXposure (MM) are more innovative than their
counterparts.

2. Among the individuals who live in a system
characterized by lower VAP. those with a higher level of
living are more innovative than individuals with a low LOL.
Further, among the higher level of living group, either of
two variables is associated with relatively higher innova-
tiveness: a higher score on individual extension contact,
or a lower educational attainment of the system.

3. In terms of the differences in the patterns of
configuration in Figure 9, compared with those in Figure 7
or 8, it is apparent that not much interaction with System
variables is detected for high VAP'S after the first Split
of the sample on the value of agricultural product of the
system. But for lower VAP'S, interaction with systemic
education is prominent. Individual levels of living become
irrelevant for higher VAP'S which is the most discriminating
attribute for lOW’VAP'S. At either level (individual or
system), education is not an important discriminator, but
when both individual and system variables are involved,
education becomes one of the best explanatory variables.

In addition to demonstrating the manner in which
individual and system variables interact, sequential inter-

action analysis allows us to formulate typologies of

70

individuals such that people are highly homogeneous within
a typology and highly heterogeneous between typologies.
Any gain in homogeneity within and heterogeneity between
will yield imporvement in predicting innovativeness. Our
assumption is that inclusion of the system variables
allowsus to formulate more homogeneous typologies and
increase the accuracy of prediction. The findings are
reported in Table 5.

From Table 5, it can be seen that inclusion of system
variables provides, with no exception, a greater range of
means and a much reduced standard deviation around the
means for all the typologies of the individuals than when
either of the two sets of variables (individual or system
variables) are considered separately. When system variables
are included, the mean value of innovativeness between the
typologies ranges from 2.25 to 7.96 and the standard devia-
tion around these mean values ranges from 1.28 to 1.91
within these typologies. Considering the typologies based
on either the individual variables or the system variables,
the mean values are quite narrow (1.95 to 7.21 for individ-
ual variables, and 2.80 to 6.69 for system variables) with
a wide range of standard deviations (1.74 to 2.33 for
individual variables and 1.64 to 2.36 for system variables).

From Figure 9 three possible typologies emerge. Those
who are generally most innovative (X = 6.69, N = 210) are
typified by a high value of agricultural products (N=57),

or by a low value of agricultural products, with a high mass

71

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72

media eXposure and frequent contact with extension personnel
(N=42). Of these 210 respondents in T1 (Table 5) with high
VAP, about half are low on the three aforementioned variables
but are still innovative because of the systemic influences
on them. This description generally fits the four subgroups
highest on systemic VAP (found in the system with a high
value of agricultural products).

A second type (T2) are those who are moderately innovative
on the average, (X=4.22, N=258). These reSpondents are
found in the system that has a low value of agricultural
product and more than 90 percent of them have low systemic
education. These respondents are typified by a high level
of living. Only one-fourth of them have high or moderate
extension contact.

The third type (T3) is the least innovative group on the
average, (X=2.25, N=212). These reSpondents have a low level
of living and are found in a system that has a low value of
agricultural products.

Still another purpose of using sequential interaction
analysis is to demonstrate the degree of increase in pre—
diction by simultaneous consideration of both individual and
system variables over and above the variance in innovativeness
explained when either individual or system variables are
considered alone. Such a simultaneous consideration results
in explaining 65 percent of the variance in innovativeness,
and is marked by an increase of 10 and 25 percent more
variance Over that eXplained by individual and system variables,

reSpectively.

73

Two-Way Analysis of Variance: One of the concerns of the
present thesis was to view the individual in the context of
the system of which he is a member. A two-way analysis of
variance is utilized to accomplish this objective wherein
differences between rows represent differences in the
characteristics of the individual, while differences between
columns correspond to the differences in the characteristics
of the system. This technique is considered apprOpriate
(l) to determine whether differences among individual and
system characteristics and interaction of the two are Signi-
ficant, and if so, (2) to determine the conditions under which
individual variables will predominate over systemic variables
and viceversa.

For an analysis within a balance theory framework, the
dichotomization of individual and system variables into high
and low levels was used to construct four typologies of
farmers. High levels for either farmers or social systems
were considered to represent "modern" and low levels to sug-
gest "traditional". These typologies are: (1) traditional
individuals living in a traditional system, (2) traditional
individuals living in a modern System. (3) modern
individuals living in a traditional system, and (4) modern
individuals living in a modern system.

Within each of these typologies, three types of pressures
on individuals are assumed to operate. One is internal social
system pressure to innovate or to resist change, depending

on the norms of the system. The second is an individual,

74

psychological pressure to innovate or reject innovations,
depending on whether the individual is modern or traditional.

The third is pressure to innovate from a source external to

 

the system, e.g., from a change agent.

These typologies and balance theory considerations led
us to anticipate that the innovativeness of individuals
would be higher when both the individual and system are
modern than when both of them are traditional (X of Cell 4 7'
X of Cell I). This notion is examined by a comparison of
means in Tables 6-13.

Tables 6-13 show the means for the four conditions on
innovativeness, the F—value for differences between the means
and their interaction. In general, we find that differences
across the individual level variable are significant for all
eight variables except credit orientation and urban pull.
Education is the only variable for which differences across
the system level variables are not Significant. The inter-
action between the individual and system level variables is
significant for all the eight variables except for the value
of agricultural product and urban pull.

The data provide support for the anticipated relation-
ship. The t-value for the difference between means in Cells
I and 4 is significant at the 5 percent level for all eight
variables. AS mentioned in Chapter I, both situations are
balanced in that the overt behavior of the individuals
demanded by the system is in line with their individual

behavior on the same variable. Innovativeness of Cell 4

75

TABLE 6. Mean Innovativeness Scores Across Individual and
System Measures of Education.

 

Individual's Systemic Education

 

 

 

Education Low High
Low 1 _ _ 2 t Value for Difference
X = 3.73 X = 3.32 Between Cell X'S
(N = 267) (N = 119) 1. 2 = 1.53
3: n = é-19
_ .... 2, = . *
High X = 5019 X = 5053 1’ g = 6.:23él'
(N = 135) (N = 159) 2, 4 = 6.57*
3 4 1 4 = 7.36*

O
F Value Across Individual
Level Variable (I) =l58.81*
F Value Across System

Level Variable (S) = 0.23
F Value for Inter-
action (IxS) = 70.20*

v- cm—wo-c-c-v

*Significant at the 5 percent level.

TABLE 7. Mean Innovativeness Scores Across Individual and
System Measures of the Value of Agricultural
Products Sold.

 

Individual's Systemic Value
Value of Agr. of Agr. Product

 

 

 

 

PrOdUCt LOW High
Low 1 2 t Value for Difference
' ‘ Between Cell X'S
x = 3.05 x = 6.05 1’ 2 = 13.79*
(N = 401) (N = 101) 1, 3 = 7,02%
2. 3 = 3-55*
_ 30 u = 7065*
High X = 4.94 X = 7.28 2, 4 = 5.24*
(N = 69) (N = 109) 1, u = 20.1h*
3 4 F Value Across Individual

Level Variable(I) = 244.35*
F Value Across System
Level Variable(S) = 71.87*
F Value for Inter-

action (I x S) = 2.99

* Significant at the 5 percent level.

76

 

 

 

 

TABLE 8. Mean Innovativeness Scores Across Individual and
System Measures of Credit Orientation.
Individual's Systemic Credit
Credit Orientation
Orientation Low High
LOW 1 X = 2.87 = 5.72 2 t Value for Difference
(N = 162) (N = 98) Between Cell X'S
1. 2 = 9.74*
- — 39 4 = 8.17*
High X = 3.36 X = 5.30 2. 3 = 7.77*
(N = 146) (N = 274) l, 3 = 1.87
3 Ll. 2. Ll’ = 1054
l, 4 =10.71*
F Value Across Individual
Level Variable (I) = .0.07
F Value Across System
Level Variable (S) = 154.15*

F Value for Inter-

action (I x 8)

*Significant at the 5 percent level.

= 5.87%

 

 

 

 

TABLE 9. Mean Innovativeness Scores Across Individual and
System Measures of Urban Pull.
Individual's Systemic Urban Pull
Urban PUll LOW High
L l 2 t Value for Difference
ow X _ 3 10 X = 5 67 Between Cell X'S
‘ ‘ ' 1, 2 = 12.48*
(N = 312) (N = 193) 3’ 4 = 2.84*
_ 2, 3 = .78*
High x = 3.18 X = 6,1 1’ 3 = 0.25
(N = 55) (N = 120) 2, 4 = 1.70
3 4 l, 4 = 13.46*
F Value Across Individual
Level Variable (I) = 2.19
F Value Across System
Level Variable (S) = 227.83*
F Value for Inter-
action (I x S) = 0.99

 

*Significant at the 5 percent level.

77

 

 

 

 

 

TABLE 10. Mean Innovativeness Scores Across Individual and
System Measures of Deferred Gratification.
Individual's Systemic Deferred
Deferred Gratification
Gratification
Low High
Low 1 _ _ 2 t Value for Difference
X ~ 5.92 X = 2.69 Between Cell'X'S
(N - 117) (N = 81) 1, 2 = 10.82*
3. 4 = 10.53*
_ _ 2, 3 = 10.29*
X = 5.91 X = 3.52 1, 3 = 0.03
(N = 152) (N = 330) 2, 4 = 3.01*
3 4 1, 4 = 9.98*

F Value Across Individual
Level Variables (I)=50.62*
F Value Across System

Level Variables(S) = 4.18*
F Value for Inter-
action (I x S) = 4.53*

 

*Significant at the 5 percent level.

 

 

 

 

TABLE 11. Mean Innovativeness Scores Across Individual and
System Measures of Extension Contact.
Individual's Systemic Extension Contact
Extens n
Contatg LOW H18“
Low 1 2 t Value for Difference
X = 2.99 X = 4.58 Between Cell X'S
(N = 292) - (N = 151) 1, 2 = 7.30}
39 11' = 5022*
—- 2’ 3 = 0.81
i = 4.85 x = 6.47 1. 3 = 7.40*
High (N = 78) (N = 159) 2, a = 6.83*
3 4 l, 4 =16.96*
F Value Across Individual
Level Variable (I) = 56.68*
F Value Across System
Level Variable (S) = 77.59*

F Value for Inter-

action (I x S)

= 24.15*

 

*Significant at the 5 percent level.

78

TABLE 12. Mean Innovativeness Scores Across Individual and
System Measures of Level of Living.

 

Individual's Systemicfigevel of Living

 

 

 

ngsinzf Low High
Low 1 _ _ 2 t Value for Difference
x = 2.47 x = 4.73 Between Cell X'S
(N = 235) (N = 120) 1. 2 = 9.80*
39 u = 5083*
_ _ 2a 3 = 0029
X = 4.65 X = 6.12 i. a = g.§g*
Hi h N = 114 N = 211 4 . = . *
s 3 ( ) ( ) 1’ u =18.25*

F Value Across Individual
Level Variable (I)=121.28*
F Value Across System
Level Variable (S)= 82.68*
F Value for Inter-

action (I x S) = 32.43*

 

*Significant at the 5 percent level.

TABLE 13. Mean Innovativeness Scores Across Individual and
System Measures of Mass Media EXposure.

 

 

 

 

 

Individual's Systemic Mass
Mass Media Media Exposure
EXposure Low High
Low 1 _ - 2 t Value for Difference
X = 3.28 X = 3.28 Between Cell X'S
(N = 215) (N = 127) 1, 2 = 00
3. 4 = 5.65*
- - 2. 3 = 3.69*
x = 4.44 X = 5.96 1, 3 = 5.08*
Iiigh (N = 110) (N = 228) 2. 4 = 9.47*
3 4 1. 4 =12.75*

F Value Across Individual
Level Variable(I) = 15.99*
F Value Across System
Level Variable(S) =112.88*
F Value for Inter-

action (I x S) = 16.78*

 

 

*Significant at the 5 percent level.

79

is greater than that of Cell 1 because the individuals in
Cell 4 have both internal and external pressures to
maintain changed condition while the individuals in Cell 1
are balanced until the external pressure to change is

felt. However, one would eXpect the greatest rate of
change over time in Cell 1, provided the external pressure
to change is strong and enduring and is felt by the members
of Cell 1.

Within the third objective, we also expected that
traditional individuals living in a modern system would be
more innovative than modern individuals living in a tradi-
tional system (X of Cell 2??? of Cell 3). This expectation
is also supported by the data for all eight variables
except education and mass media.* In general, individuals
in Cell 2 are more innovative than those in Cell 3. This
is understandable because individuals in Cell 2 are
eXperiencing a double pressure to move to Cell 4; one from
the majority of the members of the social system (Cell 4)
to move to Cell 4 (internal pressure), and the other from
outside the social system (such as from change agents) is
also being brought to bear upon the minority members in
<3e11 2 to move to Cell 4 (external pressure). Cell 3
Hmnnbers, on the other hand, have already accepted some change

azni eXperience pressure from the external sources to

*In the case of these exceptions, either the differences
amOrig individuals or among systems are not significant, i.e.,
EB range of variation is restricted.

80

maintain the changed condition. They will continue to accept
more change, but probably at a slower rate than members of
Cell 2 .

Finally, we also expected that individual innovativeness
would be affected more by system than by individual variable
(3: of Cell 2 — 1>3 - 1, and 4-3>4 - 2). . Again, this
eXpectation is supported by all eight variables in the
analysis except education and mass media eXposure. System
effects seem to make a bigger difference than differences in
individual levels of the variable. Emerging from this
hypothesis is another proposition which suggests that even
if an individual is low on a variable but lives in a system
that is high on that variable, he will be more innovative
than someone who is high on the variable but lives in a
system that is low on that variable, given that the variable

under consideration is related to innovativeness.

CHAPTER V
SUMMARY AND DISCUSSION
Summary

The present study focussed on the simultaneous and
systematic consideration of individual variables and
system variables in accounting for more variance in
individual innovativeness than when either individual or
system variables are considered alone. Individual
variables were related to communication, social and

psychological behavior of the individual. System variables

were the aggregate measures of individual variables for
each system.

Innovativeness in the present study was defined as
"the degree to which an individual is relatively earlier
in adopting new ideas than the other members of his social
system" (Rogers, 1962, p. 20). We operationalized
innovativeness as having ever used (or tried) an innovation

regardless of when it was adopted and whether its use was
(mantinued. As such, an individual's innovativeness score
153 the total score based on his reSponse regarding all ten
aagricultural innovations investigated in the present re-

Semarch. The scalability of the ten innovations was deter-

mined by Guttman scaling and factor analysis.

81

82

The social systems in the study were eight Indian
villages from the states of Maharashtra, Andhra Pradesh,
and West Bengal.hfl1flfixlcertain restrictions, these villages
were selected randomly to represent the range in village
modernization. The sample of 680 farmers was also drawn
randomly.

The major objectives of the study were threefold:

(1) To ascertain the degree to which system
variables affect the innovativeness of individual members
of a system.

(2) To determine the extent to which system
independent variables affect individual innovativeness when
the effects of individual independent variables are
controlled.

(3) To understand the way in which system variables
affect individual innovativeness under specified situa-
tions when interactions among the independent variables
are controlled.

In order to achieve these objectives, we raised a
series of logical questions. These questions are:

(1) Are there system effects? Do the properties of
Exystems affect individual innovativeness? How much
irrfluence does the system exert over the individual's
behavior?

(2) Are there system effects beyond individual
ef‘It‘ects (differences among individuals)? What is the

nature of the relationship of individual and system

83

variables with innovativeness and with each other? Can
these relationships of system effects on innovativeness
be regarded as linear?

(3) Does the strength of system effects vary with
different combinations and levels of independent system
variables and independent individual variables? Do system

effects tend to predominate in these combinations?

Presence of System Effects

System effects are the influence of systemic structure

 

and/or composition on the behavior of the members of a
social system. One of the objectives of this study was
to "ascertain the degree to which system variables affect
the innovativeness of individual members of a system."
Our analysis produced correlations of 14 of the 15 system
variables with innovativeness which were significantly
different from zero at the 5 percent level. Further
analysis indicated that correlations of all but two of the
15 individual variables were significantly different from
zero at the 5 percent level. The exceptions were credit
orientation and deferred gratification.

In the case of mass media exposure, secular orientation,
azui social participation, the "t" values for the difference
between the correlations of an individual independent
variable and a system independent variable, respectively,
with innovativeness were not significant, and hence the

1ru11vidua1 and system level measures of these variables

84

were equally good predictors of individual innovativeness.
In most cases the "t" values for the difference between the
correlations of individual independent variables and the
system independent variables were significant; in six
cases system variables predict innovativeness better, and
in another six, individual variables predict better.

The pattern of significant, zero-order correlations
of independent variables with innovativeness suggests a
threefold categorization of independent variables:

1. Those 12 variables whose individual and system
levels are both related to innovativeness, i.e., all
except the three named below (in #2 and #3).

2. Those whose individual but not system levels are
so related, i.e., caste rank.

3. Those whose system but not individual levels are
so related, i.e., credit orientation and deferred
gratification.

We conclude, on the basis of the zero-order cor-
relations, that the system-level variables are related to

innovativeness.

gaystem Effects Beyond Individual Effects

Our second objective was "to determine the extent to
Rfllich system independent variables affect individual
1runovativeness when the effects of individual independent
Variables are controlled." Within this objective we had

exIbected that system effects make a unique contribution

85

beyond individual effects in explaining innovativeness. In
general, the partial correlations of the system variables
(when the individual independent variables are controlled)
showed a significant relationship with innovativeness.

We also had eXpected, within the second objective, that
more variance in individual innovativeness would be
explained by simultaneous consideration of both individual
and system independent variables than by considering only
individual-level variables. Multiple correlations of
innovativeness with both individual and system measures of
the same attribute were run. A comparison of these
multiple correlations with the zero-order correlations of
system variables with innovativeness indicated that the
former were larger for all 15 variables.

Even clearer evidence of system effects beyond
individual effects was provided when eight independent
variables, both individual and system measures, were
combined in a series of multiple correlations. A multiple

correlation of individual measures resulted in explaining

 

48 percent of the variance in innovativeness. The equation

involving system level measures explained 41 percent of

the variance in innovativeness. But computing bgt_h

1rniividual and system-level measures in a multiple cor-

relational equation accounted for 62 percent of the variance.
Our eXpectation was that the relationship between

1n<iividual and system variables with innovativeness could

be regarded as linear without sacrificing much of the

86

explanatory power of the correlation coefficients. The
results showed that the relationship of all the individual
variables with innovativeness may safely be assumed to be
linear except for credit orientation and deferred gratifica-
tion, for which there was no significant relationship inso-
far as the zero-order correlations were concerned.
Contrarily, all the system variables were found to be

curvilinearly related with innovativeness.

System Effects and Interaction Among Variables

Our third objective was "to understand the way in which
system variables affect individual innovativeness under
specified situations when interactions among independent
variables are controlled." This objective was examined by means
of sequential interaction analysis and two-way analysis of
variance.

Three sequential interaction analyses were performed.

In the first, only individual variables were subjected to

 

configurational analysis in order to predict innovativeness.
In general, the greater the value of agricultural product
sold, and the higher the level of living of an individual,
the greater his innovativeness.
The second run was concerned only with system variables

111 predicting innovativeness. We found that the greater
the value of agricultural products sold in the system, the
SIVBater the innovativeness of the individual. However, for

those systems with a lower value of agricultural products

87

sold, deferred gratification tended to discriminate the
individual's innovativeness.

In the third run,lbg£h individual and system variables
were included in the configurational analysis. The sample
initially split on the systemic value of agricultural
products sold, followed by the individual value of
agricultural products sold, indicating that higher innova-
tiveness is associated with both individual and systemic
value of agricultural products sold. Further, among the
individuals with low value of agricultural products but
who live in a system that has a high value of agricultural
products sold, those with frequent contact with extension
personnel and high mass media eXposure were more innovative
than their counterparts.

Similarly, among the individuals who lived in a system
characterized by a low value of agricultural products sold,
those with a high level of living were more innovative.
Further, among the higher level of living category, high
innovativeness was associated with a high score on extension
contact or a low educational attainment of the system.

Thus, sequential interaction analysis provided us with
a configuration of variables organized in such a way as to
demonstrate how variables combine to maximally explain
variation in innovativeness. We were thus able to
characterize three typologies of innovativeness: (l) the
generally mggt innovative; (2) the moderately innovative;

and (3) the least innovative respondents.

88

Another finding, and perhaps the most important, from
the simultaneous consideration of both individual and system
variables in sequential interaction analysis was that the con-
figurations so obtained were more homogeneous than those con-
figurations obtained by considering only individual or system
variables alone. Such simultaneous consideration provided a
greater range of means across typologies and a much reduced
'standardidEViation withinythe‘typologies than when either.
individual or system variables were considered separately.

Interaction effects between the individual and system
variables were also found to be significant in most cases
when the data were analyzed by a two-way analysis of
variance with system and individual variables dichotomized
into high and low categories. The analysis showed that
differences across individual level variables were signi-
ficant for all the variables except credit orientation and
urban pull. Education was the only variable for which
differences across system level variables were not signi-
ficant. The interaction between the individual and system
variables was significant for all the variables except for
the value of agricultural products sold and urban pull.

For an analysis within a balance theory framework, the
dichotomization of individual and system variables into
high and low levels were used to construct four typologies
of farmers. High levels of either farmers or social systems
were considered to represent modern and low levels to

suggest traditional. Within each of these typologies,

 

89

three types of pressure on individuals were assumed to be
operative. One was internal social system pressures to
innovate or to resist change, depending on the norms of the
system. The second was an individual, psychological
pressure to innovate or to reject innovations, depending
on whether the individual was "modern" or "traditional."
The third was pressure to innovate from a source external
to the system, e.g., from a professional change agent.
Four typologies of farmers were conceived as:
I Modern individuals living in a modern system.
II Modern individuals living in a traditional sygtem.
III Traditional individuals living in a modern system.

IV Traditional individuals living in a traditional
system.

These typologies and balance theory considerations led
us to anticipate that the innovativeness of individuals
would be higher when both the individual and system are
modern than when both of them are traditional. Our data
provided support for the anticipated relationship.

Another exception was that traditional individuals liv-
ing in a modern system would be more innovative than
modern individuals living in a traditional system. This
expectation was supported by the data for all eight variables
except education and mass media.

Still another eXpectation was that individual innovative-

ness would be affected more by system than by individual

90

variables. Again, this eXpectation was supported by all
eight variables in the analysis except education and mass

media exposure.
Discussion

As mentioned earlier, two types of variables were
conceptualized as possible explanatory variables of an
individual's innovativeness. They are: (1) individual's
variables, which relate to communication, social and
psychological behavior of the individual, and (2) system
variables, which signify systemic norms on individual
variables. These two types are based on the assumption that
the characteristics of the social system, as well as
individual characteristics, facilitate or retard the process
of adoption of new ideas.

Both types of variables, in general, were found equally
effective in predicting an individual's innovativeness. In
some cases, individual variables explained more variance
in individual's innovativeness; in other cases, system
variables are more eXplanatory. This finding accords well
with the finding of van den Ban (1960), who studied the
effects of traditional and modern norms on the innovative-
ness of farmers in Wisconsin townships. He found that
although some characteristics of farmers such as education,
size of farm, etc., were positively related to their
innovativeness, township norms were even better predictors

of farmer innovativeness. A similar finding is reported

91

by Qadir (1966), who analyzed data from the Philippines.

Marsh and Coleman (1954), van den Ban (1960), and
Qadir (1966) recognized the influence of social system norms
on farmer innovativeness. However, none of them focussed
on the simultaneous and systematic consideration of bgth
individual and system variables in accounting for more
variance in individual innovativeness than that possible
by considering either individual or system variables algae.
Both Rogers (1961) and Flinn (1961 and 1963) included a
system variable to improve the prediction of innovative-
ness, but their studies lacked two major considerations:
(1) consideration of system variables, i.e., the system
level measures of all independent variables were not
included, and (2) the measure of systemic norm was not an
independent measure, but rather derived from the measure
of innovativeness, their dependent variable.

In the present study we focussed on the simultaneous
and systematic consideration of both individual and system
variables. Our results show that such consideration
results in eXplaining 14 to 21 percent more variance in ;
individual innovativeness than when either individual or =
system variables are considered alone.

Even more variance in innovativeness may be eXplained
by system variables if the nature of the relationships of
these variables is understood. In an attempt to determine
the relationship of independent variables with innovative-

ness, we found that all the individual variables may be

92

assumed to have linear relationships with innovativeness,
while all system variables seem to have a curvilinear
relationship with innovativeness. Putting system variables
in a multiple correlational analysis assumes linearity of
the relationships, and the violation of that assumption
depresses the multiple correlation coefficient. Thus, the
explanatory power of the system variables was reduced and
could be improved by using more sophisticated statistical
tools that do not impose the assumption of linearity.

Not only do the system variables have curvilinear
relationships with innovativeness, they are highly inter-
related among themselves. It is because of their high
interrelatedness that the explanatory power of one system
variable is essentially the same as for any other.*

An examination of the nature of the curvilinearity of
system variables showed that their relationships with
innovativeness can be expressed as an "S" curve, with a
point of "take-off" occurring at different points depending
on the variable in question. In a sense, this take-off
signifies a system phenomenon analogous to the psychological
threshold notion. Possibly, it is at this take-off stage
that a variable becomes a significant social object (norm)
and, as such, generates an atmosphere in favor of
innovativeness. The S“curve held for all system variables

except education. This exception is perhaps explained by

 

*Among the relationships between each of the system
variable and individual innovativeness, etaZ varies only
from .40 to .41.

93

the fact (1) that the eight social systems under investiga-
tion do not differ much from each other in reSpect to their
level of education, and (2) that their present levels of
education vary from a little less than a year to about two L,
years of schooling. These levels are far lower than the
take-off stage as found in two studies. Tumin and
Feldman (1956) suggest the notion of a "modernization
take-off" occurring at 4 to 6 years of formal schooling
for their Puerto Rican respondents. Briones and Waisanen
(1966) present similar findings from Chilean data.
Discussion of the interrelatedness of system variables
bring us close to introducing the concept of complexity of
system. Any system, especially a social system, is a
configuration of relationships in which the properties of
the system are confounded and are not attributable to one
variable or the other, but are really the effects of all
the variables. Selvin and Hagstrom (1963) Criticize the
work of Durkheim, Blau, and Davis for ignoring the inter-
relatedness of system variables. Selvin and Hagstrom use
factor analysis to reduce the dimensions along which each
system may be classified to a manageable number. These
authors also advocate that use of aggressive system variables
reduces errors of reSponse and perception much more
effectively than is possible with individual-level variables.
The result claimed is that fewer factors are needed to des-
cribe a set of systems. I

In our sequential interaction analysis, we obtained

94

many fewer configurations (predicting innovativeness) of
system variables than of individual variables. We also
found that farmers who are generally most innovative are
typically high on value of agricultural products sold,
considered both as an individual and system variable. The
next most innovative configurations are low for the
individual value of agricultural products sold, but high
on individual mass media exposure and frequency of exten-
sion contact, and are found in a system that has a high
value on agricultural products sold. This finding is
supported by Qadir (1966), who concludes that the
composition of more innovative village systems shows a
concentration of individual households with high education,
modern orientation, media contact, material possessions,
and communication facilities, which together generate a
social climate in favor of the adoption of modern ideas.
Our sequential interaction analysis also revealed a
substantial degree of interaction between individual and
system variables. This is conspicuous in the configurational
analysis, especially in the case of less innovative cate-
gories. Interaction effects between the individual and
system variables are also significant in most cases when the
data are analyzed in a two-way analysis of variance design
by dichotomizing system and individual variables into high
and low categories. The results pose an interesting and,
perhaps, theoretically significant concern with the process

by which social systems generate dissonance in individuals.

95

We found that farmers high on both individual and system
variables are high on innovativeness. However, a strong
interaction is evident in the innovativeness of farmers who
are high on one type of variable and low on the other, e.g.,
high on an individual variable but low on a system

variable. In such imbalanced situations, system effects

 

seem to predominate over individual effects, and the

 

dominance_is greater when individual effects are lower.

These findings are in accord with the general conclu-
sion of Qadir (1966), and van den Ban (1960), although
their research designs did not allow a direct test of these
propositions about balanced and imbalanced situations.
Qadir found that in a high innovative system, even individ-
uals lacking much education, mass media exposure, or a
modern orientation acted in an innovative manner. In his
study of a sample of Wisconsin townships, van den Ban also
concluded that a farmer with a high net worth, but residing
l'in a township with traditional norms, adopted fewer farm
innovations than if he were to farm in a township where
the norms were modern.

The present findings regarding the influence of the
social system on an individual's innovativeness agrees with
previous findings relative to system effects but provide
considerably more rigor. Most of the previous findings were
Speculative, not tested systematically, nor supported by
theoretical underpinnings. HOpefully, the present study

augurs the beginning of research designs which

96

simultaneously and systematically consider both individual
and system variables in predicting individual innovative-
ness and provide a rationale for the existence of system

effects.

Implications

Implications for Research

The present study was successful in explaining a rather
substantial amount of the variance in innovativeness of
farmers via a simultaneous, systematic consideration of
both individual and social system characteristics. Further,
it was unique in treating those characteristics in ana—
logous pairs, i.e., an individual measurement and a social
system measurement of a single variable.

We suggest then that this approach holds promise for
future researchers, possibly with other variables or the
same variables more precisely measured. It could well lend
itself, as Kendall and Lazarsfeld (1950) contend, to
avoidance of the "ecological fallacy" (Robinson, 1950),
'psychologistic and sociologistic fallacies" (Riley, 1963),
and "individualistic and group fallacies" (Scheuch, 1965).

This approach appears fruitful for the simultaneous,
systematic study of individual and system variables,
utilizing different social systems or a wider range of
social systems than in the present study. Extending the

scope of inquiry to cross-cultural settings might well

97

contribute to building a more adequate theory of system
effects.*

Additional variables that should be considered include
indicants of the political, economic, religious, institu-
tional development of the systems, and an index of the
amount of interpersonal communication or linkages in the
village (e.g., village size may be one indirect
indicator of interaction and composition of the social
system). All of these variables tend to vary across
systems, and are integral properties of systems. We did
not consider integral properties of the systems, having
only confined our eight system variables to aggregative
properties of the systems. We did so for certain reasons.
One was that we wanted to treat the variables in analogous
pairs (i.e., an individual-level measurement and a social
system-level measurement of the same variable). The other
was that our choice of system variables was limited to only
eight because we studied only eight social systems. We
would have included some system variables representing the
integral property of the system had our sample included
more systems. Our eXpectations, however, are that integral
variables should yield more explained variance or at least
shed more light on the complex interrelationships of systemic
concepts related to individual innovativeness. Further

research might employ criterion other than the village for

 

*A companion study (Davis, 1968) completed in the
Department of Communication, Michigan State University,
indicated that system effects, while present, are not as
strong in the context of Nigerian farmers as in the case
of Indian farmers.

 

98

defining the boundaries of the system (such as small groups
based on sociometric choices or leadership within the
village).

The present study also supports the importance of the
notion of threshold or "triggering" levels of system
variables. In both theoretical and applied fields, we
need answers to such questions as: How many years of school-
ing are necessary before education becomes a social object
(norm) for a social system and tends to bring the system
into a self-propelling stage in educational development?
Tumin and Feldman (1956) and Briones and Waisanen (1966)
suggested a minimum of 4 to 6 years. Does this level hold
generally in India and elsewhere? Lerner (1958) proposed
an Optimum level of 10 percent for his national urbaniza-
tion variable before literacy rates were effected. What is
the optimum level and under what conditions is it optimal
for the many other variables related to innovativeness?
Separate studies are needed to attack these questions.

The balance theory approach utilized in a portion of
the present study was successful within the constraints of
the data and analytic techniques, but was more suggestive
than conclusive. Future efforts might well be addressed to
a more precise measurement and analysis of individual and
system variables treated here dichotomously. They might
thus achieve a wider range of eXplanation and theoretical
generalizability.

Further, in studying the balance notion, we assumed

99

the pressures bearing on the balance-imbalance of a system
were at the ordinal level of measurement at best. (i.e.,
high and low pressure). One pressure, that from external
sources, was assumed to be constant. Our results suggest
that independent system variables tend to dominate in pre-
dicting the dependent variable, rather than individual
variables. Future investigations would do well to examine
the degree of pressures applied and their relative intens-
ity. And in so doing, a wide range of levels of external
pressure should be considered.

Finally, we must add our voice to those recommending
more attention to matters of cause and effect. The present
study was subject to certain methodological limitations.
One concerns the nature of the relationship between the
independent variables and innovativeness. We could not
determine the causal nature of these relationships. We
attempted to overcome most of the problems* for which
Tannenbaum and Bachman (1964) criticized the methodology of
Blau (1957) and Davis and others (1961), in detecting
system effects. But the basic problem of cause and effect
remains untouched by our predominately correlational
analysis. A first step toward a frontal attack on the
problem might fruitfully involve the general approach used

in the present study within a field experimental design to

 

allow manipulation of the dependent and independent variables.

 

*Most of these problems are concerned with the contamina-
tion of individual variables with system variables. A de-
tailed description of these problems was given in Chapter II.

100

Implications for Action

Results of the present research underline the importance
of system effects on individual innovativeness. The results
show direct implications for a change agency desirous of
introducing innovations in villages. These implications
might provide guidelines for change agencies in mapping
strategies for change.

One implication is that a change agency must pay atten-
tion to bgth characteristics of individuals and of social
systems when selecting prime targets of change. The agency
could then decide what programs of change might be best
introduced where and for what type of individuals to yield
maximum returns with minimum input of resources. Thus, a
change agency might better decide where to emphasize agency
contact, where the mass media facilities are most needed,
or what type of individuals most deserve educational facili-
ties. In these allocation decisions, the change agency
should consider system, as well as individual, variables.

Another implication is concerned with the notion of
thresholds. Our results indicate that all system variables
have curvilinear relationships with individual innovative-
ness. Such relationships can be expressed as an "8" curve,
with a point of "take-off" occurring at different points
depending on the variable in question. As such, a social
system may have reached the threshold level in one variable

but not in others. It may, therefore, be necessary to

 

 

lOl

concentrate efforts on those variables that have not yet
reached the threshold level and pay less attention to those
variables that have reached a transitional point. Similarly,
much more effort and inputs may be necessary for certain
communities that are far behind the take-off stage in al-
most all variables.

Still another implication is concerned with an.ig£g-
grated approach to development. By an integrated approach
we mean that the modernization program should be comprehen-
sive in that all related aSpects must be tackled simul-
taneously. Our study indicates that all system variables
are highly interrelated. Their high interrelatedness denotes
that aSpects of modernization are interrelated and that no
lasting results may be achieved if unique aSpects are dealt
with in isolation. This does not mean that particular
problems should not be given prominence, but the plans for
them should be integrated with others.

Finally, while most programs of change reach the more
modern, they may miss that part of the target population
which has the greatest nggd for change. In order to ensure
a continued effective program of change, a change agency
might do well in separating systems (or subgroups within a
system) that are balanced or imbalanced with reSpect to
systemic and individual orientations towards change. In
doing so, it will be relatively facile for the change agency
to decide (1) which systems need more attention, (2) which

systems will change more over time, and (3) which Specific

102

inputs in their individual and system form will be required
for each system. Our results suggest that in imbalanced
situations, system effects seem to predominate. Thus, for
imbalanced situations, systemic inputs, such as educational
facilities for the whole community, may be the best ap-
proach. However, attempt to change a norm of the system
may have unforeseen consequences which should be

considered well in advance.

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Tannenbaum, A. S. and Bachman, J. G. (1964), "Structural
Versus Individual Effects", American Journal of

Sociology, 69:585-595-

 

Tumin, Melvin M. and Feldman, Arnold S. (1956), "Status,
Perspective and Achievement: Education and Class
Structure in Puerto Rico", American Sociological
Review, 21, p. 465.

 

Wilson, A. B. (1959), "Residential Segregation of Social
Classes and Aspirations of High School Boys",
American Sociological Review, 24:836-845.

van den Ban, Anne Willem (1960), "Locality Group Differences
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and the AdOption of Farm Practices", Rural Sociology,
24:372-83.

107

Volkonen, T. (1966), "Individual and Structural Effects
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APPENDIX

16.

17.

18.

109

National Institute of Community Development
Hyderabad -430
Diffusion of Innovations Research Project

PHASE II - QUESTIONNAIRE

STATE A.P. M. W.B. Subject No.
VILLAGE Interviewer
Date of
NAME OF RESPONDENT Interview
Caste Time began
(Fill in without asking if the Time ended

answer is obvious)
Time elapsed
What is your caste
Religion Tribe

Education of the Respondent

 

(Interviewer write actual grade, matriculate, lst year
college, B.A., M.A., etc. If no formal education write
'None'.)

Secular Orientation

When your bullocks become too old and feeble to work
what do you do, sell them or keep them?

0 - Keep them; 1 - DK; 2 - Sell them

Do you think most people would be in favor of keeping up
a goshala in the village to feed old and useless cattle?

Do you feel that non-Hindu and lower-caste Hindus should
be allowed to eat whatever meat they wish?

Value of Agricultural Products
How many acres of land did you cultivate last year?
Acres - " '
(INTERVIEWER: If the number of acres is less than 2.5 then

stop the interview here. If it is 2.5 and above then
go on to the next question.)

l4.

19.

20.

21.

22.

28.

35.

110

What were the main crOps grown in this land this past
year?

A. How many acres ..........did you grow the past year?

B. What was your total production of .......... on this
area?

C. About how much of this ..........did you sell?
What yield did you get on your irrigated, (or having
assured water supply), main paddy crop last year
(1966)?

1 - DK; Mds./Acre 9 - NA

What yield do you get in a normal year on your irrigated
(or having assured water supply) main paddy crOp?

l - DK Mds./Acre 9 - NA
What yield do most other farmers in this area get on

irrigated (or having assured water supply), main paddy
crop in a normal year?

 

1 - DK; Mds./Acre 9 - NA
What do you think is a reall to yield for irrigated
(or having assured water supply), main paddy crop in
this area?

1 - DK; Mds./Acre 9 - NS

Innovativeness

Have you ever tried ........ in 1966?

No Yes
1. High yielding varieties - rice 0 2
2. Jawar, Bazra, Maize 0 2
Have you ever used .........?
l. Fertilizers - Am. sulphate 0 2
2. Superphosphate 0 2
3. Mixtures 0 2
4. Insecticides for plant
protection 0 2
5. Green manure 0 2
6. Cultivator or weeder 0 2
7. Improved breeding of cattle 0 2
8. Animal innoculation 0 2
9. Rat poison 0 2

111

Credit Orientation

 

 

 

41. Did you use credit for farm purposes in 1966?
0 - N0; 1 - DK; 2 - Yes
42. Would you have used (some/some more) had it been avail-
able at reasonable interest?
0 - No; 1 - DK; 2 - Yes
Extension Contact
45. Last year (1966) did you:
Exposure How many times
No DK Yes
Talk with BD0 0 l 2
Talk with VLW 0 l 2

See an agricultural
demonstration 0 l 2

See a block film on
agriculture 0 l 2

Talk with the Block
Doctor 0 1 2

Talk with a family
planning worker 0 1 2

46.

SO.

51.

Deferred Gratification

Suppose that your cash returns from the farm last year
had been twice your actual income, what would you do
with the extra money?

Social Participation

Do you hold any position(s), including membership. in
any formal organizations?

0 - No; l - DK; 2 - Yes
What are they? 0 - NA

OfficeZMembership Organizations

112

52. Where is your home?

Circle 0 - This village; 2 - Another place (Name.....)

53. When did you come to this village to stay? 9 NA
Urban Contact

54. Have you ever lived away from this village for more than
one year?

Circle 0 - No; l - DK; 2 - Yes

55. If yes, where, how long and for what purposes? (Probe
for military service).

Purpose

Name of place Duration of Stay

 

 

Another village

Town (less than
100,000)

City (100,000)
56. How many times have you visited the following places
last year?
No. of visits

Town (less than
100,000)

City (100,000)

Urban Pull

 

57. If you are offered a job in a city with double your
present income, will you go?

Circle 0 - No, 1 - DK; 2 - Yes 9 - NA

_ Mass Media Exposure
58. Do you listen to radio?
Circle 0 - No, l - DK; 2 - Yes
59. What programs do you listen to?

1 - DK 2 - Check appropriate categories below

60. Do other members of your family listen to radio?
Circle 0 - No. 1 - DK, 2 - Yes

113

61. What programs do they listen to?

l - DK 2 - Check appropriate categories below 9 NA

 

 

Type of program Respondent Family
Songs and recreational

programs

News

RRF and other farm
programs

Other

62. Did you see any cinema films during 1966? (Reference is
to commercial films, not to those shown by the Block)

Circle 0 - No, 1 - DK. 2 - Yes
63. How many?
1 - DK; 9 - NA
64. Can you read a neWSpaper?
Circle 0 - No, can't read 1 - DK 2 - Yes
65. Can you write a letter?
Circle 0 - No; l - DK. 2 - Yes

66. Did you read (did anyone read to you) any neWSpapers in
the past week? How many?

Circle 0 - No papers read/read to him
1 - Can't read, but had papers read to
him.(No.
2 - Can read, and read one or more
papers (No. )
Educational Aspirations

76. How much schooling would you like your youngest son to
have?

Circle 0 - None; 1 - DK. 2 - Some(No. of years )
9 - NA

77. Do you think this will be possible?

Circle 0 - NO; 1 - DK; 2 - Yes; 9 - NA

 

82.

83.

84.

85.

86.

87.

88.

89.

90.

91.

114

Can evil eye cause disease?
0 - No 1 - DK 2 - Yes

Have you made an offering or sacrifice to prevent sickness?
0 - No l - DK 2 — Yes

Should Harijans be allowed to draw water from all common
wells in the village?

0 - No 1 - DK 2 - Yes

Should Harijans and other children take meals together
in schools.

0 - No l - DK 2 - Yes

If your son wanted to marry a lower caste girl would you
allow it?

0 - No l - DK 2 - Yes
Do you think Harijans should be allowed to enter and
worship in all temples of the village?

0 - No l - DK 2 - Yes
In your opinion, is an illiterate village Brahmin
superior to a lower caste B.A. or M.A.?

0 - No l - DK 2 - Yes
Empathy

Let me now ask your opinion about a different subject.
There are ups and downs in everybody's life. One can
achieve a high position. On the other hand those who
are at present very well placed could go down. Please
tell us what you would do if you found yourself in the
following positions. Please don't think that we are
making fun of you.

If you were the B.D.O. of this block, what program of
agriculture would you make or conduct?

(Don't ask if the respondent is the Panchayat president)

If you were President of the Panchayat here in your vill-
age what would you do in the next year?

If you were a day laborer, what would you do to own some
land?

92-

98.
99.
100.

127a.

127b.

115

If you were District Collector what would you do to solve
some of the major problems of this area?

 

 

Political Knowledge Incorrect Correct
Who is the Prime Minister of India? 0 1
Who is the Chief Minister of your state? 0 1
Who is the M.L.A. of your area 0 1

Level of Living

I have mentioned before that one of the major purposes
of this investigation is to find out about the general
conditions of living in our villages. This is import-
ant because the government can take steps or suggest
steps for impnyement only when they have a correct idea
of the general living conditions of our village people.
To get this information, we are asking everybody about
their income, expenditure, indebtedness, things they
possess and housing conditions. Let me ask you the same
questions which I have asked others and have received
full cooperation.

Do you own the following things?

Circle No Yes
Good dress to wear for attending

fairs, weddings, etc. 0 2
Shoes 0 2
Gold Jewelry 0 2
Wrist watch or clock 0 2
Torch light 0 2
Wooden/metal furniture 0 2
Mosquito nets 0 2
Bicycle 0 2

116

128. HOUSING

'0' for None and '2' for Yes. If listed items are
present in combination with non-listed items then
Circle '2'.

No Yes
1. Brick or stone walls 0 2
2. Windows with shutters 0 2
3. Cement or stone floor 0 2
4. Tiled/tin/asbestos/cement roof 0 2
5. No. of rooms (Write actual number) 0 2
6. Separate sitting 0 2
7. Own well/tubewell 0 2
8. Separate bathroom/latrine 0 2
9. Two storied house 0 2

129 How much money (including food) does your family need
per month to live comfortably in this village?

RS.

 

130. How much tax did you pay last year?

No tax

 

RS.

 

"ImmmwmES