THE EFFECTS OF COHESION ON ORGANIZATIONAL

PERFORMANCE: A TEST OF TWO MODELS

by

LEONARD WONG, B.S., M.S.

A DISSERTATION

IN

BUSINESS ADMINISTRATION

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

May, 1992

' i, ^^ ACKNOWLEDGEMENTS ^ , .

C^TO XJ"^ would not be possible to adequately express my

appreciation to my adviaor. Dr. Robert L. Phillipa, for hia

assistance and advice not only on thia diaaertation, but

also for my entire graduate atudy and professional

development.

I also am deeply grateful to Dr. Howard McFann of the

Army Reaearch Inatitute for making the National Training

Center data available and adviaing me on ita uae.

11

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ii

LIST OF TABLES v

LIST OF FIGURES vii

CHAPTER

I. INTRODUCTION 1

Limitationa 8

II. THE THEORETICAL FRAMEWORK 12

The Conceptualization of Coheaion 12

Organizational Commitment 34

Leaderahip 38

III. THE THEORETICAL SETTING 47

The Organizational Synergy model 47

Leader-group Interactiona Model 51

Hypotheaea 54

IV. FIESEARCH DESIGN 56

Method 5 6

Sample 56

Operationalization of the Conatructa 60

Performance 67

V. DATA ANALYSIS. . . • 70

Meaaurement Model 70

Results of Regression Analysis 75

VI. IMPLICATIONS 96

Hypotheses Results 96

Model Implications 99

I I

111

Future Research 100

REFERENCES 104

APPENDICES

A. LISREL 7 OUTPUT FOR ORGANIZATIONAL SYNERGY MODEL 115

B. LISREL 7 OUTPUT FOR LEADER-GROUP INTEFIACTIONS MODEL 122

C. FIEGRESSION RESULTS FOR ORGANIZATIONAL SYNERGY MODEL 128

D. FIEGRESSION RESULTS FOR LEADER-GROUP INTERACTIONS MODEL 143

E. REVISED MODEL REGRESSION RESULTS 155

IV

LIST OF TABLES

5.1 Reliability of measures for the organizational synergy model 81

5.2 T-values for the organizational synergy model. . . 82

5.3 Reliability of measures for the leader-group interactions model 83

5.4 T-values for the leader-group interactiona model . 84

5.5 Covariance matrix for the organizational synergy model 85

5.6 Covariance matrix for the leader-group interactions model 8 6

5.7 Regreaaion reaulta for the organizational aynergy model for combined platoona, armor only, and infantry only aubgroupa; Performance = C + OC + L 87

5.8 Regreaaion reaulta for the organizational aynergy model for combined platoona, armor only, and infantry only aubgroupa; Performance = C + OC + L + (Lx OC) 88

5.9 Regreaaion reaulta for the organizational aynergy model for combined platoona, armor only, and infantry only aubgroupa; Performance = C + OC + L + (C x L) 89

5.10 Regreaaion reaulta for the organizational aynergy model for combined platoona, armor only, and infantry only aubgroupa; Performance = C + O C + L + (Cx OC) 90

5.11 Regreaaion reaulta for the organizational aynergy model for combined platoona, armor only, and infantry only subgroups; Performance = OC + L + (L x OC) + (C X L) + (C X OC) + (C X L X OC) 91

5.12 Regression reaulta for the leader-group interactiona model for combined platoona, armor only, and infantry only aubgroupa; Performance = Inatr + Supp + Drive + Goal 92

5.13 Regression resulta for the leader-group interactiona model for combined platoona, armor only, and infantry only aubgroupa; Drive = Inatr + Supp 93

5.14 Regreasion reaulta for the leader-group interactiona model for combined platoona, armor only, and infantry only aubgroupa; Coheaion = Inatr + Supp 94

5.15 Regreaaion reaulta for the leader-group interactiona model for combined platoona, armor only, and infantry only aubgroupa; Performance = Drive + Goal + (Drive x Goal) + (C X Drive) + (C x Goal) + (Drive X C X Goal) 95

VI

LIST OF FIGURES

1.1 Organizational aynergy model 10

1.2 Leader-group interactiona model 11

4.1 Organizational chart for platoona 68

4.2 Normal probability plot for performance

criterion 69

5.1 Organizational aynergy meaaurement model 79

5.2 Leader-group interactiona meaaurement model. . . . 80

Vll

CHAPTER I

INTRODUCTION

Coheaion haa traditionally been a fundamental element

in the military'a formula for combat aucceaa. Ardant du

Picq (1947), the French military writer, noted that "four

brave men who do not know each other will not dare to attack

a lion. Four leaa brave, but knowing each other well, aure

of their reliability and conaequently of mutual aid, will

attack reaolutely" (p. 110). S.L.A. Marahall, a noted

hiatorian of the U.S. Army, addreaaed the queation of what

inducea a man to face death bravely. He atated, "I hold it

to be one of the aimpleat trutha that the thing which

enablea an infantry aoldier to keep going with hia weapona

ia the near preaence or the preaumed near preaence of a

comrade" (Marahall, 1966, p. 42) .

Deapite the enthuaiaam in the context-apecific realm of

the military, the context-free literature haa yet to produce

clear and conaiatent relationahipa between coheaion and

performance. For example, Stogdill (1972) reviewed 34

atudiea which attempted to relate coheaiveneaa and

productivity. He found coheaive groupa to be more

productive in twelve atudiea, less productive in eleven

atudiea, and unrelated to productivity in the remaining

eleven studies.

More recently, Greene (1989) noted that although

cohesion of work groups is considered by practitioners and

researchers to be an important determinant of organizational

effectiveness, the importance stems from a presumed effect

on group productivity. He went on to state that textbooks

such as those by Griffin and Moorhead (1986) and

Schermerhorn, Hunt, and Osborn (1988) "directly contend that

cohesion results in high productivity when group and

organizational goals are compatible, but they fail to cite

any empirical evidence in aupport of thia view" (p. 71). In

other worda, the literature readily recognizea that coheaion

when coupled with norms enhancea performance. Thia

preaumption haa yet to receive conaiatent empirical aupport,

however.

Adding to the confuaion concerning the coheaion-

performance relationahip, the coheaion literature haa

yielded a number of different waya in which coheaion haa

been defined and operationalized. For example, coheaion haa

been conceptualized aa interperaonal attraction (e.g., Lott

& Lott, 1965); attraction to group (e.g., Seaahore, 1954); a

bidimenaional conatruct of taak and aocial coheaion (e.g.,

Anderson, 1975); and a multidimenaional construct with a

wide variety of dimensions (e.g., Yukelson, Weinberg, &

Jackson, 1984).

In an effort to overcome the ambiguity of the

cohesion-performance relationship, two models have appeared

in the literature using similar constructs of cohesion,

organizational commitment, and leadership. Phillipa, Blair,

and Schmitt (1987) propoaed the organizational aynergy model

and Schrieaheim, Mowday, and Stogdill (1979) propoaed the

leader-group interactiona model. Both modela auggeat that

cohesion haa to be directed toward performance. The

organizational aynergy model propoaea that coheaion affecta

performance when interacting with leaderahip and

organizational commitment. Figure 1.1 ahowa the

organizational aynergy model. The leader-group interactiona

model auggeata that coheaion ia moderated by group drive and

group goal acceptance. According to the Schrieaheim et al.

(1979) model, leaderahip ia an antecedent to coheaion and

group drive. Figure 1.2 illuatratea the leader-group inter­

actiona model. Thia preaent atudy teata the propoaed rela­

tionahipa between coheaion and performance poaited by the

two modela.

The modela are teated uaing data obtained from U.S.

Army unita. The unit performance meaaure uaed in thia atudy

ia the degree of unit battle aucceaa at the U.S. Army'a

National Training Center in Fort Irwin, California.

Testing the models is significant for several reasons.

First, both models attempt to clarify the ambiguous

cohesion-performance relationship found in the literature.

As stated earlier, Stogdill (1959, 1972) found that the link

between cohesion and performance was not consistently

empirically supported. Since that review, some atudiea have

provided evidence for the poaitive relationahip between

coheaion and performance (Ball & Carron, 1976; Bird, 1977;

Keller, 1986; Martena & Peteraon, 1971; Peteraon & Martena,

1972; Nixon, 1977; Widmeyer & Martena, 1978). Other atudiea

auggeated a negative relationahip (Landera & Luachen, 1971;

Zaccaro & Lowe, 1988), a aituational relationahip (Greene,

1989; Hoogatraten & Vorat, 1982; Mieaing & Preble, 1985;

Tziner and Vardi, 1982) or no relationahip at all (Melnick &

Chemera, 1974; Williama & Hacker, 1982). Even in the

atudiea that ahowed a poaitive relationahip, cauaality waa

not alwaya eatabliahed aince moat of the atudiea were croaa

aectional. Thua, with the coheaion-performance relationahip

lacking conaiatent aupport, any findinga from the teat of

either model add to the current literature.

A aecond related aignificant aapect ia the performance

meaaure and the environment in which the performance meaaure

waa aaaeaaed. Other coheaion atudiea have involved taaka

auch aa folding paper (e.g., Zaccaro & Lowe, 1988); aport

team performance (e.g., Landera et al., 1982); routine

military duties (e.g., Tziner & Vardi, 1982); and buaineaa

games (e.g., Miesing & Preble, 1985). In the present study,

the task takes place in an environment that allows group

processes to fully develop.

The uniqueness of the National Training Center (NTC)

cannot be overstated. No other training approaches the

realism routinely achieved at the NTC. A unit rotation at

the NTC lasts three weeks and pits the unit against an enemy

force trained in Soviet tactics. Unita employed in battle

are kept "honest" through the use of lasers and sensors

mounted on fighting vehicles and worn by soldiers to detect

hits and near misses of weapon systems. The task of

engaging highly competent enemy forces during day and night

missions in a maneuver area the size of Rhode Island, in

addition to the harsh deaert environment, providea the beat

external validity poaaible ahort of actual combat. The

dirt, duat, tear gaa, amoke, simulated chemical agents, and

relentless sun all contribute to the realism of an ideal

testing ground for coheaion and ita effecta on performance.

Another aignificant aapect of thia atudy ia the uae of

a narrow conceptualization of the coheaion conatruct. Aa

atated earlier, the current coheaion literature reflecta

preferencea for unitary, bidimenaional, or multidimenaional

conceptualizationa. The organizational aynergy model

advocatea a reatricted way of operationalizing coheaion.

Schrieaheim et al. (1987) acknowledge the many

conceptualizationa of coheaion, but do not apecify which

approach would be more appropriate for their model. Thia

study avoids previoua criticiams of the coheaion literature

concerning broad baaed conceptualizations. A thorough

discussion of the many conceptualizations of cohesion will

follow later in this study.

Finally, this study is significant to the U.S. military

since it addresses a key element in current doctrine.

Jacobs (in press) writes that "within the military, there is

perhaps no more important task than building cohesive combat

units." Indeed, the Army haa adopted thia view and ia

evident in U.S. Army policy publicationa auch as Operations

Field Manual FM 100-5 (1986) which states that "well-

trained, coheaive unita under good leaderahip suatain far

higher average effectiveneaa" (p. 26). The Army haa aaaumed

that higher coheaion leada to better performance without

regard to moderator variablea aa auggeated by the modela to

be teated. Thia atudy teata two modela that aeek to clarify

the moderator relationahipa. Thia atudy'a findinga are alao

relevant to policy deciaiona concerning individual and group

rotation policiea. Rotation policiea are often deaigned

with coheaion building aa a focua. Should coheaion not have

an impact aa it ia preaumed to have, theae policiea may be

premature.

Thua, the aignificance of thia atudy can be aeen in

both the context-free and context-apecific arenaa. In the

context-free, thia atudy teata modela poaited to clarify the

currently tenuoua link between coheaion and performance.

Additionally, it tests the usefulness of adopting a narrow

conceptualization of the cohesion construct. Concerning

context-specific military research, the study provides more

research addressing the military's reliance on the cohesion

concept in its search for the determinants of unit

effectiveness.

Lewin and Minton (1986) noted that "for academic

efforts to be maximally useful to interested practitioners,

they must be approached, engineered, and presented

specifically with thia uaer/conaumer orientation in mind"

(p. 525). They go on to call for reaearch in organizational

effectiveness to offer remedies for the two major problems

of effectiveneaa reaearch. Firat, the reaearch ahould

provide practitionera acceaa to and uae of relevant reaearch

findinga. Second, researchera ahould have acceaa and

experimentation with "real world" aituationa. Thia atudy

focuaea on modela that are extremely relevant to the

military practitioner. Indeed, the organizational aynergy

model waa developed by Phillipa, Blair, and Schmitt (1987)

using a military unit effectiveneaa focua. Aa pointed out

earlier, thia atudy uaea data obtained from an extremely

realiatic taak environment. Hence, teating two relevant

modela with rich data bridgea the gap between the military

practitioner and the academic.

Thia atudy teated parta of the organizational aynergy

model and leader-group interactiona model. Itema reflecting

the constructs contained in the models were selected from an

instrument administered to U.S. Army units. The measurement

properties of the items selected to represent both models

were assessed using LISREL for confirmatory factor analysis.

Reliabilities for the items as well as discriminant validity

were examined. Once the measurement models for the

organizational synergy model and the leader-group

interactions model were deemed acceptable, the actual

propositions of the two models were tested with regreaaion

analysia.

Limitationa

The atudy ia limited mainly through the uae of

aecondary data. Becauae the itema uaed to operationalize

the conatructa were not deaigned apecifically for that

purpoae, there may be aome alippage between the conatructa

and what the itema actually meaaure. Additionally, the

actual inatrument uaed to collect the data waa aixteen pagea

long. With auch a long instrument, respondent fatigue and

hence reaponae error may be preaent in the data. Another

limitation of thia atudy ia the outcome variable uaed. Unit

performance ia the outcome variable in thia atudy. An

examination of the diatribution of the performance across

units included in the study shows that although it is close

to being normally distributed, it is not. Violating a

normality assumption may affect the interpretation of the

regression analysis. Trying to correct for non-normality

may affect the more crucial linearity assumption. Finally,

both models contained triple interaction terms. Testing

such terms is difficult because of the multicollinearity

8

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in the regression equation. Multicollinearity ™ay distort

the parameter estimates.

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CHAPTER II

THE THEOFIETICAL FF(AMEWORK

In order to analyze the two models in this study, it is

first necessary to examine the theoretical underpinnings of

the models. This literature review consists of three sec­

tions. First, a review of the literature is presented to

show the evolution of the main conatruct of coheaion in the

modela to be teated. The coheaion literature review

includea a brief diacuaaion concerning why a narrow

conceptualization of coheaion ia uaed in thia atudy. The

aecond aection of the literature review examines the

development of the organizational commitment conatruct and

ita role in determining performance. Laatly, the leaderahip

component ia diacusaed in relation to ita interactive

effecta with organizational commitment, coheaion, and

performance.

The Conceptualization of Coheaion

For the laat forty yeara, the concept of coheaion haa

played a major role in group dynamica, organizational

behavior, and group therapy. The concept haa maintained ita

importance in the aocial and induatrial/organizational

psychology field through its contributions to areaa aa

diverae as decision making (Janis, 1972) , business

performance (Keller, 1976), subordinate satisfaction

(Dobbins & Zaccaro, 1986), protection from illness (Manning

12

& Fullerton, 1988), combat effectiveness (Goodacre, 1951),

conformity (Back, 1951), sport team effectiveness (Carron,

1982) and group membership (Latham & Lichtman, 1984) .

Despite the widespread use of the cohesion construct, the

research has been "dominated by confusion, inconsistency,

and almost inexcusable sloppiness with regard to defining

the construct" (Mudrack, 1989, p. 45) .

Probably the most popular definition of group cohesion

(alao referred to aa coheaiveneaa) ia that propoaed by

Festinger, Schachter, and Back (1950) who defined cohesion

as "the total field of forces which act on members to remain

in the group" (p. 164). Disagreement continues as to how

this "total field of forces" should be conceptualized,

operationalized and aaaeaaed. Throughout the literature,

coheaion haa been interpreted aa a unitary, a bidimenaional,

or a multidimenaional concept.

This section of the literature review will examine the

various interpretations of the construct of coheaion. While

mainly focusing on the recent literature (the last decade),

it is often necessary to examine the long lasting effecta of

aeveral older atudiea that permeate the coheaion literature.

The cohesion review conaiata of five parta. The firat three

parts review the context-free cohesion literature and

examine the development of various unitary, bidimensional,

and multidimensional conceptualizations of cohesion. The

fourth part examines the context-specific domain of cohesion

13

in the military. The last part of the cohesion literature

review specifically addresses studies examining the

cohesion-performance link.

Unitary Approaches

Festinger et al. (1950) pointed out two factors that

will affect the magnitude of the "force field" and can be

readily distinguished and conceptualized: attraction to

group and means control of the group. Deapite thia apparent

bidimenaional approach, Featinger and hia aaaociatea choae

to operationalize coheaion with only a aingle item--"What

three people in Weatgate or Weatgate Weat do you aee moat of

aocially?" Thia unitary operational definition of coheaion

focuaing on interperaonal attraction haa been widely uaed in

the literature. The latter view of coheaion aa the meana

control of the group waa never adopted by aubaequent

coheaion literature. The other predominant unitary view of

coheaion ia the conceptualization of coheaion as attraction

to group.

Cohesion as Interpersonal Attraction

Lott and Lott (1965) reviewed the literature from 1950

to 1962 concerning cohesion as interpersonal attraction.

Interestingly, they noted that defining cohesivenesa in

terms of interperaonal liking ia "relatively arbitrary"

since other dimensions could be used. Nevertheless, much of

the recent literature has still used the single dimension of

14

interpersonal attraction as an operational definition of

cohesion.

Scott and Rowland (1970) related.cohesion to morale in

their study of semantic differential scales. In their My

Fellow Workers scale, they essentially measured the inter­

personal attraction between workers.

Callaway and Esser (1984) tested Janis' (1972) group-

think formulation in the laboratory by manipulating group

cohesiveness and adequacy of decision procedures. Coheaive­

neaa waa conceptualized aa interperaonal attraction and waa

operationalized by uaing Back'a (1951) deceptive communica-

tiona to the aubjecta. Subjecta in the high coheaiveneaa

group were led to believe that they were membera of a ae-

lect, congenial, and effective group whoae membera had been

matched on peraonality and demographic information. Low

coheaiveneaa conditiona were obtained by telling aubjecta

that auch a match waa impoaaible. The uae of Back'a cohe­

siveness manipulation is noteworthy since thirty years

earlier Gross and Martin (1952) had strongly criticized

Back's unitary definition of cohesion and hia artificial

manipulation due to ita ephemeral effecta. The Callaway amd

Esser (1984) study found that highly cohesive groups without

adequate decision procedures tended to make the poorest

decisions.

Latham and Lichtman (1984) tested the prepotency of

social linkages in predicting commitment in voluntary

15

organizations. Their study compared cohesion and status in

explaining the level of commitment in church members.

Cohesion was conceptualized as interpersonal attraction and

was indicated by the degree of friendship, acceptance, and

love shown by other church members. Latham and Lichtman's

results showed a strong relationship between cohesion aa

interperaonal attraction and organizational commitment. In

this case, brotherly love served as the unitary conceptuali­

zation of cohesion.

Etzioni took a restrictive position on the

conceptualization of coheaion. He atated that "by coheaion,

we mean a poaitive expreaaive relationahip among two or more

actora" (Etzioni, 1975, p. 280) .

Coheaion aa Attraction to Group

Seaahore (1954) auggeated the aecond unitary concept of

cohesion as attraction to group, although he noted that the

clarity of this definition could not be entirely maintained

in the operationalized definition used in his study.

Attraction to group differs from interpersonal attraction in

that attraction to group is a group phenomenon whereas

interpersonal attraction is an individual process. Group

measures of interpersonal attraction, therefore, can be

obtained only through the imprecise aggregation of

individual attractions.

Janet Schriesheim (1980) examined group cohesiveness as

a moderator of dyadic leader-subordinate relations. In her

16

discussion of cohesiveness and group development,

Schriesheim noted that cohesiveness was associated with two

stages of Tuckman's (1965) four stage group development

process. In the norming stage, coheaiveneaa reaulta aa

group membera accept each other, the group atructure, and

the rolea the group haa developed for them. In the

performing atage, orientation toward taak becomea the focus

of group interactiona. Coheaiveneaa ia then associated with

acceptance of task-related rolea. Deapite the bidimenaional

conceptualization of coheaion, Schrieaheim operationalized

coheaion on one dimension and uaed Stogdill'a (1965) five

item aelf-report which aaka reapondenta to deacribe the

group of individuala who report to the aame auperviaor.

Turner, Hogg, Turner, and Smith (1984) examined failure

and defeat aa determinanta of group coheaiveneaa. They

choae to conceptualize coheaion aa attraction to group aince

it ia poaaible to produce poaitive attitudea towarda other

group membera even when there ia no interperaonal contact.

According to the authora, once people are defined aa membera

of a group, they perceive themaelvea aa having aimilar or

identical attributea that define the group aa a whole.

While being very aimilar to the coheaiveneaa aa manipulated

by Back (1951), the conceptualization of coheaion by Turner

and his associates is attraction to group, not interpersonal

attraction. Results of the study suggested that failure and

defeat can lead to increases in cohesion as attraction to

17

group since identification with the group helps justify and

explain behavior that incura a coat.

Dobbins and Zaccaro (1986) examined the effects of

group cohesiveness and leader behavior on subordinate satis­

faction in a military organization. Although they addreaaed

taak related norma and taak clarity in their diacuaaion of

the definition of coheaion, they operationalized coheaion aa

the degree of attraction to the group.

Bidimenaional Approachea

Aa pointed out earlier, Featinger'a (1950) original

definition of coheaion waa bidimenaional (attraction to

group and meana control of the group) although the

operational definition (interperaonal attraction) waa

unitary. Groaa and Martin (1952) found three indicatora of

coheaion in a atudy of 13 women'a living groupa at a

univeraity and criticized the Featinger et al. unitary

definition. In a reply, Schachter (1952) regarded the

problem aa empirical, not theoretical. Eisman (1959) found

five different indicators of cohesion and also questioned

the nominal definition. In 1965, Hagstrom and Selvin

applied factor analysis to data collected from 20 women's

living groups at the University of California. Their

results suggested two dimensions of social satisfaction

(satisfaction with the group and social life) and

sociometric cohesion (the proportion of best friends in the

18

group). More important than the specific dimensions, the

authors suggested that different dimensions may be necessary

for differing conditions.

The controversy continued in the literature concerning

the dimensionality of cohesion. In 1978, Bednar and Kaul

reviewed the group psychotherapy research and criticized the

ambiguity of the construct, lack of agreement between

construct and operational definition, inadequate information

on a coheaiveneaa operational definition, and the difficulty

in comparing reaulta becauae of the many waya of

operationalizing the conatruct. Evana and Jarvia (1980)

alao reviewed the literature and noted that confuaion aroae

when attraction to group, an individual conatruct, waa uaed

to approximate coheaiveneaa, a group conatruct. They

auggeated aeparating the diatinct conatructa of attraction

to group and coheaiveneaa and later developed the Group

Attitude Scale aa a aeparate meaaure of attraction to group

(Evana and Jarvia, 1986). Researchers began to make a

distinction between cohesion at a group level (attraction to

group) and cohesion at an individual level (interpersonal

attraction). The separation of interpersonal cohesion and

attraction to group was supported by Wright and Duncan

(1986) who studied 27 graduate students in 12 weekly

experiential training sessions. Wright and Duncan found

that conceptualizing cohesion as attraction to group was

better than an interpersonal attraction definition when

19

using cohesion as a predictor of individual outcome in

groups.

Carron and Chelladurai (1981) specifically sought to

determine if cohesiveness is a single or multidimensional

construct. They studied 99 male athletes and administered

portions of the Sport Cohesiveness Questionnaire (Martens,

Landers, and Loy, 1972) over two occaaiona. Factor analyaia

revealed two factora accounting for 83.5% of the variance.

The firat factor waa labeled "individual to group coheaion"

and was the degree the individual perceived a sense of

association with the total group. The first factor

contained items measuring enjoyment, aenae of belonging, and

value of memberahip. The aecond factor waa labeled "group

aa a unit coheaion" and waa the individual'a perception of

the group aa a total unit. The aecond factor contained

items measuring teamwork and closeness. The two factors

identified by Carron and Chelladurai approximated the

previously used unitary dimensions of attraction to group

and interpersonal attraction. Carron and Chelladurai also

noted that although the perception of team cohesion was

moderated by the type of group membership (individual

performance and team performance), only task motivation

showed consistency across the two sport types.

Six years earlier, Anderson (1975) pointed out that

before understanding cohesion, it was first necessary to

make a distinction between types of groups. One type of

20

group is the friendship, psyche, or socioemotional group.

Members belong to them for emotional satisfaction. The

other type of group is the task oriented group that exists

to accomplish a task. Anderson also noted that these two

types of groups are not mutually exclusive and can be

present in all groups. His study using 386 female

undergraduates in task and socioemotional triads provided

support for attraction to the group and interpersonal

attraction dimensions of cohesion. More significantly,

however, Anderaon auggeated that while value aimilarity led

to interperaonal attraction, goal-path clarity in taak

oriented groupa led to a coheaiveneaa other than

interperaonal attraction.

Wheeleaa, Wheeleaa, and Dickaon-Markham (1982) examined

the aocial and taak dimenaiona of amall group communication

and developed a meaaure of group coheaion with 75

undergraduatea participating in a two-week aocial action

project. Wheeleaa et al. conceptualized coheaion aa

interperaonal attraction, but more importantly, they made a

diatinction between the aocial and taak dimensions of the

group.

Instead of keeping the task dimension separate from

cohesion, Tziner (1982) pointed out that two types of

cohesion exist: socioemotional cohesion and instrumental

(task) cohesion. Zaccaro and Lowe (1988) set out to

empirically verify this bidimensional nature of cohesion.

21

Hypothesizing the two dimensions of interpersonal and task

cohesion, they manipulated interpersonal cohesion by placing

subjects in the high interpersonal cohesion group in an

exercise designed to enhance member liking (e.g.,

introductions and discussion about themselves). Subjects in

the low interpersonal cohesion conditions performed an

exercise designed to inhibit member attraction and stimulate

perceived diaaimilarity. High-taak coheaive groupa received

a cover story that emphasized the importance of both the

atudy and the member taak performance. The taak waa folding

sheeta of paper into "moon tenta" in a 15 minute period.

The atudy ahowed that high taak coheaion increaaed group

performance while high group interperaonal coheaiveneaa did

not reault in any greater performance effort. Thua, by

using a bidimenaional approach, they were able to aee the

differing effecta of taak and interperaonal coheaion on

performance. Zaccaro and McCoy (1988) performed the aame

experiment with a diajunctive taak (i.e., the group muat

adopt a aingle aolution to the excluaion of all other

aolutiona). The factor analyaia done in their atudy alao

aupported the taak and interperaonal dimenaiona of coheaion.

Johnaon and Fortman (1988) examined the dimenaiona of

cohesion by uaing factor analyaia and cluster analysis on

the Gross Cohesivenesa Scale (Gross, 1957) administered to

144 undergraduates participating in an unrelated study. The

authors noted that although cohesion consists of social and

22

task cohesion, the Gross Cohesiveness Scale only measures

social cohesion. Additionally, their analysis supported

dividing social cohesion into affective and cognitive

components.

Anderson's (1975) distinctions between types of groups

highlights a major reason for the disagreement on the

interpretation of coheaion. Featinger et al. (1950)

originally uaed the coheaion concept with aocial groupa.

Thus, interperaonal attraction became important aa evidenced

by their operationalization of coheaion. Therapy group

researchers used the cohesion concept in determining how to

maintain group memberahip. Conaequently, attraction to

group became a major conaideration (Evana and Jarvia, 1986).

Aa aport teama and intact organizationa became intereated in

coheaion, the concept waa often linked to performance (e.g..

Ball and Carron, 1976). Taak coheaion then became aalient.

It appeara that the diaagreement over the dimenaiona of

coheaion may not originate from the concept itaelf, but

inatead reaulta from the varied applicationa of the concept.

With the identification of two diatinctiona in the

conceptualization of coheaion--the individual veraua the

group and taak veraua aocial concerna, it became neceaaary

to move to a multidimenaional conceptualization of cohesion.

Multidimensional Approaches

Yukelson, Weinberg, and Jackson (1984) modified items

contained in past cohesion studies, developed their own

23

items based on the literature, and interviewed coaches for

their perceptions on the dimensions of the cohesion concept.

They developed a 41 item instrument which they administered

to 196 male and female intercollegiate basketball players on

16 teams. Factor analysis revealed four factors. The first

factor was labeled quality of teamwork and represented a

sense of how well teammates work together toward successful

team performance. The second factor waa attraction to group

which aignified the attraction dimenaion of coheaion

previoualy uaed in unitary conceptualizationa. The third

factor waa unity of purpoae which reflected a commitment to

the norma and atrategiea the team waa atriving to achieve.

The final factor waa labeled valued rolea and reflected a

aenae of identification with group memberahip. The obvious

criticism of this atudy waa that although it recognized the

poaaible multidimenaionality of the coheaion concept, it waa

clearly poat hoc analyaia without any a priori theory driven

dimenaiona poaited.

Carron, Widmeyer, and Brawley (1985) attempted to aolve

the many equivocal reaulta coming from atudiea in coheaion.

They pointed out that inadequate meaaurement procedurea

stemmed from the lack of conceptual clarity in the cohesion

research. They proposed that cohesion consists of two

categories consisting of two constructs each. The first

category was labeled group integration which represented the

closeness, similarity, and bonding within the group as a

24

whole. Individual attractions to group was the second

category posited and represented the interaction of motives

working on the individual to remain in the group. Each

category then consisted of task and social orientations.

Thus, the distinction between group and individual

attractions, and task and social concerns were adopted in

the Carron et al. model.

Carron and his aaaociatea developed the Group

Environment Queationnaire (GEQ) which contained all four

dimenaiona of the coheaion concept. The GEQ waa

adminiatered to 247 athletea from 26 intact teama from

intercollegiate and municipal adult leaguea in Canada.

Factor analyaia aupported the authora' conceptualization.

Aaide from making the dimenaionality diatinctiona of the

coheaion concept, thia atudy waa noteworthy in the

realization of the impact of aocial deairability biaaea in

aelf-reporting coheaion in an intact team or organization.

Piper et al. (1983) examined the effecta of coheaion on

group memberahip in a therapeutic aetting. Uaing 45 adulta

in 9 therapy groupa. Piper and hia aaaociatea adminiatered

questionnaires to explore the different types of bonding

occurring in the groups. They pointed out that bonding may

occur between participants, between participanta and the

leader, and between participanta and the group. Factor

analysis confirmed the three types of bonding with

additional subtypes in each factor. Interestingly, Piper et

25

al. advocated that despite subtypes such as compatibility to

the group, mutual stimulation and effect, and compatibility

with the leader, only commitment to the group should be used

as a conceptualization of cohesion. They argued that

adopting a broad definition of cohesion brings in many more

group processes than suggested in the original Festinger et

al. (1950) formulation. Thus, although Piper et al.

confirmed the multidimensionality of cohesion, they proposed

to restrict cohesion to a narrow definition to avoid future

problems in conceptual clarity.

Military Coheaion

In the military context, coheaion ia often interpreted

aa aome level of bonding or aupport. Aa in the context-free

literature, the conceptualizationa have been unitary and

multidimenaional. A unitary conceptualization ia evidenced

by Manning and Fullerton (1988) who demonatrated that highly

coheaive unita provide their membera protection from the

stresses of military life. They argued that the

conceptualization of cohesion as interpersonal attraction

falls short of the cohesion as interpersonal bonding

characteristic in military units. They suggested instead

conceptualizing cohesion as social support which is an

individual's belief that he or she is cared for, loved,

esteemed, and a member of a network of mutual obligations.

They measured unit cohesion by asking soldiers for their

26

perceived social support from family, friends, unit, and

Army. Thus, cohesion was defined unitarily, but the support

group was multidimensional.

Many other military atudiea have focuaed on coheaion aa

the reaistance to diaintegration. Thia focua ia much like

the alternative nominal definition auggeated by Groaa and

Martin (1952) in their criticiam of Featinger et al. (1950).

In their classic study of World War II Wehrmacht soldiers,

Shils and Janowitz (1948) argued that:

When the individual's immediate group, and ita aupporting formationa, met hia baaic organic needa, offered him affection and eateem from both officera and comradea, aupplied him with a aenae of power and adequately regulated hia relationa with authority, the element of aelf-concern in battle, which would lead to diaruption of the effective functioning of hia primary group, waa minimized, (p. 281)

Significant in the Shila and Janowitz atudy waa the

incluaion of taak and aocial coheaion and the incluaion of

the role of the leader.

The definition offered by a atudy done at the National

Defenae Univeraity defines military coheaion aa the bonding

together of membera of a unit or organization in auch a way

aa to auatain their will and commitment to each other,

their unit, and the miaaion (Johna et al., 1984). The

dimensions found in this broad conceptualization of cohesion

can be viewed as peer, organizational, and task cohesion

(Henderson, 1985). By focusing on the bonding to others and

group, instead of attraction to others and group, the

definition focuses on the ability of the group to avoid

27

disintegration under stress. This definition is exemplary

of the current military view of cohesion. It should be

noted that this conceptualization specifically posits that

cohesion is positively related to mission accomplishment.

Siebold and Kelly (1988) hypothesized three types of

bonding in Army unita. Theae were horizontal bonding among

peera, vertical bonding between leadera and aubordinates,

and organizational bonding between all membera and their

unit and the Army. Each level of bonding alao had affective

and inatrumental componenta. An inatrument containing itema

reflecting the three dimenaiona waa given to 70 platoona

(1015 aoldiera) at four Army poata. Factor analyaia of the

data aupported the three dimenaiona and aubtypea. Siebold

and Kelly'a multidimenaional approach with affective and

inatrumental (taak) componenta ia certainly comprehenaive,

but auffera from much of what Piper et al. (1983) warned

againat: comprehenaiveneaa at the expenae of conceptual

clarity. For example, it ia difficult to aeparate the

bonding between aubordinatea and their leadera from

relationahip-oriented leaderahip.

Griffith (1988) aurveyed 8,869 aoldiera in 93 U.S. Army

companies and found four dimensions of cohesion with factor

analysis. The first factor was the quality of instrumental

and affective relationships among junior enlisted men

(peers). The second factor was the quality of relationships

between subordinates and their leaders. The third factor

28

was the internalization of Army values. The fourth factor

was soldier confidence in weaponry and leaders. The first

three factors can be viewed as analogous to the familiar

attraction to others, group, and leaders.

Little (1969) in his classic work, "Buddy Relations and

Combat Performance," observed American soldiers during the

Korean War. From hia obaervationa of coheaion and combat

performance. Little warned againat a broad conceptualization

of coheaion. He atated that:

Even in the amalleat unit there ia an "iron framework" of organization which aervea aa a baaia of aocial control. However, the contribution of theae reaearch findinga to military primary groupa haa often been overinterpreted and overextended to the point of creating a "human relationa" theory of organization which faila to give aufficient emphaaia to authority, power atructure, the environmental context, and organizational goala aufficient acope. (pp. 191-192)

The Piper et al. (1983), Etzioni (1975), and Little

(1969), atudiea are aimilar in that deapite many yeara and

many atudiea apent aearching for the varioua dimenaiona of

cohesion, these researchers suggested a move back to a

unitary conceptualization of cohesion. A narrow definition

permits the researcher to sharply focus on one aspect of

group dynamics called cohesion and then avoid labeling other

emerging group processes as dimensions of cohesion. The

many dimensions that arose in the literature may have been

an attempt to account for the unexplained variance due to

moderating and intervening variables. Unfortunately, such

variables and dimensions are endless (e.g., confidence in

29

weaponry as a dimension of cohesion?). Adding to the

confusion was the multitude of cohesion instruments that

evolved while the concept of cohesion was explored.

Phillipa, Blair, and Schmitt (1987) take a narrow

approach for the aame reaaons. In order to avoid a "human

relations theory" (Little, 1969) based on a multidimensional

conceptualization, their organizational synergy model uses a

narrow definition of cohesion. Somewhat similar to Etzioni

(1975), the authora define peer coheaion aa "the degree of

positive, affective relationships between peer group

members" (p. 152).

Coheaion and Performance

Stogdill'a (1972) review of the coheaion-performance

literature aet the tone for aubaequent reaearch into

clarifying the coheaion-performance relationahip. Stogdill

propoaed that there muat be aomething to give the coheaion

direction. In hia review, he auggeated that group drive

would provide the interactive effecta with coheaion.

Keller (1986) uaed Seaahore'a five item meaaurement of

coheaion aa attraction to group in hia atudy of performance

in research and development project groups. Using

independent variables of cohesiveness, physical propinquity,

job satisfaction, and innovative orientation, he found that

only cohesiveness significantly correlated with all four

criteria used to measure performance. It should be noted

30

that physical propinquity was suggested by Lott and Lott

(1965) as an antecedent of cohesion as interpersonal

attraction. in Keller's atudy, phyaical propinquity ahowed

no significant relationship with performance and was

independent of cohesiveness as attraction to group. The

construct of innovative orientation parallels that of

Stogdill's (1972) group drive in that it provided a

direction for the group's efforts.

Miesing and Preble (1985) examined cohesion in groups

participating in a complex business simulation. Cohesion

was found to be a significant factor in explaining high

performance. The researchera noted, however, that in order

to attain high performance in a aimulation aetting, high-

performance norma muat be preaent. Thia view ia aimilar to

that propoaed earlier by Seaahore (1954). High-performance

norma can be introduced by group proceaaea or through a

atrong and aecure leader willing to provide direction yet to

ahare power with group membera.

Tziner and Vardi (1982) attempted to teat the effecta

of different combinationa of group coheaiveneaa and command

style on the performance effectiveness of active tank crews.

Level of cohesivenesa waa determined by comparing who the

subjects had for their tank crew versus their desires of who

they wanted in their crew. The results showed performance

effectiveness increased in combinations of low cohesiveness

with people oriented command style and high cohesiveness

31

with a command style oriented to both people and task.

Tziner and Vardi noted that their conceptualization of

cohesion focused on interpersonal relations. Thus, by

definition, this aspect of the group may not necessarily

contribute directly to performance effectiveness. Group

norms, ability, and size may moderate effectiveness more

than interpersonal attraction.

Interestingly, Tziner and Vardi (1982) noted that

personal compatibility may be inadequate as a measure of

cohesiveness They suggested future reaearch in examining

inatrumental or taak relationahipa to include a notion of

the norma involved in determining performance. Tziner and

Vardi'a atudy can be compared with the poaition taken by

Etzioni (1975). He argued that actora will have "poaitive

emotional inveatment in each other and that theae

inveatmenta ... are governed by norma" (p. 280). When

conaidering coheaion aa interperaonal attraction and ita

relationahip to performance, the valence of group norma

becomes important. This parallels Seashore's (1954)

moderating role of norms.

Williams and Hacker (1982) examined whether team

cohesion in women's intercollegiate field hockey was a cause

for or an effect of aucceaaful aport performance. Their

study was prompted by the many studies advocating a positive

relationship between cohesion and performance in sport teams

(e.g.. Ball & Carron, 1976; Martens & Peterson, 1971;

32

widmeyer & Martens, 1978). The authors conceptualized

cohesion as the unity of a team and was operationalized as

seven items measuring interpersonal attraction, personal

power or influence, value membership, sense of belonging,

enjoyment, teamwork, and closeness. Their results showed a

causal flow from succeaa to increaaed coheaiveneaa and from

increased cohesiveness to greater satisfaction. Thus, they

noted that while coaches may not need to be concerned with

building cohesivenesa (friendahipa) in order for their team

to be more aucceaaful, coheaiveneaa may atill be important

aince it leada to more aatiafaction. Nevertheleaa, the

Williama and Hacker atudy providea no aupport for the

coheaion-performance link.

Zaccaro and Lowe (1988) manipulated taak baaed coheaion

and interperaonal coheaion for aubjecta required to perform

an additive taak. The atudy'a findinga ahowed that high

taak coheaion facilitatea performance, whereaa interperaonal

attraction had no effect. Zaccaro and McCoy (1988)

manipulated taak and interperaonal coheaion for aubjecta

performing a diajunctive taak and found that both types were

necessary for higher performance.

The Seashore (1954), Stogdill (1972), Tziner and Vardi

(1982), Etzioni, (1975), and Miesing and Preble (1985)

studies all point to the need for direction in the form of

"high-performance norms," "group drive," or "innovative

orientation" to clarify the cohesion-performance linkage.

33

The cohesion literature shows an evolutionary process

in the conceptualization of the construct. As researchers

attempted to broaden the use of the cohesion construct, the

conceptualization of the construct itself began to broaden.

With multidimensional views, the link to performance became

increasingly ambiguous. The cohesion-performance

literature showa the utility of a narrow conceptualization

of coheaion with aome interactive conatruct providing

direction. Taak coheaion can be viewed aa interperaonal

attraction baaed on a mutual deaire to accompliah a goal.

Thua, it may be beneficial to reatrict coheaion to

interperaonal attraction and then add goal acceptance or

norma aa aeparate componenta of a model to ahow the

coheaion-performance relationahip. The organizational

synergy model assumes a narrow conceptualization of cohesion

and introduces direction through organizational commitment

and leadership. The next part of this literature review

concerns the construct of organizational commitment.

Organizational Commitment

Conceptualizations of organizational commitment have

gone through a developmental process similar to that of

cohesion. An extensive review of the theoretical and

empirical work done on the concept of organizational

commitment is found in Mowday, Porter, and Steers' (1982)

book on the psychology of commitment and other employee-

organization linkage variables. Reichers (1985) also

34

reviewed the commitment literature and current

conceptualizations. Instead of a similar extensive review

of the literature, this literature review will focus on

possible alternative conceptualizations of organizational

commitment, and more importantly, the link from

organizational commitment to performance.

Conceptualizations of Organizational Commitment

Reichers (1985) pointed out three possible

conceptualizationa of organizational commitment. The firat

definition ia baaed on Becker'a (1960) notion of aide beta.

With thia definition, commitment ia a function of the coata

and benefita aaaociated with organizational memberahip. A

aide bet conceptualization aaaumea that individuala atake

aome unrelated aapect of their Uvea on continued

organizational memberahip. For example, individuala can

have increaaed commitment due to atatua, age, tenure, or the

possession of organization-specific skills. Becker's side

bet conceptualization haa been uaed by reaearchera auch aa

Hrebiniak and Alluto (1972), Farrell and Ruabult (1981), and

Sheldon (1971).

The second conceptualization of organizational

commitment takes an attributional approach. With this

approach, attributions are made in order to maintain

consistency between one's behavior and attitudes. Salancik

(1977) argued that behaviors that are explicit, irrevocable,

35

volitional, and public bind the individual to the behavior

and hence increase organizational commitment. This approach

is also supported by the work of O'Reilly and Caldwell

(1980).

The third conceptualization found in the organizational

commitment literature views commitment aa occurring when

individuala identify with and extend effort towarda

organizational goala and valuea. The moat common veraion of

thia goal congruence conceptualization ia that of Porter,

Steera, Mowday, and Boulian (1974) and later popularized by

Mowday, Porter, and Steera (1982). Porter et al. (1974)

poaited that organizational commitment conaiata of (a) a

belief in and acceptance of organizational goala and valuea,

(b) the willingneaa to exert effort towarda organizational

goal accompliahment, and (c) a atrong deaire to maintain

organizational memberahip. Thia view of organizational

commitment ia much like that of coheaion except it ia at a

higher level of analyaia.

Organizational Commitment and Performance

Moat of the atudiea examining organizational commitment

have either viewed it aa a dependent variable or aa an

independent variable in the prediction of turnover and

abaenteeiam. Concerning the main effect of organizational

commitment and performance, Meyer and Allen (1984; Allen &

Meyer, 1987) made an intereating diatinction between the

36

Becker (1960) aide bet conceptualization and Porter'a (1974,

1982) goal congruence definition. They uaed the terms

c^ntj-nuance commitm. nt and affective commitment,

respectively, to characterize the two approaches. Although

both conceptualizations reflect links between the employee

and the organization, the nature of the links is quite

different. In the goal congruence conceptualization,

employees remain with the organization because they want to

do so, whereas those individuala with the aj^e bet

commitment remain becauae they need to do ao. Meyer et al.

(1989) examined the relationahip of affective and

continuance commitment to performance in a atudy of food

aervice organization managera in Canada. Performance waa

meaaured by auperviaor ratinga. The atudy found that

affective (goal congruence) commitment correlated poaitively

with performance while continuance (aide bet) commitment

correlated negatively.

O'Reilly and Chatman (1986) alao provided aupport for a

main effect relationahip between goal congruence commitment

and performance. In their atudy, aide bet commitment waa

poaited aa being derived from Kelman'a (1958) compliance

basis for attitude change. Similarly, goal congruence

commitment was posited as being derived from the

internalization or identification bases for attitude change.

Their findings suggested that commitments based on

internalization or identification are important correlates

37

of individuals' willingness to expend time, effort, and

money on behalf of the organization. Commitments based on

compliance, on the other hand, were significantly correlated

only with an intent to stay with the organization.

Angle and Perry (1981) examined the organizational

commitment relationship to performance with a sample of 24

bus aervice organizationa. Uaing the goal congruence

commitment, their findinga indicated that organizational

commitment waa aaaociated with organizational adaptability,

turnover, and tardineaa rate, but not with the operating

coata performance meaaure.

Decotiia and Summera (1987) uaed a goal congruence

approach in their atudy of 367 managerial employeea.

Intereatingly, they found that organizational commitment waa

predictive of individual motivation and objective job

performance, but not of auperviaora' ratinga of job

performance.

Leaderahip

After reviewing aeveral thouaand empirical atudiea of

leadership, Stogdill (1974) failed to find any single type

of leader behavior consistently related to higher

performance. Leadership then either moderates other

relationships or is moderated by other factors in its

relationship to performance. The two models to be tested in

this study approach the leadership-performance link

differently. The Phillips et al. (1987) model assigns

38

leadership a direct effect on performance and interactive

effects with organizational commitment and cohesion. The

Schriesheim et al. (1979) model posits that leadership has

no direct effect yet is moderated by cohesion and group

drive. This section of the literature review consists of

three parts. The first part reviews the literature

examining the roles of leadership and cohesion in relation

to performance. The aecond part examinea the atudiea

addreaaing leaderahip, and group drive. Finally, a akilla

approach to leaderahip ia diacuaaed aince thia atudy

examinea the poaaibility of auch an approach. More detailed

reviewa of the leaderahip field can be found in recent

reviewa by Yukl (1989a, 1989b), Smith and Peteraon (1989),

and Baaa (1990).

Leaderahip and Coheaion

Throughout the development of the leaderahip field,

reaearchera have aought to find a conaiatent element related

to leader effectiveneaa. After reaearcher failed to find

univeral traita that led to leader effectiveneaa,

researchers began examining leader behaviors. The search

continued, except instead of traits, universally effective

leader behaviors were studied (e.g., Blake and Mouton,

1964). In the early 1960's, the focus of leadership

researchers shifted to the realization that situational

factors interacted with leader behavior to produce differing

39

degrees of effectiveness. A trend through many of the

leader behavior studies, however, was the dichotomization of

behaviors into instrumental and supportive (task and

relationship, initiating structure and consideration)

behaviors (e.g., Schriesheim & Stogdill, 1975; Likert, 1967;

Hersey & Blanchard, 1977). It should be noted that the

cohesion literature, aa diacuaaed earlier, developed in a

similar manner with taak and aocial diatinctiona.

One of the firat aituational leaderahip theoriea to

conaider the interaction of leaderahip and coheaion waa

Houae's (Houae, 1971) path-goal theory. According to thia

theory, atructuring leader behaviora are effective and

acceptable to a aubordinate only if the work environment

lacka other aourcea of role clarification auch aa coheaion.

Schrieaheim (1980) argued that coheaion ia aaaociated with

acceptance of taak-related rolea. In her atudy,

Schrieaheim examined the moderator effecta of coheaion on

leaderahip. Her findinga aupported the hypotheaia that

leader atructuring behavior waa more poaitively related with

performance in groupa with low coheaion. Converaely, her

atudy alao found that leader conaideration ia more

positively related to performance in groups with high

cohesion. It should be noted, however, that the study used

a self-rated performance measure. Social desirability

biases are probable with such methods.

Tziner and Vardi (1982) studied the performance of tank

40

crews under different combinations of group cohesion and

leader style. Interestingly, their findings were opposite

those of Schriesheim (1980). In the Tziner and Vardi study,

groups with low cohesion performed better with a leadership

style that emphasized a people orientation. Groups with

high cohesion performed better with leaders using a task-

oriented command style. The conflicting findings can be

resolved when considering the groups analyzed in each study.

Schriesheim studied public utility workers with over 70% of

the subjects being clerical workers. In her atudy, role

clarification waa a major factor in determining self-rated

performance. It would be expected that task-oriented leader

style would provide the role clarification not provided by

group cohesion.

In the Tziner and Vardi study, the groups were tank

crews who had well defined roles. Groups with low cohesion

were not lacking role clarity, but inatead lacked

interperaonal attraction. Thua, in the latter atudy,

aupportive leader behavior waa more effective in groupa with

low coheaion. The diacrepanciea between the atudiea can be

seen as resulting from the conceptualization of cohesion

discussed earlier. Schriesheim viewed low cohesion as

lacking role clarity. Tziner and Vardi viewed low cohesion

as lacking personal attraction. It should be noted that

this study uses a conceptualization more in line with the

Tziner and Vardi study.

41

Fiedler (1967), in his contingency model of leadership,

addressed the interaction of leadership and cohesion in the

assessment of the leader-member relations component of the

situational favorableness scale. The underlying hypothesis

for the use of cohesion as an element of the leader-member

relations scale is derived from Fiedler's (1958) work.

Specifically, Fiedler hypothesized that the leader's ability

to contribute to the group's productivity requires that the

group's structure enable him to communicate effectively with

all membera, and that the membera be willing to follow the

directiona of the leader. Fiedler pointa out that coheaion

fulfilla the taak of providing atructure to allow the leader

to communicate with the group and allowa the leader to

devote his influence to the task rather than having to

direct his efforts mainly toward group maintenance. Fiedler

and Garcia (1987) alao aummarized the leader member

relationa reaearch which includea coheaion. Other

leaderahip reaearch that addreaaea coheaion can be found in

comprehenaive reviewa auch aa Baaa (1990).

In another atudy, Fiedler (1963) uaed four aeparate

experimenta that provided conaiatent aupport that the

leader'a intelligence predicta group performance in coheaive

groups, but not in uncohesive groups. He argued that the

leader directly influences the effectiveness of the group

only if the group is cohesive. If the group is uncohesive,

the power of the leader is diverted from performance to

42

maintenance of the group. This assertion contradicts the

organizational synergy model (Phillips et al., 1987) which

proposes that a leader can overcome the low cohesion of a

group and still directly affect performance.

Kerr and Jermier (1978) addressed the effect of

cohesion on leadership and proposed that group cohesion can

reduce the effectiveneaa of both aupportive and inatrumental

leader behavior. Their argumenta for their hypotheaia

parallel thoae given by Schrieaheim (1980) and Tziner and

Vardi (1982). Kerr and hia colleaguea (Howell et al., 1990)

later modified their position and stated that an enhancer

for leader directiveneaa and aupportiveneaa ia the creation

of coheaive work groupa with high performance norma. Thua,

coheaion enhancea leaderahip rather than merely aubatituting

for it.

Leaderahip and Group Drive

Early behavioral reaearchera auch aa Lewin, Lippitt,

and White (1939) provided the baaia for aupport that

instrumental and supportive leadership positively impacted

on group drive. Group drive is conceptualized as

representing the "intensity with which members invest

expectation and energy on^ehalf of the group." (Stogdill,

p. 27). Schriesheim et al. (1979) cite a study

done by Greene and Schriesheim (1977) that found that both

types of leader behaviors led to group drive. More

recently, Podsakoff and Todor (1985) examined the

43

relationship of leader reward and punishment behavior with

group drive. They found that leader contingent reward and

contingent punishment behavior was positively related to

group drive. They also found that noncontingent punishment

and reward behavior were negatively related to group drive.

In this present atudy, leaderahip ia viewed with two

perspectives. In the organizational synergy model (Phillips

et al., 1987), no distinction is made between instrumental

or supportive leadership. Indeed, the model simply aaaerts

that effective leaderahip interacta with coheaion and

organizational commitment. The leader-group interactiona

model, on the other hand, uaea the familiar dichotomy of

inatrumental and aupportive leaderahip. In keeping with the

original model formulationa, the atudy will uae a leader

effectiveneaa rating baaed on leader competenciea for the

organizational aynergy model and a dichotomized leaderahip

rating (inatrumental and aupportive) for the leader-

interactiona model.

A Skilla Approach to Leaderahip

The majority of recent leaderahip theoriea have dealt

with leader behaviora. Reaearchera have deacribed what

leaders do, what leaders should do to be more effective, and

what leaders should do in order to be more effective in

different leadership situations (Wright & Taylor, 1984).

Consequently, leader behavior was described in very general

44

terms (e.g., democratic, autocratic, considerate,

authoritarian, relationship-oriented, task-oriented).

Unfortunately, the theories do not specify how to perform

this type of behavior. Thus, a well meaning, but

incompetent democratic leader can destroy an organization.

As Wright and Taylor (1984) pointed out, "it is not merely

what leaders do, but how well they do it which determines

how effective they are" (p. 15). A akilla approach to

leaderahip (e.g., Katz, 1955; Hollander, 1960; Miachel,

1968) attempta to account for thia notion of competency of

the leader.

Thia atudy alao uaea a akilla approach to leaderahip

due to a practical conaideration of the atudy. The atudy

focuaea on organizational leadera who are placed in the role

of a leader with organizational atructure already in place

and organizational objectivea already eatabliahed. If

lieutenanta become platoon leadera by fiat, then a major

concern ia how to develop the lieutenanta prior to them

becoming platoon leadera. Skilla can be developed and theae

may later prove to be the baaea for leader behaviora. For

the purpoaea of leader training, a akilla approach to

leadership is advantageous.

The skills addressed in this study are similar to

Clement and Ayres' (1976) nine leader competencies. Clement

and Ayres pointed out that their nine competencies were

developed to "classify skills and competencies representing

45

essential requirements for effective organizational

leadership functioning" (p. 4). The nine leader skills

proposed in their study are: communication, human

relations, counseling, supervision, technical, management

science, decision making, planning, and ethics.

Importantly, they also proposed that necessary leadership

skills vary according to organizational level. Thus, at the

platoon leader level, Clement and Ayres would argue that

communication, human relations, counseling, supervision, and

technical skills are more relevant to performance. The

skill approach to leaderahip cloaely parallela the

competenciea propoaed by Quinn (1988), Mintzberg (1973),

Wright and Taylor (1984), or Kotter (1982).

46

CHAPTER III

THE THEORETICAL SETTING

The review of the coheaion literature pointa out that

the cohesion-performance relationship has not been

consistently empirically supported. The two modela teated

in this study attempt to clarify the relationship by

bringing in leadership and group processes.

The Organizational Synergy Model

According to Phillips, Blair, and Schmitt (1987),

organizational synergy may be viewed as consisting of three

main and interactional components: peer cohesion,

leaderahip, and organizational commitment. Organizational

aynergy ia more than the aum of ita componenta. Inatead,

organizational aynergy ia a quantum leap in performance due

to the interactive nature of ita componenta. Peer coheaion

ia defined aa the degree of poaitive, affective

relationahipa between peer group membera. Leaderahip ia not

apecified exactly, but the authora auggeat leader behaviora

ranging from initiating atructure and conaideration to

Bass's (1985) model of transactional and transformational

leadership. Whatever method of operationalizing is used,

the authors state that the focus is on effective leadership.

Finally, the authors suggest a definition of organizational

commitment such as that posited by Mowday, Porter, and

Steers (1982). In this conceptualization, organizational

47

commitment can be portrayed as having three major

components: (a) a person's strong belief in and an

acceptance of the organization's goals, (b) a person's

willingness to exert considerable effort on behalf of the

organization, and a (c) a person's definite desire to

maintain membership (Porter et al., 1974).

In the military context, the role of organizational

commitment in the organizational synergy model can be viewed

as a mid-range response to the extreme views of military

cohesion. Prior to World War II, military researchers

stressed that military cohesion was to a great extent a

function of the belief in the "rightness" of the larger

cause which was at issue in the war (e.g., the "Why We

Fight" films prior to WWII). After the war, Shils and

Janowitz (1948) found that the tenacity of the Wehrmacht was

not due to ideological reasons, but due to the bonds of the

primary group. Gabriel and Savage (1978) took this view to

the extreme by asserting that for the German soldier, "the

"cause," Nazi ideology, or even nationalism, waa never the

driving force in hia capacity to fight" (p. 36). Inatead,

Gabriel and Savage attribute coheaion with effective leader­

ship as the main aspect of unit effectiveness. The organi­

zational synergy model softens the Gabriel and Savage ap­

proach and re-introduces the "larger cause" through the

construct of organizational commitment.

48

The components of organizational synergy can have both

main and interactive effects, except for peer cohesion which

only has an interactive effect on increased performance.

The essence of the organizational synergy concept can be

seen in the equation:

OS = L + OC + (L X OC) + (C X L) + (C x OC) + (C x L x OC)

where OS = organizational synergy, C = peer cohesion, L =

leader effectiveness, and OC = organizational commitment.

Phillips et al. (1987) provided six examples to illustrate

the interactive aspects of the model. They can be summa­

rized as follows:

Case 1: Peer cohesion is very high, leader influence

is low, and organizational commitment is low. In this case,

organizational synergy and hence performance would be low

despite the high coheaion. Thia condition waa exemplified

in the Hawthorne atudiea.

Caae 2: Peer coheaion ia very high, leader influence

is very high, and organizational commitment is relatively

low. In this situation, organizational synergy would be

high due to the interaction between cohesion and leadership.

This is the condition described by Shils and Janowitz (1948)

when analyzing the German Army during World War II.

Case 3: Peer cohesion is low, leader influence is

high, and organizational commitment is low. In this

situation, the model suggests a moderate degree of

organizational synergy due to leadership. Leadership thus

49

overcomes the negative effects of cohesion and poor

organizational commitment.

Case 4: Peer cohesion is low, leader influence is

high, and organizational commitment is fair. This situation

would result in higher organizational synergy than described

in Case 3 due to the interactive leadership and organiza­

tional commitment effect in addition to main effects.

Case 5: Peer cohesion is high, leader influence is

low, and organizational commitment ia high. In this situa­

tion, the model asserts that in spite of the leader, organi­

zational synergy may still be high.

Case 6: All componenta are high. Thia ia the

aituation that Phillipa et al. (1987) argue organizational

synergy takes place. Performance goes beyond a mere

additive effect of cohesion, leadership, and organizational

commitment. Inatead, the additive effect ia enhanced by the

powerful interactive effecta.

Peer coheaion will lead to greater aynergy only when

combined with linkagea to the leader, to the organization,

or to both. Peer coheaion, by itaelf, can have no impact on

unit performance. Thua, unit performance will be influenced

by the main effecta of leaderahip and organizational

commitment, and the interactive effecta of any combination

of the three componenta of organizational synergy. The

model posits that the key components may substitute for one

another. That is, there can be effective unit performance

50

without a high degree of peer cohesion, if there is

effective leadership or a high degree of organizational

commitment. Organizational synergy will be the highest,

however, when all three components are high.

Leader-group Interactions Model

Schriesheim, Mowday, and Stogdill (1979) proposed a

leader-group interactions model that is similar to the model

posited by Phillips et al. (1987). Like the organizational

synergy model, the leader-group interactions model rests on

the proposition that coheaion doea not have a direct effect

on performance. The modela differ in the role of leaderahip

and the direct effect of organizational commitment on per­

formance. In the Phillipa et al. model, leaderahip haa an

interactive effect with coheaion on performance. In the

Schrieaheim et al. model, leaderahip ia an antecedent to

group drive and group coheaion. Group drive ia the group'a

vigor or enthuaiaam (Stogdill, 1972) that parallela the

willingneaa-to-expend-effort component of the Porter et al.

(1974) conceptualization of organizational commitment. Goal

acceptance ia the parallel conatruct to the ahared values

and goals component of the Porter et al. conceptualization

of organizational commitment. The essence of the

Schriesheim et al. model is that leader behavior influences

group performance through its impact on group drive and

cohesion. Group drive and cohesion interact and are

moderated by group goal acceptance. The leader-group

51

interaction model indicates that group performance is more

highly related to the group characteristics of drive and

cohesiveness than to leadership. This is contrasted by the

organizational synergy model's asaertion that leaderahip can

have a direct effect on group performance and can overcome

the low coheaion and low organizational commitment.

Integrating the commitment literature with the coheaion

literature, it ia poaaible to aee a dual role for

organizational commitment in the organizational aynergy

model. Firat, the coheaion literature auggeata a need for a

normative moderator to clarify the coheaion-performance

relationahip. The goal congruence conceptualization of

organizational commitment bringa into the model ahared

valuea and direction for the coheaion exiating in the group.

Second, the commitment-performance literature auggeata that

the goal congruence commitment conceptualization alao haa a

main effect on performance (e.g., Decotiia & Summera, 1987).

The organizational aynergy model adopta both theae viewa in

poaiting a moderator effect upon coheaion and a direct

effect upon performance from organizational commitment.

The Schrieaheim et al. (1979) model doea not uae the

label organizational commitment. Inatead, the leader-group

interactions model divides the previously discussed Porter

et al. (1974) conceptualization into group drive and group

goal acceptance. Group drive is conceptualized as

representing the "intensity with which members invest

52

expectation and energy on behalf of the group" (Stogdill,

p. 27). This definition parallels the willingness to expend

effort component of the Porter et al. (1974)

conceptualization of organizational commitment except it is

at the group level instead of the organizational level.

Likewise, the group goal acceptance component of the

Schriesheim et al. model parallels the shared values and

goals component of the Porter et al. (1974) definition.

Schriesheim et al. posit that group drive provides

motivation while the goal acceptance provides the direction

of the energy of the group. One can see how it strongly

parallels the organizational synergy model by dissecting the

organizational commitment conceptualization into the two

components of group drive and group goal acceptance.

Empirical support of the effects of group drive and

goal acceptance was provided by a atudy done by Greene

(1989). In hia atudy, goal acceptance, and to a leaaer

degree, group drive were identified aa aourcea of variance

in productivity and both were found to moderate the

relationship between cohesion and productivity. The

findings suggest, contrary to the Schriesheim et al. (1979)

model, that group goal acceptance is antecedent to group

drive. Greene qualified his findings, however, by raising

the possibility that causation may be hard to interpret due

to his use of developing rather than mature groups. For the

purposes of this study, however, Greene's findings provide

53

empirical support for the moderating effects of group drive

and goal acceptance on the cohesion and performance rela­

tionship.

Hypotheses

The hypotheses tested in this study can be categorized

by each model. For the organizational synergy model, the

hypotheses are:

H j : Cohesion will not have a direct effect on

performance.

H2: Effective leadership will have a direct, positive

effect on performance.

H^: Organizational commitment will have a positive,

direct effect on performance.

H^: Effective leadership and organizational commitment

will have an interactive effect on performance.

He: Coheaion and leaderahip will have an interactive

effect on performance.

H/-: Coheaion and organizational commitment will have

an interactive effect on performance.

HT: Coheaion, organizational commitment, and

leaderahip will have an interative effect on performance.

The organizational aynergy model doea not hypotheaize a

direct effect of coheaion on performance. H-j is included as

a test of the null hypothesis.

54

The hypotheses based on the leader-group interactions

model are:

Kg: Neither instrumental nor supportive leader behav­

iors will have a direct effect on performance.

Hg: Instrumental and supportive leader behaviors will

have a positive, direct effect on group drive.

H-J Q: Instrumental and supportive leader behaviors will

have a positive, direct effect on cohesion.

Hii: Group drive, coheaion, and goal acceptance will

have an interactive effect on performance.

H-|2* Group drive will not have a direct effect on

performance.

H]_3: Group goal acceptance will not have a direct

effect on performance.

Aa with the teat of the organizational aynergy model,

several null hypotheses were included to verify the

postulates of the model. Thus, Hg, H ^ , and H-|2 were

included.

55

CHAPTER IV

RESEARCH DESIGN

Method

Approximately two weeks prior to deployment to the

National Training Center, U. S. Army units were administered

instruments by Army Research Institute researchers. Items

from this questionnaire were aggregated at the platoon level

to operationalize the constructs of cohesion, organizational

commitment, group goal acceptance, group drive, instrumental

leadership, supportive leaderahip, and leaderahip

effectiveneaa. During the actual rotation at the National

Training Center, the unita were evaluated on a aeriea of

miaaiona to include attacka, defenaea, movementa, and live

fire exerciaea. Subject matter experta accompanied each

unit and gave a rating of aucceaaful or unaucceaaful per­

formance.

The data were aubjected to a confirmatory factor

analyaia uaing LISREL 7 (Joreakog and Sorbom, 1989). Once

the meaaurement model waa aaaeaaed, the hypotheaea were

teated uaing linear regreaaion. LISREL waa not uaed for all

tests due to the large number of interactive relationships.

A detailed discussion of the methodology used in this study

follows.

Sample

The sample consists of 101 platoons from U.S. Army

armor and infantry units. Forty-two platoons were infantry

56

platoons and fifty-nine were armor platoons. An armor

platoon contains sixteen men and an infantry platoon

contains thirty-two men. The data were collected from four

rotations (separate deployments) at the National Training

Center.

The unit of analysis for this study is the platoon.

Figure 4.1 shows the organizational structure of the two

types of platoons uaed in thia analyaia. Coheaion,

organizational commitment, group drive, and group goal

acceptance are meaaured at the platoon level by aggregating

individual reaponaea. Leaderahip concerna the leaderahip of

apecific platoon leadera, except for the meaaurement of

aupportive leaderahip which could only be meaaured by uaing

the aubordinate leadera' perception of platoon leaderahip in

general (i.e., the platoon leader or the platoon aergeant).

Thus, any findings including the supportive leadership

construct must be qualified by pointing out that the process

of leadership is the focus rather than a single individual's

behaviors. Performance is also measured at the platoon

level. Haney (1980) noted that there are four

considerations when addressing unit of analysis: (a)

purpose, (b) evaluation design, (c) statistical

considerations, and (d) practical considerations. The

following discussion will address the level of analysis used

in this study in relation to these considerations.

57

The purpose of both models being tested is to determine

effectiveness at the unit level while considering group

processes. Indeed, the main construct in the study is

cohesion which is a group construct. In the military con­

text, early researchers such as Shils and Janowitz (1948)

implied that squad or team level was where cohesion was moat

important. In a military organization, companiea are

compriaed of platoona which are aubdivided into aquada which

are divided into teams. Marlowe et al. (1985) argued that

the coheaion waa beat atudied at the company level. It ia

at the platoon level, however, that aoldiera are often croaa

attached to other unita. Additionally, the coheaion at the

aquad level ia often affected by policiea at the platoon

level. The preaent atudy uaea data gathered from an

inatrument uaing a platoon level of analyaia in the phraaing

of the itema concerning coheaion. Thua, coheaion ia the

aggregation of individuala' reaponaea about their

perceptiona of the platoon'a coheaion. Additionally, the

deaign of the National Training Center'a evaluation ayatem

providea a performance rating at the platoon level and

higher.

Statiatical conaiderationa include the effect on

reliability of aggregating individual reaponaea at the

platoon level as opposed to using only individual responses.

Shaycroft (1962) proposed that group mean reliability will

be higher than individual score reliabilities. When

58

aggregating, the expected value of the error term approaches

zero. Justification for aggregation is also provided by

Schneider and Bowen (1985) and Schneider (1975, 1983) and

George (1990). A disadvantage of using a platoon level of

analysis is the reduced sample size. Since structural

equation modeling usually requires a large sample size, a

reduced N may affect the significance of the findings.

A practical consideration when addressing the unit of

analysis issue is determining how to mitigate the impact of

missing data. By uaing the platoon level, a low reaponae

rate in a particular aquad ia not problematic aince the unit

of analyaia ia ahifted to the higher platoon level. It muat

be aaaumed, however, that the reapondenta that are

aggregated at the platoon level are repreaentative of the

entire platoon. For the infantry platoona, the mean number

of aquad membera who were aggregated to meaaure group

conatructa of coheaion, group goal acceptance, group drive,

and organizational commitment waa 14.6 aoldiera. For armor

platoons, it ^^^ Q - 7 qnl (;i-i pra. The mean number of squad

leaders in infantry platoons aggregated to measure

leadership was 3.4 sergeants. For armor platoons, the mean

number of tank commanders (squad leaders and platoon

sergeant) was 2.5 sergeants.

Rousseau (1985) also argued that when unit

characteristics are to be assessed through perceptual data,

the unit members should be divided into groups to avoid

59

common method bias. In this study, cohesion, organizational

commitment (to include group drive, and group goal

acceptance) were measured using perceptiona by the aquad

members. Leadership of the platoon leader was assessed by

the perceptions of the squad leaders and the platoon

sergeant (the subordinate leaders). Finally, performance

was determined by the subject matter experts. Common method

bias is minimized in this study since the constructs are

measured using three different sources.

The present atudy takea individual perceptiona

concerning group proceaaea and examinea the affect upon

group performance. Eaaentially, all conatructa are raiaed

to the group level. Another method that may have been uaed

ia the varient paradigm (Danaereau, Alutto, & Yammarino,

1984) . The unique aapect of the varient approach ia that it

allowa one to conduct analyaea that permit choicea among

hypotheaized relationahipa among variablea at multiple

levela of analyaia.

Operationalization of the Conatructa

The itema were taken from a larger 17 page inatrument

administered by the Army Research Institute. In the

organizational synergy model, cohesion was operationalized

by using responses from the squad members (not the leaders)

to two items:

60

A. Almost All

B. Most

C. Some

D. Few

E. Nearly None

C12. of the squad members in this platoon trust each other.

C13. of the squad members in this platoon care about each other.

As stated earlier, the organizational synergy model

advocated a narrow definition of cohesion along the lines of

the Etzioni (1975) conceptualization. The two items shown

above focus sharply on the affective bonds between group

membera and exclude other factora auch aa attraction to the

group. It ahould be noted that the coheaion evaluated by

the itema ia only between aquad membera. Thua, a coheaion

between aquad membera and aquad leadera (vertical coheaion)

ia not being addreaaed. Thia ia a key aapect of the organi­

zational aynergy conceptualization of coheaion. Theae two

itema, aa well aa all the itema in the inatrument, were

reverse coded. With reverse coding, higher numbers reflect

higher levels of the construct being measured.

Organizational commitment in the organizational synergy

model is composed of three facets: (a) a belief and

acceptance of organizational goals, and values, (b) a

willingness to exert effort toward organizational goal

accomplishment, and (c) a strong desire to maintain

organizational membership (Porter et al., 1974). Two items

from the squad member instruments were used to

61

operationalize the belief and acceptance of organizational

goals and values (group goal acceptance).

A. B. C. D. E. Almost Most Some Few Nearly All None

CI. of the squad members in this platoon uphold and support Army values such as loyalty, honesty, and devotion to duty.

Cll. of the squad members feel they are serving their country.

One item also from the squad member instrument was used to

measure willingness to exert effort towards organizational

goal accomplishment (group drive).

A. B. C. D. E. Very More than About the Less than Very Hard Most Same as Most Little

People Most People People

CDl. How hard do you work and try to do as good a job as possible?

Two items from squad members were used to operationalize the

willingness to maintain organizational membership (group

membership).

A. Almost All

CA8. platoon.

B. C. D. E. Most Some Few Nearly

None

of the squad members are proud to be i

A. B. C. D. E. Strongly Agree Neither Disagree Strongly Agree Agree Nor Disagree

Disagree

CA14. In combat, I would rather serve with this platoon than with some other platoon.

62

A key issue here is the level of analysis when

addressing the organization. In operationalizing the goal

acceptance component of organizational commitment, the items

infer that the organization is the Army as a whole. The

group effort component moves away from the Army aa the

organization and leavea the reapondent free to decide to

which goala the item refera. The group memberahip component

of organizational commitment ia meaaured by itema referring

to memberahip to the platoon rather than the Army. Thua, a

limitation in uaing theae itema ia that the organization to

which commitment may be directed may be the Army, the

platoon, or even the reapondent'a company or battalion.

Leaderahip aa deacribed in the organizational aynergy

model ia equated with hierarchical coheaion. The authora

atate, however that "the aame factora that reault in

effective leaderahip in general alao reault in improving

hierarchical coheaion" (Phillipa et al., 1987, p. 153).

Thua, the organizational aynergy model ia more concerned

with effective leaderahip than breaking it down into

componenta auch aa inatrumental or aupportive.

Thia atudy uaed an index of four leaderahip akilla

poasessed by the platoon leader aa perceived by the aquad

leaders. This approach uses the "master manager" concept of

Quinn (1988). According to the competing values approach

(Quinn, 1988) leader effectiveness is determined by the

ability of the leader to have behavior in each of the four

63

competing quadrants consisting of the open systems model,

the rational goal model, the human relations model, and the

internal process model. Thus, leader effectiveness is not

the resulting level of group performance. Instead, it is

the ability of the leader to be skilled in many different

leader behaviors.

Squad leader and platoon sergeant responses to the

following items were used to measure effective leadership.

How well does [your platoon leader] perform these akilla?

Exceptionally Very Well Barely Poorly Well Well Adequate

L4. Deciaion making

L8. Technical and Tactical Proficiency

L9. Initiative

L13. Soldier team development

The firat item tapa the rational goal quadrant. The aecond

item addreaaea the internal proceaa quadrant. The open

systems quadrant is measured by the third item, and the

human relations quadrant is reflected by the fourth item.

The original instrument had thirteen leader skills. Only

four were chosen because the responses were so highly

correlated (average r = .800).

The leader-group interactions model also uses cohesion

as a construct. Cohesion is operationalized the same as

described above for the organizational synergy model. The

leader-group interactions model does not use organizational

commitment, but instead uses only two components of it:

64

group effort (drive) and goal acceptance. These two

concepts are also operationalized as described above for the

organizational synergy model. As stated earlier, the exact

level of analysis for the items are not specified for the

respondent.

The leader-group interactiona model dichotomizea lead­

erahip into the two familiar typea of leader behavior.

Inatrumental leader behaviora are directed toward taak

accompliahment, while aupportive leader behaviora are di­

rected toward group maintenance (Stogdill, 1974; Schrieaheim

et al., 1979).

Originally, this atudy aet out to divide the leader

akilla deacribed above into thoae reflecting inatrumental

and thoae reflecting aupportive behaviora. Thua, inatrumen­

tal akilla would include:

L2. Planning

L3. Superviaion

• L4 . Deciaion making

L6. Management

^L8. Technical and Tactical Proficiency

Supportive skills or those directed toward group maintenance

would include:

LI. Communication

Lll. Flexibility

^L13. Soldier team development

65

Unfortunately, confirmatory factor analysis showed that the

leader skills could not be dichotomized into instrumental

and supportive behavior.

Hence, an alternate method of tapping instrumental and

supportive leadership had to be used. Items were selected

from the questionnaire that addressed supportive and

instrumental leader behaviors instead of supportive or

instrumental skills. The instrumental leadership items

taken from the squad leader and platoon sergeant instruments

are:

A. Almost Always

B. Often

C. Sometimes

D. Seldom

E. Almost Never

[My platoon leader]

T10PL4. Tells soldiers specifically what they do right.

T10PL6. Tells soldiers specifically how to improve.

The items used to operationalize supportive leader behavior

are:

A. B. C. D. E. Strongly Agree Neither Disagree Strongly Agree Agree Nor Disagree

Disagree

CA3. Leaders in this platoon encourage me to train and do my best.

CA5. Leaders in this platoon keep soldiers informed about what is going on in the unit.

As stated earlier, any findings using the supportive

leadership construct must be qualified that the leadership

was not the specific behaviors of the platoon leader.

66

Performance

Performance was measured at the platoon level. The

performance data came from subject matter experts who

accompanied each platoon into simulated battle. Each

platoon fought in an average of six battles during the force

on force phase of the National Training Center deployment.

If a platoon received a successful then it was assigned a

score of "1" for that battle. If it received an

unsuccessful rating, then it was assigned a "0" for that

battle. Performance scores were then calculated as the

percentage of success in all the battles fought.

Descriptive statistics for the performance criterion

are as follows:

N 101 Mean _0^J3Q^LM-Std Dev 0.19882 Variance 0.039529 Skewness 0.193968 Kurtosis -0.59043 W:Normal 0.947497 Prob<W 0.0009

The W statistic is the Shapiro-Wilk atatiatic (Shapiro and

Wilk, 1965) which ia the ratio of the beat eatimator of the

variance to the uaual corrected aum of aquarea eatimator of

the variance. The W atatiatic for the performance criterion

ia large, therefore the dependent variable appeara not to be

normally diatributed. Graphically, however, the normal

probability plot in Figure 4.2 aupporta the aaaumption of

normality of the performance criterion. If the data (marked

with asterisks) are from a normal distribution, they should

tend to fall along the reference line (marked with plus

aigns).

67

Infantry Platoon Armor Platoon

Platoon Leader Platoon Leader I I (3 men)

Platoon Sergeant Platoon Sergeant I I (3 men)

I 1 1 I 1 Squad Ldr Squad Ldr Squad Ldr Tank Cdr Tank Cdr (10 men) (10 men) (10 men) (3 men) (3 men)

Figure 4.1 Organizational chart for platoons.

68

0 . 8 2 5 +

0 . 4 2 5 +

0 . 0 2 5 + * +•

+ *

++ •k-k

* * * * *

* * * + +++

+ * + * * * *

* * * * *

+++ * * * * * * *

+++ * * * * * *

+ + + + + + + + + + - 2 - 1 0 +1 +2

Figure 4.2 Normal probability plot for performance criterion.

69

CHAPTER V

DATA ANALYSIS

Measurement Model

Once the constructs were operationalized, two

measurement models were tested. Figure 5.1 and Figure 5.2

show the measurement models for the organizational synergy

model and the leader-group interactions model, respectively.

Reliability and validity were then assessed. Reliabilities

from previous studies using identical items were not

available. Reliabilities were calculated in thia atudy uaing

aeveral techniquea. Cronbach'a alpha (Cronbach, 1951) waa

first computed for the items. Although Cronbach's alpha is

widely used, it equals reliability only if the itema are

atrictly parallel or, at leaat, eaaentially tau-equivalent.

Otherwiae alpha only aervea aa a lower bound for reliability

(Novick and Lewia, 1967). Theta (Zeller and Carminea,

1980), a reliability more appropriate for itema that are not

homogeneoua, waa also computed from principal componenta

analyaia. Individual reliabilitiea (aquared multiple

correlationa) and compoaite reliabilitiea were calculated

using LISREL (Bagozzi and Yi, 1988). Diacriminant validity

was also calculated with a confirmatory factor analysis

using LISREL. The LISREL output for the organizational

synergy model is found in Appendix A and the output for the

leader-group interactions model is found in Appendix B.

70

Regression analysis followed the confirmatory factor

analysis.

Organizational Synergy Measurement Model

Table 5.1 shows the reliabilities for individual items

and constructs contained in the organizational synergy

model. For composite reliabilities, values greater than

about .6 are desirable (Bagozzi and Yi, 1988). For the

organizational synergy model, all composite reliabilities

are adequate. One of the items included in the

organizational commitment construct, CDl, has a low

individual reliability with a squared multiple correlation

of only .195. Since it was only one of five itema included

in the conatruct and it waa the only available item to tap

the effort component of organizational commitment, it waa

retained. The T-valuea (Table 5.2) alao eatimate the preci-

aion of each parameter eatimate. Aa expected, the T-value

for CDl pointa to poaaible problema with that item.

Fornell and Larcker (1981) atate that a teat of

diacriminant validity ia to compare the compoaite

reliabilitiea of the latent conatructa with the aquare of

the phi matrix. If the compoaite reliabilitiea are larger,

discriminant validity is supported. For the organizational

aynergy model, all compoaite reliabilitiea are larger than

the aquare of the phi valuea. Diacriminant validity ia

supported.

71

The confirmatory factor analyaia of the meaaurea used

in testing the organizational synergy model (Appendix A)

reveals a chi-square of 41.74 (d.f. = 41) and a p-value

of .439. Nonsignificant chi-square values are desirable.

The model has an adjusted goodness-of-fit index (AGFI)

of .889 indicating a satisfactory fit and good discriminant

validity. Another measure of model fit to the data is the

root mean aquare reaidual. Low valuea are deairable. The

value for the organizational aynergy model ia .016, again

pointing toward a good fit. It ia important to note,

however, that LISREL worka beat for larger aample aizea (N =

200) . With a aample aize of 101, a Type II error may reault

(Bagozzi and Yi, 1988).

Wold'a (1985) partial leaat aquarea (PLS) technique ia

aimilar to LISREL in that both atructural relationa among

latent variablea and relationahipa between latent variablea

and obaerved variablea may be modelled. The PLS technique,

however, ia more auitable for the analyaia of amaller

samples such as the present study. LISREL was used in the

present study because it is more widely used in the

literature and the effects of a smaller sample size can be

assessed with the program.

Since the AGFI is only satisfactory and not excellent

(above .90), examining the modification indices provides

some insight to where the model is losing fit. Modification

indices greater than 3.84 suggest that freeing the

72

corresponding parameter will significantly improve the model

(Bagozzi and Yi, 1988) . In the organizational synergy

model, the fit would be increased if item CI was allowed to

load with the cohesion construct. Item CI asks:

CI. of the squad members in this platoon uphold and support Army values such as loyalty, honesty, and devotion to duty.

The high modification indice may be a reault of a theoreti­

cal or a methodological reaaon. Theoretically, item CI

determinea the amount of ahared valuea held in the platoon.

Researchers (Festinger, 1950; Goodnow and Tagiuri, 1952)

have long held that shared values may lead to cohesion,

although the reaearch haa been unable to determine which ia

the antecedent. Methodologically, item CI may load on the

cohesion measure because it was on the same page as the

other two items measuring cohesion. Freeing the item to

load on cohesion would increase the fit, but modifications

other than trivial changes should be treated cautiously and

cross validated (Bagozzi and Yi, 1988). Since the fit is

already satisfactory, no adjustments were made.

Leader-group Interactions Measurement Model

Table 5.3 shows the reliability of measures for the

leader-group interactions model. Group drive, the single

item CDl, is the same item that had a low reliability when

used with the organizational synergy model. Because the

leader-group interactions model proposes that group drive is

the vigor or enthusiasm rather than the effort of the group,

73

and the actual item is very susceptible to social

desirability biases, the error variance was set at 30%.

Thus, the reliability was based on item CDl only measuring

70% of the actual construct of group drive. With that

adjustment, the reliability of group drive changes to .699

reflecting the preset reliability. Supportive leadership

did not achieve the .6 composite reliability rule of thumb.

Item CA3 has a very low individual reliability, but since

the alpha and theta reliabilities were adequate, the item

was not deleted. An examination of the T-values (Table 5.4)

also points to item CA3 being weak on measuring supportive

behavior.

The confirmatory factor analysis for the leader-group

interactions model (Appendix B) reveals a chi-aquare of

21.60 (d.f. = 18) with a p-value of .250. Aa with the

organizational aynergy model, a nonaignifleant chi-aquare

value ia deairable. The model yielda an AGFI of .891 which

ia a aatiafactory fit. The root mean aquare reaidual ia a

small .015, also indicating a good model fit. Thus, the

data fit the model well. Using the Fornell and Larcker

(1981) test of discriminant validity, however, the phi value

for goal acceptance exceeds the composite reliability for

cohesion. This indicates that cohesion and goal acceptance

may be explaining the same variance and thus discriminant

validity may be threatened. The possible problems in

discriminant validity are mitigated, however, by the

74

satisfactory adjusted goodness of fit index and the

modification indices indicating that letting items in goal

acceptance load on cohesion would not provide any

significantly better model fit. Examining the modification

indices for values higher than 3.84 also shows that

adjusting item CA3 would not affect the model fit.

Analysis of the measurement models for both the

organizational synergy model and the leader-group

interactions model reveals satisfactory model fit.

Reliabilities were satisfactory except for a questionable

supportive leader behavior in the leader-group interactions

model. Discriminant validity waa aupported with good AGFI

valuea for the confirmatory factor analyaia. Further

validity aaaeaament waa not poaaible given the limited

aecondary data. Ideally, validity would be aaaeaaed through

multiple group analyaia, croaa validation, or validity

generalization (Bagozzi and Yi, 1988) .

Reaulta of Regreaaion Analyaia

Multiple linear regreaaion analyaia waa performed to

test the hypotheses of the organizational synergy model and

the leader-group interactions model. The covariance matrix

for the items are found in Table 5.5 and Table 5.6. The

regressions were performed on three groups. First, the

hypotheses were tested with infantry and armor platoons

combined (N = 101). Then only armor platoons were tested

75

(N = 59), followed by only infantry platoons (N = 42). A

theoretical difference between infantry and armor results

was possible because of structural differences. In an

infantry platoon, the soldiers see and interact with each

other in a field environment. In a tank platoon, soldiers

may see and interact with mainly their fellow tank crew

members instead of the entire platoon. This may affect

group variables such as cohesion due to differences in

"sheer contact" (Festinger, 1953).

Constructs were entered into the regression equations

using an index formed by the items assessed in the

measurement model. Index coefficients were the lambdas

(loadings) as determined by the confirmatory factor analysis

using LISREL. The main effect variables were entered into a

regression equation. Each interactive term was then placed

in the equation (without other interactive terms) and the

relationships were examined.

Organizational Synergy Model Regression Results

The organizational synergy model can be expressed in

equation form as:

OS = L + OC + (L X OC) + (C X L) + (C X OC) + (C x L x OC)

where OS = organizational synergy (performance), C = peer

cohesion, L = leader effectiveness, and OC = organizational

commitment. Five regressions were run for the combined.

76

armor only, and infantry only groups. The regression

equations analyzed were:

(1) Performance = C + OC + L

(2) Performance = C + O C + L + (Lx OC)

(3) Performance = C + OC + L -i- (C x L)

(4) Performance = C + O C + L + (Cx OC)

(5) Performance = OC + L + (L x OC) + (C x L) +

(C X OC) + (C X L X OC) .

Because the multiplicative interaction terms are, by

definition, highly correlated with the terms included in it,

multicollinearity allowa the parameter eatimates to be

subject to large fluctuations. To overcome these "bouncing

betas," a technique propoaed by Smith and Saaaki (1979) waa

implemented. Eaaentially, the multicollinearity ia greatly

reduced by centering the conatituent variablea around

minimizing conatanta before forming the multiplicative

function. Aa a check for multicollinearity, the variance

inflation factor (VIF) waa calculated for each of the

interaction terma. A VIF above 10 indicatea

multicollinearity problema. With the Smith and Saaaki

method, the VIF'a for all interaction terma were well below

10. The organizational aynergy model regreaaion reaults, to

include the VIF's, are found in Appendix C. The adjusted

R^, beta weights, and p-values for each group and each

regression equation specified above are summarized in Tables

5.7 through 5.11.

77

Although cohesion does not have a main effect on

performance according to the organizational synergy model,

it was included in several regression equations as a test of

the null hypothesis.

Leader-group Interactions Model Regression Results

Hypotheses concerning the leader-group interactions

model were tested with three subgroups of combined, infantry

only, and armor only platoons with four regression

equations. The four equations tested (using the notation of

Instr = inatrumental leaderahip, Supp = aupportive

leaderahip. Goal = group goal acceptance. Drive = group

drive, and C = coheaion) were:

(1) Performance = Inatr + Supp + Drive + Goal

(2) Drive = Inatr + Supp

(3) Coheaion = Inatr -i- Supp

(4) Performance = Drive + Goal + (Drive x Goal) +

(C X Drive) + (C x Goal) + (Drive x C x Goal).

Aa with the organizational aynergy model, the predicted

null hypotheaea concerning the effecta of inatrumental and

aupportive leaderahip are teated in aome of the regreaaion

equationa. The leader-group interactiona model regreaaion

results are found in Appendix D. Summarized results of the

adjusted R values, beta weights, and p-values are found in

Tables 5.12 through 5.15.

78

c^ t^ t o ^0

LllA

AJLP

X X X X X

c z

c c c

O

O

c

c o

•H 4J r3 N

•H

c

o

c

t^ to

79

CO c/tf CO

U-P

CO CO CO C O CO c ^ c ^ (^6 c/6

o T3

z C

a E C

c z w c o

o c

c (J

c

CM

c

80

Table 5.1: Reliability of measures for the organizational synergy model.

Measure

Cohesion

C12

C13

squared multiple correlation

.784

.907

Organizational Commitment

CI

Cll

C8

CA14

CDl

.405

.688

.879

.756

.195

Leadership Effectiveness

alpha theta composite reliabilty

.915 .918 .919

.856 .865 .817

.947 .947 .853

L4 .763

L8

L9

L13

.842

.872

.789

81

Table 5.2: T-values for the organizational synergy model.

Cohesion Organizationl Commitment Leadership

C12 10.953 .000 TOOO

C13 12.302 .000 .000

6.947 .000

10.034 .000

12.253 .000

10.804 .000

4.511 .000

.000 10.951

.000 11.903

.000 12.266

.000 11.259

CI

Cll

C8

CA14

CDl

L4

L8

L9

L13

.000

.000

.000

.000

.000

.000

.000

.000

.000

82

Table 5.3: Reliability of measures for the leader-group interactions model.

Measure

Cohesion

C12 C13

squared multiple c correlation

.760

.935

alpha

.915

theta

918

composite reliabilty

.918

Goal Acceptance

CI .487

Cll 563

.687 .687 .691

Group Drive

CDl .699

Instrumental Leadership

T10PL4

T10PL6

.493

.878

.794 .795 .811

Supportive Leadership

CA3

CA5

.171

.589

.715 .700 .500

83

Table 5.4: T-values for the leader-group interactions model. (Goal = group goal acceptance. Drive = group drive, Instr = instrumental leadership, Supp = supportive leadership)

C12

C13

CI

Cll

CDl

T10PL4

T10PL6

CA3

CA5

Cohesion

10

12

.692

.602

.000

.000

.000

.000

.000

.000

.000

Goal

.000

.000

7.443

8.087

.000

.000

.000

.000

.000

Drive

.000

.000

.000

.000

.000

.000

.000

.000

.000

Instr

.000

.000

.000

.000

.000

6.635

8.507

.000

.000

Supp

.000

.000

.000

.000

.000

.000

.000

3.546

5.1250

84

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.470

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91

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76

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( M r ) i H f H c o ' * » H ^ e o o N r > i H r H U r - l U « H Q t J l J 3 t - 3 f H O O U < U iJ

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85

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86

Table 5.7: Regression results for the organizational synergy model for combined platoons, armor only, and infantry only subgroups. (C = cohesion, OC = organizational commitment, L = leadership)

Performance = C + OC + L

Beta Weight p-value adj. R'

Combined platoons

C

OC

L

-.037

.081

.049

777

517

148

-.003

Armor only

C

OC

L

.059

.006

.029

.716

.971

.519

-.037

Infantry only

OC

.123

.226

.097

.565

.221

.050

197

87

Table 5.8 Regression results for the organizational synergy model for combined platoons, armor only, and infantry only subgroups. (C = cohesion, OC = organizational commitment, L = leadership)

Performance = C + O C + L + (Lx OC)

OC

L

L X OC

Armor on

C

OC

L

L X OC

ly

Beta Weight

Combined platoons

-.035

.073

.051

-.048

065

009

024

051

p-value

.794

.568

.139

.709

693

954

609

761

adj. R'

-.013

-.054

Infantry only

OC

L

L X OC

.118

.189

.096

.134

585

333

056

548

.182

88

Table 5.9: Regression results for the organizational synergy model for combined platoons, armor only, and infantry only subgroups. (C = cohesion, OC = organizational commitment, L = leadership)

Performance = C + OC + L +" (C x L)

2 Beta Weight p-value adj. R

Combined platoons

C -.053 .695 -.010

OC .084 .507

L .053 .127

C X L -.086 .553

Armor only

C -.045 .786 -.049

OC -.008 .961

L .015 .752

C X L .115 .546

Infantry only

C .022 .915 .261

OC .184 .302

L .077 .112

C X L -.507 .052

89

Table 5.10 Regression results for the organizational synergy model for combined platoons, armor only, and infantry only subgroups. (C = cohesion, OC = organizational commitment, L leadership)

Performance = C + O C + L + (Cx OC)

Beta Weight p-value adj. R'

Combined platoons

C

OC

L

C X OC

-.039

.092

.053

-.093

.771

.465

.127

.749

-.009

Armor only

C

OC

L

C X OC

.083

.007

.022

.377

.622

.965

.623

.369

-.039

Infantry only

OC

L

C X OC

.106

.213

.098

.224

.626

.255

.051

.591

180

90

Table 5.11: Regression results for the organizational synergy model for combined platoons, armor, and infantry only subgroups. (C = cohesion, OC = organizational commitment, L = leadership)

Performance = OC + L + (L x" OC) + (C x L) -i-(C X OC) + (C X L X OC)

Combined OC

L

L X OC

C X L

C X OC

C X L X (

platoons

DC

Beta Wei

.030

.045

-.021

.134

.329

-.142

.ght p-value

.802

.408

.920

.866

.829

.787

adj. R2

-.0274

Armor only OC

L

L X OC

C X L

C X

C X

OC

L X

Infantry OC

L

L X OC

OC

on ly

C X L

C X OC

C X L X OC

.113

.010

.078

.572

1.16

.269

.146

1.57

.178

2.07

5.89

2.15

.519

.891

.756

.554

.552

.674

-.0699

.615

.317

.649

.323

.154

.145

318

91

Table 5.12 Regression results for the leader-group interactions model for combined platoons, armor only, and infantry only subgroups. (Instr = instrumental leadership, Supp = supportive leadership. Drive = group drive. Goal = group goal acceptance)

Performance = Instr + Supp + Drive + Goal

Beta Weight p-value ad j . R'

Combined platoons

Instr

Supp

Drive

Goal

.020

.137

-.467

.136

.539

.197

.104

.233

.026

Armor only

Instr

Supp

Drive

Goal

.025

.208

.435

.067

541

104

269

605

006

Infantry only

Instr

Supp

Drive

Goal

.049

.131

.293

.423

390

516

461

010

182

92

Table 5.13: Regression results for the leader-group interactions model for combined platoons, armor only, and infantry only subgroups. (Instr = instrumental leadership, Supp = supportive leadership. Drive = group drive)

Drive = Instr + Supp

Beta Weight

Combined platoons

p-value adj. R'

Instr

Supp

-.001

.019

922

635

-.018

Armor only

Instr

Supp

010

030

.488

.510

-.023

Infantry only

Instr

Supp

032

094

.188

.279

-.002

93

Table 5.14 Regression results for the leader-group interactions model for combined platoons, armor only, and infantry only subgroups. (Instr = instrumental leadership, Supp = supportive leadership)

Cohesion = Instr + Supp

Beta Weight p-value adj. R^

Combined platoons

Instr

Supp

-.011

.030

.788

.817

-.019

Armor only

Instr

Supp

.034

.017

499

910

-.024

Infantry only

Instr

Supp

.064

.101

349

683

-.029

94

CO Z < o >-

< OS OC < OQ Q^ Z i CO

"J -

CN O O O

I

>

-5 »-0) _J-v> O O Si X -Q «> 3

u o a

c o

a C

I is i/i o a.

lo O O 00

T3

"o U v> u* 0) u

a X

"D O c 0) a o o

4>

o >

'6 a

O w

a X

UJ

cn 5 J 2 « o J. a>

3 2

Q) C2.

o £ S I O OC

s -I c 0.

<A

o Of

^ o

.sion results for the leader-group ictions model for combined platoons, armor and infantry only subgroups. (Drive = drive. Goal = group goal acceptance, C = on)

mance = Drive + Goal + (Drive x Goal) + rive) + (C X Goal) + (Drive x C x Goal)

Beta Weight

.547

7.18

.208

3.57

.478

4.76

p-value

104

434

925

069

294

442

adj. R'

.054

D D D -.278 .552 -.012

Goal

Drive X Goal

C X Drive

C X Goal

DriveCGoal

. 268

•1 .08

•2.94

1 .36

. 2 8 5

.984

. 7 2 1

.240

. 0 6 9

.974

I n f a n t r y o n l y D r i v e

G o a l

D r i v e x G o a l

C X D r i v e

C X G o a l

D r i v e C G o a l

• .519

1 2 . 9

3 . 6 4

7 . 9 4

.010

8 . 4 8

. 247

. 2 5 8

. 2 6 1

. 1 1 8

. 9 8 5

. 272

. 3 5 7

95

CHAPTER VI

IMPLICATIONS

Hypotheses Results

As a result of the regression analysis, specific

hypotheses were supported or unsupported as follows.

Organizational Synergy Model

H-j : Coheaion will not have a direct effect on perform­

ance. This hypothesis was supported in all three subgroups

since there were no aignificant parameter eatimatea for

coheaion in any of the regreaaion analyaea..

H2: Leaderahip will have a direct, poaitive effect on

performance. Intereatingly, thia hypotheaia waa only aup­

ported in the infantry aubgroup. With infantry platoona,

leaderahip effectiveneaa waa aignificant at the p < .050

level. Aa atated earlier, thia ia probably due to the

organizational atructure differencea between armor and

infantry platoona. In an armor platoon, the platoon leader

may have far leaa leaderahip diacretion (Hunt and Oaborn,

1982) due to the phyaical aeparation of the four tank crewa.

Ho: Organizational commitment will have a poaitive,

direct effect on performance. Thia hypotheaia waa

unsupported in all subgroups. It should be noted, however,

that this conceptualization of organizational commitment was

the three component construct of Mowday et al. (1974). The

narrower construct of group goal acceptance was tested in

96

the leader-group interactions model regression analysis. In

that analysis, group goal acceptance was significant at the

p < .01 level.

H^: Leadership and organizational commitment will have

an interactive effect on performance. This hypothesis was

unsupported in all subgroups.

H^: Cohesion and leadership will have an interactive

effect on performance. This hypothesis was unsupported in

the combined and armor only samples. In the infantry only

sample, it was significant at the p > .052 level and the

parameter was negative. To examine the effects of the

interaction, the partial derivative of the regression

equation (where Y = performance, X-j = cohesion, and X2 =

leadership) over X2 yields the following:

dY = .077 - ,501X-^, dX2

This equation will yield zero when X-j has a value

of .15, so when X- ia above .15, the equation will be

negative, and it will be poaitive when X-j ia below .15.

Thua the inflection point of the alope—the value of X- at

which a change in the direction of the alope occura--ia .15.

The values of cohesion for the infantry subsample range from

1.02 to 1.97 with a mean of 1.45 and a median of 1.41.

Comparing the inflection point with the range in values for

platoon cohesion, it appears that higher leadership

effectiveness is associated with higher levels of unit

performance in those platoons with low cohesion. In

97

platoons with high cohesion, leadership effectiveness tends

to affect performance adversely. This is similar to the

Kerr and Jermier (1978) view of cohesion as a substitute for

leadership, and actually may suggest that cohesion may act

as a neutralizer of leadership.

Hg: Cohesion and organizational commitment will have

an interactive effect on performance. This hypothesis was

unsupported in all aubgroupa.

H-y: Coheaion, organizational commitment, and

leadership will have an interactive effect on performance.

This hypothesis was not supported. It should be noted,

however, that multicollinearity may have affected the

parameter values when the triple interaction term was

included.

The Leader-group Interactions Model

Hg: Neither instrumental nor supportive leader behav­

iors will have a direct effect on performance. This was

supported in all the subgroups, but like H-^, it may be due

more to a lack of statistical power.

HQ: Instrumental and supportive leader behaviors will

have a positive, direct effect on group drive. This

hypothesis was not supported.

H-IQ: Instrumental and supportive leader behaviors will

have a positive, direct effect on cohesion. This hypothesis

was not supported.

98

H-J 2' Group drive, cohesion, and goal acceptance will

have an interactive effect on performance. This hypothesis

was not supported. As with the organizational synergy

model, multicollinearity may have affected the parameter

values due to the inclusion of the triple interaction term.

^12* Group drive does not have a direct effect on

performance. This hypothesis was supported but may be due

to lack of statistical power rather than the lack of

existence of a relationship.

^12' Group goal acceptance does not have a direct

effect on performance. This hypothesis was supported in the

combined and armor only subgroups. Interestingly, group

goal acceptance waa aignificant at the p > .01 level for the

infantry platoona.

Model Implications

This study did not support either the organizational

synergy model or the leader-group interactions model. Both

models attempted to clarify the relationship between

cohesion and performance. The most salient reason for both

models to be unsupported by the data is the exclusion of

other necessary variables. For example, Schriesheim et al.

(1979) also proposed that group task moderates the effects

of cohesion on productivity. Phillips and Wong (1990)

augmented the organizational synergy model with variables

such as training and equipment readiness. Perhaps with a

dependent variable such as platoon battle performance,

99

cohesion is moderated by many more variables than specified

in either model. More comprehensive models, however, are

difficult to test.

Another reason for the nonsupport of the models may

focus on the site of the performance criterion, the National

Training Center. Because it is the National Training Center

and not the National Evaluation Center, units are allowed to

fail and learn from their mistakes. In this type of

learning environment, units may fail, but still maintain and

build high cohesion in the learning process. Cohesion may,

as the models propose, impact on unit performance. The

effect, however, may be hidden by the succesa/unaucceaful

rating found in the criterion variable.

Finally, aa atated in the meaaurement model diacuaaion,

conatruct validity waa not totally eatabliahed for the

measurement model. Secondary data analysis does not include

the choice and selection of indicants nor any aspects of

instrument construction. Thus, in this study since the items

do not come from standard scales, measurement properties of

the items have not been established. More critical,

however, is the potential limitation of invalidity. Items

chosen to represent a construct may, in fact, be measuring

something else. The items measuring organizational

commitment do not exactly match the Mowday, Porter, and

Steers' (1982) conceptualization of organizational

commitment. Additionally, group drive may have to be

100

measured with only a single indicant. A slippage between

what the item measures and how it is used may make

interpretation difficult. As Hyman (1972) pointed out,

however, "one must take what one can find" (p. 23).

It appears then, that the cohesion-performance

relationship remains elusive. What then did the study

reveal other than nonsupport for the organizational synergy

model and the leader-group interactions model? First, the

test of the organizational synergy model showed that

leadership does indeed make a difference. Effective

leadership was found to have an effect on performance in

infantry platoons. This study provides longitudinal,

empirical support to studies such as Smith, Carson and

Alexander (1984) that leadership does lead to increased

organizational performance. Of course, to completely follow

up on this finding, a validation study must be done.

The test of the leader-group interactions model showed

that group (or organizational) goal acceptance also led to

better unit performance. It is important to keep in mind

that goal acceptance was operationalized as the shared

values of devotion to duty and the belief that the soldiers

were serving their country. Support for the relationship

between notions of patriotism and performance contrasts with

views of theorists such as Gabriel and Savage (1978) who

believed that "the 'cause' ... was never the driving force

in [the German soldier's] capacity to fight" (p. 36). If

101

values and a belief in a larger cause can affect

performance, then maybe leadership or any means that affects

these values (transformational leadership) may significantly

raise performance.

Future Reaearch

The aignificant relationahip between leaderahip and

performance for infantry unita deaervea a cloaer look. To

aee how much variance would be explained by the goal

acceptance and leaderahip variablea, a atepwiae regression

was done (Appendix E). Goal acceptance explains 19% while

leadership explains nearly 8% of the variance in unit

performance (in infantry units). Both parameter estimates

were significant. Of course, thia analyaia ia purely poat

hoc and muat be explored in further reaearch.

On a more general level, thia atudy examinea only one

facet of the many determinanta of organizational

effectiveneaa. Both the organizational aynergy model and

the leader-group interactiona model addreaa only the "human"

aide of organizational effectiveneaa. Quinn and Rohrbaugh

(1983) point out that the human relationa view ia only one

of four competing approachea. The adaptive, integrative,

and goal-attainment functiona of organizationa alao have to

be considered. Future reaearch would include other factora

such as training, environment, or equipment.

102

This study used a narrow conceptualization of cohesion.

It did not, however, compare the effects on performance when

using a broader conceptualization. Both the organizational

synergy model and the leader-group interactions model

include constructs that cohesion researchers have included

in their conceptualizations of cohesion. Future research is

needed in testing the power of using a more restrictive

definition.

Finally, effective leadership was associated with

higher performance in infantry platoons, but not in the

armor platoons. It was posited that such differences might

have resulted from atructural differencea. Further reaearch

could explore the effecta of atructure on the impact of

leaderahip on performance in auch unita.

103

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114

APPENDIX A

LISREL 7 OUTPUT FOR

ORGANIZATIONAL SYNERGY MODEL

DA NI=27 NO=101 MA=CM LA C12 C13 CI Cll C8 CA14 CDl LI L2 L3 L4 L5 L6 L7 L8 L9 LIO Lll L12 L13 L14 T10PL4 T10PL5 T10PL6 CA2 CA4 CA5 SE 1 2 3 4 5 6 7 11 15 16 20/ CM FI=C:\PRELIS\DAT.COV MO NX=11 NK=3 PH=ST LK CHSN ORGCOM LDRSHP PA LX 2(10 0) 5(0 1 0) 4 (0 0 1) OU SE TV MI

COVARIANCE

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13

C12

.268

.229

.138

.198

.265

.224

.054 -.039 -.016 -.008 -.037

NUMBER NUMBER NUMBER NUMBER NUMBER NUMBER

OF INPUT VARIABLES 27 OF Y -OF X -OF ETA OF KSI

VARIABLES VARIABLES

0 11

- VARIABLES 0 - VARIABLES 3

OF OBSERVATIONS

MATRIX TO BE ANALYZED C13

.275

.153

.227

.289

.237

.052 -.037 -.019 -.014 -.035

CI

.210

.147

.204

.149

.055 -.040 -.003 -.006 -.020

Cll

.374

.358

.300

.058 -.066 -.032 -.018 -.046

101

C8

.573

.433

.092

.036

.009

.009

.011

CA14

.470

.085 -.021 .023 .012

-.026

115

COVARIANCE MATRIX TO BE ANALYZED CDl L4 L8

PARAMETER SPECIFICATIONS LAMBDA X

CHSN

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13 PHI

1 2 0 0 0 0 0 0 0 0 0

CHSN

CHSN 0 ORGCOM 12 LDRSHP 13

THETA DELTA C12 C13

15 THETA DELTA

CDl

21

16

ORGCOM

0 0 3 4 5 6 7 0 0 0 0

ORGCOM

0 14

CI

TT"

L4

22

LDRSHP

0 0 0 0 0 0 0 8 9

10 11

LDRSHP

0

Cll

18

L8

23

L9 L13

CDl L4 L8 L9

L13

. 0 9 1

. 0 1 2

. 0 3 3

. 0 2 7

. 0 0 4

. 7 6 6

. 5 9 5

. 584

. 562

. 7 2 3

. 6 0 8

. 5 5 3 . 6 8 6 . 5 5 5 654

C8

"T9"

L9

"2?

CA14

To"

L13

""2^

116

INITIAL ESTIMATES (TSLS) LAMBDA

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13

PHI

CHSN ORGCOM LDRSHP

THETA DELTA C12 C13

X CHSN

.458

.499

.000

.000

.000

.000

.000

.000

.000

.000

.000

CHSN

1.000 .854

-.070

CI

ORGCOM

.000

.000

.350

.501

.664

.597

.129

.000

.000

.000

.000

ORGCOM

1.000 -.037

LDRSHP

.0.00

.000

.000

.000

.000

.000

.000

.769

.779

.767

.722

LDRSHP

1.000

Cll C8 CA14

.058 .026 THETA DELTA

CDl L4

087

L8

123

L9

132

L13

.113

.074 .175 .117 .098 .132 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES

C12 C13 CI Cll C8 CA14

.784 .906 .584 .670 .769 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES CDl L4 L8 L9 L13

.760

.183 .772 .839 .857 .798 TOTAL COEFFICIENT OF DETERMINATION FOR X - VARS IS 999

117

LISREL ESTIMATES (MAXIMUM LIKELIHOOD) LAMBDA X

CHSN ORGCOM LDRSHP

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13 PHI

CHSN ORGCOM LDRSHP

THETA DELTA C12

.058 THETA DELTA

CDl

.458

.499

.000

.000

.000

.000

.000

.000

.000

.000

.000

CHSN

1.000 .835

-.062

C13

.026

L4

.000

.000

.292

.507

.709

.596

.133

.000

.000

.000

.000

ORGCOM

1.000 -.021

CI

.125

L8

.000

.000

.000

.000

.000

.000

.000

.764

.780

.773

.718

LDRSHP

1.000

Cll

.117

L9

C8

.070

CA14

.115

L13

.073 .182 .114 .088 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES C12 C13 CI Cll C8

138

CA14

.784 .907 .405 .688 .879 .756

SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES CDl L4 L8 L9 L13

nr95 .763 .842 .872 .789 TOTAL COEFFICIENT OF DETERMINATION FOR X - VARS IS .999

CHI-SQUARE WITH 41 DEGREES OF FREEDOM = 41 GOODNESS OF FIT INDEX = .931

ADJUSTED GOODNESS OF FIT INDEX = ROOT MEAN SQUARE RESIDUAL =

SUMMARY STATISTICS FOR FITTED RESIDUALS SMALLEST FITTED RESIDUAL = -.058 MEDIAN FITTED RESIDUAL = .000 LARGEST FITTED RESIDUAL = .035

74 (P = .439)

.889

.016

118

STEMLEAF PLOT - 518 - 41 - 3185 - 21544 - 11876633210 - 019777644322211110000000000000

01233456666 110034466 211269 31135

SUMMARY STATISTICS FOR STANDARDIZED RESIDUALS STANDARDIZED STANDARDIZED STANDARDIZED PLOT

RESIDUAL = RESIDUAL = RESIDUAL =

-2 473 000 041

SMALLEST MEDIAN LARGEST STEMLEAF - 215 - 11764211100 - 0 187777 6555544433311100000000000000

011133334677889 111123578 2 137 310

LARGEST POSITIVE STANDARDIZED RESIDUALS RESIDUAL FOR CI AND C13 = 3 RESIDUAL FOR CA14 AND C8 = 2

041 658

STANDARD ERRORS LAMBDA X

CHSN ORGCOM LDRSHP

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13 PHI

.042

.041

.000

.000

.000

.000

.000

.000

.000

.000

.000

CHSN

.000

.000

.042

.051

.058

.055

.029

.000

.000

.000

.000

ORGCOM

.000

.000

.000

.000

.000

.000

.000

.070

.066

.063

.064

LDRSHP

CHSN .000 ORGCOM .040 LDRSHP .106

THETA DELTA C12 C13

.013 .013

CI

019

.000

.106 .000

Cll

019

C8

.019

CA14

.021

119

THETA DELTA CDl L4 L8 L9 L13

.010

T-VALUES

.031

LAMBDA

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13 PHI

CHSN ORGCOM LDRSHP

10 12

2C

X CHSN

.953

.302

.000

.000

.000

.000

.000

.000

.000

.000

.000

CHSN

.000 1.972 -.585

.023

ORGCOM

.000

.000 6.947

10.034 12.253 10.804 4.511 .000 .000 .000 .000

ORGCOM

.000 -.202

.020

LDRSHP

.000

.000

.000

.000

.000

.000

.000 10.951 11.903 12.266 11.259

LDRSHP

.000

.025

THETA DELTA C12 C13 CI Cll C8

4.454 2.049 THETA DELTA

CDl

6.751

L4

5.982

L8

3.567

L9

CA14

5.493

L13

6.959 5.830 4.957 4.399 5.619

MODIFICATION INDICES AND ESTIMATED CHANGE MODIFICATION INDICES FOR LAMBDA X

CHSN ORGCOM LDRSHP

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13

.000

.000 9.622 2.326 2.770 1.620 .067 .655 .163 .885 .684

.000

.000

.000

.000

.000

.000

.000 1.213 .173

1.023 .423

.000

.000

.121 1.293 .083 .238

1.243 .000 .000 .000 .000

120

ESTIMATED CHANGE FOR LAMBDA X CHSN ORGCOM LDRSHP

C12 C13 CI

Cll C8

CA14 CDl L4 L8 L9

L13 NO NON-ZERO NO NON-ZERO

.000

.000

.247

.131 -.169 -.118 -.015 -.039 .017 .036

-.035 MODIFICATION MODIFICATION

MAXIMUM MODIFICATION

.001 -.001 .000 .000 .000 .000 .000

-.053 .017 .039

-.028 INDICES INDICES

INDEX IS

FOR FOR 9. ,62

.000

.000 -.013 -.044 .011 .019 .031 .000 .000 .000 .000 PHI THETA DELTA ! FOR ELEMENT ( 3, 1) OF

LAMBDA X

121

APPENDIX B

LISREL 7 OUTPUT FOR

LEADER-GROUP INTERACTIONS MODEL

DA NI=27 NO=101 MA=CM LA C12 C13 CI Cll C8 CA14 CDl LI L2 L3 L4 L5 L6 L7 L8 L9 LIO Lll L12 L13 L14 T10PL4 T10PL5 T10PL6 CA3 CA4 CA5 SE 1 2 3 4 7 22 24 25 27/ CM FI=C:\PRELIS\DAT.COV MO NX=9 NK=5 PH=ST LK CHSN GOAL DRIVE PA LX 2(1 0 0 0 0) 2(0 1 0 0 2(0 0 0 1 0) 2 (0 0 0 0 VA 1 LX(5,3) VA .0273 TD(5,5) OU SE TV MI

INSTR SUPP

0) 1)

1 (0 0 0 0 0)

COVARIANCE

NUMBER OF NUMBER OF NUMBER OF NUMBER OF NUMBER OF NUMBER OF

MATRIX TO BE C12 C13

INPUT VARIABLES 27 Y - VARIABLES 0 X - VARIABLES 9 ETA - VARIABLES 0 KSI - VARIABLES 5 OBSERVATIONS 101

ANALYZED CI Cll CDl T10PL4

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

.268

.229

.138

.198

.054 -.021 -.004 .002

-.008 COVARIANCE

T10PL6

.275

.153

.227

.052 •.015 •.004 .002 .028 MATRIX

.210

.147

.055

.016

.033

.044

.019 TO BE CA3

.374

.058 -.025 -.030 .081 .016

ANALYZED CA5

091 008 004 025 018

879 587 099 209

T10PL6 CA3 CA5

907 156 279

.492

.131 .346

122

PARAMETER SPECIFICATIONS LAMBDA X

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

CHSN

1 2 0 0 0 0 0 0 0

PHI

CHSN GOAL

DRIVE INSTR SUPP

CHSN

0 9

10 12 15

THETA DELTA C12 C13

GOAL

0 0 3 4 0 0 0 0 0

GOAL

0 11 13 16

CI

DRIVE

0 0 0 0 0 0 0 0 0

DRIVE

0 14 17

Cll

INSTR

0 0 0 0 0 5 6 0 0

INSTR

0 18

CDl

SUPP

0 0 0 0 0 0 0 7 8

SUPP

0

T10PL4

19 20 THETA DELTA

T10PL6 CA3

21 22 23 24

CA5

25 26 27

INITIAL ESTIMATES LAMBDA X

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

PH]

CHSN GOAL

DRIVE INSTR SUPP

CHSN

.452

.506

.000

.000

.000

.000

.000

.000

.000

CHSN

1.000 .960 .435

-.027 .033

(TSLS)

GOAL

.000

.000

.318

.461

.000

.000

.000

.000

.000

GOAL

1.000 .560

-.020 .300

DRIVE

.000

.000

.000

.000

.252

.000

.000

.000

.000

DRIVE

1.000 .029 .230

INSTR

.000

.000

.000

.000

.000

.666

.882

.000

.000

INSTR

1.000 .618

SUPP

.000

.000

.000

.000

.000

.000

.000

.418

.314

SUPP

1.000

123

THETA DELTA C12 C13

063 .019 THETA DELTA T10PL6 CA3

CI

109

CA5

Cll

.161

CDl

027

T10PL4

.436

.129 .318 .247 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES

C12 C13 CI Cll CDl T10PL4

.763 .932 .482 .569 .699 .505 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES

T10PL6 CA3 CA5

.858 .354 .284 TOTAL COEFFICIENT OF DETERMINATION FOR X - VARS IS 999

LISREL ESTIMATES (MAXIMUM LIKELIHOOD) LAMBDA X

CHSN GOAL DRIVE INSTR SUPP

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

PHI

CHSN 1 GOAL

DRIVE INSTR SUPP

THETA DELTA C12

.064 THETA DELTA

T10PL6

.451

.507

.000

.000

.000

.000

.000

.000

.000

CHSN

.000

.961

.418

.014

.083

C13

.018

CA3

.000

.000

.320

.459

.000

.000

.000

.000

.000

GOAL

1.000 .579 .003 .157

CI

.108

CA5

.000

.000

.000

.000

.252

.000

.000

.000

.000

DRIVE

1.000 .023 .178

Cll

.164

.000

.000

.000

.000

.000

.658

.892

.000

.000

INSTR

1.000 .682

CDl

.027

.000

.000

.000

.000

.000

.000

.000

.290

.451

SUPP

1.000

T10PL4

.446

.111 .408 .142 SQUARED MULTIPLE CORRELATIONS FOR X - VARIABLES C12 C13 CI Cll CDl

.760 .935 .487 .563 .699

T10PL4

.493

124

SQUARED MULTIPLE CORRELATIONS FOR X -T10PL6 CA3 CA5

VARIABLES

.878 .171 .589 TOTAL COEFFICIENT OF DETERMINATION FOR X - VARIABLES IS .999

CHI-SQUARE WITH 18 DEGREES OF FREEDOM = GOODNESS OF FIT INDEX = .956

ADJUSTED GOODNESS OF FIT INDEX = ROOT MEAN SQUARE RESIDUAL =

21.60 (P = .250)

.891

.015

RESIDUAL RESIDUAL

SUMMARY STATISTICS FOR FITTED SMALLEST FITTED RESIDUAL =

FITTED FITTED PLOT

21 650 7600 9943321110000000000000 124456789 25

MEDIAN LARGEST STEMLEAF - 3 - 2 - 1 - 0

RESIDUALS -.032 .000 .060

0 1 2 3 4 5 6

02

0 SUMMARY STATISTICS FOR STANDARDIZED RESIDUALS SMALLEST MEDIAN LARGEST STEMLEAF - 1 - 1 - 0

0 0 0 1 1

STANDARDIZED STANDARDIZED STANDARDIZED PLOT

7 332100 8765 33332210000000000000 1234 77 0123333 6

RESIDUAL = RESIDUAL = RESIDUAL =

-1 663 000 616

125

STANDARD ERRORS LAMBDA X

C12 C13 CI Cll CDl

T10PL4 T10PL6

CA3 CA5

PHI

CHSN GOAL

DRIVE INSTR SUPP

CHSN

.042

.040

.000

.000

.000

.000

.000

.000

.000

CHSN

.000

.053

.119

.109

.130 THETA DELTA C12

.013

C13

.013 THETA DELTA

T10PL6

.138

T-VALUES

1

CA3

.064

LAMBDA X

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

(

10 12

PHI

CHSN GOAL

DRIVE INSTR SUPP

CHSN

.692

.602

.000

.000

.000

.000

.000

.000

.000

CHSN

.000 17.998 3.504 -.127 .639

GOAL

.000

.000

.043

.057

.000

.000

.000

.000

.000

GOAL

.000

.135

.127

.152

CI

.018

•

GOAL

.000

.000 7.443 8.087 .000 .000 .000 .000 .000

GOAL

.000 4.287 .027

1.031

DRIVE

.000

.000

.000

.000

.000

.000

.000

.000

.000

DRIVE

.000

.127

.153

Cll

.031

CA5

069

DRIVE

.000

.000

.000

.000

.000

.000

.000

.000

.000

DRIVE

.000

.182 1.165

INSTR

.000

.000

.000

.000

.000

.099

.105

.000

.000

INSTR

.000

.135

CDl

.013

INSTR

.000

.000

.000

.000

.000 6.635 8.507 .000 .000

INSTR

.000 5.064

SUPP

.000

.000

.000

.000

.000

.000

.000

.082

.088

SUPP

.000

T10PL4

.098

SUPP

.000

.000

.000

.000

.000

.000

.000 3.546 5.125

SUPP

.000

126

THETA DELTA C12 C13

4.764 1.404 THETA DELTA

T10PL6

CI Cll

5.867 5.225

CA3 CA5

CDl

2.131

T10PL4

4.561

.800 6.395 2.066

MODIFICATION INDICES AND ESTIMATED CHANGE MODIFICATION INDICES FOR LAMBDA X

CHSN GOAL DRIVE INSTR

NO NO NON-ZERO MODIFICATION INDICES FOR THETA DELTA MAXIMUM MODIFICATION INDEX IS 2.76 FOR ELEMENT (8,4) LAMBDA X

SUPP

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

.000

.000 1.290 1.290 .000 .125 .125 .015 .015

ESTIMATED

C12 C13 CI

Cll CDl

T10PL4 T10PL6

CA3 CA5

CHSN

.000

.000 -.293 .420 .000

-.026 .035

-.009 .013

.090

.090

.000

.000

.000

.080

.080

.043

.043 CHANGE FOR

GOAL

.060 -.068 .000 .000 .000

-.021 .028 .015

-.023

.790

.790 1.610 1.610 .000 .019 .019 .776 .776

LAMBDA X DRIVE

.036 -.041 .084

-.121 .000 .012

-.016 .073

-.114 NON-ZERO MODIFICATION INDICES FOR PHI

.053

.053 1.533 1.533 .000 .000 .000

2.764 2.763

INSTR

-.007 .008 .057

-.081 .000 .000 .000

-.503 .781

1.601 1.601 .516 .516 .000 .000 .000 .000 .000

SUPP

-.042 .048 .037

-.054 .000 .004

-.006 .000 .000

OF

127

APPENDIX C

REGRESSION RESULTS FOR

ORGANIZATIONAL SYNERGY MODEL

Model 1: Direct effects with infantry and armor combined

Analysis of Variance

Source DF

Model 3 Error 95 C Total 98

Sum of Squares

0.10443 3.72651 3.83094

0 0

Mean Square

03481 03923

F Value

0.887

Prob>F

0.4506

Root MSE Dep Mean C.V.

0.19806 0.33910

58.40659

R-square Adj R-sq

0.0273 -0.0035

Parameter Estimates

Variable DF

INTERCEP 1 COHESION 1 ORGCOM 1 LEADER 1

Parameter Estimate

0.148721 -0.037586 0.081278 0.048937

Standard Error

0.16282920 0.13224840 0.12496908 0.03358756

T for HO: Parameter=0

0 . 9 1 3 0 . 2 8 4 0 . 6 5 0 1 .457

ProbIT

0.3634 0.7769 0.5170 0.1484

128

Model 2 Leadership x Organizational Commitment interaction term (infantry and armor combined).

Analysis of Variance

Source Sum of

DF Squares Mean

Square F Value Prob>F

Model 4 Error 94 C Total 98

0.10998 3.72096 3.83094

0.02749 0.03958

0.695 0.5975

Root MSE Dep Mean C.V.

0.19896 0.33910 58.67271

R-square Adj R-sq

0.0287 -0.0126

Parameter Estimates

Parameter Standard T for HO Variable

INTERCEP COHESION ORGCOM LEADER LOC

Variable INTERCEP COHESION ORGCOM LEADER LOC

DF

1 1 1 1 1

DF 1 1 1 1 1

Estimate

0, -0, 0, 0

-0

.151433

.034826

.072955

.050936

.048206

Error Parameter=0

0.16373134 0.13305524 0.12749150 0.03416009 0.12875322

Variance Inflat ion

0.00000000 2.54683584 2.60948324 1.03079951 1.07494278

0.925 -0.262 0.572 1.491

-0.374

Prob

0, 0. 0. 0. 0,

> IT

.3574

.7941

.5685

.1393

.7089

129

Model 3: Cohesion x Leadership interaction term (infantry and armor combined).

Analysis of Variance

Source DF Squares

Model 4 Error 94 C Total 98

0.11840 3.71254 3.83094

Sum of Square

0.02960 0.03950

Mean F Value

0.749

Prob>F

0.5608

Root MSE Dep Mean C.V.

0.19873 0.33910

58.60625

R-square Adj R-sq

0.0309 -0.0103

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob >

INTERCEP 1 0.157538 0 COHESION 1 -0.053067 0 ORGCOM 1 0.083610 0 LEADER 1 0.052801 0 CL 1 -0.085558 0

16405680 13522881 12545754 03432253 14383686

0 . 9 6 0 - 0 . 3 9 2

0 . 6 6 6 1 .538

- 0 . 5 9 5

0 0 0 0 0

3394 6956 5068 1273 5534

Variable DF Variance Inflation

INTERCEP 1 0 COHESION 1 2 ORGCOM 1 2 LEADER 1 1 CL 1 1

00000000 63669474 53261967 04298770 12103759

130

Model 4: Cohesion x Organizational Commitment interaction term (infantry and armor combined).

Analysis of Variance

Source DF

Model 4 Error 95 C Total 99

Sum of Squares

0.12536 3.81942 3.94478

0 0

Mean Square

03134 04020

F Value

0.780

Prob>F

0.5412

Root MSE Dep Mean C.V.

0.20051 0.33571

59.72745

R-square Adj R-sq

0.0318 -0.0090

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > IT

INTERCEP 1 0.126182 COHESION 1 -0.039124 ORGCOM 1 0.092576 LEADER 1 0.052606 COC 1 -0.093318

0.16417834 0.769 0.4441 0.13388490 -0.292 0.7708 0.12630422 0.733 0.4654 0.03416537 1.540 0.1269 0.29028254 -0.321 0.7486

Variable DF Variance Inflation

INTERCEP 1 COHESION 1 ORGCOM 1 LEADER 1 COC 1

0.00000000 2.54653741 2.53777987 1.01727331 1.01177065

131

Model 5: Cohesion x Leadership x Organizational Commitment interaction term (infantry and armor combined).

Analysis of Variance

Source DF

Model 4 Error 94 C Total 98

Sum of Squares

0.12130 3.70965 3.83094

Mean Square

0.03032 0.03946

F Value

0.768

Prob>F

0.5484

Root MSE Dep Mean C.V.

0.19866 0.33910

58.58342

R-square Adj R-sq

0.0317 -0.0095

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob >

INTERCEP 1 0.176487 0 COHESION 1 -0.046074 0 ORGCOM 1 0.073750 0 LEADER 1 0.04 9777 0 CLOC 1 -0.043929 0

16875446 13328267 12587523 03371373 06719723

1 -0 0 1

-0

046 346 586 476 654

0 0 0 0 0

2983 7304 5593 1432 5149

Variable DF Variance Inflation

INTERCEP 1 0 COHESION 1 2 ORGCOM 1 2 LEADER 1 1 CLOC 1 1

00000000 56334594 55149958 00710002 08382046

132

Model 6: Direct effects with armor only.

Analysis of Variance

im of Mean Source DF Squares Square F Value Prob>F

Model 3 0.03461 0.01154 0.282 0.8379 Error 57 C Total 60

Root MSE 0.20214 R-square 0.0146 Dep Mean 0.31344 Adj R-sq -0.0372 C.V.

Sum of Squares

0.03461 2.32912 2.36373

0.20214 0.31344 64.49091

Mean Square

0.01154 0.04086

R-square Adj R-sq

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > |T|

INTERCEP 1 0.320072 0.23029031 1.390 0.1700 COHESION 1 -0.059507 0.16264601 -0.366 0.7158 ORGCOM 1 0.005911 0.16239878 0.036 0.9711 LEADER 1 0.028834 0.04447241 0.648 0.5194

133

Model 7:

Source

Model Error C Total

Leadership x Organizational Commitment interaction term with armor only.

Analysis of Variance

Root MSE Dep Mean C.V.

Sum of DF Squares

4 0.03849 56 2.32524 60 2.36373

0.20377 0.31344 65.00993

Mean Square

0.00962 0.04152

R-square Adj R-sq

F Value

0.232

Prob>F

0.9194

0.0163 -0.0540

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > |T|

INTERCEP 1 COHESION 1 ORGCOM 1 LEADER 1 LOC 1

Variable DF

0.337233 0.065426 0.009430 0.024314 0.050396

Variance Inflation

0.23883226 0.16509390 0.16410984 0.04720519 0.16482387

1 0 0 0 0

412 396 057 515 306

0 0 0 0 0

1635 6934 9544 6085 7609

INTERCEP 1 COHESION 1 ORGCOM 1 LEADER 1 LOC 1

0.00000000 2.43779166 2.35872469 1.15252839 1.11306175

134

Model 8 Cohesion x Leadership interaction term with armor only.

Analysis of Variance

Source DF

Model 4 Error 56 C Total 60

Sum of Squares

0.04985 2.31389 2.36373

Mean Square

0.01246 0.04132

F Value

0.302

Prob>F

0.8757

Root MSE Dep Mean C.V.

0.20327 0.31344 64.85105

R-square Adj R-sq

0.0211 -0.0488

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > T|

INTERCEP 1 0.357977 0 COHESION 1 -0.045022 0 ORGCOM 1 -0.007935 0 LEADER 1 0.015772 0 CL 1 0.114973 0

23984340 16528490 16489014 04962626 18935551

1, 0, 0, 0, 0,

.493

.272

.048

.318

.607

0. 0. 0. 0. 0.

.1412

.7863

.9618

.7518

.5462

Variable DF Variance Inflation

INTERCEP 1 COHESION 1 ORGCOM 1 LEADER 1 CL 1

0.00000000 2.45542253 2.39288990 1.28003142 1.28307326

135

Model 9: Cohesion x Organizational Commitment interaction term with armor only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

DF

4 56 60

MSE Mean

Sum of Squares

0.07334 2.37951 2.45285

0.20613 0.30980 66.53753

Mean Square

0.01834 0.04249

R-square Adj R-sq

F Value

0.432

0.0299 -0.0394

0.7853

Parameter Estimates

Variable

INTERCEP COHESION ORGCOM LEADER COC

Variable

INTERCEP COHESION ORGCOM LEADER COC

DF

1 1 1 1 1

DF

1 1 1 1 1

Parameter Standard T for HO: Estimate Error Parameter=0

0.351562 0.24793386 1. -0.083355 0.16811153 -0. 0.007239 0.16634819 0. 0.022616 0.04668215 0. 0.376832 0.41576796 0.

Variance Inflation

0.00000000 2.37716452 2.33195384 1.10175243 1.13099445

.418

.496

.044

.484

.906

Prob

0. 0. 0. 0. 0.

> ITI

.1617

.6220

.9654

.6299

.3686

136

Model 10: Cohesion x Leadership x Organizational Commmitment interaction term with armor only.

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Prob>F

Model 4 0.07303 0.01826 0.446 0.7746 Error 56 2.29070 0.04091 C Total 60 2.36373

Root MSE 0.20225 R-square 0.0309 Dep Mean 0.31344 Adj R-sq -0.0383 C.V. 64.52534

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob >|T|

INTERCEP 1 0.354372 0.23311573 1.520 0.1341 COHESION 1 -0.055521 0.16278481 -0.341 0.7343 ORGCOM 1 -0.000924 0.16263849 -0.006 0.9955 LEADER 1 0.014526 0.04688162 0.310 0.7578 CLOC 1 0.090788 0.09368165 0.969 0.3367

Variance Variable DF Inflation

INTERCEP 1 0.00000000 COHESION 1 2.40580870 ORGCOM 1 2.35154575 LEADER 1 1.15392121 CLOC 1 1.11653470

137

Model 11: Direct effects with infantry only.

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Prob>F

Model 3 0.36158 0.12053 4.114 0.0134 Error 35 1.02537 0.02930 C Total 38 1.38695

Root MSE 0.17116 R-square 0.2607 Dep Mean 0.37623 Adj R-sq 0.1973 C.V. 45.49365

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > |T|

INTERCEP 1 -0.358711 0.22028479 -1.628 0.1124 COHESION 1 0.123058 0.21186467 0.581 0.5651 ORGCOM 1 0.226118 0.18140285 1.246 0.2209 LEADER 1 0.097307 0.04800730 2.027 0.0503

138

Model 12: Leadership x Organizational interaction term and infantry only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

DF

4 34 38

MSE Mean

Sum of Squares

0.37255 1.01439 1.38695

0.17273 0.37623

45.91015

Mean Square

0.09314 0.02984

R-square Adj R-sq

F Value

3.122

0.2686 0.1826

Prob>F

0.0273

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > |T

INTERCEP 1 -0.292348 0.24777127 -1.180 0.2462 COHESION 1 0.117866 0.21397557 0.551 0.5853 ORGCOM 1 0.189349 0.19284094 0.982 0.3331 LEADER 1 0.095931 0.04849995 1.978 0.0561 LOC 1 -0.134797 0.22225331 -0.607 0.5482

Variance Variable DF Inflation

INTERCEP 1 0.00000000 COHESION 1 2.66528848 ORGCOM 1 2.94275124 LEADER 1 1.01486931 LOC 1 1.35255845

139

Model 13: Cohesion x Leadership interaction term with infantry only.

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Prob>F

Model 4 0.47073 0.11768 4.367 0.0059 Error 34 0.91621 0.02695 C Total 38 1.38695

Root MSE 0.16416 R-square 0.3394 Dep Mean 0.37623 Adj R-sq 0.2617 C.V. 43.63193

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > IT

INTERCEP 1 -0.088694 0.25026968 -0.354 0.7252 COHESION 1 0.022526 0.20924423 0.108 0.9149 ORGCOM 1 0.183677 0.17525267 1.048 0.3020 LEADER 1 0.076910 0.04714497 1.631 0.1120 CL 1 -0.506808 0.25181804 -2.013 0.0521

Variance Variable DF Inflation

INTERCEP 1 0.00000000 COHESION 1 2.82183370 ORGCOM 1 2.69087148 LEADER 1 1.06171198 CL 1 1.38302995

140

Model 14: Cohesion x Organizational Commitment interaction term with infantry only.

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Prob>F

Model 4 0.37037 0.09259 3.097 0.0282 Error 34 1.01657 0.02990 C Total 38 1.38695

Root MSE 0.17291 R-square 0.2670 Dep Mean 0.37623 Adj R-sq 0.1808 C.V. 45.95951

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > |T|

INTERCEP 1 -0.307934 0.24143805 -1.275 0.2108 COHESION 1 0.106316 0.21624953 0.492 0.6261 ORGCOM 1 0.213528 0.18472530 1.156 0.2558 LEADER 1 0.097865 0.04850979 2.017 0.0516 COC 1 -0.224582 0.41415448 -0.542 0.5912

Variance Variable DF Inflation

INTERCEP 1 0.00000000 COHESION 1 2.71639352 ORGCOM 1 2.69447609 LEADER 1 1.01310118 COC 1 1.17431256

141

Model 15 Cohesion x Leadership x Organizational Commitment interaction term with infantry only.

Analysis of Variance

Source

Model Error C Total

DF

6 32 38

Sum of Squares

0.59028 0.79667 1.38695

Mean Square

0.09838 0.02490

F Value

3.952

Prob>F

0.0046

Root MSE Dep Mean C.V.

0.15778 0.37623

41.93804

R-square Adj R-sq

0 0

4256 3179

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > Tj

INTERCEP 1 1.100934 0 ORGCOM 1 -0.146438 0 LEADER 1 -0.157065 0 COC 1 5.899596 4 LOC 1 0.178822 0 CL 1 2.078627 2 COCL 1 -2.154656 1

83509150 1.318 28816842 -0.508 15457753 -1.016 04400576 1.459 38996352 0.459 07132183 1.004 44532667 -1.491

0 0 0 0 0 0 0

1967 6148 3172 1544 6496 3231 1458

Variable DF Variance Inflation

INTERCEP 1 ORGCOM 1 LEADER 1 COC 1 LOC 1 CL 1 COCL 1

0.00000000 7.87496993 12.35438929

134.46741666 4.99009233

101.28509470 345.11059119

142

APPENDIX D

REGRESSION RESULTS FOR

LEADER-GROUP INTERACTIONS MODEL

Model 1 Performance = instrumental leadership + supportive leadership + group drive + group goal acceptance (infantry and armor combined).

Analysis of Variance

Source

Model Error C Total

DF

4 94 98

Sum of Squares

0.25129 3.57965 3.83094

Mean Square F Value

0.06282 0.03808

1.650

Prob>F

0.1683

Root MSE Dep Mean C.V.

0.19514 0.33910

57.54781

R-square Adj R-sq

0.0656 0.0258

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > |T|

INTERCEP 1 0.390470 0 INSTR 1 0.020170 0 SUPP 1 0.137248 0 DRIVE 1 -0.467352 0 GOAL 1 0.135899 0

29349405 1.330 03271185 0.617 10573110 1.298 28450496 -1.643 11328594 1.200

0.1866 0.5390 0.1974 0.1038 0.2333

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1 DRIVE 1 GOAL 1

0.00000000 1.24845118 1.25910427 1.18880849 1.19992161

143

Model 2 Drive = instrumental leadership + supportive leadership (infantry and armor combined).

Analysis of Variance

Source

Model Error C Total

DF

2 96 98

Sum of Squares

0.00141 0.55789 0.55930

Mean Square

0.00070 0.00581

F Value

0.121

Prob>F

0.8860

Root MSE Dep Mean C.V.

0.07623 1.01056 7.54359

R-square Adj R-sq

0 -0

0025 0183

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob >

INTERCEP 1 INSTR 1 SUPP 1

0.985880 -0.001251 0.019540

0.05467528 0.01275773 0.04106067

18.032 -0.098 0.476

0.0001 0.9221 0.6352

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1

0.00000000 1.24434852 1.24434852

144

Model 3 Cohesion = instrumental leadership + supportive leadership (infantry and armor combined).

Analysis of Variance

Source DF Sum of

Squares Mean

Square F Value Prob>F

Model Error C Total

2 96 98

0.00524 5.68938 5.69462

0.00262 0.05926

0.044 0.9568

Root MSE Dep Mean C.V.

0.24344 1.52768 15.93547

R-square Adj R-sq

0.0009 -0.0199

Parameter Estimates

Variable DF Parameter Eatimate

Standard T for HO: Error Parameter=0 Prob >

INTERCEP 1 INSTR 1 SUPP 1

1.514850 -0.010968 0.030490

0.17460162 0.04074091 0.13112431

8.676 -0.269 0.233

0.0001 0.7883 0.8166

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1

0.00000000 1.24434852 1.24434852

145

Model 4: Performance = group drive + group goal acceptance + (group drive x cohesion x goal acceptance) for infantry and armor combined.

Analysis of Variance

Source Model Error C Total

Root Dep C.V.

DF 6

93 99

MSE Mean

Sum of Squares 0.44058 3.50421 3.94478

0.19411 0.33571

57.82166

Mean Square F Value Prob>F 0.07343 1.949 0.0810 0.03768

R-square 0.1117 Adj R-sq 0.0544

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > IT I

INTERCEP 1 -8.153330 11 DRIVE 1 -0.547345 0 GOAL 1 7.180226 9 DG 1 0.208311 2 CD 1 -3.571304 1 CG 1 0.478197 0 DCG 1 4.767512 6

34224326 33368708 13515023 20781239 94146164 45284931 17149185

0. 1. 0. 0. 1, 1, 0,

.719

.640

.786

.094

.839

.056

.773

0. 0. 0. 0, 0, 0, 0,

.4740

.1043

.4339

.9250

.0690

.2937

.4418

146

Model 5: Performance = instrumental leadership + supportive leadership + group drive + group goal acceptance with armor only.

Analysis of Variance

Source DF Sum of

Squares Mean

Square F Value Prob>F

Model Error C Total

4 55 59

0.17190 2.18338 2.35527

0.04297 0.03970

1.083 0.3741

Root MSE Dep Mean C.V.

0.19924 0.31496 63.25888

R-square Adj R-sq

0 0

0730 0056

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > IT

INTERCEP 1 0.612384 0 INSTR 1 -0.025323 0 SUPP 1 0.208238 0 DRIVE 1 -0.435468 0 GOAL 1 -0.067375 0

40942788 1.490 04117917 -0.615 12598025 1.653 39009969 -1.116 16276937 -0.414

0.1404 0.5411 0.1040 0.2691 0.6805

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1 DRIVE 1 GOAL 1

0.00000000 1.21964875 1.19470101 1.17792715 1.22144327

147

Model 6 Drive = instrumental leadership -i- supportive leadership with armor only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

DF

2 57 59

MSE Mean

Sum of Squares

0.00358 0.30370 0.30728

0.07299 1.02520 7.11994

Mean Square

0.00179 0.00533

R-square Adj R-sq

F Value

0.336

0.0117 -0.0230

Prob>F

0.7160

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO Error Parameter=0 Prob > |T|

INTERCEP 1 INSTR 1 SUPP 1

1.010045 -0.010298 0.030260

0.06488831 0.01476402 0.04563701

15.566 -0.698 0.663

0.0001 0.4883 0.5100

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1

0.00000000 1.16811718 1.16811718

148

Model 7: Cohesion = instrumental leadership + supportive leadership with armor only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

DF

2 57 59

MSE Mean

Sum of Squares

0.03821 3.50277 3.54098

0.24790 1.57999 15.68965

Mean Square

0.01910 0.06145

R-square Adj R-sq

F Value Prob>F

0.311 0.7340

0.0108 -0.0239

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > |T|

INTERCEP 1 1.702646 0.22036918 7.726 0.0001 INSTR 1 -0.034046 0.05014052 -0.679 0.4999 SUPP 1 -0.017564 0.15498924 -0.113 0.9102

Variance Variable DF Inflation

INTERCEP 1 0.00000000 INSTR 1 1.16811718 SUPP 1 1.16811718

149

Model 8 Performance = group drive + group goal acceptance + (group drive x cohesion x goal acceptance) with armor only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

MSE Mean

DF

6 54 60

Sum of Squares

0.21875 2.23410 2.45285

0.20340 0.30980 65.65550

Mean Square

0.03646 0.04137

R-square Adj R-sq

F Value

0.881

0.0892 -0.0120

Prob>F

0.5150

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > IT

INTERCEP 1 DRIVE 1 GOAL 1 DG 1 CD 1 CG 1 DCG 1

0.239411 16 -0.278634 0 0.268036 13

-1.078116 3 -2.943137 2 1.366114 0 0.285756 8

42947771 46935592 21654902 00108194 47707594 73880381 93629806

0, 0, 0, 0, 1, 1, 0,

.015

.594

.020

.359

.188

.849

.032

0. 0, 0, 0, 0, 0, 0,

.9884

.5552

.9839

.7208

.2400

.0699

.9746

150

Model 9 Performance = instrumental leadership + supportive leadership + group drive + group goal acceptance with infantry only.

Analysis of Variance

Source DF

Model 4 Error 34 C Total 38

Sum of Squares

0.37158 1.01537 1.38695

0 0

Mean Square

09289 02986

F Value

3.111

Prob>F

0.0277

Root MSE Dep Mean C.V.

0.17281 0.37623

45.93225

R-square Adj R-sq

0 0 2679 1818

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > ITI

INTERCEP 1 -0.145261 0 INSTR 1 0.049341 0 SUPP 1 0.130887 0 DRIVE 1 -0.293249 0 GOAL 1 0.42364 3 0

45391368 -0.320 05668342 0.870 19955447 0.656 39336372 -0.745 15527086 2.728

0 0 0 0 0

7509 3901 5163 4611 0100

Variable DF Variance Inflation

INTERCEP 1 0 INSTR 1 1 SUPP 1 1 DRIVE 1 1 GOAL 1 1

00000000 70803366 62053304 13665081 15911922

151

Model 10 Drive = instrumental leadership + supportive leadership with infantry only.

Analysis of Variance

Source DF Sum of

Squares Mean

Square F Value Prob>F

Model Error C Total

2 36 38

0.01122 0.20816 0.21937

0.00561 0.00578

0.970 0.3888

Root MSE Dep Mean C.V.

0.07604 0.98803 7.69614

R-square Adj R-sq

0 -0

0511 0016

Parameter Estimates

Variable DF Parameter Eatimate

Standard T for HO Error Parameter=0 Prob > ITI

INTERCEP 1 INSTR 1 SUPP 1

1.030061 0.031956 0.094549

0.09803538 0.02380074 0.08602312

10.507 1.343

-1.099

0.0001 0.1878 0.2790

Variable DF Variance Inflation

INTERCEP 1 INSTR 1 SUPP 1

0.00000000 1.55532467 1.55532467

152

Model 11: Coheaion = inatrumental leaderahip + supportive leadership with infantry only.

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Prob>F

Model 2 0.04419 0.02210 0.470 0.6288 Error 36 1.69258 0.04702 C Total 38 1.73677

Root MSE 0.21683 R-square 0.0254 Dep Mean 1.44719 Adj R-sq -0.0287 C.V. 14.98295

Parameter Estimates

Parameter Standard T for HO: Variable DF Estimate Error Parameter=0 Prob > IT I

INTERCEP INSTR SUPP

Variable

INTERCEP INSTR SUPP

1 1 1

DF

1 1 1

1.405372 0.064454

-0.100843

Variance Inflation

0.00000000 1.55532467 1.55532467

0, 0, 0,

.27955131

.06786865

.24529793

5.027 0.950

-0.411

0.0001 0.3486 0.6834

153

Model 12 Performance = group drive + group goal acceptance + (group drive x cohesion x goal acceptance) with infantry only.

Analysis of Variance

Source

Model Error C Total

Root Dep C.V.

MSE Mean

DF

6 32 38

Sum of Squares

0.63663 0.75031 1.38695

0.15313 0.37623

40.69972

Mean Square

0.10611 0.02345

R-square Adj R-sq

F Value

4.525

0.4590 0.3576

Prob>F

0.0020

Parameter Estimates

Variable DF Parameter Estimate

Standard T for HO: Error Parameter=0 Prob > IT

INTERCEP 1 -15.325778 13 DRIVE 1 -0.519642 0 GOAL 1 12.913594 11 DG 1 3.641123 3 CD 1 -7.941529 2 CG 1 0.010867 0 DCG 1 8.480422 7

90928920 44067525 22110630 18153450 97521242 57468800 59687081

•1 1 1 1 2 0 1

102 179 151 144 669 019 116

0 0 0 0 0 0 0

2788 2470 2583 2609 0118 9850 2726

154

APPENDIX E

Model 1

Source

Model Error C Total

REVISED MODEL REGRESSION RESULTS

Performance = leadership + group goal acceptance.

Analysis of Variance

DF

2 36 38

Root MSE Dep Mean C.V.

Sum of Squares

0.37191 1.01504 1.38695

0.16792 0.37623

44.63092

Mean Square F Value

0.18595 0.02820

R-square Adj R-sq

6.595

0.2681 0.2275

Prob>F

0.0036

Parameter Estimates

Variable DF

INTERCEP 1 LEADER 1 GOAL 1

Parameter Estimate

-0.331550 0.091475 0.414522

Standard T for HO Error Parameter=0

0.19684082 0.04686935 0.14033614

-1.684 1.952 2.954

Prob > IT

0.1008 0.0588 0.0055

Stepwise Regression

Step

1 2

Var Number Entered In

GOAL LEADER

1 2

Partial R**2

0.1907 0.0774

Model R**2

0.1907 0.2681

C(p)

3.831 2.115

8.7191 3.8091

Prob>F

0.0054 0.0588

155