THE	PARADOX	OF	ASYMMETRIC	COMPETITION:	ASYMMETRIC	RIVALRY	AND	ASYMMETRIC	
COMPETITIVE	ANALYSIS	
By	
Sonia	Manjeshwar	

A	DISSERTATION	
Submitted	to	
Michigan	State	University	
in	partial	fulfillment	of	the	requirements	
for	the	degree	of	
Retailing	–	Doctor	of	Philosophy	
2017	

ABSTRACT	
THE	PARADOX	OF	ASYMMETRIC	COMPETITION:	ASYMMETRIC	RIVALRY	AND	ASYMMETRIC	
COMPETITIVE	ANALYSIS	
By	
Sonia	Manjeshwar	
The	literature	on	asymmetric	competition	has	largely	portrayed	asymmetry	to	be	a	
competitive	advantage.	However,	this	assertion	has	been	made	on	the	basis	of	studies	that	
assume	perfect	rationality	and	ignore	cognitive	limitations	of	firms.	The	salience	of	assuming	
cognitive	limitations	becomes	evident	when	we	decompose	the	concept	of	asymmetric	
competition	into	its	individual	components	of	interfirm	rivalry	and	competitive	analysis	(Chen	
1996).	At	the	action	level	of	interfirm	rivalry,	asymmetry	captures	the	one-sided,	actionresponse	dynamic	between	competing	firms,	and	denotes	the	competitive	advantage	of	
attacking	your	rivals	with	impunity.	Seen	from	a	focal	firm’s	point	of	view,	asymmetry	at	the	
cognitive	level	of	competitive	analysis	implies	a	dangerous	situation,	indicating	the	firm’s	
inability	to	recognize	or	respond	to	competitive	threats	from	outwardly	insignificant	
competitors	(Chen	1996;	Desarbo,	Grewal,	and	Wind	2006).		
The	purpose	of	this	study	is	to	develop	a	theoretical	understanding	of	the	paradoxical	
phenomenon	of	asymmetric	competition	and	provide	insight	into	why	such	contradicting	
outcomes	occur.	Furthermore,	we	consider	that	competitive	experience	is	embedded	within	
the	larger	context	of	institutional	norms,	political	systems,	and	social	network	structures	
(Desarbo,	Grewal	and	Wind	2006;	Grewal	and	Dharwadkar	2002).		

Using	data	from	41	publicly	traded	firms	in	the	U.S.	retail	industry	observed	over	a	tenyear	period (2003-2012),	we	examined	the	paradoxical	nature	of	asymmetric	competition.	Our	
results	strongly	suggest	that	while	strategic	adaptations	to	a	context’s	logic	of	competition	lead	
to	the favorable consequence of asymmetric	rivalry,	some	of	the	same	strategic adaptations	
lead	to	the	adverse	effect	of	asymmetric	competitive	analysis.		We	found	that	firms’	defensive	
manipulation	of	political	logics	in	the	form	of	money	spent	on	campaign	finance	and	lobbying	is	
significantly	and	positively	related	to	the	focal	firm’s	ability	to	carry	out	greater	number	of	
competitive	actions	relative	to	rivals.	However,	defensive	manipulation	of	political	logics	was	
also	found	to	be	significantly	and	positively	related	to	the	focal	firm’s	market	share	erosion,	
indicating	impaired	awareness	that	characterizes	asymmetric	competitive	analysis.	
Similarly,	we	found	that	when	firms	had	a	hierarchical	distribution	of	positional	
embeddedness	among	their	key	decision-makers,	they	were	able	to	perform	more	competitive	
actions	compared	to	their	rivals.	However,	we	also	found	that	a	hierarchical	distribution	of	
positional	embeddedness	among	key	decision-makers	to	also	be	positively	and	significantly	
related	to	the	firms	engaging	in	a	narrow	repertoire	of	competitive	actions,	indicating	
exploitative	behaviors	associated	with	asymmetric	competitive	analysis.	
Our	results	bear	important	implications	for	future	research,	managerial	practices,	and	
public	policy,	on	a	topic	that	has	been	largely	overlooked	in	the	marketing	strategy	literature.	

Copyright	by	
SONIA	MANJESHWAR	
2017	

To	Sean,	I	love	you.	

v	

ACKNOWLEDGEMENTS	

This	research	was	supported	with	funding	from	the	College	of	Communication,	Arts	&	
Sciences,	and	the	Graduate	School	of	Michigan	State	University.	My	dissertation	would	have	
been	an	impossible	feat	without	the	mentorship	of	my	stellar	dissertation	committee.	I	would	
like	to	begin	by	thanking	my	committee	chair	and	long-time	mentor,	Dr.	Brenda	Sternquist,	who	
has	fostered	my	creativity,	encouraged	me	to	step	outside	my	comfort	zone,	motivated	me	to	
find	inspiration	in	the	unlikeliest	of	places,	to	be	bold,	to	take	risks	and	most	of	all,	to	find	joy	in	
my	work.	I	am	also	grateful	to	Dr.	Linda	Good,	who	at	times	has	had	to	fill	in	the	role	of	being	
my	surrogate	mother	at	MSU,	and	to	whom	I	credit	the	meticulous,	detail-oriented	approach	
taken	in	my	research.	Many	thanks	to	Dr.	Patricia	Huddleston,	for	impressing	upon	me	to	never	
lose	sight	of	the	quintessential	“why	we	care?”	question	in	my	research.	A	huge	thank	you	goes	
out	to	Dr.	Roger	Calantone,	who	despite	his	impossible	schedule	took	a	genuine	interest	in	my	
research	and	was	extremely	generous	with	his	time	and	advice.		Finally,	I	would	like	to	express	
my	heartfelt	gratitude	to	my	wonderful	family	and	friends	for	their	unconditional	love	and	
unwavering	support.	I	feel	incredibly	lucky	and	blessed	to	have	you	all	in	my	life.	

vi	

TABLE	OF	CONTENTS	
LIST	OF	TABLES…………………………………………………………………………………………………………………………	ix	
LIST	OF	FIGURES………………………………………………………………………………………………………………………..	x	
CHAPTER	1:	

Introduction	............................................................................................................	1	

CHAPTER	2:	 Conceptual	Development	.....................................................................................	12	
2.1	
Asymmetric	Competition:	Asymmetric	Analysis	and	Asymmetric	Rivalry	....................	12	
2.2	
The	Red	Queen	.............................................................................................................	17	
2.3	
Strategic	Adaptation	to	Logics	of	Competition	............................................................	21	
2.3.1	 Institutional	Logics	....................................................................................................	22	
2.3.2	 Political	Logics	..........................................................................................................	24	
2.3.3	 Social	Logics	..............................................................................................................	25	
2.4	
Problems	of	Strategic	Adaptation	to	Logics	of	Competition	........................................	29	
CHAPTER	3:	 Hypotheses	Development	....................................................................................	36	
3.1	
Institutional	Logics	and	Asymmetric	Competitive	Rivalry	............................................	36	
3.2	
Political	Logics	and	Asymmetric	Competitive	Rivalry	...................................................	43	
3.3	
Social	Logics	(Structural	Embeddedness)	and	Asymmetric	Competitive	Rivalry	..........	48	
3.4	
Social	Logics	(Positional	Embeddedness)	and	Asymmetric	Competitive	Rivalry	..........	54	
3.5	
Social	Logics	(Positional	Embeddedness)	and	Asymmetric	Competitive	Analysis	........	60	
3.6	
Political	Logics	and	Asymmetric	Competitive	Analysis	.................................................	67	
CHAPTER	4:	 Methodology	........................................................................................................	73	
4.1	
Sample	and	Data	..........................................................................................................	73	
4.2	
Competitive	Actions	of	Retailers	..................................................................................	76	
4.2.1	 Interrater	Reliability	.................................................................................................	80	
4.3	
Dependent	Variables	....................................................................................................	81	
4.3.1	 Asymmetric	Rivalry	with	competitors	......................................................................	82	
4.3.2	 Asymmetric	Competitive	Analysis	in	the	focal	firm	..................................................	83	
				4.3.2.1				Action	Repertoire	Simplicity	..................................................................................	84	
				4.3.2.2				Market	Share	Erosion	............................................................................................	84	
4.4	
Independent	Variables	.................................................................................................	85	
4.4.1	 Proactive	manipulation	of	the	institutional	logics	by	focal	firm	...............................	86	
4.4.2	 Defensive	manipulation	of	political	logics	by	focal	firm	...........................................	87	
4.4.3	 Structural	Embeddedness	........................................................................................	87	
4.4.4	 Positional	Embeddedness	........................................................................................	90	
4.5	
Control	Variables	..........................................................................................................	96	
4.5.1	 Firm	Size	...................................................................................................................	96	
4.5.2	 Slack	Resources	........................................................................................................	97	
4.5.3	 Citizens	United	v.	FEC	...............................................................................................	97	
4.5.4	 Debt	Ratio	.................................................................................................................	98	
vii	

4.5.5	 Board	Size	.................................................................................................................	98	
4.5.6	 Industry	Growth	.......................................................................................................	99	
4.5.7	 Industry	Concentration	............................................................................................	99	
CHAPTER	5:	 Results	................................................................................................................	107	
5.1	
Panel	data	analysis	procedure	....................................................................................	109	
5.2	
Diagnostic	Checks	.......................................................................................................	112	
5.3	
Tests	of	Hypotheses-	Asymmetric	Rivalry	..................................................................	113	
5.4	
Tests	of	Hypotheses-	Asymmetric	Competitive	Analysis	............................................	118	
CHAPTER	6:	 Discussion	and	Conclusions	................................................................................	122	
6.1	
Discussion	...................................................................................................................	123	
6.2	
Managerial	and	Policy	Implications	............................................................................	133	
6.3	
Limitations	and	Future	Research	................................................................................	137	
6.4	
Conclusions	.................................................................................................................	138	
APPENDICES	………………………………………………………………………………………………………………………….140	
Appendix	A:	Content	Analysis	Schedule	.................................................................................	141	
Appendix	B:	Software	Version	Information	............................................................................	146	
Appendix	C:	Results	of	Panel	Data	Random-Effects	Analyses	for	Asymmetric	Rivalry	(Relative	
Competitive	Activity)	..............................................................................................................	147	
Appendix	D:	Results	of	Panel	Data	Random-Effects	Analyses	for	Asymmetric	Competitive	
Analysis	...................................................................................................................................	149	
BIBLIOGRAPHY……………………………………………………………………………………………………………………….151	

viii	

LIST	OF	TABLES	
	
Table	1:	Firm	and	industry	group	information	.............................................................................	75	
	
Table	2:	Competitive	actions	of	retailers	announced	in	news	media	articles	were	coded	under	
the	above	retail	industry-specific	action	categories	............................................................	78	
	
Table	3:	Examples	of	action	category	coding	...............................................................................	79	
	
Table	4:	Competitive	action	statistics	itemizing	the	number	of	actions	of	a	particular	type	and	
the	statistics	of	those	actions	...............................................................................................	80	
	
Table	5:	Variable	descriptive	statistics	.........................................................................................	92	
	
Table	6:	Summarization	of	all	operationalized	variables	...........................................................	101	
	
Table	7:	Chow	test	of	poolability	................................................................................................	111	
	
Table	8:	Hausman	test	results	....................................................................................................	111	
	
Table	9:	Breusch-Pagan	Test	of	Heteroscedasticity	...................................................................	112	
	
Table	10:	Results	of	Augmented	Dickey-Fuller	test	for	Unit	Roots/Stationarity	........................	113	
	
Table	11:	Panel	data	results	for	random-effects	models	of	asymmetric	rivalry	(relative	
competitive	activity)	...........................................................................................................	116	
	
Table	12:	Panel	data	results	for	random-effects	regression	of	asymmetric	competitive	analysis
	............................................................................................................................................	120	
	
Table	13:	Versioning	information	for	R,	IDE,	and	R	libraries	used	in	this	work.	.........................	146	
	
Table	14:	Panel	data	results	for	random-effects	models	of	asymmetric	rivalry	(relative	
competitive	activity)	...........................................................................................................	147	
	
Table	15:	Panel	data	results	for	random-effects	regression	of	asymmetric	analysis	.................	149	
	
	

	

	

ix	

LIST	OF	FIGURES	
Figure	1:	An	example	of	a	board	interlock.	..................................................................................	26	
Figure	2:	An	example	of	an	indirect	board	interlock.	...................................................................	28	
Figure	3:	The	paradox	of	asymmetric	competition	......................................................................	35	
Figure	4:	The	affiliation	two-mode	network	of	board	interlocks	.................................................	56	
Figure	5:	The	paradox	of	asymmetric	competition	......................................................................	82	
Figure	6:	Indirect	board	interlocks	among	general	merchandisers	in	2012	.................................	88	
Figure	7:	A	closer	look	at	the	two	indirect	board	interlocks	between	Target	and	Walmart	from	
the	year	2012	.......................................................................................................................	89	
Figure	8:	Histogram	of	the	number	of	board	interlocks	for	each	director	serving	on	the	board	of	
Sears	department	stores	in	2004	.........................................................................................	93	
Figure	9:	Histogram	from	Publix	chain	of	supermarket	stores	indicating	the	number	of	board	
interlocks	for	each	director	serving	on	the	board	of	Publix	in	2004	....................................	94	
Figure	10:	Histogram	of	number	of	board	interlocks	from	the	CVS	Pharmacy	Drugstores	in	2004
	..............................................................................................................................................	95	
Figure	11:	Correlation	matrix	.....................................................................................................	108	
Figure	12:	Panel	data	procedure	................................................................................................	110	
Figure	13:	Money	spent	by	firms	on	campaign	contributions	and	lobbying	..............................	125	
Figure	14:	Board	Composition	based	on	the	distribution	of	positional	embeddedness	in	the	
boardroom	and	the	overall	positional	embeddedness	of	the	firm	in	the	intercorporate	
board	network	....................................................................................................................	135	
	

	

x	

	

Chapter	1: Introduction	

“And	how	many	hours	a	day	did	you	do	lessons?”	said	Alice,	in	a	hurry	to	
change	the	subject.
“Ten	hours	the	first	day,”	said	the	Mock	Turtle:	“nine	the	next,	and	so	on.”
“What	a	curious	plan!”	exclaimed	Alice.
“That's	the	reason	they're	called	lessons,”	the	Gryphon	remarked:	“because	
they	lessen	from	day	to	day.”	
Lewis	Carroll,	Alice’s	Adventures	in	Wonderland	(1865,	p.	145)	
Studies	have	demonstrated	that	decision-makers	in	organizations	have	certain	innate	
cognitive	and	inferential	limitations,	which	makes	extracting	lessons	from	experience	to	be	
both	challenging	and	unreliable.	Scholars	have	attributed	these	cognitive	limitations	to	
psychological	factors	of	bounded	rationality,	heuristics	and	biases,	as	well	as	sociological	factors	
pertaining	to	decision-makers’	tacit	beliefs	regarding	interpersonal	interactions.	These	
limitations	demonstrate	their	corresponding	effect	on	reducing	organizational	efficiency	and	
responsiveness	(Levinthal	and	March	1988;	Simon	1955;	Tversky	and	Kahneman	1983;	
Edmondson	1999).	While	cognitive	processes	of	strategists	have	been	shown	to	impact	how	
firms	identify	competitors	and	why	firms	get	blindsided	by	seemingly	unknown	competitors	
(Porac	and	Thomas	1990;	Zajac	and	Bazerman	1991;	Zahra	and	Chaples	1993),	existing	research	
has	fallen	short	of	incorporating	such	cognitive	limitations	into	studies	of	dynamic	competition,	
particularly	those	competitive	encounters	that	are	asymmetric	in	nature.		Competition	is	

	

1	

asymmetric	when	a	focal	firm	competes	more	intensely	with	its	rival,	compared	to	the	intensity	
with	which	the	rival	competes	with	the	focal	firm	(Desarbo,	Grewal	and	Wind	2006;	Chen	
1996).		The	salience	of	cognitive	limitations	become	evident	when	we	decompose	the	concept	
of	asymmetric	competition	into	its	individual	components	of	interfirm	rivalry	and	competitive	
analysis	(Chen	1996).	At	the	action	level	of	interfirm	rivalry,	asymmetry	captures	the	one-sided,	
action-response	dynamic	between	competing	firms,	and	denotes	the	competitive	advantage	of	
attacking	your	rivals	with	impunity.	On	the	other	hand,	seen	from	a	focal	firm’s	point	of	view,	
asymmetry	at	the	cognitive	level	of	competitive	analysis	implies	a	dangerous	situation,	
indicating	the	firm’s	inability	to	recognize	or	respond	to	competitive	threats	from	outwardly	
insignificant	competitors	(Chen	1996;	Desarbo,	Grewal,	and	Wind	2006).		The	purpose	of	this	
study	is	to	develop	a	theoretical	understanding	of	the	paradoxical	phenomenon	of	asymmetric	
competition	and	provide	insight	into	why	such	contradicting	outcomes	occur.		
While	scholars	in	marketing	and	strategy	have	acknowledged	the	problem	of	
asymmetric	competitive	analysis,	theoretical	development,	as	well	as	empirical	research	of	this	
phenomenon,	has	been	scarce.		Furthermore,	existing	research	discounts	the	cognitive	bounds	
and	biases	of	decision-makers,	reducing	asymmetric	competitive	analysis	to	a	simplistic	
problem	that	occurs	due	to	the	misidentification	of	competitors.	Instead,	research	has	been	
mostly	limited	to	defining	competitors	along	the	parameters	of	strategic	group	membership	or	
benchmarking	rivals	along	certain	supply-side	and	demand-side	factors	(Carpenter,	Cooper,	
Hanssens,	and	Midgley	1988;	Desarbo,	Grewal,	and	Wind	2006;	Chen,	Su	and	Tsai	2007).	
Additionally,	while	scholars	have	acknowledged	the	presence	of	blind	spots	and	biases	that	
make	competitive	analysis	subjective,	empirical	studies	of	competitive	interaction	assume	

	

2	

rational	choice	in	their	models	of	competitive	analysis,	with	firms	analyzing	and	attacking	rivals	
on	the	basis	of	objective	criteria	such	as	market	overlap,	multimarket	contact,	and	resource	
similarity	(Chen	1996;	Upson,	Ketchen,	Connelly	and	Ranft	2012).		
In	reality,	competitive	analysis	goes	beyond	simply	identifying	competitors,	and	includes	
a	cognitive	component	that	draws	lessons	from	competitive	experience,	and	occurs	in	a	
complex	reality	that	is	shaped	by	contextual	factors	that	enable	a	focal	firm	to	formulate	an	
appropriate	course	of	action	against	competitive	threats	(Porter	1980;	Zahra	and	Chaples	1993;	
Barnett	2008).		Insights	from	organizational	learning	theory	suggest	that	boundedly	rational	
organizations	seek	to	simplify	competitive	experience	by	drawing	lessons	from	recent	
competitors	and	from	competitive	encounters	that	occur	on	home	turfs	(Levinthal	and	March	
1993).	Organizations	also	tend	to	specialize	in	a	narrow	set	of	competencies	by	frequently	
exploiting	tried	and	tested	strategies,	thereby	simultaneously	gaining	competence	in	a	narrow	
repertoire	of	competitive	actions	and	reducing	the	incentive	to	develop	alternative	strategies.	
Thus,	although	the	tendency	to	simplify	and	specialize	lessons	of	competitive	experience	are	an	
efficient	way	to	deal	with	complexity	for	boundedly	rational	firms,	these	processes	also	lead	to	
myopia	(Levinthal	and	March	1993),	because	simplifying	competitive	experience	by	treating	
current	competitors	and	immediate	market	environment	with	relevance	puts	the	focus	on	a	
narrow	range	of	competitors,	which	compromises	competitor	identification,	a	precursor	to	
competitive	analysis.		Additionally,	specializing	in	a	narrow	niche	of	competitive	actions	leads	to	
competence	traps.	Strategies	that	were	successful	in	their	original	contexts	trap	firms	into	
reutilizing	them	until	they	are	no	longer	effective,	and	reduce	the	incentive	to	develop	
alternate	adaptive	responses	(Cohen	and	Levinthal	1994).	

	

3	

Furthermore,	competitive	experience	is	embedded	within	the	larger	context	of	
institutional	norms,	political	systems,	and	social	network	structures	that	determine	the	validity	
and	legitimacy	of	competitive	actions	(Desarbo,	Grewal	and	Wind	2006;	Grewal	and	
Dharwadkar	2002).	At	the	same	time,	firms	are	known	to	be	more	than	just	passive	conformists	
of	institutional	norms,	and	are	known	to	act	entrepreneurially	to	manipulate	the	institutional	
environment	to	their	advantage	(Oliver	1991).		The	existence	of	multiple	institutional	logics	in	
markets,	make	markets	inefficient	and	costly	for	firms	to	adapt	to	each	logic.		Firms	are	known	
to	circumvent	the	problem	of	multiple	institutional	logics	by	initiating	innovative	change	that	
prevail	over	multiple	logics,	and	building	legitimacy	for	the	innovative	product	or	service	with	
the	help	of	branding	or	trademarks	(Suchman	1995;	Mendonca,	Pereira	and	Godinho	2004).		
Likewise,	studies	on	corporate	political	activity	have	shown	firms	to	manipulate	political	logics	
in	order	to	defend	their	self-interests	and	enhance	their	competitive	advantage	(Stigler	1971).		
Such	manipulation	of	political	logics	by	firms	involves	using	specific	tactics	to	create	resource	
asymmetries	and	increase	their	rivals’	costs,	such	as	lobbying	and	political	campaign	
contributions	(Hillman	and	Hitt	1999;	Oliver	and	Holzinger	2008;	Capron	and	Chatain	2008).	
Lastly,	firms	are	known	to	strategically	embed	themselves	into	the	surrounding	social	network	
to	gain	access	to	network	resources	and	reduce	a	rival’s	ability	to	respond	to	competitive	
actions	(Gnyawali	and	Madhavan	2001).	Although,	the	importance	of	context	in	competitive	
structures	is	evident	from	the	aforementioned	research,	there	is	a	gap	in	the	literature	with	
respect	to	the	context-specificity	of	asymmetric	competition.			
This	study	aims	at	addressing	the	gaps	that	have	been	identified	in	the	literature	with	
respect	to	the	context-specificity	and	the	cognitive,	inferential	limitations	of	asymmetric	

4	

competition.		More	specifically,	our	goal	is	to	address	these	gaps	in	the	literature	to	understand	
the	contradictory	implications	of	asymmetric	competition	with	respect	to	the	positive	outcome	
of	asymmetric	rivalry	and	the	negative	outcome	of	asymmetric	competitive	analysis.	
Evolutionary	theories	of	adaptive	learning	such	as	the	Red	Queen	theory	of	competition	may	
offer	an	answer	as	to	why	such	opposing	competitive	asymmetries	exist	(Barnett	and	Hansen	
1996;	Barnett	2008).	The	Red	Queen	refers	to	competition	as	a	self-reinforcing	race,	in	which	
organizations	adapt	to	their	context’s	logic	of	competition,	making	them	stronger	competitors,	
which	trigger	attempts	in	rivals	to	respond	to	a	focal	firm’s	competitive	moves,	evolving	into	a	
process	of	reciprocal	causality.		While	successful	adaptations	to	competition	generate	lessons	
that	inform	future	courses	of	action	in	firms,	over	time,	these	lessons	are	overexploited,	turn	
obsolete	and	trap	the	firm	into	using	the	same	competencies,	leading	to	asymmetric	
competitive	analysis	and	the	erosion	of	competitive	gains	(Barnett	2008).		
A	key	assumption	of	the	Red	Queen	theory	is	the	context	specificity	of	competition,	
where	inter-firm	rivalry	is	assumed	to	vary	considerably	across	contexts.		Thus,	what	it	takes	to	
create	asymmetric	rivalry	in	one	context	could	turn	into	a	losing	strategy	once	the	context	
changes	over	time	or	space.	Achieving	asymmetric	rivalry	depends	on	how	well	adapted	the	
focal	firm	is	to	the	specific	competitive	logic	that	prevails	in	its	context.		This	competitive	logic,	
also	known	as	the	logic	of	competition,	details	the	formal	and	informal	rules	or	principles	that	
determine	who	can	compete,	how	they	compete,	on	what	criteria	they	succeed	or	fail,	and	
what	are	the	consequences	of	success	or	failure	(Barnett	2008).		However,	logics	of	competition	
vary	over	time	and	space	and	while	strategic	alignment	with	the	context’s	logic	of	competition	
creates	favorable	asymmetries	with	rivals,	once	the	context’s	logic	of	competition	changes,	the	

5	

same	strategic	adaptations	affect	the	competitive	analysis	of	the	boundedly	rational	focal	firm,	
making	it	difficult	for	the	firm	to	anticipate	and	respond	to	competitive	threats	from	unknown	
rivals	(Levinthal	and	March	1993;	Barnett	2008).			
Advocating	the	application	of	multiple	theories	to	explain	complex	phenomena,	Weick	
(2007,	p.	16)	asserts	that	“It	takes	richness	to	grasp	richness”.		Accordingly,	we	use	the	Red	
Queen	as	our	model’s	overarching	theory,	and	apply	multiple	theoretical	insights	to	understand	
the	complexity	of	asymmetric	competition.	To	resolve	the	paradox	of	asymmetric	competition,	
we	follow	Poole	and	Van	De	Ven’s	(1989)	recommendation	and	situate	the	paradox	at	two	
different	levels	of	the	firm,	at	the	level	of	competitive	analysis	and	at	the	level	of	competitive	
rivalry.			
The	main	argument	in	our	paper	is	as	follows:	Asymmetric	rivalry	occurs	when,	relative	
to	its	competitors,	the	focal	firm	strategically	adapts	to	the	logics	of	competition,	prevailing	in	
the	institutional,	political	and	social	context	of	the	market	structure.	However,	paradoxically,	
some	of	the	same	strategic	adaptations	create	systematic	errors	in	the	focal	firm’s	lessons	of	
competition,	leading	to	the	unintended	consequence	of	asymmetric	competitive	analysis.	For	
example,	strategically	adapting	to	the	context’s	political	logic	of	competition,	by	way	of	
financing	political	campaigns	and	government	lobbying,	enables	the	focal	firm	to	carry	out	a	
greater	number	of	competitive	actions	compared	to	its	rivals.		The	rationale	being,	currying	
political	favors	allows	the	firm	to	enact	legislation,	pass	laws	and	set	industry	standards,	
without	having	to	worry	about	adapting	to	market	contingencies.	However,	the	power	of	being	
able	to	influence	the	market	rather	than	adapt	to	its	contingencies,	reduces	the	firm’s	incentive	
to	develop	new	adaptive	capabilities.	The	strategy	of	manipulating	the	political	logics	is	

6	

exploited	and	refined	into	a	skill,	leading	to	the	atrophy	of	capabilities	necessary	in	responding	
to	change.		
Our	study	modeled	asymmetric	competition	in	terms	of	the	action-response	dynamic	of	
firms,	by	using	methodology	from	competitive	dynamics	theory	(Smith,	Grimm	and	Gannon	
1992).	To	that	end,	we	defined	asymmetric	rivalry	as	the	total	number	of	competitive	actions	
taken	by	the	focal	firm,	relative	to	rival	firms.	In	line	with	competitive	dynamics	methodology,	
an	action	was	defined	as	“externally	directed,	specific	and	observable	competitive	moves	
initiated	by	the	firm	to	enhance	its	relative	competitive	position”	(Smith,	Ferrier	and	Ndofor	
2001;	p.	321).	We	conceptually	defined	asymmetric	competitive	analysis	as	the	reduced	ability	
in	the	focal	firm	in	responding	to	competitive	threats.	We	operationalized	the	construct	of	
asymmetric	competitive	analysis	in	two	ways.	First,	in	terms	of	action	repertoire	simplicity	that	
measures	the	extent	to	which	firms	engage	in	only	a	narrow	set	of	repetitive	actions,	typical	of	
exploitative	learning	behaviors	of	asymmetric	competitive	analysis.	And	second,	we	use	the	
metric	of	market	share	erosion,	to	capture	impaired	awareness	associated	with	asymmetric	
competitive	analysis	(Ferrier,	Smith	and	Grimm	1999).		
The	context’s	institutional,	political	and	social	logics	of	competition	were	
operationalized	in	the	following	ways:	we	measured	the	proactive	manipulation	of	the	
context’s	institutional	logic	of	competition	by	the	number	of	active	registered	trademarks	and	
service-marks	filed	annually	by	the	firm	each	year;	and	we	operationalized	the	defensive	
manipulation	of	political	logics	by	the	total	amount	spent	on	campaign	finance	and	lobbying	by	
firms	annually.		

7	

Adaptation	to	the	context’s	social	logics	is	captured	at	two	conceptual	levels:	positional	
and	structural	embeddedness.	Scholars	have	proposed	board	interlocks	to	be	indicators	of	
firms’	network	embeddedness	(Mizruchi	1996;	Granovetter	1985).		A	board	interlock	occurs	
when	a	board	director	affiliated	with	one	company	sits	on	the	board	of	directors	of	another	
company,	joining	the	two	companies	into	an	interlock.	Positional	embeddedness	highlights	the	
informational	benefits	that	accrue	from	network	centrality,	i.e.,	the	extent	to	which	firms	
occupy	a	central	position	in	the	network	by	virtue	of	being	connected	to	many	significant	ties	
(Wasserman	and	Faust	1994).		Positional	embeddedness	is	operationalized	in	two	ways,	first,	
using	degree	centrality,	expressed	in	terms	of	the	number	of	corporate	boards	that	the	focal	
firm	is	connected	to	via	interlocks.	Secondly,	networks	of	interlocking	board	directors	represent	
a	particular	type	of	network	known	as	affiliation	or	two-mode	networks	that	have	two	distinct	
sets	of	nodes	or	social	entities	(corporate	boards	and	directors),	where	ties	occur	between	
corporations	as	well	as	between	directors	serving	on	the	corporation’s	board	(Borgatti	and	
Everett	1997;	Robins	and	Alexander	2004;	Opsahl	2013).		In	network	research,	a	distinction	is	
made	between	two-node	sets	based	on	which	node	set	is	more	responsible	for	the	
embeddedness	of	the	firm	(more	responsible	for	tie	creation).	Scholars	consider	both	nodes	of	
directors	and	corporate	boards	as	primary	nodes	(Mizruchi	1996;	Shropshire	2010;	Opsahl	
2013).	Therefore,		we	also	captured	positional	embeddedness	among	board	members	of	focal	
firm	by	using	a	metric	of	inequality,	such	as	the	Gini	coefficient,	to	gauge	the	impact	of	having	
board	members	that	vary	in	embeddedness	(degree	centrality)	on	the	focal	firm’s	board.	Since	
research	suggests	that	a	lopsided	distribution	of	positional	embeddedness	among	board	

8	

directors	influences	group	dynamics	and	decision-making	in	firms,	we	model	its	impact	on	
asymmetric	competition	(He	and	Huang	2011).			
Structural	embeddedness	refers	to	the	structure	and	configuration	of	a	firm’s	network	
of	ties,	which	help	organizations	gain	access	to	unique	information	through	indirect	ties,	to	
monitor	competitors,	and	to	enjoy	reputation	effects	of	being	associated	with	high	status	
partners	(Burt	2010).		Structural	embeddedness	is	operationalized	by	the	number	of	Fortune	
1000	firm	directors	on	a	focal	firm’s	corporate	board,	indirect	corporate	board	ties	to	rival	
firms,	and	the	representation	of	financial	institution	representatives	on	the	focal	firm’s	board.	
Our	study	sets	forth	to	answer	the	question:	Why	are	firms	their	own	worst	enemies?	
Specifically,	why	do	strategic	adaptations	made	by	firms	to	the	context’s	logic	of	competition	
create	favorable	asymmetries	with	rivals,	and	why	do	some	of	those	same	strategic	adaptations	
create	the	unfavorable	consequences	of	asymmetric	competitive	analysis?	Unlike	previous	
studies	we:	(a)	consider	the	cognitive	and	inferential	limitations	of	firms	in	dynamic	competitive	
encounters	that	are	asymmetric	in	nature;	and	(b)	take	into	account	the	context-specificity	of	
competitive	interaction.			
Our	results	strongly	suggest	that	while	strategic	adaptations	to	a	context’s	logic	of	
competition	lead	to	asymmetric	rivalry,	some	of	the	same	adaptations	lead	to	the	adverse	
effect	of	asymmetric	competitive	analysis.		We	found	that	firms’	defensive	manipulation	of	
political	logics	is	significantly	and	positively	related	to	the	focal	firm’s	ability	to	carry	out	greater	
number	of	competitive	actions	relative	to	rivals.	However,	defensive	manipulation	of	political	
logics	was	also	found	to	be	significantly	and	positively	related	to	its	market	share	erosion,	
indicating	impaired	awareness	of	asymmetric	competitive	analysis.		Similarly,	we	found	that	

9	

when	firms	had	a	hierarchical	distribution	of	positional	embeddedness	among	its	key	decisionmakers	they	were	able	to	perform	more	competitive	actions	compared	to	their	rivals.	However,	
we	also	found	that	a	hierarchical	distribution	of	positional	embeddedness	among	its	key	
decision-makers	to	be	positively	and	significantly	related	to	the	firms	engaging	in	a	narrow	
repertoire	of	competitive	actions,	indicating	exploitative	behaviors	associated	with	asymmetric	
competitive	analysis.	Our	results	have	important	implications	for	the	concept	of	asymmetric	
competition,	a	topic	that	has	been	largely	overlooked	in	the	marketing	strategy	literature.		
Our	findings	challenge	existing	conceptualizations	of	asymmetric	competitive	analysis.	
Namely,	asymmetric	competitive	analysis	has	been	attributed	to	reduced	awareness	that	
occurs	due	to	firms	ignoring	rivals	who	are	not	similar	in	resources	or	share	common	markets,	
meaning	firms	are	very	vigilant	of	rivals	that	are	similar	to	them	in	terms	of	resources	and	
market	commonality	(Chen	1996;	Upson,	Ketchen,	Connelly	and	Ranft	2012).	However,	in	our	
study	we	found	compelling	evidence	of	asymmetric	competitive	analysis	in	firms	that	were	
similar	in	resources	and	had	sufficient	market	overlap,	demonstrating	that	these	factors	alone	
are	not	sufficient	determinants	of	competitive	analysis.	By	explicitly	modelling	the	cognitive	
limitations	of	firms	and	the	context-specificity	of	competition	in	our	study,	we	offer	a	better	
understanding	of	the	complex	phenomenon	of	asymmetric	competition.	
This	thesis	proceeds	as	follows.	The	next	section	reviews	the	literature	on	asymmetric	
competition,	and	outlines	the	theoretical	framework	for	the	relationship	between	strategic	
adaptation	by	firms	to	context’s	logic	of	competition	and	asymmetry	at	the	levels	of	rivalry	and	
competitive	analysis.	In	the	third	section	we	use	the	theoretical	framework	as	a	guide	for	
developing	specific	hypotheses	that	relate	strategic	adaptation	of	firms	to	asymmetric	rivalry	

10	

and	asymmetric	competitive	analysis.		In	the	fourth	section	we	describe	our	data,	measures,	
and	methods,	and	in	the	fifth	section	we	provide	our	results.		Next,	we	discuss	our	findings,	
considering	how	our	results	extend	theory	and	extant	literature.	We	also	offer	the	managerial	
and	policy	implications	of	our	findings.	Finally,	we	conclude	with	limitations	and	suggestions	for	
future	research.	

11	

Chapter	2: Conceptual	Development	
2.1 Asymmetric	Competition:	Asymmetric	Analysis	and	Asymmetric	Rivalry	
Theoretical	perspectives	from	the	Resource-Based	View	(RBV)	theory	have	
acknowledged	that	the	existence	of	asymmetry	in	the	vector	of	resources	possessed	by	firms	is	
a	source	of	sustainable	economic	rent	(Amit	and	Schoemaker	1993).	It	is	implied	that	the	
asymmetry	in	resources	possessed	by	each	firm	influences	competitive	analysis,	and	causes	
firms	to	form	a	unique	approach	toward	each	of	their	competitors	(Collis	1991).	Extending	the	
idea	of	asymmetric	resources	from	the	RBV	to	the	Dynamic	Capabilities	View	theory	(DCV),	
Miller	(2003)	argued	that	asymmetries	refer	to	the	distinctive	capabilities	of	firms	that	are	
developed	sequentially	through	path-dependent	learning.	Distinctive	capabilities	are	found	in	
the	firm’s	executive	talent,	social	ties	and	tacit	knowledge,	and	which	enable	the	firm	to	stay	
ahead	of	its	competitors.		
However,	the	identification	of	such	asymmetries	is	not	a	perfect	recipe	for	success,	
because	path-dependent,	incremental	learning	tends	to	have	some	inherent	flaws	(Miller	
2003).	For	instance,	bounds	on	the	rationality	of	decision-makers	prevent	firms	from	making	
accurate	connections	between	capabilities	and	outcomes,	especially	when	the	causal	linkages	
are	complex	and	convoluted,	and	outcomes	are	far	removed	from	the	original	source	(Miller	
2003).	When	firms	experience	success,	they	are	compelled	to	turn	the	successful	event	into	a	
learning	moment.	However,	this	learning	experience	may	become	problematic	when	success	is	
attributed	to	an	incorrect	or	flawed	capability.	This	phenomenon,	known	as	superstitious	
learning	reinforces	the	defective	connections	between	capabilities	and	outcomes,	and	as	a	

	

12	

result	faulty	capabilities	are	repeatedly	employed	because	they	are	wrongly	believed	to	have	
led	to	success	in	the	past	(Levitt	and	March,	1988).	Although	Miller	(2003)	acknowledged	the	
cognitive	limitations	involved	in	the	identification	of	asymmetric	capabilities,	his	work	did	not	
elaborate	on	the	consequences	of	such	faulty	learning.	Furthermore,	the	studies	on	the	
asymmetry	of	resources	and	capabilities	from	the	RBV	and	DCV	perspective	do	not	explain	how	
relative	differences	in	competitive	advantage	affect	competitive	behavior	between	firms	in	the	
market	context.			
The	earliest	evidence	of	asymmetric	competitive	behavior	in	the	marketing	literature	is	
a	study	done	by	Carpenter,	Cooper,	Hanssens,	and	Midgley	(1988),	who	described	the	
competitive	structure	of	a	market	to	be	asymmetric	in	terms	of	the	differential	effect	a	focal	
firm’s	marketing	actions	had	on	the	market	share	of	other	firms	competing	in	the	market.	The	
source	of	asymmetric	competition	is	attributed	to	the	unique	features	of	a	focal	firm’s	
marketing	mix	strategy	such	as	exclusive	distribution	rights	or	branding	that	either	protected	a	
firm	from	their	competitors’	marketing	actions	or	made	them	particularly	vulnerable	to	their	
competitor’s	actions.	For	example,	the	authors	found	a	firm’s	profitability	to	be	dependent	
upon	the	cross-price	elasticity	of	two	similar	brands,	such	that	a	price	decrease	by	a	
substitutable	brand	decreased	the	profitability	of	its	closest	rival	(Carpenter	et	al.	1988).		
Additionally,	they	also	found	that	some	asymmetries	could	offer	only	temporary	advantage	due	
to	the	periodic	tweaking	of	marketing	mix	elements	such	as	price	and	advertising	dollars	to	
match	competitors	(Carpenter	et	al.	1988).	However,	while	this	study	demonstrates	the	
presence	of	asymmetric	competition	in	market	structures,	it	offers	a	rather	static	view	of	
competitive	behavior.	Competitive	situations	in	the	real	world	are	dynamic,	firms	feel	the	

	

13	

effects	of	each	other’s	moves	and	are	prone	to	retaliating	against	competitive	attacks	(Porter	
1980).	Asymmetric	competition,	as	seen	from	a	purely	situational	perspective,	as	a	setting	in	
which	resources	of	a	focal	firm	create	a	situation	of	asymmetry	in	the	distribution	of	profits	in	
the	rest	of	the	firms	in	the	market,	underemphasizes	the	relational	aspect	of	competitive	
behavior,	and	by	extension,	the	rivalry	that	exists	between	competing	firms	(Kilduff,	Elfenbein,	
and	Staw,	2010).	Understanding	asymmetric	competition	can	be	enhanced	by	considering	the	
relational	context	of	competition,	also	known	as	rivalry	since	competing	firms	share	a	history	of	
repeated	competitive	interactions	as	opposed	to	a	single	isolated	competitive	interaction.		The	
relational	aspect	of	understanding	asymmetric	competition	is	critical	because	it	implies	that	no	
two	firms	within	an	industry	analyze	competitive	threat	symmetrically,	and	consequently	no	
two	firms	will	engage	in	actions	symmetrically.	Additionally,	while	Carpenter	et.	al	(1988)	
demonstrated	that	marketing	actions	that	improved	firm	performance	could	also	make	the	firm	
vulnerable	to	competitive	attacks,	their	study	is	rooted	in	a	rational	choice	model	that	excludes	
the	bounds	of	human	decision-making,	and	the	biases	inherent	in	human	perception	that	affect	
competitive	analysis	(Porac	and	Thomas	1990).	
In	the	strategy	literature,	Chen’s	(1996)	Competitive	Dynamics	Theory	uses	elements	
from	game	theory	to	conceptualize	competitive	asymmetry	in	terms	of	a	sequence	of	moves	
and	countermoves	between	rival	firms	along	the	dimensions	of	market	commonality	and	
resource	similarity.		Each	firm’s	awareness	of	a	competitive	threat,	its	motivation	to	attack	(or	
respond)	to	a	threat,	and	its	capability	to	attack	(or	retaliate)	a	rival	firm,	depend	upon	the	
degree	of	difference	in	the	similarity	of	resources,	overlapping	markets,	and	multimarket	
contact	shared	with	its	competitors	(Chen	1996;	Upson,	Ketchen,	Connelly	and	Ranft,	2012).		

	

14	

Consequently,	smaller	rivals	are	considered	an	insignificant	threat	and	allowed	wider	latitude	of	
actions	without	retaliation	from	the	larger	firm.	As	such	the	competitive	dynamics	theory	
demonstrates	both	favorable	and	unfavorable	implications	of	competitive	asymmetry	
depending	on	which	firm	is	the	focal	firm	under	consideration	(Chen	1996).		For	example,	from	
a	focal	firm’s	point	of	view,	asymmetric	rivalry	is	advantageous	because	it	provides	a	wide	
leeway	of	competitive	actions	against	larger	rivals	(Chen,	1996).	Conversely,	the	inability	to	
recognize	and	respond	to	competitive	threats	from	seemingly	small	competitors	exposes	a	
deep	flaw	in	the	competitive	analysis	of	the	larger	firm.		
However,	while	it	is	true	that	size	may	play	a	role	in	competitive	analysis,	firms	may	also	
not	react	to	rival	actions	because	they	lack	information	or	have	not	developed	a	sufficiently	
sensitive	information	system	to	detect	a	change	in	the	marketing	mix	actions	of	their	rivals	
(Carpenter	et	al.,1988).	Most	firms	do	not	collect	information	about	competitors	in	a	
systematic,	organized	manner,	but	act	on	the	basis	of	informal	assessments,	conjectures	and	
intuition	gained	through	scraps	of	piecemeal	intelligence	gained	through	the	course	of	
competitive	experience	(Porter	1980).		Additionally,	a	theoretical	model	that	assumes	rational	
choice	in	the	perception	of	competitive	threat	purely	along	the	dimensions	of	market	overlap	
and	resource	similarity	is	highly	restrictive	and	simplistic,	because	evidence	suggests	that	
identification	of	competitive	threats	is	heavily	influenced	by	the	inherent	biases	of	human	
cognition	that	create	blind-spots	into	managerial	attempts	of	analyzing	their	competitive	
landscape	(Bazerman	2002).	Furthermore,	competitive	actions	are	also	influenced	by	factors	
that	extend	beyond	the	market	components	(competitors,	customers,	suppliers)	of	their	task	
environment,	to	include	nonmarket	factors	(political	agents,	regulatory	bodies,	network	

	

15	

associations).	The	nonmarket	factors	that	populate	firms’	task	environment	may	not	directly	
participate	in	the	market	transactions	of	firms	but	wield	enough	authority	to	affect	the	
outcome	of	competition	through	positive	endorsement,	withdrawal	of	support,	or	sanctions	on	
firms,	and	evidence	suggests	that	firms	are	aware	that	their	strategic	actions	are	contingent	
upon	the	effective	management	of	nonmarket	factors	from	their	task	environment	(Boddewyn	
1988).	
In	addition	to	the	gaps	identified	in	the	literature,	the	single	recurring	theme	that	cuts	
across	the	current	literature	depicts	asymmetric	competition	as	a	paradoxical	phenomenon.	On	
one	hand,	competitive	asymmetry	leads	to	favorable	outcomes	(superior	competitive	
advantage	relative	to	rivals	for	focal	firm,	attack	without	retaliation).	However,	on	the	other	
hand	there	is	an	implication	that	asymmetry	can	also	create	vulnerabilities	in	the	focal	firm	
under	consideration.	Our	aim	is	to	reconcile	these	contradicting	themes	to	explain	this	paradox	
as	it	occurs	within	the	context	of	the	bounded	rationality	of	firms	and	their	key	decisionmakers,	as	well	as	the	broad	context	of	the	task	environment	of	firms.		
	The	current	literature	on	asymmetric	competition	has	singularly	focused	on	competitive	
outcomes	to	the	exclusion	of	the	larger	context	of	the	firm’s	task	environment.	While	the	
former	captures	the	configuration	of	firms	that	compete	with	one	another	at	a	given	level	of	
the	value	chain,	the	latter	includes	the	competitive	structures	among	firms	as	well	as	the	broad	
context	of	social	networks,	institutional	framework	and	regulatory	agencies	that	frame	the	
competitive	structures	of	firms	(Desarbo,	Grewal	and	Wind	2006;	Thompson	1967).	Rivalry	is	
also	a	function	of	the	market	structure	or	the	environmental	context	in	which	firms	compete.		
Competitive	moves	are	often	made	with	attention	to	their	implications	for	not	only	each	of	the	

16	

firm’s	rivals	but	also	with	keeping	the	context	in	mind	(Chen	1996).		Furthermore,	consideration	
of	managerial,	social	and	cognitive	factors	into	competitive	analysis	has	been	strongly	
supported	by	scholars	in	the	strategic	groups	literature	(Porac	and	Thomas	1990;	Zajac	and	
Bazerman	1991).	Accordingly,	a	theoretical	explanation	of	competitive	asymmetry	would	be	
lacking	if	it	did	not	extend	the	contextual	considerations	of	a	firm’s	environment	to	the	
dynamics	of	competitive	rivalry.		
With	this	in	mind,	and	on	the	basis	of	the	gaps	identified	in	the	current	literature	with	
respect	to	the	contradictory	nature	of	asymmetric	competition,	the	assumption	of	perfect	
rationality,	and	the	absence	of	context	in	the	extant	literature	on	asymmetric	competition,	we	
extend	the	Red	Queen	theory	as	a	general	theory	of	competition	to	understand	asymmetric	
competition	as	it	occurs	at	the	level	of	interfirm	rivalry	and	at	the	level	of	competitive	analysis.	
Using	theoretical	insights	from	organizational	learning,	institutional	theory,	social	network	
theory,	resource	dependence,	and	group	dynamics,	we	postulate	a	paradox	of	asymmetric	
competition,	where	strategic	adaptations	by	firms	lead	to	favorable	asymmetries	with	rival	
firms,	effectively	reducing	the	action	capability	of	rivals.		At	the	same	time,	some	of	the	same	
strategic	adaptations	create	unfavorable	asymmetries	with	respect	to	the	competitive	analysis	
of	firms,	reducing	their	ability	to	assess	competitive	threats	effectively.		

2.2 The	Red	Queen	
The	Red	Queen	is	a	dynamic	theory	of	competition	that	uses	a	synthesis	of	
organizational	learning	theory	(Levitt	and	March	1988)	and	organizational	ecology	(Hannan	and	

17	

Freeman	1989),	to	explain	why	some	organizations	are	more	competitive	compared	to	others	
and	why	such	competitive	advantage	is	temporary	(Barnett	and	Hansen	1996;	Barnett	2008).	
The	theory	is	named	after	the	character	of	the	Red	Queen	in	Lewis	Carroll’s	Through	the	
Looking	Glass,	and	is	inspired	by	a	dialogue	between	the	Red	Queen	and	Alice,	where	the	
exhausted	Alice	complains	to	the	Red	Queen	that	although	she	seems	to	be	running	as	fast	as	
she	can,	she	is	still	under	the	tree	where	she	started.	The	Red	Queen	answers:	“Now,	here,	you	
see,	it	takes	all	the	running	you	can	do	to	keep	in	the	same	place.	If	you	want	to	get	somewhere	
else,	you	must	run	at	least	twice	as	fast	as	that.”	(Carroll	1872,	p.36).	In	an	influential	article,	
evolutionary	biologist	Van	Valen	(1973)	used	this	metaphor	to	describe	the	survival	of	species	
as	an	evolutionary	arms-race	between	coevolving	species.	According	to	Van	Valen,	the	best	a	
species	can	do	to	survive	is	to	quickly	and	tirelessly	keep	up	with	an	adversary’s	adaptations	
(Martin	2010).		
	In	the	business	context,	Barnett	and	Hansen	(1996)	introduced	the	coevolutionary	
theory	of	the	Red	Queen	to	explain	the	competitive	one-upmanship	between	firms	in	an	
industry.	Organizations	adapt	to	the	constraints	of	their	market	context,	which	consist	of	key	
elements	of	their	task	environment,	including	competitors	for	markets	and	resources,	suppliers	
of	capital,	goods	and	labor,	customers	or	clients,	and	stakeholders	such	as	government	
agencies,	trade	associations	and	regulatory	bodies	(Thompson	1967).	The	successful	adaption	
to	the	task	environment	improves	the	organization’s	performance	and	becomes	a	learning	
moment	for	the	firm,	but	at	the	same	time	also	increases	the	pressure	faced	by	other	firms	in	
its	competitive	context,	who	aspire	to	catch-up	to	the	firm’s	success	and	are	motivated	to	carry	
out	their	own	adaptive	strategies	in	response	to	the	original	firm’s	strategic	actions	(Cyert	and	

18	

March	1963).	The	improvements	made	by	the	responding	firms	in	turn	trigger	a	selfaccelerating	process	of	reciprocal	competition	that	goes	back	and	forth	between	competing	
firms	(Barnett	and	McKendrick	2004).	In	addition	to	these	baseline	effects,	the	Red	Queen	
theory	also	considers	the	undesirable	consequences	of	the	lessons	learned	from	adapting	to	
competitive	constraints	(Barnett	and	Hansen	1996).		Drawing	on	Levitt	and	March’s	(1988)	
concept	of	“competency	traps”,	the	theory	states	that	competitive	experience	has	a	tendency	
to	stagnate	into	outdated	lessons,	which	can	lead	to	myopia	and	failure	in	firms.		This	occurs	
because	organizations	have	a	tendency	to	simplify	experience	by	confining	lessons	to	recent	
events	and	on	competitive	encounters	that	occurred	on	the	firm’s	home	turf,	and	by	
specializing	in	those	select	few	lessons	of	competition	that	the	firm	believes	led	to	success	in	
the	past	(Levitt	and	March	1988).	
Studies	of	Red	Queen	competition	have	demonstrated	that	these	flawed	methods	of	
learning	reduced	the	viability	(Barnett	and	Hansen,	1996)	and	founding	rates	of	firms	(Barnett	
and	Sorenson	2002),	competitiveness	in	the	global	industry	(Barnett	and	McKendrick	2004),	and	
have	led	to	hazardous	change	(Barnett	and	Pontikes	2008).	Barnett	and	McKendrick	(2004)	
found	that	contrary	to	Porter’s	(1980)	hypothesis,	organizations	did	not	benefit	from	facing	
strong	competitors	in	their	country	of	origin	because	their	adaptive	responses	had	become	
specialized	to	the	market	in	which	they	originally	operated	in.	As	a	result,	lessons	learned	from	
competing	in	one’s	home	country	did	not	carry	over	into	a	foreign	market.	Using	logic	from	
organizational	learning	theory,	the	authors	argued	that	when	firms	successfully	adapt	to	a	
particular	market’s	context,	they	develop	capabilities	that	are	well	suited	for	that	particular	
context,	and	the	successful	adaption	leads	to	a	continuation	in	the	exploitation	of	those	

	

19	

capabilities	to	the	detriment	of	exploring	new	capabilities	(Barnett	and	McKendrick	2004).	
Structural	inertia	associated	with	size	and	past	performance,	also	reduced	the	ability	of	firms	to	
develop	new	responses	to	combat	new	rivals	in	the	foreign	market	(Barnett	and	McKendrick	
2004).		Barnett	and	Pontikes	(2008)	also	found	evidence	of	biases	in	organizations	that	had	
survived	a	history	of	Red	Queen	competition,	particularly	when	this	competitive	experience	
was	recently	acquired	(Barnett	and	Pontikes	2008).		Specifically,	they	found	sampled	firms	
overestimated	their	ability	to	succeed	in	new	ventures	based	on	recent	success	in	competitive	
markets.		This	so-called	“success	bias”	made	organizations	more	likely	to	engage	in	risky	actions	
related	to	market	entry,	which	ultimately	resulted	in	failure	(Barnett	and	Pontikes	2008).		
The	theorization	of	the	Red	Queen	competition	describes	a	dynamic	process	in	which	
firms	adapt	to	competition	in	ways	that	create	a	competitive	pressure	on	other	firms	to	catchup,	causing	them	to	retaliate	and	so	on.		However,	the	empirical	strategy	for	the	Red	Queen	
competition	theory	is	based	on	a	population-ecology	model	(Barnett	and	Hansen	1996;	Barnett	
2008;	Hannan	and	Freeman	1989).	Therefore,	the	methodology	of	what	appears	to	be	a	selfexciting	theory	of	competitive	action	and	response	between	firms	has	been	consigned	to	a	
rather	static	investigation	in	the	form	of	survival	rates	and	failure	rates	of	firms	exposed	to	
competition	(Swaminathan	2009).	
This	missing	link	of	organizational	action	has	been	modeled	by	Derfus,	Maggitti,	Grimm	
and	Smith	(2008)	in	their	test	of	the	dynamic	Red	Queen	process	to	demonstrate	that	a	focal	
firm’s	actions	increased	their	own	performance	and	at	the	same	time	also	increased	the	
number	and	speed	of	rivals’	actions,	which	in	turn	negatively	affected	the	focal	firm’s	
performance.	Although	their	study	modeled	the	dynamic	process	of	the	theory,	it	offered	a	

20	

rather	sanitized	view	of	the	negative	effects	of	Red	Queen.	For	instance,	contrary	to	Derfus	et	
al.’s	(2008)	assertion,	negative	consequences	of	Red	Queen	competition	do	not	simply	refer	to	
the	reduced	performance	of	the	focal	firm	on	account	of	rival	actions.		Under	the	Red	Queen	
theory,	a	firm’s	competitiveness	evolves	through	the	process	of	competitive	moves	and	
countermoves,	where	the	very	strengths	that	make	organizations	good	at	adapting	to	the	
constraints	of	competition	in	the	short	run,	backfire	in	the	form	of	a		
“competency	trap”	leading	to	negative	consequences	in	the	long	run	(Barnett,	2008).	Firms	
form	competitive	repertoires	that	place	them	ahead	of	their	close	rivals,	but	on	the	downside,	
these	repertories	are	limited,	as	they	have	been	developed	keeping	a	certain	set	of	rivals	in	
mind.	Consequently,	this	makes	the	firm	vulnerable	to	threats	from	unfamiliar	rivals.	
Additionally,	the	study	by	Derfus	et	al.	(2008)	also	ignores	the	impact	of	the	wider	context	that	
determines	the	rules	or	principles	on	which	firms	compete,	the	so-called	“logics	of	competition”	
that	we	discuss	in	the	following	section.			

2.3 Strategic	Adaptation	to	Logics	of	Competition	
In	the	Red	Queen	theory,	logics	of	competition	refer	to	a	system	of	principles	that	
determine	who	can	compete,	how	they	compete,	on	what	criteria	they	succeed	or	fail,	and	
what	the	consequences	are	of	success	or	failure	are	(Barnett	2008).	Competiveness	requires	
that	a	firm	perform	better	than	its	rivals,	according	to	the	context’s	logic	of	competition,	and	
adaptation	to	the	context’s	logic	of	competition	determines	what	it	takes	to	win	in	a	particular	
context.		Logics	of	competition	can	be	formal	or	informal,	and	can	cut	across	various	political,	

21	

social,	institutional	and	technological	contexts.		For	instance,	government	regulations	can	
determine	who	is	allowed	to	compete	in	a	given	context,	while	in	other	instances,	a	firm’s	
connections	in	a	social	network	determine	its	access	to	network-related	resources	and	
information.	Therefore,	firms	in	a	Red	Queen	competitive	scenario	will	strategically	adapt	to	
the	context’s	logic	of	competition	to	create	favorable	asymmetries	with	rivals.		These	logics	of	
competition	fall	under	the	institutional,	political	and	social	spheres	of	influence	in	the	firms’	
task	environment,	and	are	respectively	known	as	institutional	logics,	political	logics	and	social	
logics	of	the	Red	Queen	competition	(Barnett	2008).	
	
2.3.1 Institutional	Logics	
The	Red	Queen’s	conceptualization	of	firms	as	strategic	actors	in	their	environment	is	
consistent	with	Oliver’s	(1991)	extension	of	the	institutional	framework	to	explain	the	strategic	
actions	of	firms	in	response	to	institutional	demands	and	expectations.	Whereas	earlier	
research	on	institutional	theory	focused	on	the	organization’s	categorical	conformity	to	
institutional	pressures,	the	adoption	of	a	limited	range	of	socially	sanctioned	organizational	
structures,	and	the	rigidity	and	persistence	of	institutional	norms	for	legitimacy	(DiMaggio	and	
Powell	1983;	Meyer	and	Rowan	1977;	Scott	1995),	later	research	has	acknowledged	the	role	of	
actors’	self-directed	agency	in	envisioning	and	enacting	changes	to	the	contexts	in	which	they	
are	embedded	in	(Oliver	1991;	Dacin,	Goodstein	and	Scott	2002;	Misangyi,	Weaver,	and	Elms	
2008;	Greenwood	and	Suddaby	2006).	
This	second	movement	of	research	in	institutional	theory	has	a	strategic	focus	that	
adopts	the	decision-maker’s	perspective	and	puts	emphasis	on	the	deliberate	attempts	by	firms	

	

22	

at	proactively	manipulating	logics	by	implementing	evocative	symbols	to	garner	legitimacy	from	
stakeholders	(Suchman	1995).	Although	for	most	organizations,	conformity	to	institutional	
pressures	is	seen	as	the	path	to	legitimacy,	for	some	firms,	conformity	is	insufficient,	inefficient	
and	even	costly	(Suchman	1995;	Barnett	2008).	For	instance,	the	Red	Queen	theory	argues	that	
competitive	landscapes	are	often	inundated	with	multiple,	competing	institutional	logics.	These	
institutional	logics	may,	for	example,	take	the	form	of	competing	services,	competing	products	
or	a	new	logic	of	transacting	with	suppliers	and	customers.	Existence	of	multiple,	competing	
logics	requires	the	firm	to	adapt	to	each	logic,	which	is	inefficient	for	the	firm	due	to	constraints	
of	time	and	cost	(Barnett	2008).		For	these	reasons,	firms	often	attempt	to	drive	out	competing	
institutional	logics	and	actively	manipulate	which	institutional	logics	come	to	prevail	in	an	
industry	(Barnett,	2008).	Firms	engage	in	manipulation,	i.e.	they	purposefully	and	
opportunistically	attempt	to	influence	the	institutionalized	values,	beliefs,	or	definitions,	and	
criteria	of	acceptable	practices	or	performance	standards	in	an	industry	(Oliver,	1991).	
Additionally,	such	manipulation	is	a	proactive	initiative	because	decision-makers	have	a	
predefined	strategy	that	they	want	seen	as	the	dominant	logic	in	the	environment.	Firms	that	
engage	in	the	proactive	manipulation	of	the	institutional	logics	are	originators	of	innovative	
practices,	products	and	services	that	depart	from	existing	practice.	Innovative	activities	that	
diverge	from	the	norm	also	necessitate	active	mobilization	of	resources	in	order	to	build	
legitimacy	for	the	divergent	change.	This	can	be	achieved	with	the	help	of	trademarks	and	
branding	to	convey	quality,	credibility	and	purpose	to	the	end-user.		This	exercise	often	leads	to	
a	gain	in	pragmatic	legitimacy,	which	refers	to	legitimacy	given	to	a	firm	by	its	most	immediate	
audience	members	(e.g.	consumers)	based	on	the	innovative	logic’s	expected	value	(Suchman	

23	

1995).		Legitimacy	gained	from	manipulating	institutional	logics	enhances	the	supply	of	
resources	to	the	firm,	but	more	importantly,	it	ensures	stability	and	continuity	of	organizational	
activities	(Parsons	1960).			

2.3.2 Political	Logics	
Actors’	self-directed	agency	in	adapting	to	the	logics	of	competition	can	also	occur	when	
government	policy	alters	logics	of	competition	(Barnett	2008).	The	government	can	be	a	source	
of	tremendous	uncertainty	because	it	has	the	power	to	impact	the	economic	decisions	of	firms,	
to	grant	or	withdraw	legitimacy,	to	enact	laws	that	raise	the	cost	of	product	substitutes,	to	
sanction	economic	conduct,	and	alter	the	competitive	structure	of	the	market	(Boddewyn,	
1988;	Hillman	and	Hitt,	1999).	Firms	that	perceive	a	high	dependence	on	government	policy	
and	those	that	perceive	the	need	to	defend	their	tangible	and	intangible	resources	are	more	
likely	to	engage	in	corporate	political	activity	(Hillman	and	Hitt	1999).		Managing	the	political	
environment	also	allows	firms	to	leverage	the	power	of	the	government	to	defend	their	relative	
market	positions.		As	noted	by	Stigler	(1971),	public	policy	is	designed	for	the	industry’s	benefit,	
and	all	industries	with	sufficient	political	power	strive	to	manipulate	the	state	into	obtaining	
governmental	favors.			
Politically	motivated	strategies	are	built	on	the	premise	that	the	political	logics	of	a	
given	context	operate	as	centers	of	exchange,	where	firms	seek	to	secure	objectives	of	private	
gain	in	exchange	of	agreed-upon	monetary	contributions	or	sensitive	information	to	draft	policy	
reform	(Keim	and	Zeithaml	1986;	Buchanan	1987;	Hillman	and	Hitt	1999).		Due	to	the	coercive	
and	beneficial	powers	of	the	government,	many	firms	formulate	strategies	that	are	specifically	

24	

geared	toward	affecting	the	political	logics	of	their	market	structure	(Oliver	and	Holzinger	
2008).	Often	these	politically	motivated	strategies	involve	specific	tactics	such	as	government	
lobbying	or	financial	contributions	to	candidates	and	political	action	committees.	Scholars	
suggest	that	manipulation	of	political	logics	is	a	long-term	strategy	that	evolves	into	a	dynamic	
capability,	enabling	firms	to	quickly	exploit	political	opportunities	and	eliminate	rivals	by	raising	
their	cost	of	competing	in	the	industry	(Hillman	and	Hitt	1999;	Oliver	and	Holzinger	2008).		
	
2.3.3 Social	Logics	
Firms	also	display	entrepreneurship	and	strategic	action	in	their	social	logics	of	
competition.	Social	network	theory	states	that	competing	firms	are	embedded	in	a	network	of	
ties	that	influences	their	competitive	behavior	and	strategic	outcomes	(Gnyawali	and	
Madhavan	2001;	Baum	and	Dutton	1996;	Dacin,	Ventresca	and	Beal	1999;	Burt	1992;	
Granovetter	1985).	Inherent	in	embedded	networks	is	a	type	of	competitive	advantage	known	
as	social	capital,	which	includes	benefits	such	as	timely	and	novel	information,	tacit	knowledge,	
tangible	resources,	greater	control	and	maneuverability	(Moran	2005).	In	the	strategy	
literature,	the	network	of	board	interlocks	has	been	widely	used	to	test	the	embeddedness	
perspective	on	interfirm	network	ties	(Davis,	1991;	Mizruchi,	1996;	Shrophsire	2010;	Haynes	
and	Hillman	2010;	Beckman,	Schoonhoven,	Rottner	and	Kim	2014).		
A	board	interlock	occurs	when	a	board	director	from	one	firm	also	sits	on	the	board	of	
another	firm,	and	creates	an	interlock	or	tie	between	the	two	firms	(refer	to	Figure	1	for	an	
illustration	of	a	board	interlock)	(Mizruchi	1996).	In	the	literature,	board	interlocks	have	been	
cited	as	an	important	indicator	of	network	embeddedness	(Mizruchi	1996;	Burt	1991)	and	are	

	

25	

associated	with	a	type	of	competitive	advantage	known	as	board	capital.	Board	capital	
constitutes	both	social	capital	from	the	directors’	ties	to	external	organizations,	as	well	as	
human	capital,	which	refer	to	directors’	qualifications,	expertise,	knowledge,	and	skills	(Hillman	
and	Dalziel	2003;	Haynes	and	Hillman	2010).		

James A. Johnson

BOARD INTERLOCK

	
Figure	1:	An	example	of	a	board	interlock.	A	board	interlock	exists	between	the	firms	Target	
Corporation	and	Goldman	Sachs	because	board	director,	James	A.	Johnson	is	a	board	member	
of	both	firms,	connecting	the	two	firms	into	an	interlock.	
	
The	literature	identifies	structural	embeddedness	and	positional	embeddedness	as	
mechanisms	of	social	capital	that	influence	interfirm	behavior	and	competitive	advantage	
(Gulati	and	Gargilio	1999;	Burt	1992;	Granovetter	1992).		Structural	embeddedness	refers	to	
the	structure	and	configuration	of	an	actor’s	network	ties,	and	emphasizes	the	role	that	
network	structure	plays	in	attracting	social	capital	and	competitive	advantage	through	strategic	
ties.	Structurally	embedded	organizations	gain	access	to	unique,	restricted	information	through	
	

26	

indirect	ties,	and	benefit	from	reputation	effects	by	being	associated	with	high	status	partners	
(Burt	2010).		The	resource-dependence	theory	states	that	board	directors	perform	an	
important	function	of	resource	provision	that	encompasses	a	wide	range	of	specific	duties	that	
include,	for	instance,	granting	legitimacy	or	enhancing	the	reputation	of	the	firm	(Selznick	
1949),	aiding	strategy	formulation,	providing	expertise,	advising	top	management	on	important	
firm	decisions	(Lorsh	and	MacIver	1989;	Mintzberg,	1983),	establishing	relations	with	important	
stakeholders	(Burt	1980;	Hillman,	Cannella	and	Paetzold,	2000),	and	facilitating	access	to	
financial	capital	(Mizruchi	and	Stearns	1988).	Moreover,	firms	strategically	form	indirect	board	
interlocks	as	a	monitoring	mechanism	or	to	signal	collusion	with	rival	firms	in	the	industry	
(Mizruchi	1996).	Section	8	of	the	Clayton	Act	of	1914	specifically	prohibits	board	interlocks	
between	firms	competing	in	the	same	markets	(Mizruchi	1996).	However,	indirect	board	
interlocks,	which	occur	when	directors	from	two	companies	sit	on	the	board	of	the	third,	host	
firm,	are	legal	and	prevalent	among	competitors	(Burt,	1980;	Vance,	1983).	
Positional	embeddedness	highlights	the	informational	benefits	that	accrue	from	
occupying	particular	positions	in	the	network.	For	example,	network	centrality,	which	refers	to	
the	position	of	an	individual	actor	in	the	network,	denotes	the	extent	to	which	the	actor	
occupies	a	strategic	position	in	the	network	by	virtue	of	being	involved	in	many	significant	ties	
(Wasserman	and	Faust,	1994).	The	more	central	an	organization’s	network	position,	the	more	
likely	it	will	have	better	access	to	a	wide	range	of	information	from	a	large	number	of	
connections	in	the	network	(Gulati	and	Gargiulo	1999).	Directors	with	high	network	centrality,	
as	seen	in	the	high	number	of	board	appointments	to	other	firms,	are	in	a	unique	position	to	
have	access	to	a	multiple	sources	of	information	(Davis	1991).	Holding	multiple	directorships

27	

H. Lee Scott, Jr.

James A. Johnson

INDIRECT INTERLOCK BETWEEN WALMART AND TARGET
	
Figure	2:	An	example	of	an	indirect	board	interlock.	Director	H.	Lee	Scott	Jr.	is	a	board	member	of	Walmart	Inc.,	and	director	James	A.	
Johnson	is	a	board	member	of	Target	Corporation.	Target	and	Walmart	cannot	form	a	board	interlock	due	to	regulations	of	the	
Clayton	Act	of	1914	which	prohibits	interlocks	between	industry	rivals.	However,	both	directors	also	sit	on	the	board	of	Goldman	
Sachs.		Therefore,	Walmart	and	Target	are	connected	through	an	indirect	board	interlock,	which	is	legal.	
	

28	

expands	the	pool	of	knowledge,	broadens	experience	and	enables	firms	to	vicariously	learn	
about	the	implementation	and	outcome	of	strategies	at	other	firms	(Useem	1988;	Shrophsire	
2010).		

2.4 Problems	of	Strategic	Adaptation	to	Logics	of	Competition	
While	positional	embeddedness,	as	seen	in	the	form	of	multiple	director	ties	to	outside	
firms,	brings	a	wealth	of	information	to	the	corporation,	studies	have	also	warned	about	the	
potential	problems	of	poor	knowledge	transfer	and	diminished	engagement	in	firm	strategy	
due	to	multiple	board	appointments	taking	up	the	time	and	resources,	which	are	necessary	for	
directors	to	contribute	adequately	to	each	firm	(Shropshire	2010;	Ferris,	Jagannathan	and	
Pritchard	2003).	A	firm’s	receptivity	to	additional	sources	of	information	diminishes	as	the	focal	
board’s	network	centrality	in	the	interlocking	directorate	increases	(Shropshire	2010).	A	firm’s	
board	is	more	likely	to	be	receptive	and	attentive	to	a	small,	manageable	source	of	information.	
Given	the	challenges	of	weighing	a	large	amount	of	information	from	multiple	embedded	ties,	
even	when	multiple	directors	bring	pertinent	information	to	the	boardroom,	information	
overload	may	prevent	the	firm	from	reaping	the	benefit	of	directors	with	high	network	
centrality	(Shropshire	2010)	
Evidence	suggests	that	decision-makers	rely	on	a	variety	of	simplifying	strategies	or	
rules	of	thumb,	called	heuristics,	when	making	decisions	(Tversky	and	Kahneman,	1974).	As	the	
implicit	guidelines	that	direct	our	judgment,	heuristics	serve	as	a	mechanism	for	coping	with	
the	complex	environment	surrounding	our	decisions.		Information	overloaded	actors	often	
engage	in	a	decision-making	heuristic	known	as	satisficing,	which	means	that	they	settle	for	a	

29	

satisfactory	choice	instead	of	searching	till	they	reach	the	most	optimal	solution	(Simon	1955).	
In	general,	heuristics	are	helpful	because	they	provide	managers	in	a	time-crunch	a	simple	way	
of	dealing	with	a	complex	world.	However,	they	are	often	misused	which	can	lead	to	serious	
errors	or	biases	in	judgments	(Tversky	and	Kahneman,	1974;	Bazerman	and	Moore	2012).		
Competitive	Dynamics	theory	suggests	that	the	problem	of	bias	in	competitive	analysis	
is	not	a	trivial	one.	Awareness	is	a	key	precursor	to	organizational	action	and	motivates	the	firm	
to	respond	to	a	competitive	threat	(Chen	1996).	If	the	knowledge	transfer	due	to	high	
positional	embeddedness	(high	network	centrality	of	interlock	ties)	is	poor,	then	managers	will	
have	a	reduced	ability	in	identifying	competitive	threats	and	will	find	themselves	blindsided	by	
a	surprise	attack.		Recent	studies	on	boardroom	interaction	have	acknowledged	the	importance	
of	using	group	dynamics	theory	to	understand	boardroom	decision	styles	and	its	effect	on	firm	
outcomes	(He	and	Huang,	2011;	Beckman,	et	al.,	2014).	Furthermore,	the	distribution	of	the	
network	centrality	of	individual	directors	on	a	focal	firm’s	board	has	been	associated	with	
creating	a	lopsided	power-dynamic	in	the	board	room,	and	has	been	shown	to	have	an	adverse	
effect	on	decision-making,	firm	performance	and	the	firm’s	ability	to	attract	financial	resources	
(He	and	Huang,	2011;	Beckman,	et	al.,	2014).		
Additionally,	while	strategic	adaptation	to	the	context’s	logic	of	competition	allows	firms	
to	have	relative	competitive	advantage,	some	of	the	same	adaptations	create	problems	in	the	
way	firms	process	lessons	learned	from	competitive	experience	(Barnett,	2008;	Barnett	and	
Hansen	1996).		Organizational	learning	presumes	interpretation	of	experience,	and	involves	
drawing	lessons	from	memory	and	pooling	personal	experience	with	knowledge	gained	through	
the	experiences	of	others	(Levinthal	and	March	1993).	However,	bounded	rationality,	coupled	

30	

with	problems	arising	from	competing	against	the	backdrop	of	a	complex	and	changing	context,	
makes	it	difficult	to	extract	perfect	lessons	from	experience	and	to	retain	them	(March,	Sproull,	
and	Tamuz,	1991).	Organizational	learning	theory	states	that	organizations	try	to	cope	with	the	
problems	of	learning	from	experience	through	the	process	of	simplification,	by	reducing	
adaptation	in	one	part	of	an	organization	in	order	to	make	learning	more	efficient	in	another	
part	(Lounamaa	and	March,	1987).	As	a	result	of	simplifying	experience,	organizations	tend	to	
focus	attention	and	narrow	their	competence,	becoming	increasingly	specialized	in	a	narrow	
repertoire	of	adaptive	responses.	
Initially,	the	simplification	of	experience	and	specialization	of	adaptive	response	
improves	organizational	performance,	but	this	eventually	leads	to	“traps	of	learning”	by	
simultaneously	reducing	the	incentive	for	exploring	new	strategies	or	achieving	competency	by	
exploring	new	capabilities	(Levinthal	and	March	1993).	One	such	trap	of	learning	is	known	as	a	
“competency	trap”	where	the	firm	that	excels	at	a	particular	strategy,	tends	to	engage	in	that	
strategy	with	greater	frequency,	resulting	in	gaining	more	competency	with	that	strategy	but	at	
the	cost	of	exploring	better	alternatives.	Gaining	competency	with	a	particular	strategy	is	selfreinforcing,	meaning,	firms	tend	to	engage	in	the	strategies	at	which	they	have	greater	
competency	more	frequently	compared	to	strategies	with	which	they	are	less	competent	at.	
This	results	in	the	firm	gaining	greater	experience	with	that	particular	strategy.	Becoming	more	
experienced	with	a	particular	strategy	translates	into	becoming	more	competent	at	that	
strategy	over	others	and	invites	further	utilization	and	prioritization	of	that	strategy	over	
others.		This	self-reinforcing	feedback	loop	of	learning	makes	it	attractive	for	the	organization	
to	continue	with	this	distinctive	competency	(Levinthal	and	March	1993).		While	this	

	

31	

specialization	leads	to	an	immediate	competitive	advantage,	it	is	rarely	sustainable.	The	
organization	becomes	essentially	trapped	in	the	exploitation	of	the	same	competency,	
alienating	itself	from	acquiring	other	forms	of	experience,	and	eventually	becomes	susceptible	
to	environmental	change	(David,	1985).	
Perspectives	from	structural	inertia	theory	state	that	stakeholders	both	demand	and	
reward	organizational	leadership	that	demonstrates	the	ability	to	reproduce	successful	
capabilities	repeatedly	and	consistently.		There	is	pressure	and	incentive	on	organizational	
leadership	to	maintain	the	status	quo	and	to	resist	having	to	frequently	adapt	to	environmental	
demands.	Successful	competencies	become	institutionalized	into	standardized	routines,	which	
lower	the	cost	of	reproducing	these	competencies	over	and	over	again.		However,	the	same	
factors	that	make	competencies	reproducible	make	it	resistant	to	change	(Hannan	and	
Freeman	1984).	As	a	result,	organizational	leadership	becomes	increasingly	unresponsive	to	
environmental	contingencies.		
Another	trap	of	learning	from	experience	occurs	when	organizations	that	wield	power	in	
their	market	environments	lack	the	incentive	to	develop	their	adaptive	skills.	For	example,	firms	
that	pursue	a	strategy	of	defensively	manipulating	the	political	logics	of	their	market	
environment	impose	their	policies,	products,	and	strategies	over	other	firms,	instead	of	
learning	to	adapt	to	a	dynamic	environment	(Levinthal	and	March	1993).	The	continued	usage	
of	political	power	by	firms	using	tactics	such	as	campaign	financing	and	government	lobbying	
changes	a	short-term	asset	into	a	long-run	liability.		In	the	short-term,	the	ability	to	dictate	an	
environment	makes	it	attractive	for	the	organization	to	maintain	this	strategy	because	it	reaps	
profits	from	defending	its	strategic	interests,	without	having	to	worry	about	adapting	to	

32	

competition	from	other	firms.	Over	the	course	of	time,	when	the	environment	undergoes	
economic,	political,	or	demographic	changes	that	are	beyond	the	firm’s	control,	the	
organization	pays	a	heavy	penalty	due	to	the	atrophy	of	adaptive	skills	that	are	necessary	to	
prevent	being	blindsided	by	new	competition	(Levinthal	and	March	1993).		
To	summarize,	there	is	a	gap	in	the	literature	with	respect	to	competitive	scenarios	that	
are	asymmetric	in	nature.		Adopting	a	dynamic	view	of	competition	indicates	that	asymmetry	
has	contradicting	implications	for	firms	when	we	decompose	the	static	concept	of	competition	
into	its	dynamic	parts:	the	action	part	of	rivalry	and	the	cognitive	component	of	analysis.	When	
asymmetric	competition	is	studied	at	the	level	of	asymmetric	rivalry,	where	one	firm	competes	
with	little	to	no	retaliation	from	its	rivals,	asymmetry	is	a	competitive	advantage.		However,	
when	asymmetry	occurs	at	the	cognitive	level	of	competitive	analysis,	wherein	one	firm	is	
unaware	or	unable	to	neutralize	the	other	firm’s	threat,	it	turns	into	a	destructive	liability.		
To	explain	this	paradox,	we	enlist	the	aid	of	the	theory	of	Red	Queen	competition,	
which	provides	a	context	for	asymmetric	competition,	and	is	flexible	enough	to	allow	a	multitheoretical	explanation	of	the	favorable	and	unfavorable	consequence	of	asymmetric	rivalry	
and	asymmetric	competitive	analysis.	Strategic	adaptation	to	the	context’s	logic	of	competition	
leads	to	favorable	asymmetric	rivalries	for	the	focal	firm.	Specifically,	proactive	manipulation	of	
institutional	logics	(by	way	of	legitimizing	innovations	through	trademarking)	that	challenges	
existing	institutional	logics,	and	the	defensive	manipulation	of	the	political	logics	(safeguarding	
corporate	interests	by	engaging	in	a	political	activity)	by	the	focal	firm,	create	favorable	
asymmetric	rivalry,	enabling	the	focal	firm	to	engage	in	a	greater	number	of	competitive	
actions	relative	to	its	rivals.		Similarly,	firms	that	strategically	adapt	to	the	social	logics	of	

	

33	

competition,	by	increasing	their	positional	and	structural	embeddedness	within	the	network	of	
interlocking	director	ties	improve	their	asymmetric	rivalry	with	rival	firms.		
	However,	some	of	these	same	strategic	adaptations	to	logics	of	competition	also	lead	
to	unfavorable	asymmetries	for	the	focal	firm.		We	assume	that	firms	are	boundedly	rational,	
which	creates	the	potential	for	cognitive	biases	to	occur	during	competitive	analysis.	
Asymmetric	competitive	analysis	occurs	because	organizations	fail	to	adapt	rationally,	because	
the	cognitive	biases	of	boundedly	rational	firms	trap	firms	into	exploiting	existing	strategies	
over	alternative	routines.		Problems	in	competitive	analysis	arise	depending	on	how	firms	
choose	to	respond	to	the	constraints	of	their	competitive	environment.		More	specifically,	we	
argue	that	defensive	manipulation	of	political	logics	creates	competency	traps	and	biases	in	the	
focal	firm,	and	leads	to	asymmetric	competitive	analysis,	as	evidenced	by	the	erosion	of	market	
share	in	the	focal	firm	and	a	simple	repertoire	of	competitive	actions.	In	addition	to	the	limits	
on	the	cognitive	capacity,	we	also	assume	that	adaptive	learning	in	firms	is	constrained	by	
decision-makers’	tacit	norms	of	group	dynamics,	thereby	reducing	the	effectiveness	of	
embedded	ties.	Citing	the	problems	identified	in	the	literature	we	argue	that	greater	positional	
embeddedness	leads	to	the	unfavorable	consequence	of	asymmetric	competitive	analysis.	We	
formally	present	these	arguments	and	the	theoretical	rationale	behind	the	relationships	
identified	in	the	conceptual	model	(Figure 3),	in	the	following	chapter	where	we	develop	
specific	hypotheses	based	on	our	theoretical	framework.		

34	

roac
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INSTITUTIONAL LOGICS
s ra emar s

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+

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an

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POLITICAL LOGICS

H6

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s am a n nance

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e ness

s nanc a rm e resen a es
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s ar e Share ros on
sAc on e er o re
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SOCIAL LOGICS
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Figure	3:	The	paradox	of	asymmetric	competition.	Strategic	adaptation	to	logics	of	competition	leads	to	asymmetric	rivalry	and	
asymmetric	competitive	analysis.	
	

35	

Chapter	3: Hypotheses	Development	
We	now	develop	arguments	based	on	the	theoretical	framework	discussed	in	the	
previous	chapter	to	predict	relationships	between	logics	of	competition	and	asymmetric	
competition.	Specific	hypotheses	are	developed	to	predict	relationships	between	logics	of	
competition	and	asymmetric	competition.	We	begin	by	stating	hypotheses	related	to	
strategic	adaptation	to	institutional,	political	and	social	logics	of	competition	and	
asymmetric	rivalry	in	Sections	3.1	–3.4.		Next,	we	argue	why	strategic	adaptations	to	the	
political	and	social	logics	(positional	embeddedness)	of	competition	lead	to	asymmetric	
competitive	analysis	in	Sections	3.5	and	3.6.	

3.1 	Institutional	Logics	and	Asymmetric	Competitive	Rivalry	
Institutional	logics	are	defined	by	Thornton	and	Ocasio	(1999,	p.	804)	as	“the	socially	
constructed,	historical	patterns	of	material	practices,	assumptions,	values,	beliefs,	and	rules	by	
which	individuals	produce	and	reproduce	their	material	subsistence,	organize	time	and	space,	
and	provide	meaning	to	their	social	reality”.		They	refer	to	the	“organizing	principles”,	which	
are	supplied	by	higher	order	social	institutions	such	as	organizations,	the	government,	and	
professional	affiliations,	and	shape	actors’	world	view	and	interpretation	of	the	organizational	
field	(Friedland	and	Alford	1991,	p.258).	These	higher	order	institutional	logics	inform	the	
cognition	of	decision-makers,	and	determine	how	actors	within	organizations	come	to	interpret	
and	share	an	understanding	of	which	strategic	interests	to	pursue	and	which	decisions	to	
execute	(DiMaggio	1997;	Jackall	1988).	For	instance,	changes	in	institutional	logics	affect	which	
36	

economic	forces	gain	the	attention	of	firms,	which	ones	are	deemed	problematic,	and	whether	
they	are	addressed	with	a	corresponding	change	in	the	strategy	and	structure	of	the	responding	
organization	(Fligstein	1990;	Thornton	and	Ocasio	1999).			
The	theoretical	perspective	of	institutional	logics	incorporates	the	traditional	viewpoint	
of	institutional	theory,	wherein	the	institutional	environment	comprised	of	regulatory	
structures,	normative	pressures,	and	cultural-cognitive	systems	shape	organizational	and	
managerial	activity	(Zucker	1977;	DiMaggio	and	Powell	1983).	While	the	definition	of	
institutional	logics	acknowledges	the	meaning	and	importance	of	institutions,	it	differs	from	the	
classical	institutional	theory	assumption	of	the	perfunctory	pursuit	of	legitimacy	by	firms	and	
the	resulting	isomorphism	of	firms	in	the	organizational	field	(DiMaggio	and	Powell	1983;	
Thornton	and	Ocasio	2008).	In	the	classical	institutional	theory	view,	survival	and	performance	
depends	largely	on	firms	passively	embracing	legitimacy	by	conforming	to	institutional	
pressures,	instead	of	actively	pursuing	strategies	to	bend	the	institutional	pressures	to	their	
advantage	(DiMaggio	and	Powell	1983;	Meyer	and	Rowan	1977).	The	institutional	logics	view	
assumes	that	managerial	practices	and	outcomes	are	a	product	of	the	individual	actor’s	
interpretation	of	their	institutional	structure	and	that	individual	agency	is	embedded	in	the	
institutional	environment	(Friedland	and	Alford	1991;	Thornton	and	Ocasio	1999).	Under	this	
view,	organizations	are	perceived	to	be	agents	of	change,	that	behave	in	an	entrepreneurial	
manner	to	shape	and	alter	the	outcome	of	institutional	logics	rather	than	take	institutional	
pressures	for	granted	for	the	end	goal	of	legitimacy	(Thornton	2004;	Barnett	2008).	
Additionally,	instead	of	assuming	that	a	single,	dominant	institutional	logic	overruns	
organizational	fields	and	constrains	firms	to	adopt	similar	coping	strategies	(isomorphism),	the	

37	

institutional	logics	view	assumes	the	organizational	field	of	firms	to	have	simultaneously	
occurring	multiple,	fragmented,	and	competing	logics	(Friedland	and	Alford	1991;	Schneiberg	
2007).	Furthermore,	evidence	suggests	that	firms	respond	to	the	multiplicity	of	institutional	
logics	in	market	settings	differently,	indicating	that	isomorphism	is	not	the	main	response	to	
institutional	pressures	(Greenwood,	Diaz,	Li	and	Lorente	2010).			
To	illustrate,	in	the	Red	Queen	theory,	institutional	logics	refer	to	a	special	type	of	logic	
of	competition	that	determines	the	industry’s	barriers	to	entry,	industry	rivalry,	the	criteria	on	
which	rivals	compete,	their	stratagems,	and	the	outcome	of	competition	(Barnett	2008).		
Consequently,	institutional	logics	orchestrate	a	meta-competition	of	sorts	between	firms	
contesting	one	form	of	institutional	logic	over	another.	For	instance,	contests	between	firms	
could	occur	between	firms	competing	over	unique	rights	to	a	distribution	system,	over	
alternative	modes	of	payment,	or	competing	standards	for	certifying	quality	of	goods	and	
services.	The	winner	of	the	meta-competition	between	multiple	institutional	logics	sets	the	
tone	for	the	context’s	logic	of	competition	and	therefore	dictates	what	rules,	practices	or	
procedures	will	prevail	in	the	industry	that	other	firms	are	obligated	to	follow.		Furthermore,	
institutional	environments	have	been	characterized	as	being	pluralistic	and	fragmented	
environments	in	which	various	organizations	and	stakeholders	make	conflicting	demands	on	
their	constituent	organizations	(Meyer	and	Rowan,	1977).	D’Aunno,	Sutton	and	Price	(1991)	
argue	that	organizations,	when	met	by	conflicting,	plural	demands	from	their	institutional	
environment,	are	constrained	in	their	ability	to	respond	to	these	demands	individually.	For	
example,	strategically	adapting	to	each	of	the	competing	logics	is	a	costly	proposition	when	
there	are	high	fixed	costs	for	doing	so.	Thus,	actors	are	motivated	to	act	as	agents	of	

	

38	

institutional	change	not	only	for	strategic	reasons	of	market	dominance,	but	also	for	practical	
reasons	of	ease	and	efficiency,	and	more	importantly	because	adjusting	to	competing	
institutional	logics	is	a	cost	prohibitive	affair	(Barnett	2008).		
For	these	reasons,	firms	often	attempt	to	rule	out	certain	institutional	logics	and	actively	
try	to	manipulate	which	institutional	logics	come	to	prevail	in	an	industry	(Barnett	2008).	
Manipulation	refers	to	the	purposeful	and	opportunistic	attempt	to	influence	the	
institutionalized	values,	beliefs,	or	definitions,	and	criteria	of	acceptable	practices	or	
performance	standards	in	an	industry	(Oliver	1991).	Social	actors	seek	to	achieve	their	
efficiency-related	objectives	by	creating	or	transforming	an	existing	institutional	logic	with	the	
help	of	the	resources	available	to	or	created	by	them	(DiMaggio	1988;	Maguire,	Hardy,	and	
Lawrence	2004;	Seo	and	Creed	2002).	According	to	Oliver	(1991),	motivation	to	manipulate	
institutional	logics	by	firms	is	high	when	there	is	multiplicity	of	constituents	and	constraints	on	
the	organization.	An	onslaught	of	heterogeneous	institutional	arrangements	is	likely	to	make	
actors	question	these	arrangements.	Instead	of	taking	these	heterogeneous	institutional	
arrangements	for	granted,	actors	are	motivated	to	differentiate	themselves	from	them,	and	if	
necessary,	diverge	from	competing	logics	(Emirbayer	and	Mische	1998;	Seo	and	Creed	2002;	
Sewell	1992;	Battilana,	Leca	and	Boxenbaum	2009).	The	Red	Queen	theory	states	that	
entrepreneurial	organizations	revolutionize	industries	by	introducing	innovative	approaches	
that	imply	a	new	logic	of	competition	(Barnett	2008).	This	involves	manipulating	market	
environments	by	creating	practices,	services	or	products	that	others	are	motivated	to	follow	for	
the	purpose	of	redefining	the	legitimacy	of	extant	institutional	rules,	norms,	or	practices	
(Suchman	1995;	DiMaggio	and	Powell	1983).		

39	

Organizations	that	initiate	calculated,	interest-driven	changes	that	transform	existing	or	
create	new	institutional	logics	are	termed	as	institutional	entrepreneurs	in	the	institutional	
theory	literature	(DiMaggio	1988;	Greenwood	and	Suddaby	2006).		Institutional	
entrepreneurship	involves	initiating	innovative	changes	within	the	boundaries	of	an	
organization	or	within	the	broader	institutional	context	in	which	an	actor	is	embedded	in,	that	
diverge	from	the	prevailing	logic	of	a	market’s	institutional	context	(Battilana,	Leca	and	
Boxenbaum	2009).	Extant	research	illustrates	trademarks	as	indicators	of	innovative	activity	in	
service	firms,	standing	in	as	surrogates	for	strategic	change,	in	addition	to	reinforcing	the	
differentiation	of	the	product	or	service	offerings	(Schmoch,	2003;	Mendonca,	Pereira	and	
Godinho	2004;	Hipp	and	Grupp	2005).	
Implementing	divergent	change	is	challenging	and	resource-intensive	because	of	the	
“liability	of	newness”	associated	with	innovations	(Misangyi,	Weaver	and	Elms	2008;	Aldrich	
and	Fiol	1994).	Therefore,	financial	resources	are	used	during	early	stages	of	the	process	to	
bypass	challengers	of	a	proposed	change	(Greenwood,	Suddaby,	and	Hinnings,	2002).	Resource	
commitments	are	also	made	to	build	legitimacy	for	the	new	innovative	change	(Greenwood	and	
Suddaby	2006).	The	manipulation	of	institutional	logics	to	promote	an	innovative	idea	involves	
building	pragmatic	legitimacy	with	the	organization’s	immediate	audience	(Suchman	1995).	
Pragmatic	legitimacy,	which	refers	to	the	legitimacy	conferred	upon	the	organization	by	its	
most	immediate	audience	(e.g.	customers),	involves	support	for	an	organization	based	on	the	
innovation’s	practical	use	and	its	expected	value	to	its	audience.	Because	pragmatic	legitimacy	
refers	to	the	direct	exchange	between	a	focal	organization	and	its	specific	constituents,	its	
manipulation	involves	investing	in	a	strategy	that	requires	persuading	consumers	to	value	

	

40	

certain	offerings	over	others	(Suchman	1995).	Additionally,	institutional	theory	views	
companies	and	their	brands	as	being	embedded	in	the	institutional	environment,	which	
consists	of	the	cultural	meanings,	ideals	and	accepted	social	norms	that	serve	as	guidelines	to	
which	companies	conform	to	signal	legitimacy	to	key	audiences	(DiMaggio	and	Powell	1983;	
Suchman	1995).	For	example,	the	registration	of	a	trademark	for	a	new	product	or	service	
confers	pragmatic	legitimacy	because	brands	facilitate	consumer	choice	and	frame	the	new	
product	or	service	offering	as	having	credibility.	Trademarks	also	convey	significant	and	
sustained	resource	commitments	because	a	history	of	investment	in	research	and	development	
precedes	the	product	or	service	being	trademarked.		Additionally,	unlike	patents	and	
copyrights,	trademarks	need	to	be	renewed	indefinitely,	as	firms	would	not	be	able	to	justify	
sustaining	a	trademark	(with	its	associated	costs	of	renewal	fees,	filing	legal	documents,	etc.)	if	
their	products	had	no	distinguishing	advantages	or	attributes	in	relation	to	other	offerings	in	
the	market	(Mendonca,	Pereira	and	Godinho	2004).	Securing	legitimacy	is	important	because	in	
addition	to	being	a	catalyst	to	obtaining	resources,	legitimacy	also	ensures	that	firms	can	
introduce	innovative	change	relatively	smoothly	(Parsons	1960).		
Thus,	proactive	manipulation	of	institutional	logics	is	a	strategic	action	that	involves	
mobilizing	resources,	and	making	an	irreversible	commitment	to	implement	divergent	change.	
Porter’s	general	strategy	theory	argues	that	rivals	are	less	motivated	to	attack	and	less	likely	to	
immediately	respond	to	strategic	actions	for	two	main	reasons	(Porter	1980).	One,	if	there	is	
uncertainty	surrounding	the	effectiveness	of	strategic	actions,	it	is	highly	likely	that	rivals	will	
not	deploy	resources	to	retaliate	or	intimidate	the	firms	to	retreat	(Porter	1980;	Chen,	Smith	
and	Grimm	1992).	Accordingly,	rival	firms	will	wait	to	respond	or	respond	slowly	to	strategic	

41	

actions	(Smith,	Grimm,	Gannon	and	Chen	1991).	Secondly,	as	manipulating	logics	involves	
mobilizing	significant	resources,	rivals	will	be	hesitant	to	commit	resources	to	an	unproven	
strategy	(Chen,	Smith	and	Grimm	1992).	Additionally,	since	innovative	actions	diverge	from	the	
prevailing	institutional	norm,	rivals	may	not	be	able	to	anticipate	the	extent	or	the	threat	
potential	of	such	actions.	For	example,	the	technology	of	RFID	is	a	revolutionary	method	for	
managing	supply	chain	and	distribution	of	goods	in	the	retail	industry.		However,	when	
Walmart	adopted	RFID	for	inventory	management	in	2003,	other	retailers	were	hesitant	and	
slow	to	commit	to	a	brand	new	technology	that	required	a	major	investment	in	infrastructure	
and	involved	a	high	level	of	coordination	with	suppliers	(Sliwa	2005).	
To	summarize,	competing	on	multiple	institutional	logics	creates	constraints	in	the	
efficiency-related	goals	of	firms.		Firms	try	to	overcome	this	constraint	by	manipulating	
institutional	logics	in	order	to	shape	the	outcome	of	the	meta-competition	between	logics	of	
competition.	The	manipulation	of	institutional	logics	is	a	strategic	action	that	is	innovative	and	
resource-intensive,	which	deters	retaliation	from	rivals	and	creates	favorable	competitive	
asymmetries	for	the	focal	firm.			
Therefore,	we	hypothesize:	

H1:	Proactive	manipulation	of	institutional	logics	is	positively	related	to	focal	firm	
asymmetric	rivalry	with	rival	firms.		

42	

3.2 Political	Logics	and	Asymmetric	Competitive	Rivalry	
Logics	of	competition	are	also	shaped	by	the	institutional	gatekeepers	and	policy	makers	
in	the	political	environment	of	firms	(Barnett	2008;	Zuckerman	1999).	Porter	(1980)	
acknowledges	the	government	as	one	of	the	critical	forces	that	shape	industry	competition,	and	
recognizes	government	policy	as	potentially	influencing	most,	if	not	all,	aspects	of	industry	
structure	either	directly	or	indirectly.	The	task	environment	of	firms	comprised	of	regulatory	
bodies	that	include	various	government	agencies	are	a	source	of	considerable	environmental	
uncertainty	to	firms	(Thompson	1967).	For	instance,	government	policy	can	alter	the	size	of	an	
industry	through	antitrust	legislation,	and	regulatory	agencies	can	enact	laws	that	determine	
the	entry	and	exit	barriers	of	an	industry	and	indirectly	create	de	facto	competitors	overnight	
(Porter	1980).	Legislation	pertaining	to	employment	practices,	environmental	standards	and	
taxation	guidelines	can	raise	the	cost	structures	of	firms	and	consequently	alter	demand	for	
products	and	services	through	substitutes	(Hillman	and	Hitt	1999;	Stigler	1971).	The	
government	wields	considerable	power	to	intervene	in	economic	transactions	and	issue	
sanctions	that	grant	or	withdraw	legitimacy,	and	is	ultimately	a	source	of	tremendous	
environmental	uncertainty	to	firms	(Pfeffer	and	Salancik	1978;	Jacobson,	Lenway	and	Ring	
1993;	Boddewyn	1988;	Porter	1980;	Williamson	1979).	Theoretical	perspectives	from	resourcedependence	theory	suggest	that	a	perceived	high	dependence	on	government	policy	motivates	
firms	to	reduce	dependence	by	pursuing	political	action	strategies	(Stigler	1971;	Pfeffer	and	
Salancik	1978).		Firms	that	perceive	environmental	uncertainty	and	a	high	dependence	on	
government	policy	will	follow	a	relational,	long-term	approach	to	political	action,	rather	than	a	
discrete,	transactional	one	(Hillman	and	Hitt	1999).	
	

43	

As	opposed	to	complying	with	government	forces,	firms	will	engage	in	defensive	
political	activity	to	preemptively	thwart	undesirable	policies	that	challenge	the	status	quo	or	
threaten	to	constrain	competitive	advantage	(Oliver	and	Holzinger	2008).	As	such,	corporations	
will	seek	to	manipulate	the	government	through	specific	strategies	to	obtain	special	political	
favors	with	government	officials	(Stigler	1971).	Scholars	have	compared	political	institutions	to	
markets	of	exchange,	consisting	of	suppliers	of	public	policy	(elected	officials,	candidates,	
regulatory	bodies)	and	demanders	of	policy	(firms	vying	for	public	policy)	(Buchanan	1987;	
Hillman	and	Keim	1995;	Hillman	and	Hitt	1999).	According	to	these	theorists,	corporations	use	
incentives	such	as	campaign	contributions	and	constituent-specific	information	in	exchange	of	
public	policy	that	benefits	corporate	interests	(Hillman	and	Hitt	1999;	Stigler	1971).	To	that	end,	
exchange	theory	states	that	corporations	will	engage	in	specific	relational	strategies	based	on	
the	nature	of	resources	exchanged.	For	instance,	firms	will	engage	in	relational	political	
strategies	that	provide	policy	makers	with	analytical	information	pertaining	to	the	pros	and	
cons	of	specific	issues	related	to	their	constituency,	with	the	aim	of	aiding	elected	officials	in	
framing	policies	(Aplin	and	Hegarty	1980).	
A	particularly	common	tactic	associated	with	the	political	strategy	of	providing	
information	to	policy	makers	by	individuals	representing	the	firm’s	interest	is	government	
lobbying.	Yet	another	corporate	political	strategy	involves	the	use	of	financial	incentives	to	
appeal	to	nonelected	and	elected	state	officials	(Hillman	and	Hitt	1999).	Evidence	suggests	that	
financial	favors	extended	by	corporate	CEOs	in	France	were	reciprocated	by	elected	officials	in	
the	form	of	privileged	access	to	government	subsidy	programs	(Bertrand,	Kramarz,	Schoar	and	
Thesmar	2004).	For	political	decision-makers,	especially	in	the	United	States,	campaign	

44	

financing	is	a	very	important	resource	that	affects	the	candidate’s	chance	of	getting	elected	to	
office	(Glass	2012).	Thus,	corporations	also	use	tactics	that	directly	support	political	candidates	
through	campaign	donations	or	indirectly	through	Political	Action	Committee	(PAC)	
contributions	to	support	their	relational	approach	to	political	strategy	(Hillman	and	Hitt	1999).		
Resource-dependence	theory	argues	that	reducing	government	dependence	increases	a	
firm’s	power	(Pfeffer	and	Salancik	1978).	Through	their	use	of	defensive	political	strategies,	
firms	reduce	dependence	on	the	political	logics,	and	in	exchange	harness	their	increased	
political	power	to	improve	their	market	position,	hinder	rivals’	ability	to	compete,	influence	
policy	makers	to	pass	legislation	favorable	to	the	firm,	increase	the	cost	of	substitutes	and	
complements,	and	lobby	to	maintain	protective	price	controls	(Capron	and	Chatain	2008;	
Stigler	1971;	Keim	and	Zeithaml	1986).	A	focal	firm	that	defensively	manipulates	the	political	
environment	does	not	necessarily	improve	its	own	adaptive	skills,	instead	they	restrict	a	rival’s	
ability	to	compete.	Defensive	political	strategies	such	as	lobbying	are	pursued	to	undermine	the	
legitimacy	of	rivals’	products	and	services	to	consumers,	clients	or	other	stakeholders	in	the	
institutional	environment	(Oliver	and	Holzinger	2008;	Capron	and	Chatain	2008).	The	resulting	
market	power	enables	politically	connected	firms	to	impose	their	products,	policies	and	
standards	on	the	market	context,	and	carry	out	their	strategies	without	having	to	worry	about	
environmental	contingencies	(Levinthal	and	March	1993).	The	ability	to	control	rival’s	resources	
through	political	power	is	a	source	of	competitive	advantage	to	firms	(Capron	and	Chatain	
2008).		
Additionally,	a	defensive	political	strategy	that	involves	cultivating	relationships	with	
elected	officials	through	lobbying	tactics	leads	to	the	creation	of	political	social	capital,	defined	

45	

as	“benefits	that	firms	secure	through	direct	or	indirect	social	ties	to	policy	agents	that	facilitate	
government	lobbying	in	favor	of	current	firm	interests”	(Oliver	and	Holzinger	2008,	P.510).	
Similar	to	social	capital,	political	social	capital	promotes	trust	between	parties	engaged	in	a	
relational	political	strategy	(corporations	and	policy	makers).	It	enhances	the	ability	of	the	firm	
to	exert	influence	over	the	other	party	and	ensures	mutual	benefit	and	cooperation	(Oliver	and	
Holzinger	2008;	Hillman	and	Hitt	1999).		More	importantly,	political	social	capital	is	a	dynamic	
capability,	which	when	deployed	enables	firms	to	defend	their	current	market	position	and	
strategic	assets	to	maintain	competitive	advantage	(Teece,	Pisano	and	Shuen,	1997;	Blyler	and	
Coff	2003;	Oliver	and	Holzinger	2008).	Firms	that	enjoy	superior	political	connections	tend	to	
deploy	a	greater	number	of	competitive	actions	relative	to	other	firms	in	the	market	(Capron	
and	Chatain	2008).	
The	defensive	manipulation	of	political	logics	represents	a	strategic	action	that	is	
designed	to	cope	with	the	constraints	of	political	intervention,	to	create	a	defendable	position	
in	the	industry,	and	consequently	accrue	superior	competitive	advantage	for	the	firm.		In	the	
strategic	management	literature,	strategic	actions	involve	a	long-term	commitment	of	
considerable	resources,	high	sunk	costs,	an	overhaul	of	organizational	structure	and	radical	
realignment	of	the	organization-environment	fit	(Thompson	1967;	Porter	1980;	Galbraith	and	
Kazanjian	1986;	Chen,	Smith	and	Grimm	1992).	Industry	leaders	such	as	Walmart	have	
overhauled	their	organizational	structure	to	strategize	public	policy	and	pursue	government	
relations	(Wilson	2015).	Chen,	Smith	and	Grimm	(1992)	found	evidence	that	strategic	actions	
are	less	likely	to	invite	countervailing	actions	from	rival	firms,	thereby	allowing	firms	to	attack	
with	impunity	(Chen,	Smith	and	Grimm	1992).	

	

46	

Their	findings	are	rooted	in	Schelling’s	(1960)	thesis	on	competitive	interaction,	which	
argued	that	the	likelihood	of	a	rival’s	retaliation	is	dependent	upon	the	extent	to	which	the	
focal	firm	can	convince	its	opponent	about	their	commitment	to	a	strategic	action.	Thus,	firms	
that	signal	a	continued	commitment	of	substantial	resources	to	manipulate	the	contingencies	
of	public	policy	are	able	to	thwart	their	rival’s	progress	and	ability	to	compete	(Capron	and	
Chatain	2008).	
A	relational	approach	to	the	political	strategy	using	specific	tactics	such	as	lobbying	
requires	special	expertise,	commitment	of	substantial	resources	and	a	dedicated	team	of	
lobbyists	for	cultivating	political	relationships	(Hillman	and	Hitt	1999;	Capron	and	Chatain	
2008).	Political	social	capital	accrued	through	connections	with	elected	officials	is	tacit	knowhow	and	therefore	is	not	something	that	can	be	easily	imitated	by	other	firms.	Capabilities	that	
are	rare,	inimitable	and	non-substitutable	offer	an	asymmetric	advantage	over	other	firms	
(Barney	1991;	Miller	2003).	From	an	outsider’s	perspective,	the	connection	between	the	
capability	and	favorable	performance	outcomes	is	not	immediately	recognizable.	Schelling	
(1960)	notes	that	the	lack	of	knowledge	surrounding	the	expected	effectiveness	of	certain	
strategies,	especially	those	that	require	considerable	implementation,	puts	another	restraint	on	
a	countervailing	response.		Scholars	have	deemed	corporate	political	activity	to	have	
tremendous	causal	ambiguity	due	to	the	multiplicity	of	constituents,	institutional	constraints,	
and	the	information	asymmetry	between	actors	in	the	political	process.	And,	unlike	several	
market	strategies	which	can	be	evaluated,	there	is	no	metric	for	assessing	the	effectiveness	of	a	
political	strategy	(Hart	2004;	Baumgartner,	Berry,	Hojnacki,	Kimball,	and	Leech	2009;	Hansen	
1991).		The	time	constraints	involving	reallocation	of	resources,	realignment	of	organizational	

47	

processes	and	the	lack	of	familiarity	associated	with	an	unproven	strategy	are	associated	with	
fewer	and	delayed	responses	from	rivals	(Chen,	Smith,	and	Grimm	1992).	Due	to	the	costs	of	
political	organization,	many	firms	do	not	have	dedicated	personnel	for	organizing	and	
formulating	political	strategies	(Capron	and	Chatain	2008).	Consequently,	it	is	possible	that	only	
a	fraction	of	a	firm’s	competitors	has	resources	dedicated	to	influence	political	markets,	
thereby	allowing	the	firm	to	have	a	free	reign	over	the	market.	
In	conclusion,	firms	differ	in	their	ability	to	influence	their	political	environment	because	
of	their	differing	political	connections	and	resource	endowments.	Firms	that	have	strong	
political	connections	are	more	likely	to	intervene	in	political	markets	to	shape	policy	pertaining	
to	their	resource	environment	in	their	favor.	Firms	that	enjoy	superior	political	connections	are	
more	likely	to	deploy	competitive	actions	relative	to	its	rivals.		Taking	these	various	arguments	
together,	we	hypothesize:	
	
H2:	Defensive	manipulation	of	political	logics	is	positively	related	to	focal	firm	asymmetric	
rivalry	with	rival	firms.	
	

3.3 Social	Logics	(Structural	Embeddedness)	and	Asymmetric	Competitive	
Rivalry	
The	Red	Queen	theory	views	competition	as	a	constraint	operating	on	firms	in	an	
industry.	Firms	stay	one	step	ahead	of	rivals	by	overcoming	the	constraints	of	the	logics	of	
competition	in	their	market	environment	(Barnett	and	McKendrick	2004).	Constraints	of	

	

48	

competition	are	often	embedded	into	the	structural	configuration	of	the	social	network	
surrounding	firms.	Economic	behavior	of	firms	is	said	to	be	embedded	in	the	structure	of	
network	ties,	and	firms’	purposeful	competitive	actions	and	outcomes	of	those	actions	are	
impacted	by	their	structural	embeddedness	in	the	network	(Gynawali	and	Madhavan	2001).	
Overcoming	the	structural	constraints	of	the	social	logics	of	competition	provides	access	to	a	
unique	source	of	competitive	advantage,	known	as	social	capital,	which	consists	of	unique	
benefits	such	as	access	to	assets,	access	to	status,	and	access	to	unique	information.		Social	
capital	includes	tangible	assets	such	as	financial	capital,	equipment,	as	well	as	intangible	or	tacit	
knowledge	that	flows	between	connected	firms	in	a	network	(Madhavan,	Koka	and	Prescott	
1998;	Gnyawali	and	Madhavan	2001).	Status-related	benefits	of	social	capital	refer	to	the	
legitimacy,	prestige,	and	recognition	that	flow	from	high-status	firms	to	low	status	firms	
(Podolny	1993;	Gnyawali	and	Madhavan	2001).	Network	constraints	present	in	the	structure	of	
networks	deny	access	to	social	capital.		For	instance,	we	cite	two	forms	of	network	constraints	
that	prevent	firms	from	gaining	access	to	asset	and	status	benefits—network	closure	and	
network	constraints	due	to	regulatory	framework—and	discuss	how	firms	overcome	such	
constraints	of	the	social	logics	of	competition	by	strategically	embedding	themselves	into	the	
network	structure	(Burt	1992;	Burt	2010;	Mizruchi	1996).			
In	structural	embeddedness	theory,	network	closure	refers	to	a	network	that	is	marked	
by	dense,	interconnected	and	close	ties,	where	membership	is	closed	off	to	outsiders	that	do	
not	belong	to	the	network	(Burt	2010).	Burt’s	(1992)	structural	holes	theory	argues	that	firms	
navigate	around	the	constraint	of	network	closure	(missing	holes)	by	pursuing	a	type	of	“Tertius	
strategy”	that	involves	structurally	embedding	such	closed	networks	into	a	second	relationship	

49	

(Burt	2010).	For	example,	a	focal	firm	trying	to	gain	access	to	capital	and	wanting	to	mitigate	its	
outsider	status	to	potential	investors,	invites	top	level	executives	of	investment	banks	or	
venture	capitalists	to	join	the	rank	of	its	board	of	directors.	The	resulting	tie	produces	two	
effects:	framing	and	endorsement.		
Framing	effect	occurs	when	meaning	derives	in	some	part	from	the	context	in	which	a	
person	or	entity	is	viewed	(Burt	2010).	In	a	frame-oriented	society,	every	entity	is	set	in	an	
environment	or	a	frame	of	reference	and	the	entity	is	evaluated	within	this	frame	of	reference	
(Sternquist	2007).	Affiliation	with	an	established	insider	frames	the	outsider	firm	as	a	favorable	
business	associate	to	potential	investors	or	business	partners.	Signaling	theory	further	explains	
how	structurally	embedded	organizations	tied	to	a	common	partner	utilize	reliable	information	
about	each	other	from	that	partner	(Baker	1990;	Gulati	1995;	Spence	1973).	Outside	parties	
often	lack	firsthand	knowledge	about	the	inner	workings	of	a	firm	and	must	turn	to	visible	signs	
for	indicators	of	reliability	(DiMaggio	and	Powell	1983).		Sharing	common	ties	with	a	potential	
partner	signals	that	the	partner	is	regarded	as	reliable	and	trustworthy,	and	will	be	as	
cooperative	with	the	focal	firm	as	with	the	other	organizations	that	the	partner	is	affiliated	with	
(Gulati	and	Gargulio	1999).	Secondly,	the	affiliation	is	also	an	implicit	endorsement,	and	carries	
a	reputation	cost	for	the	endorsing	entity.		If	the	relations	with	the	focal	firm	fails,	then	it	
reflects	badly	on	the	party	that	endorsed	the	focal	firm	to	the	closed	network	(Burt	2010).	By	
appointing	directors	with	ties	to	other	reputable	organizations	to	their	corporate	board,	the	
firm	signals	to	potential	investors	and	stakeholders	that	it	is	a	reputable	and	legitimate	
enterprise,	worthy	of	their	endorsement	(Mizruchi	1996).	Consequently,	financial	institutions	

50	

are	more	likely	to	lend	capital	to	a	firm	that	has	well-known,	reputable	directors	on	its	board	
(DiMaggio	and	Powell,	1983;	Mizruchi,	1996).		
In	addition	to	assets,	status-related	benefits	are	also	embedded	in	the	structure	of	
interfirm	network	ties.	Status	is	a	function	of	an	individual’s	personal	social	capital	that	is	
supplied	by	the	individual’s	personal	network	and	connections	with	elite	institutions	(Belliveau,	
O’Reilly	and	Wade	1996;	Certo	2003).	In	general,	high	status	organizations	are	favored	in	
competitive	races	(Barnett	2008).	Because	tie-related	status	produces	a	“Mathew	Effect”	of	
sorts	where	high	status	firms	continue	gaining	advantage	relative	to	peers,	in	the	form	of	
preferential	access	to	resources,	faster	organizational	growth,	and	access	to	capital	(Merton,	
1968;	Podolny,	Stuart	and	Hannan	1996;	Podolny	1993;	Benjamin	and	Podolny	1999;	Barnett	
2008).	The	social	capital	from	board	directors	network	connections	also	includes	sensitive	
information	pertaining	to	the	competitive	environment	of	firms,	including	updates	on	strategies	
and	policies	at	rival	firms	(Davis	1991;	Haunschild	1993).	Additionally,	director’s	networks	also	
supply	firms	with	knowledgeable	and	highly	qualified	managerial	talent	(Rosenstein,	Bruno,	
Bygrave,	and	Taylor	1993).	Directors	that	have	ties	to	strategically	related	organizations	are	
able	to	provide	better	advice	and	counsel	to	the	focal	firm,	and	increase	firm	performance	
(Carpenter	and	Westphal	2001;	Westphal,	1999).	
We	now	turn	our	attention	to	another	kind	of	network	constraint	that	is	artificially	
created	through	stakeholder	intervention	and	impacts	competitive	behavior.	Network	ties	are	
known	to	reduce	uncertainty,	improve	market	intelligence,	and	enable	collusive	behavior	
through	improved	coordination	among	industry	peers	(Mizruchi	1996;	Burt	1992).	Following	
this	rationale,	regulatory	constraints	on	tie	formation	between	industry	firms	will	also	alter	the	

51	

context’s	logic	of	competition	and	contribute	to	competitive	uncertainty.	Due	to	the	
questionable	legality	of	collusive	behavior	between	industry	firms,	direct	ties	among	close	rivals	
are	usually	prohibited	by	regulation	(Mizruchi	1996;	Burt	1992).		For	example,	Section	8	of	the	
Clayton	Act	of	1914	specifically	prohibits	direct	interlocks	between	firms	competing	in	the	same	
markets	(Mizruchi	1996).	However,	indirect	interlocks,	which	occur	when	directors	from	two	
firms	sit	on	the	board	of	the	third,	host	firm,	are	legal	and	prevalent	among	competitors	(Burt	
1980;	Vance	1983).	
Structural	embeddedness	involves	actors’	reliance	on	indirect	channels	of	
communication	for	information	on	other	actors,	and	applies	more	generally	to	structurally	
equivalent	firms	that	are	indirectly	connected	through	a	common	tie	such	as	indirect	interlock	
(Gulati	and	Gargiulo	1999).	This	indirect	channel	of	communication	serves	as	a	search	and	
monitoring	mechanism	for	rival	strategies	and	actions,	and	for	evaluating	each	other’s	relative	
progress	(Burt	1987).	Indirect	interlocks	with	rival	firms	resemble	Granovetter’s	(1973)	concept	
of	“weak	ties”,	where	weak	ties	are	the	indirect	ties	initiated	by	actors	in	a	network	to	
manipulate	constraints	of	network	structure	and	gain	socially	distant	information	and	ideas.	
When	the	regulatory	framework	poses	constraints	on	network	structure,	indirect	interlocks	
have	the	dual	advantage	of	being	legal,	and	at	the	same	time	avoiding	the	redundancy	of	
information	that	comes	with	direct	ties	(Granovetter	1973).		
	Through	such	strategic	embeddedness,	firms	also	gain	the	unique	opportunity	to	learn	
about	decision-making	styles	at	rival	firms	when	their	own	directors	interact	with	rival	firm	
directors	as	they	jointly	serve	on	corporate	boards	of	a	third	firm	(Carpenter	and	Westphal	
2001).	Evidence	suggests	that	knowledge	obtained	from	weak	ties	is	even	more	useful	when	it	

	

52	

is	obtained	from	such	a	trusted	and	competent	source	as	a	board	director	(Levin	and	Cross	
2004).	Boards	are	often	composed	of	highly	competent	individuals	such	as	bankers,	financial	
representatives,	retired	government	officials,	and	top	management	of	Fortune	1000	firms,	who	
bring	along	with	them	expertise,	experience,	and	skills	to	provide	advice	and	counsel	to	the	
boards	they	serve	on	(Baysinger	and	Butler	1985;	Gales	and	Kesner	1994).	Competence	is	a	
critical	dimension	of	trust	and	amplifies	the	perceived	usefulness	and	the	likelihood	of	
mobilizing	the	information	received	through	these	indirect	ties	between	firms	into	a	concrete	
plan	of	action	(Mayer,	Davis	and	Schoorman	1995;	Levin	and	Cross	2004).		
To	sum	up	the	above	arguments,	structural	embeddedness	by	the	focal	firm	overcomes	
network	constraints	of	regulatory	framework	and	network	closure	to	give	access	to	competitive	
advantage	in	the	form	of	social	capital.	Structural	embeddedness	occurs	when	firms	circumvent	
network	constraints	by	inviting	directors	from	reputable	institutions	to	serve	on	their	board.		
Similarly,	firms	can	also	structurally	embed	regulatory	constraints	on	tie	formation	through	
indirect	ties	with	rivals	as	a	noisy	channel	of	communication	that	relays	rival	strategies	and	
actions.	Structural	embeddedness	gives	access	to	competitive	advantage	called	social	capital,	
making	firms	more	likely	to	carry	actions	and	enables	the	focal	firm	to	carry	out	competitive	
actions	with	impunity.	

Therefore,	we	hypothesize:	

H3:	Structural	embeddedness	is	positively	related	to	focal	firm	asymmetric	rivalry	with	rival	
firms.	

53	

3.4 Social	Logics	(Positional	Embeddedness)	and	Asymmetric	Competitive	
Rivalry	
Firms	also	use	their	position	in	the	inter-organizational	network	to	alter	the	logics	of	
competition	(Barnett	2008).	Positional	embeddedness	refers	to	the	overall	position	of	a	focal	
firm	in	its	social	network	of	ties,	which	gives	access	to	resources	that	ultimately	help	the	focal	
firm	in	gaining	competitive	advantage	over	rivals.	Positional	embeddedness	refers	to	the	
construct	of	network	centrality,	which	describes	the	notion	of	being	connected	to	many	
significant	ties	that	put	the	focal	firm	in	a	position	of	strategic	advantage	(Freeman	1979;	Gulati	
and	Gargiulo	1999).	The	resulting	strategic	advantage	includes	access	to	salient	information	
such	as	market	intelligence	on	rivals	in	the	industry,	including	nuanced	details	about	the	
competitive	intent,	strategic	posture	and	resources	of	other	firms	in	the	competitive	space	
(Harrigan	1986;	Gnyawali	and	Madhavan	2001).	Therefore,	positional	embeddedness	can	
influence	an	organization’s	ability	to	access	fine-grained	information	about	the	competitive	
environment.		
Evidence	supports	the	view	that	by	virtue	of	being	involved	in	several	ties,	central	actors	
have	a	more	accurate	picture	of	the	neighboring	network	(Krackhardt	1990),	and	have	access	to	
a	large	web	of	market	intelligence	(Gulati	1999;	Powell,	Koput	and	Smith-Doerr	1996).	Central	
organizations	are	also	more	embedded	in	the	flow	of	information	and	resources	in	the	network	
compared	to	non-central	or	peripheral	actors	(Cook	and	Emerson	1978),	which	gives	them	early	
access	to	novel	information	(Rogers	1995;	Valente	1995).	We	assert	that	positional	
embeddedness	gives	firms	a	competitive	advantage,	and	a	firm’s	network	centrality	serves	as	a	

54	

resource	of	market	intelligence	and	guides	the	focal	firm	in	adapting	to	context’s	logic	of	
competition.	
An	example	of	how	positional	embeddedness	is	demonstrated	at	the	inter-firm	level	of	
network	centrality	is	the	specific	case	of	direct	board	interlocks.	A	board	interlock	occurs	when	
a	director	or	a	top-level	executive	from	the	focal	firm	also	serves	as	a	director	on	another	firm’s	
board,	joining	the	two	firms	into	an	interlock	(Shropshire	2010).	The	focal	firm’s	network	
centrality	is	based	on	the	number	of	interlocked	ties	formed	with	other	firms	in	the	
intercorporate	board	network	(Mizruchi	1996).	Consequently,	when	a	firm	has	directors	that	
serve	on	multiple	boards,	the	firm	accrues	benefits	related	to	network	centrality	such	as	social	
capital,	early	access	to	information,	technology,	capital,	and	prestige	(Davis	1991).	In	terms	of	
information	flows	via	board	interlocks,	research	suggests	that	it	is	important	to	consider	flow	of	
information	not	only	at	the	network	level	between	interlocked	firms,	but	also	at	the	level	of	the	
individual	nodes	(interpersonal	ties	of	directors	serving	on	the	board)	(Shropshire	2010;	Chen,	
Dyball	and	Wright	2009;	Kang	2008).	This	distinction	is	important	because,	networks	of	
interlocking	directors	represent	a	particular	type	of	network	known	as	affiliation,	bipartite	or	
two-mode	networks	(Borgatti	and	Everett,	1997;	Robins	and	Alexander,	2004).		An	affiliation	
network	represents	two	distinct	sets	of	nodes	or	social	entities,	and	each	tie	represents	a	social	
actor’s	affiliation	with	a	group.	In	the	case	of	board	directors,	a	director	on	a	corporation’s	
board	simultaneously	links	firms	in	the	interlock	(focal	firm	interlocked	with	other	firms)	as	well	
as	to	other	directors	on	the	board	they	serve	on	(See	Figure	4).		
	

	

55	

2

1

3
B
A

C

4
Panel I

2

1

3
B
A

C

4
Panel II
	
Figure	4:	The	affiliation	two-mode	network	of	board	interlocks.	A	board	interlock	occurs	when	a	
director	simultaneously	sits	on	the	boards	of	two	(or	more)	firms,	connecting	the	firms	into	an	
interlock.	Because	the	affiliation	network	is	two-mode,	we	can	view	the	interlocks	in	two,	
equally-important	ways:
•

Panel	I:	Firm-Firm	network.	Firm	4	is	connected	to	firms	1,	2,	and	3	through	board	
interlocks	via	individual	directors	A,	B	and	C.	

•

Panel	II:	Director-Director	network.	Directors	A,	B	and	C	are	also	connected	to	each	
other	as	board	members	of	Firm	4.	
	

	

56	

The	duality	and	intertwining	of	linkages	in	affiliation	structures	implies	that	neither	node	
should	be	considered	in	isolation,	and	scholars	in	the	bipartite	interlocking	directorate	network	
literature	advocate	considering	both	nodes	in	research	(Mizruchi	1996;	Shropshire	2010;	Opsahl	
2013).	Bearing	this	in	mind,	we	extend	the	discussion	of	positional	embeddedness	to	the	
distribution	of	network	centrality	of	individual	directors	on	the	focal	firm’s	board.			
Boardroom	interactions	are	germane	to	understanding	how	information	flows	from	
network	centrality	are	used	to	advise	strategic	action	by	firms.		For	instance,	similar	to	He	and	
Huang	(2011),	we	recognize	that	corporate	boards	are	composed	of	people,	and	that	as	
humans,	elements	of	interpersonal	group	relations	such	as	conflict,	teamwork,	and	social	
hierarchy	would	affect	decision-making	in	the	boardroom.		A	large	body	of	research	on	small	
group	dynamics	indicates	that	people	are	naturally	prone	to	guiding	their	interactions	with	each	
other	on	the	basis	of	an	informal	hierarchical	order,	which	is	formed	due	to	our	tendency	to	use	
social	cues	as	a	guide	to	draw	inferences	of	each	other’s	status	and	authority	(Magee	and	
Galinsky	2008).	Centrality	at	the	director	level	indicates	the	number	of	firms	whose	boards	an	
individual	director	serves	on.	Being	invited	to	serve	on	a	board	is	considered	prestigious.	It	also	
expands	an	individual	director’s	pool	of	available	sources	of	information,	provides	broader	
exposure	to	and	awareness	of	current	strategies	at	other	firms.	These	benefits,	among	others,	
allow	directors	with	high	network	centrality	to	be	acknowledged	as	being	competent,	
influential,	and	respected	by	peers	on	the	board.		Therefore,	directors	will	use	the	number	of	
board	directorships	concurrently	held	by	their	co-directors	as	a	social	cue	to	deduce	the	social	
hierarchy	or	ranking	in	the	boardroom.	The	greater	the	degree	of	such	inequality	in	distribution	

	

57	

of	directorships	across	the	board,	the	easier	it	becomes	for	directors	to	perceive	an	informal	
hierarchy	in	the	boardroom	(Blau	1977;	He	and	Huang	2011).		
We	argue	that	informal	hierarchy	in	the	boardroom	due	to	some	directors	having	more	
centrality	compared	to	others,	will	affect	the	focal	firms’	ability	to	make	use	of	the	information	
sourced	from	the	intercorporate	board	network.		Directors	develop	repositories	of	knowledge	
after	witnessing	first-hand	the	implications	of	strategic	decisions	made	at	other	firms	
(Carpenter	and	Westphal	2001).		However,	transforming	this	knowledge	into	strategic	actions	
requires	an	organizational	structure	that	facilitates	its	translation	into	a	definitive	plan.		
Research	from	social	hierarchy	literature	argues	that	people	default	to	an	informal	hierarchical	
order,	as	opposed	to	a	more	egalitarian	group	structure,	because	hierarchy	provides	clear	
guidelines	for	group	interaction	and	coordination	(Magee	and	Galinsky	2008).	
The	informal	hierarchy	among	board	directors	facilitates	coordination	among	directors	
and	promotes	consensus	building	for	approving	strategic	decisions	(He	and	Huang	2011).			This	
is	important	for	several	reasons.	First,	time-crunched	directors	need	effective	ways	to	facilitate	
important	decisions	in	a	very	limited	amount	of	time,	given	that	board	meetings	are	infrequent	
and	short	in	duration.	Second,	the	absence	of	clear	rules	or	procedures	to	guide	expectations	
and	involvement	in	boardroom	discussions	necessitates	clear	leadership	to	provide	guidance	on	
the	rules	of	engagement	and	discussion,	for	example,	when	to	speak,	with	whom	speak,	and	
how	to	make	board	interactions	smoother	and	more	effective	(He	and	Huang	2011).	An	
ostensible	hierarchy	in	the	boardroom	helps	directors	interact	more	productively,	enables	
better	decision-making,	and	ultimately	results	in	better	financial	performance	for	the	firm	
(Finkelstein	and	Mooney	2003;	He	and	Huang,	2011).		

	

58	

Board	directors	with	high	positional	embeddedness	are	key	to	executing	good	decisions,	
and	work	as	catalysts	in	the	boardroom	to	persuade	other	directors	and	executive	leadership	to	
adopt	a	particular	line	of	action.	Directors	differentiate	in	the	level	of	deference	given	to	fellow	
board	members	on	the	basis	of	the	number	of	board	memberships	held	by	each	director,	with	
highly	connected	board	directors	commanding	more	authority	in	the	boardroom	compared	to	
those	that	don’t	hold	many	directorships	(He	and	Huang	2011).	To	have	a	functioning	board,	
you	need	a	style	of	leadership	that	discourages	dysfunctionality	and	promotes	consensus	
building	while	drawing	meaningful	lessons	from	competing	viewpoints.	The	resolution	of	
conflict	in	groups	involves	the	exercise	of	authority	and	status	(Sell,	Lovaglia,	Mannix,	
Samuelson,	and	Wilson	2004).	Task-related	conflicts,	defined	as	mild	disagreement	over	
differences	in	ideas,	viewpoints,	and	opinions	pertaining	to	the	group’s	task,	are	likely	to	be	
reduced,	because	less	central	(and	thus	lower	ranking)	directors	cooperate	with	and	defer	to	
more	central	(higher	ranked)	fellow	directors	to	handle	matters	of	greater	strategic	importance	
(Amason	and	Sapienza	1997).	Directors	who	have	high	centrality	on	the	focal	firm’s	board	tend	
to	also	leverage	their	position	to	act	as	arbitrators	to	resolve	deadlocked	ideas	or	to	endorse	
good	ideas	proposed	by	lower	ranking	directors	(Leblanc	and	Gillies	2005).		
Evidence	suggests	that	a	firm’s	embeddedness	in	a	rich	network	of	ties	is	what	makes	it	
more	likely	to	develop	competitive	capabilities	compared	to	others	that	are	not	as	well	
positioned	to	access	new	ideas,	information	and	opportunities	(McEvily	and	Zaheer	1999).	By	
explicitly	modeling	the	bipartite	structure	of	the	board	interlock	in	our	theoretical	framework,	
we	are	able	to	derive	a	more	nuanced	understanding	of	how	positional	embeddedness	works	
as	a	two-pronged	strategy	for	acquiring	asymmetric	competitive	advantage.		Positional	

	

59	

embeddedness	at	the	firm	level	provides	access	to	market	intelligence,	while	distribution	of	
positional	embeddedness	at	the	focal	firm	board	level,	facilitates	decision-making	and	
responsiveness	to	act	upon	network-sourced	intelligence.			
To	sum	up	the	previous	arguments,	in	comparison	to	rival	firms,	greater	positional	
embeddedness	of	the	focal	firm	in	the	form	of	network	centrality,	creates	a	competitive	
advantage	due	to	the	enhanced	access	to	market	information.	Additionally,	the	distribution	of	
positional	embeddedness	on	the	focal	firm’s	board,	facilitates	the	interpretation	of	the	market	
information	to	inform	strategic	decisions.	The	improved	decision-making	capability	enables	the	
focal	firm	to	carry	out	a	greater	number	of	competitive	actions	relative	to	rival	firms.		
Therefore,	we	hypothesize:	
	
H4:	Positional	embeddedness	is	positively	related	to	focal	firm	asymmetric	rivalry	with	rival	
firms.	
	

3.5 Social	Logics	(Positional	Embeddedness)	and	Asymmetric	Competitive	
Analysis		
Prior	research	shows	that	corporate	boards	are	more	likely	to	be	influenced	to	
strategically	adapt	when	presented	with	unique	information	that	cannot	be	accessed	through	
other	sources	(Haunschild	and	Beckman	1998).		However,	focal	firms	with	a	high	number	of	
interlocks	due	to	their	central	position	in	the	network,	may	be	inundated	with	vast	amounts	of	
information,	making	them	susceptible	to	information	overload	(Porter	and	Donthu	2008;	

	

60	

Simmel	1950).		Ferris,	Jagannathan	and	Pritchard	(2003)	assert	that	directors	who	have	
upwards	of	three	interlocks	lack	the	time	and	resources	to	devote	adequate	attention	to	each	
firm.	Hence,	even	if	the	firm	has	access	to	unique	and	relevant	information	through	its	board	
interlocks,	the	boardroom	may	not	reap	the	benefits	of	centrality	due	to	the	onerous	task	of	
evaluating	a	large	amount	of	information	from	multiple	sources.	Firms	cope	with	information	
overload	by	practicing	the	heuristic	of	satisficing	when	it	comes	to	making	decisions	(Shropshire	
2010).		Satisficing	is	a	rational	theory	of	choice	that	describes	the	process	by	which	boundedly	
rational	actors,	facing	time	and	computation	costs,	search	for	alternatives	until	a	“satisfactory”	
solution	is	found	(Simon	1955;	Simon	1956;	Winter	2000).		Satisficing	contrasts	with	
optimization	theory,	wherein	a	firm	concludes	a	search	upon	finding	the	most	optimal	solution	
as	opposed	to	settling	for	a	solution	that	is	adequate	or	satisfactory.	

	

Organizational	learning	theory	states	that	diverse	interpretations	of	information	
improves	learning	and	increases	the	organization’s	capability	of	undertaking	a	broad	range	of	
actions.	However,	it	also	states	that	the	interpretation	of	information,	i.e.	giving	meaning	to	
information	or	developing	shared	understanding	of	information,	is	affected	when	information	
exceeds	processing	capacity	(Huber	1991).		This	implies	that	over-embeddedness	of	directors	
may	compromise	the	ability	to	generate	meaningful	lessons	from	market	intelligence	and	
consequently	compromise	the	ability	to	engage	in	a	wide	range	of	competitive	actions.	
Similarly,	insights	from	team	and	organizational	learning	theory,	indicate	that	over	
embeddedness	in	terms	of	multiple	board	interlocks	could	lead	to	exploitative,	as	opposed	to	
exploratory,	behaviors	at	both	director	and	corporation	levels	of	the	affiliation	network	level	
(March	1991;	O’Leary,	Mortensen	and	Woolley	2011).	When	individuals	concurrently	belong	to	

	

61	

multiple	teams,	the	group	faces	difficulties	in	sharing	and	integrating	information	to	generate	
new	knowledge	and	ideas	(Wilson	et	al.	2007).	In	contrast,	individuals	working	on	a	smaller	
number	of	teams	are	able	to	allocate	more	time	per	team,	resulting	in	active	experimentation,	
creative	problem	solving	and	the	ability	to	appreciate	nuanced	information	mindfully	(DeMarco	
2002).	
Team	learning	theory	states	that	learning	gets	undermined	when	individuals	serve	on	
multiple	teams	simultaneously	because	multiple	concurrent	team	membership	sets	time	limits	
on	how	well	teams	give	attention	to,	and	how	well	they	integrate	novel	information	
successfully.	This	leads	to	the	prioritization	of	only	those	tasks	that	are	critical	and	timesensitive	(O’Leary,	Mortensen	and	Woolley	2011).	Therefore,	individuals	will	prefer	exploiting	
the	same	action	repertoires	that	have	been	utilized	in	the	past,	and	are	less	likely	to	deviate	
from	the	past	actions.	(Soda	and	Bizzi	2012).	Repeated	exploitation	of	the	adaptive	processes	is	
likely	to	be	effective	in	the	short	run	but	becomes	self-destructive	over	time	(Huber	1991).	The	
frequency	with	which	an	action	is	used	leads	to	gaining	competency	with	that	particular	action.	
And,	the	more	competent	the	firm	becomes	at	using	the	same	actions,	the	more	likely	they	use	
that	same	set	of	actions	or	behaviors.	Thus,	a	firm	develops	competency	traps	that	prevent	it	
from	engaging	in	exploratory	behaviors	or	from	developing	new	competencies	with	which	to	
attack	rivals	(Levinthal	and	March	1993).	
A	varied	interpretation	of	information	is	also	necessary	to	inform	competitive	analysis.		
When	centrality	is	unevenly	distributed	on	the	corporate	board	of	the	focal	firm,	directors	with	
high	status	tend	to	dominate	boardroom	discussions,	while	the	perspectives	of	other	board	
members	receive	less	attention.	As	a	result,	the	firm	is	not	able	to	draw	on	the	expertise	of	the	

	

62	

entire	board	(Wittenbaum	and	Bowman	2005;	Beckman	et	al.	2014).	Also,	Mannix	and	Sauer	
(2006)	argue	that	group	hierarchy	interacts	with	the	dissemination	and	exchange	of	
information	to	create	group	discussion	bias.	The	propensity	to	introduce	and	consider	
commonly	held	information	at	the	expense	of	exchanging	and	considering	information	uniquely	
possessed	by	members	creates	discussion	bias	and	poor	group	decisions	(Stasser,	Taylor,	and	
Hanna	1989;	Stasser	and	Titus	1985;	Stasser	and	Stewart	1992).		
Earlier,	we	discussed	how	the	distribution	of	network	centrality	creates	an	informal	
hierarchy	in	the	boardroom	structure	(He	and	Huang	2011).		We	argued	that	this	informal	
hierarchy	on	a	corporation’s	board	facilitates	decision-making,	and	allows	the	firm	to	undertake	
more	competitive	actions	compared	to	rival	firms	that	have	a	more	egalitarian	boardroom	
structure.		However,	differences	in	the	distribution	of	director	ties	have	a	tendency	to	create	a	
power	dynamic	in	the	boardroom,	which	leads	to	differences	in	the	power	wielded	by	each	
director	in	boardroom	interactions	(Beckman	et	al.	2014;	Emerson	1962;	Ibarra	and	Andrews	
1993).	In	essence,	such	power	imbalances	between	directors	of	a	firm	are	likely	to	result	in	
principal-principal	problems	rather	than	the	traditional	principal-agent	problems	because	
conflicts	are	likely	to	occur	between	the	various	principal	board	members	(Garg	2013).		
Additionally,	Emerson’s	(1956;	1964)	theory	of	power	relations,	states	that	the	inherent	
instability	of	power	hierarchies	set	into	motion	balancing	operations	by	less	powerful	entities	to	
reduce	the	power	dynamic	in	the	relationship	to	a	more	equitable	level.	For	example,	recent	
research	demonstrates	that	hierarchies,	whether	earned	or	arbitrarily	assigned,	make	
individuals	less	inclined	to	cooperate	due	to	a	decreased	investment	by	lower	ranked	
individuals	(Cronin,	Acheson,	Hernandez	and	Sanchez	2015).	This	low	inclination	to	cooperate	

	

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on	the	part	of	less	powerful	entities	can	be	explained	with	the	help	of	the	balancing	operation	
motivational	withdrawal,	which	refers	to	decreased	motivational	investment	on	the	part	of	the	
less	powerful	member.	The	lower	ranking	director	might	lose	some	interest	in	cooperation	due	
to	the	impact	of	frustrations	and	demands	imposed	by	the	higher-ranking	directors.	
Motivational	withdrawal	is	one	of	the	balancing	operations	by	which	the	tensions	in	an	
unbalanced	hierarchy	situation	are	reduced	by	the	lower	ranking	director.		The	lower	ranking	
director’s	motivational	orientations	and	commitment	toward	different	aspects	of	director	
duties	will	intimately	reflect	this	process	(Emerson	1962).	
Additionally,	the	theory	of	signal	detection	explains	that	a	person’s	success	at	
identifying	a	potential	threat	is	not	only	dependent	on	the	person’s	competence	or	perceptual	
ability,	but	also	on	the	anticipated	consequences	of	the	relayed	responses.	This	implies	that	the	
willingness	to	relay	a	threat	detection	response	may	stem	from	a	conservative	criterion	that	
leads	the	person	to	withhold	the	detection	of	the	threat	(Natsoulas	1967;	Wickens	and	Huey	
1993).	And	indeed,	research	shows	that	when	a	low-status	group	member	relays	unique	
information,	it	is	likely	to	be	viewed	with	more	cynicism	than	when	a	high-status	member	
shares	unique	information	(Stewart	and	Stasser	1995;	Wittenbaum	2000).	As	a	result,	low	
status	members	learn	that	communicating	shared	information	is	more	rewarding	and	leads	to	
an	improvement	of	status,	compared	to	communicating	unique	information	(Wittenbaum	and	
Bowman	2005).			
Emerson	argues	that	because	status	is	universally	perceived	as	a	prized	commodity,	a	
low-status	individual	could	engage	in	another	kind	of	balancing	operation.	By	giving	status	
(deference)	to	the	high-status	individual,	the	low	status	individual	restores	the	balance	of	

	

64	

power	and	creates	dependent	hierarchies	(Emerson	1964).	Additionally,	a	director’s	concern	for	
self-preservation,	coupled	with	a	desire	for	status	and	aspirations	to	scale	the	corporate	board	
hierarchy	to	gain	additional	outside	directorships,	will	lead	to	ingratiatory	behaviors	(Shropshire	
2010).	Deference,	flattery,	and	downplaying	one’s	opinions	to	give	validity	to	others	are	
examples	of	ingratiatory	behaviors	(Gordon	1996;	Westphal	and	Stern	2006).	
As	agents	of	shareholders,	board	directors	are	obligated	to	scrutinize,	evaluate,	and	
monitor	the	way	management	operates,	ensuring	the	best	interests	of	the	shareholders	
(Hillman	and	Dalziel	2003).	Conscientious	execution	of	monitoring	and	resource	provision	
duties	may	create	tension	for	a	director	who	wishes	to	capitalize	on	opportunities	in	the	board	
directors’	labor	market	(Hillman	and	Dalziel	2003;	Westphal	and	Khanna	2003).	However,	
provision	of	advice	and	counsel	may	create	a	conflict	of	motivation	for	directors,	known	as	the	
ingratiator’s	dilemma,	which	may	diminish	the	likelihood	that	a	director	will	share	relevant	
knowledge	or	if	they	do	share	it	that	the	information	will	be	presented	influentially	(Jones	1964;	
Shropshire	2010).	This	mirrors	the	classic	agency	problem	of	the	misalignment	of	goals	between	
the	agent	and	the	principal.	
Agency	theorists	see	the	primary	function	of	board	directors	as	monitoring	agents	who	
are	appointed	to	protect	the	best	interests	of	principals	(shareholders)	(Eisenhardt	1989;	
Jensen	and	Meckling	1976;	Mizruchi	1983).		Board	directors	are	obligated	to	ensure	that	the	
management	operates	in	the	best	interests	of	the	shareholders—an	obligation	that	is	met	by	
scrutiny,	evaluation,	and	regulation	of	the	actions	of	the	top	management	by	the	board	
directors	(Hillman	and	Dalziel	2003).	Conscientious	execution	of	this	role	may	create	an	internal	
conflict	for	a	director	who	wishes	to	capitalize	on	opportunities	of	gaining	multiple	

	

65	

directorships	(Hillman	and	Dalziel	2003;	Westphal	and	Khanna	2003).	According	to	agency	
theory,	misalignment	between	the	goals	of	the	agent	and	principal	results	in	the	classic	agency	
problem	of	moral	hazard.	Moral	hazard	refers	to	a	lack	of	effort	on	the	agent’s	part	and	implies	
that	the	agent	is	shirking	and	behaving	in	a	self-interest	seeking	manner	(Eisenhardt	1989).	
Directors	who	are	motivated	by	their	own	career	prospects	have	less	incentive	to	monitor	
management,	because	flattery	and	favors	as	opposed	to	honest	advice	may	help	a	director	
garner	invitations	to	additional	outside	boards	(Shropshire	2010;	Westphal	and	Stern	2007).		
High	centrality	may	also	have	a	negative	effect	on	a	firm’s	willingness	to	deviate	from	
the	past	and	contribute	to	competitive	inertia,	resulting	in	asymmetric	competitive	analysis.		
External	network	ties	carry	positive	referrals	from	those	that	they	are	connected	to,	and	build	a	
favorable	reputation	of	actors	as	being	dependable	and	reliable	individuals	(Wong	and	Boh	
2010).		Social	judgment	of	actors	is	rarely	made	on	the	basis	of	a	rational	explanation	such	as	a	
specific	attribute	that	links	to	past	performance,	and	is	almost	entirely	based	upon	actors’	
connections	or	network	ties	to	other	parties	(Anderson	and	Shirako	2008;	Benjamin	and	
Podolny	1999;	Bitektine	2011;	Fombrun	and	Shanley	1990;	Rao	1994;	Rindova,	Williamson,	
Petkova	and	Sever	2005).	This	means	that	directors	who	are	concerned	about	their	favorable	
social	judgment,	reputation	and	reliability,	will	be	less	inclined	to	sign-off	on	strategic	actions	
that	deviate	from	what	has	worked	before.	Deviating	from	established	actions	carries	the	risk	of	
being	ambiguous,	distorting	perceptions	and	jeopardizing	their	favorable	social	judgment	
(Anderson	and	Shirako	2008;	Bromley	1993;	Deephouse	and	Carter	2005;	Pfarrer,	Pollock	and	
Rindova	2010;	Phillips	and	Zuckerman	2001).		

66	

In	conclusion,	positional	embeddedness	leads	to	firms	adopting	a	satisficing	approach	to	
problem	solving	due	to	the	volume	of	information	from	having	multiple	ties.		As	a	result,	firms	
engage	in	exploiting	a	narrow	repertoire	of	tried	and	tested	strategic	actions.	When	action	
repertoires	are	versatile	as	opposed	to	being	simple,	managers	are	challenged	to	monitor	rival	
responses	and	to	respond	adaptively	to	those	responses.	In	contrast,	a	narrow	repertoire	of	
actions	reduces	the	firm’s	knowledge	base	because	firms	get	to	experience	and	act	on	fewer	
challenges	(Miller	and	Chen	1993;	Miller	1993;	Levitt	and	March	1988).	Simplicity	of	action	
repertoires	blindsides	managers	and	confines	firms	to	a	narrow	set	of	skills	and	experiences,	
and	prevents	the	firm	from	keeping	up	with	the	competitive	environment	(Miller	1993).		
Additionally,	a	lopsided	power	dynamic	in	the	boardroom,	that	stems	from	having	a	few	
directors	with	more	positional	embeddedness	than	others,	also	contributes	to	poor	competitive	
analysis.		Lower	ranking	board	members	are	less	likely	to	participate	in	discussions	or	share	
unique	information.	As	a	result,	the	firm	is	not	able	to	draw	on	the	expertise	of	its	entire	board.	
Therefore,	we	hypothesize:	

H5:	Positional	embeddedness	is	positively	related	to	asymmetric	competitive	analysis	in	focal	
firm.	

3.6 Political	Logics	and	Asymmetric	Competitive	Analysis	
By	pursuing	a	defensive	political	strategy,	a	focal	firm	is	able	to	reduce	its	dependence	
over	the	regulatory	framework,	and	at	the	same	time	leverage	the	power	of	the	government	to	

67	

influence	its	task	environment	(Hillman	and	Hitt	1999;	Santos	and	Eisenhardt	2005).	By	using	
tactics	such	as	government	lobbying	and	campaign	contributions,	firms	acquire	political	power	
to	meet	their	strategic	goals.	Firms	that	gain	power	through	defensive	and	manipulative	
political	actions,	essentially	have	a	free	reign	over	the	market	environment	since	they	are	able	
to	dictate	their	market	actions,	products,	services	and	strategies	to	the	task	environment	
consisting	of	customers,	clients	and	other	firms,	instead	of	learning	to	adapt	to	an	exogenous	
environment	(Levinthal	and	March	1993;	Oliver	and	Holzinger,	2008).	
While	this	may	sound	like	an	ideal	situation	from	the	politically-connected	firm’s	
perspective,	it	is	essentially	walking	into	a	“trap	of	power”	(Levinthal	and	March	1993,	p.	102).	
Traps	of	power	occur	when	firms,	exploit	their	power	to	impose	environments	instead	of	
developing	adaptive	skills	to	their	task	environment,	resulting	in	the	atrophy	of	adaptive	
capabilities	(Levinthal	and	March	1993).		Political	power	kills	the	incentive	to	adapt,	learn,	
innovate,	and	breeds	complacency,	because	the	firm	can	force	its	standards,	products,	services	
and	strategies	on	the	market	context	instead	of	adapting	to	change	(Deutsch	1966).	Thus,	
although	a	defensive	political	strategy	may	enhance	a	firm’s	performance,	it	is	unlikely	to	
generate	sustainable	competitive	advantage,	because	the	defensive	firm’s	dynamic	capabilities	
reside	not	in	seeking	new	areas	for	improvement,	but	in	retaining	and	rehashing	the	same	
strategic	assets	and	competencies	it	already	specializes	in.	Doing	so	protects	the	firm’s	current	
position,	but	does	not	necessarily	improve	its	capabilities	(Oliver	and	Holzinger	2008).	
Moreover,	having	the	power	to	influence	your	peers	means	that	there	are	no	genuine	learning	
moments.	During	good	times,	only	exceptionally	mediocre	actions	result	in	failed	outcomes,	so	
firms	may	inaccurately	infer	sub-optimal	actions	to	be	a	cause	of	success.	Learning	becomes	

	

68	

superstitious	because	firms	assume	that	their	actions	genuinely	lead	to	positive	outcomes,	
when	in	reality	it	was	their	ability	to	control	the	political	environment	that	led	to	a	successful	
outcome	(Levitt	and	March	1988).		
Acquiring	political	social	capital	by	manipulating	the	political	environment	occurs	over	
the	long	term,	and	as	a	dynamic	capability,	is	embedded	in	a	set	of	established	and	
institutionalized	routines	within	organizations	(Hillman	and	Hitt	1999).		The	political	activity	of	
lobbying	and	contributing	to	campaigns	is	described	as	a	routinized,	standard	operating	
procedure	in	firms,	which	is	institutionalized	and	developed	over	time	to	deal	with	the	
uncertainty	of	the	political	environment	(Hadani	and	Schuler	2013).		Consequently,	the	more	an	
organization	responds	to	competitive	forces	by	playing	a	defensive	political	strategy,	the	
greater	its	competence	will	center	on	defensive	political	strategy.	By	comparison,	an	
organization	that	rarely	or	never	responds	to	competition	by	manipulating	the	political	logics	
will	only	have	the	option	of	improving	when	confronted	by	competitive	threats.		Thus,	
organizations	with	a	history	of	defensive	strategy	are	less	likely	to	have	developed	routines	for	
improving	in	the	face	of	competition	because	they	were	busy	exploiting	their	capability	of	
influencing	the	market	(Barnett	2008).		
Overreliance	on	exploiting	the	successful	capabilities	is	self-reinforcing	by	nature.	The	
better	the	firm	is	at	influencing	its	environment	using	a	defensive	strategy,	the	more	likely	they	
will	continue	using	it.	The	more	frequently	a	firm	employs	a	defensive	political	process	the	
more	experience	they	have	with	it.	Experience	with	the	capability	results	in	fine	tuning	it	into	a	
competency	(Levinthal	and	March	1993).	Theory	from	organizational	learning	states	that	when	
firms	exploit	a	proven	solution,	they	abandon	exploring	other	options	that	help	in	coping	with	

	

69	

the	challenges	of	competition.	Knowledge	and	experience	with	old	competencies	inhibit	effort	
to	change	because	firms	learn	about	alternative	strategies	through	direct	involvement.	Since	
manipulating	political	logics	alienates	the	firm	from	gaining	experience	with	alternative	
strategies	it	makes	them	more	vulnerable	to	change	(Levinthal	and	March	1993;	Cohen	and	
Levinthal	1994).	Eventually	the	firm’s	ability	to	influence	the	environment	is	likely	to	be	
overwhelmed	by	economic,	political,	or	demographic	changes	beyond	its	control.	Competitive	
advantage	acquired	through	a	defensive	political	strategy	is	fleeting,	because	changes	in	the	
task	environment,	including	political	upheaval,	stakeholder	action	and	more	importantly,	the	
Schumpeterian	creative	destruction	brought	about	by	innovative	actions	of	new	challengers,	
will	expose	the	focal	firm’s	lack	of	adaptive	skills	(Oliver	and	Holzinger	2008;	Barnett	2008;	
Levinthal	and	March	1993;	Schumpeter	1950).	
This	atrophy	of	adaptive	capabilities	resulting	from	a	controlled	competitive	experience	
is	similar	to	the	deprivation	of	trial-and-error	learning	which	is	common	among	children	of	
overprotective	parents.	Parental	overprotectiveness	serves	simultaneously	to	reinforce	
dependency	and	prevent	the	child	from	developing	independent,	autonomous	behaviors	
(Bornstein	1992).	Dependency	occurs	because	children	quickly	learn	that	being	dependent	on	
others	(with	minimal	effort	on	their	own	part)	is	highly	rewarding.	Similarly,	firms	that	engage	
in	protectionism	reinforce	their	dependency	on	governmental	regulations	at	the	cost	of	
developing	their	competitive	skills.	
We	argue	that	firms	manipulate	the	political	logics	to	defend	their	market	position	
against	competitors.	Firms	achieve	this	by	lobbying	for	regulation	that	limits	new	entrants	or	by	
raising	the	cost	of	substitutes	and	complementary	products	to	make	competitor	offerings	

	

70	

undesirable	(Stigler	1971;	Oliver	and	Holzinger	2008;	Hillman	and	Hitt	1999;	Capron	and	
Chatain	2008).	By	reducing	their	competitive	experience	to	a	small,	restricted	pool	of	rivals,	
firms	become	more	vulnerable	to	cognitive	biases	(Barnett	2008).		This	occurs	because	
experience	derived	from	a	restricted	sample	size	of	competing	firms	challenges	the	validity	of	
lessons	learned,	since	they	lack	the	complete	range	of	experiences	for	making	meaningful	
inferences	(March,	Sproull	and	Tamuz	1991;	Lounamaa	and	March	1987;	Levitt	and	March	
1988).	Firms	that	lack	multiple	events	of	competitive	engagement	will	use	whatever	
information	they	can	extract	from	this	limited	sample	of	competitive	encounters	to	inform	
competitive	analysis	(March,	Sproull	and	Tamuz	1991).		Limiting	competitive	experience	to	a	
narrow	range	of	industry	rivals,	makes	firms	myopic	because	their	adaptive	responses	are	
specialized	and	tailored	to	a	select	group	of	rivals,	and	therefore	poorly	adapted	to	other	
possible	forms	of	competition	(Barnett	2008;	Levinthal	and	March	1993).		They	may	suffer	from	
a	bounded	awareness	problem,	where	firms	become	blindsided	to	the	changing	competitive	
landscape	around	them	and	become	susceptible	to	a	type	of	cognitive	bias	known	as	the	
focusing	illusion	(Bazerman	and	Chugh	2005).	The	focusing	illusion	refers	to	the	tendency	of	
people	to	make	judgments	based	on	their	attention	to	only	a	subset	of	available	information,	to	
overweigh	that	information	and	to	underweigh	unattended	information.	For	example,	firms	
may	engage	in	political	activity	due	to	mimetic	pressures	from	other	firms,	or	for	reasons	of	
managerial	self-aggrandizement,	all	of	which	are	unrelated	to	firms’	outcomes.		Furthermore,	
there	is	no	clear	metric	of	measuring	the	impact	of	political	logics	on	firms’	performance,	and	
with	the	political	process	being	notoriously	fickle,	there	is	considerable	causal	ambiguity	
between	manipulation	of	political	logics	and	its	effect	on	firms’	competiveness	(Hart	2004)	

	

71	

Taking	these	various	arguments	together,	we	conclude	that	although	defensive	
manipulation	of	political	logics	has	clear	benefits	in	terms	of	reduced	competition,	this	strategy	
also	makes	the	organization	vulnerable	to	being	blindsided	by	competition	and	having	its	
competitive	gains	eroded	away.	
Based	on	the	preceding	rationale,	we	hypothesize:	
	
H6:	Defensive	manipulation	of	political	logics	is	positively	related	to	asymmetric	competitive	
analysis	in	focal	firm.	
	

	

	

72	

Chapter	4: Methodology	
	

4.1 Sample	and	Data	
	To	ensure	that	our	hypothesized	firm-level	strategic	responses	to	logics	of	competition	
and	their	impact	on	asymmetric	competition	can	be	traced	to	the	same	context,	our	sampling	
procedure	established	screening	guidelines	(Derfus	et	al.	2008).	First,	to	give	context	to	our	
study,	we	selected	the	U.S.	retail	industry	because	of	its	long	history	of	well-recognized	rivalry	
between	firms,	a	known	set	of	industry	rivals,	a	clearly	defined	boundary	between	strategic	
groups	within	the	industry,	and	the	prominent	documentation	of	competitive	actions	by	retail	
firms	in	the	media.	Second,	we	defined	the	domestic	retail	industry	on	the	basis	of	the	North	
American	Industry	Classification	System	(NAICS)	classification	code	of	firms	listed	under	the	4445	NAICS	industry	codes	for	the	retail	industry.	Third,	from	this	industry	classification	system,	
we	selected	firms	that	were	publicly	traded	and	that	were	classified	as	competing	in	the	
industry	under	a	distinct	strategic	group	or	market	segment,	as	defined	by	the	six-digit	NAICS	
code	classification,	and	compared	it	to	the	National	Retail	Federation’s	system	of	classifying	
firms	on	the	basis	of	the	percentage	of	sales	from	a	particular	category.	This	ensured	that	we	
could	trace	the	effect	of	the	predicted	strategic	actions	of	a	focal	firm	on	asymmetric	rivalry	
with	its	closest	rivals	within	the	industry.		
Nine	strategic	groups	met	our	criteria:	general	merchandisers,	supermarkets,	
drugstores,	department	stores,	sporting	goods,	hardware	and	lumber	stores,	home	ware	
retailers,	office	supply,	and	footwear	retailing.	

	

73	

Fourth,	we	included	only	those	firms	that	represented	at	least	forty	percent	or	more	of	
the	industry	group	sales	generated	by	firms	that	were	publicly	traded	in	the	year	2012,	or	firms	
with	net	sales	equal	to	or	greater	than	10	per	cent	of	the	sales	of	the	strategic	group	leader	(in	
terms	of	sales),	had	distinct	operations	in	the	U.S.	market,	and	reported	performance	in	the	
specific	U.S.	market.	
		Error!	Reference	source	not	found.	lists	all	the	firms	sampled	in	the	study,	their	NAICS	
code,	strategic	group	and	the	average	percentage	of	sales	accounted	for	by	the	strategic	group	
for	the	year	2012.	Additionally,	we	sampled	our	firms	over	time	so	that	we	could	make	causal	
inferences	about	the	relationship	between	our	predictors	and	our	dependent	variables.	This	
approach	resulted	in	a	sample	of	41	U.S	retail	firms	observed	over	a	sampling	period	of	ten	
years	from	2003-2012.	We	have	a	balanced	panel	data	consisting	of	410	firm-year	observations.		
	
	
	
	
	
	
	
	
	
	
	

	

74	

Firm	and	firm	group	information	
Firm	Name	

NAICS	Code	

Strategic	Group	

Sales	%	

Kroger	Co.	
Safeway	Inc.	
Supervalu	Inc.	
Whole	Foods	Market	Inc.	
Delhaize	Group	
Koninklijke	Ahold	
Walmart	Inc.	
Target	Corp.	
Dollar	General	Corp.	
Family	Dollar	Stores	
Dollar	Tree	Inc.	
Amazon.com	Inc.a
Macy’s	Inc.	
Sears	Holdings	Corp.	
JC	Penney	Co.	
Kohl’s	Corp.	
Belk	Inc.	
Dillards	Inc.	
Sports	Chalet	Inc.	
Hibbet	Sports	
Dicks	Sporting	Goods	
Cabelas	
Big	5	Sporting	Goods	
Pier	1	Imports	
Williams	Sonoma	
Bed	Bath	&	Beyond	
Home	Goods	Inc.	
Genesco	Inc.	
Foot	Locker	Inc.	
DSW	Inc.	
Finish	Line	Inc.	
Collective	Brands	Inc.	
Shoe	Carnival	Inc.	

445110	

Supermarkets	

45.16	

452990	

General	Merchandise	

82.66	

452111	

Department	Stores	

50.64	

451110	

Sporting	Goods	

50.52	

442299	

Housewares	

56.11	

448210	

Footwear	

42.01	

Table	1:	Firm	and	industry	group	information.	Note	that	Sales	%	is	the	percentage	of	the	total	
industry	group	sales	represented	by	the	selected	firms	(e.g.	the	summed	sales	of	Kroger,	
Safeway,	Supervalu,	Whole	Foods	Market,	Delhaize,	and	Koninklijke	comprise	45.16%	of	the	
total	industry	sales.	

75	

Table	1:	(cont'd).	
Rite	Aid	Corp.	
Walgreens	Co.	
CVS	Caremark	Corp.	
Office	Depot	Inc.	
Officemax	Inc.	
Staples	Inc.	
Home	Depot	Inc.	
Lowe’s	Inc.	

446110	

Drugstores	

88.74	

453210	

Office	Supply	Stores	

59.86	

444110	

Building	Supplies	

47.61	

a	

Amazon.com	is	treated	as	its	own	industry	group.	It	represents	16.53%	sales	in	
the	non-store	retailers	group.	

4.2 Competitive	Actions	of	Retailers	
Consistent	with	the	theoretical	framework	of	the	Red	Queen	theory	regarding	the	effect	
of	strategic	responses	to	the	logics	of	competition	within	a	specific	environment,	we	focused	on	
actions	that	were	specific	and	observable	to	firms	in	the	retailing	industry.	We	followed	the	
methodology	from	previous	research	on	competitive	dynamics	to	code	our	dependent	variable	
of	asymmetric	competitive	rivalry	(Derfus	et.al.	2008;	Smith	et	al.	1992).	The	methodology	
known	as	structured	content	analysis	uses	content	analysis	to	draw	relevant	information	from	
published	newspaper	and	magazine	articles	(Jauch,	Osborn	and	Martin	1980).			
Our	main	argument	underlines	competitive	actions	as	deliberate	announcements	that	
focal	firms	want	to	signal	to	their	rivals.	Since	actions	and	responses	that	are	observable	to	
customers,	competitors,	and	other	industry	watchers	tend	to	be	reported	in	the	business	press,	
past	research	captured	such	observable	actions	through	structured	content	analysis	of	
newspaper	and	trade	magazine	articles	(Derfus	et	al.	2008;	Basdeo	et	al.	2006;	Ferrier	et	al.,	

76	

1999;	Miller	and	Chen	1994;	Smith,	Grimm,	Gannon	and	Chen	1991;	Young,	Smith	and	Grimm	
1996).		The	structured	content	analysis	(Jauch,	Osborn	and	Martin	1980)	was	conducted	by	
coding	actions	into	categories	on	the	basis	of	a	content	analysis	schedule	(See	Appendix	A).		
Therefore,	we	only	searched	reputable	and	widely	circulated	news	sources	that	were	
easily	available	to	retailers	competing	in	the	US	retail	industry.			We	relied	on	two	primary	
sources	for	retailer	competitive	actions:	company	websites	for	press	releases	and	
announcements,	and	Infotrac’s	General	Business	File	ASAP	database.	The	latter	covers	activities	
of	companies	and	industries	from	over	4,000	leading	business	and	trade	publications,	and	
offers	a	wide	selection	of	US	and	regional	news	publications	and	services	such	as	the	Wall	
Street	Journal,	The	NY	Times,	PR	Newswire,	PR	News,	as	well	as	retail	trade	publications	such	as	
Progressive	Grocer,	Women’s	Wear	Daily,	Chain	Store	Age	and	Drug	Store	News,	to	name	a	
few.		
We	searched	for	each	of	the	forty-one	firms	listed	in	our	study	in	the	search	function	of	
the	Infotrac’s	General	Business	File	ASAP	database	and	set	the	search	limits	to	the	period	20032012.	To	capture	the	full	extent	of	competitive	actions	in	the	retail	industry,	we	coded	
competitive	actions	under	quintessential	retailing	action	categories	(See	

e 	and	

e 3	for	

definitions	of	action	categories	and	examples	of	action	coding).	The	categories	were:		
assortment	mix,	services	strategy,	supply	chain	management,	pricing,	promotion,	and	
geographic	expansion	(Levy	and	Weitz	2012).	We	coded	a	total	of	34,058	actions	for	all	years	
(Table	4	for	breakdown	of	actions	under	each	category).		

77	

Definitions	of	Action	Categories	
Action	Category	

Definition	

Assortment	Mix	

Actions	pertaining	to	the	development	of	an	assortment	plan,	
including	the	breadth	and	depth	of	merchandise	that	the	focal	
retail	firm	offers	in	a	merchandise	category.	
Any	form	of	persuasive	marketing	communication	activities	
designed	to	inform	customers	about	a	product	or	service,	and	
to	influence	them	with	incentives	or	excitement-generating	
programs	to	purchase	those	products	or	services.	
Actions	pertaining	to	the	pricing	of	the	good	or	service	
offered	by	the	retailer	to	convey	optimal	customer	value	and	
perceived	benefits	over	price	paid.	
Activities	and	programs	designed	by	the	retailer	to	make	the	
shopping	experience	convenient	and	rewarding	for	the	
customer.	
The	set	of	activities	employed	by	the	retailer	to	seamlessly	
and	efficiently	integrate	their	suppliers,	warehouses,	stores	
and	transportation	intermediaries	to	deliver	the	right	quantity	
of	merchandise,	at	the	right	time	and	right	location.	
Number	of	new	stores	opened	in	a	calendar	year,	minus	
renovated	stores.	Obtained	from	company	annual	reports.	

Promotion	

Pricing	Strategy	

Services	Strategy	

Supply	Chain	
Management	

Geographic	Expansion	

Table	2:	Competitive	actions	of	retailers	announced	in	news	media	articles	were	coded	under	
the	above	retail	industry-specific	action	categories.	

78	

Examples	of	Action	Coding	
Action	
Category	

Article	Title	

Excerpt	

Publication	
Source	

Citation	
Date	

Assortment	
Mix	

Macy's	is	
Lagerfeld's	
Latest	Fashion	
Project	

Women’s	Wear	
Daily	

July	21,	
2011	

Promotion	

Whole	Foods	
Debuts	Weekly	
Online	Cooking	
Show	
Staples	Debuts	
'Dollar	Deals'	

Karl	Lagerfeld	aims	to	give	Macy's	contemporary	business	a	
kick	come	fall…The	multitasking	designer	is	the	latest	to	
design	an	exclusive	capsule	collection	for	the	department	
store	group,	which	will	launch	Aug.	31	in	235	of	Macy's	800plus	stores,	and	on	macys.com.	
Natural	and	organic	grocer	Whole	Foods	Market,	based	
here,	yesterday	launched	"Secret	Ingredient,"	its	first-ever	
weekly	online	cooking	show.		

Progressive	
Grocer	

May	8,	2007	

Pricing	
Strategy	

Services	
Strategy	

Supply	Chain	
Management	

Taking	a	page	out	of	McDonald's	marketing	book,	Staples	
InformationWeek	 November	
has	launched	its	own	"dollar	deals."	The	program,	which	will	
7,	2005	
run	for	the	month	of	January,	offers	Sharpies,	Bic	pens,	
writing	pads,	paper	clips	and	other	items	for	$1.		
Wal-Mart	Pharmacy	Mail	Services,	a	mail	order	pharmacy	
PR	Newswire	
July	25,	
provided	by	Wal-Mart	Stores,	Inc.,	has	added	a	new	cost2003	
effective	and	convenient	way	to	order	diabetes	
maintenance	medications	online.	

Wal-Mart	
Launches	New	
Online	Mail	
Order	Service	
for	Diabetic	
Supplies	
Home	Depot	To	 The	Home	Depot,	which	already	has	spent	$1	billion	over	
Increase	Use	Of	 the	last	three	years	to	overhaul	its	IT	infrastructure,	will	
SAP	
deploy	SAP	for	Retail	merchandising	and	supply-chain	
applications.	

InformationWeek	 May	18,	
2005	

Table	3:	Examples	of	action	category	coding.	Excerpts	from	articles	that	contained	retailers’	competitive	actions	and	the	action	
category	they	were	coded	under.	

79	

Competitive	Action	Statistics	

Sum	
Pct.	of	
Total	
Mean	
Std.	Dev.	
Min.	
Max.	

Geographic	
Expansion	

Assortment	 Service	 Promotion	 Pricing	 Supply	
Chain	

30802	
90.44	

1038	
3.05	

692	
2.03	

945	
2.77	

420	
1.23	

192	
0.48	

75.13	
128.61	
0	
937	

2.53	
4.15	
0	
24	

1.69	
3.35	
0	
23	

2.30	
3.66	
0	
24	

1.02	
2.44	
0	
16	

0.40	
0.84	
0	
6	

Table	4:	Competitive	action	statistics	itemizing	the	number	of	actions	of	a	particular	type	and	
the	statistics	of	those	actions.	

4.2.1 Interrater	Reliability	
To	verify	the	reliability	of	our	coding	for	competitive	actions,	two	coders	independently	
recoded	a	random	subsample	of	the	total	actions	collected	from	the	news	media	sources	(n	=	
320) into	each	of	the	five1	action	categories	of	assortment	mix,	services	strategy,	supply	chain
management,	pricing	and	promotion.		We	tested	for	coding	reliability	using	Perreault	and	
Leigh’s	(1989)	reliability	index	(!" ),	which	is	used	for	assessing	the	reliability	of	judgment-based	
qualitative	data	found	in	marketing	studies.	The	Perreault-Leigh	!" 	index	is	a	widely	accepted	
tool	for	accessing	reliability	of	marketing	actions	and	is	more	rigorous	than	simple	percentage	
agreement	measures	of	reliability	(Ferrier,	Smith	and	Grimm	1999;	Grayson	and	Rust	2001).		
This	test	yielded	a	value	of	!" = 0.93,	which	indicates	a	high	degree	of	coding	reliability	(Ferrier,	

1

	Actions	pertaining	to	geographic	expansion	were	collected	from	annual	reports,	in	terms	of	
the	total	number	of	new	stores	opened	in	a	calendar	year,	minus	renovated	stores.	
80	

Smith	and	Grimm	1999).	

4.3 Dependent	Variables	
We	now	discuss	the	operationalization	of	our	dependent	variables,	asymmetric	rivalry	
and	asymmetric	competitive	analysis.		We	operationalized	asymmetric	rivalry	in	terms	of	
relative	competitive	activity	of	the	focal	firm,	compared	to	rivals.	
We	operationalized	the	construct	of	asymmetric	competitive	analysis	in	two	ways.	First,	
in	terms	of	action	repertoire	simplicity,	which	measures	the	extent	to	which	firms	engage	in	a	
narrow	set	of	actions,	typical	of	exploitative	learning	behaviors	of	asymmetric	competitive	
analysis.	And	second,	we	used	the	metric	of	market	share	erosion	to	capture	impaired	
awareness	associated	with	asymmetric	competitive	analysis	(Ferrier,	Smith	and	Grimm	1999).	
We	explain	these	measures	in	greater	detail	in	the	following	sub	sections	and	specify	the	
measures	for	all	variables	in	our	model	(Figure ).	

81	

roac
H

+

e

an

a on

INSTITUTIONAL LOGICS
s ra emar s

e ens e
+

AS
s ea

H2

an

a on

POLITICAL LOGICS

H6

sLo
n
s am a n nance

AS

AL
e om e
Ac
+

e

S r c ra m e

e ness

s nanc a rm e resen a es
s or ne
rec ors
s n rec n er oc s

+

H4

os ona m e

A AL S S

s ar e Share ros on
sAc on e er o re
Sm c

SOCIAL LOGICS
H3

+

e ness

H5

+

s e ree en ra
s n oe c en

Figure	5:	The	paradox	of	asymmetric	competition.	Operationalization	of	measures	for	
independent	variables	(institutional,	political	and	social	logics	of	competition)	and	dependent	
variables	(asymmetric	rivalry	and	asymmetric	competitive	analysis)	are	shown.	

4.3.1 Asymmetric	Rivalry	with	competitors	
Asymmetric	rivalry	is	defined	as	the	total	number	of	competitive	actions	taken	by	the	
focal	firm,	relative	to	rival	firms.	We	operationalize	asymmetric	rivalry	using	a	method	similar	to	
the	competitive	dynamics	literature	for	relative	competitive	activity	(Chen	1996;	Ferrier	et	al.,	
1999;	Smith,	Grimm,	Gannon	and	Chen	1991;	Young,	Smith	and	Grimm	1996).		Specifically,	
asymmetric	rivalry	is	measured	by	subtracting	a	focal	firm’s	(	total	number	of	actions	in	a	given	
year	)*+ 	from	the	average	total	number	of	actions	taken	by	all	competitors	in	a	focal	industry	

82	

group.	This	is	given	by	
RCA+* = )*+ −

1
1−1

)2+ ,	
23*

where	RCA	is	the	relative	competitive	activity	and	1	is	the	total	number	of	firms	under	
consideration.	
Positive	values	indicate	asymmetric	rivalry	(greater	number	of	actions	relative	to	rival	
others	in	industry	group).	For	example,	in	the	Drugstore	group	(NAICS	446110),	relative	
competitive	activity	was	computed	with	CVS	Pharmacy	as	the	focal	firm,	and	Rite	Aid	and	
Walgreens	Pharmacy	as	the	rival	firms.		When	Walgreens	Pharmacy	was	the	focal	firm,	CVS	and	
Rite-Aid	were	rival	firms.2		

4.3.2 Asymmetric	Competitive	Analysis	in	the	focal	firm	
Asymmetric	competitive	analysis	in	the	focal	firm	is	defined	as	the	reduced	ability	in	
responding	to	competitive	threats	relative	to	rival	firms.	We	used	action	repertoire	simplicity	
and	market	share	erosion	of	the	focal	firm	as	proxy	measures	for	asymmetric	analysis	in	the	
focal	firm	(Chen	et	al,	1999).		

2

	For	the	purpose	of	this	study	we	included	Amazon	inc.	in	the	General	Merchandise	category	
for	a	number	of	reasons.	First,	its	strategic	orientation	and	target	market	are	closely	aligned	
with	general	merchandisers	as	opposed	to	other	online	retailers,	which	tend	to	have	a	more	
niche	focus.		Second,	from	press	reports	we	gleaned	that	general	merchandise	retailers	such	as	
Walmart	and	Target	are	paying	close	attention	to	and	competing	with	Amazon	as	a	rival.		
83	

4.3.2.1 Action	Repertoire	Simplicity	
Action	repertoire	simplicity	captures	the	competency	trap	dynamic	of	asymmetric	
competitive	analysis.	We	reasoned	that	firms	that	have	a	diminished	perception	of	competitive	
threat	will	not	be	motivated	to	launch	a	wide	variety	of	competitive	actions.	Following	extant	
research	on	competitive	dynamics	this	variable	will	be	operationalized	using	a	Herfindahl	action	
concentration	index	(Ferrier	et	al.	1999;	Miller	and	Chen	1996),	and	will	be	calculated	as	
follows:	
)7
18

6=
7

where	18	 is	the	total	number	of	actions	(18 =

9

7 )7 )	and	)7 /18	 is	the	share	or	proportion	of	

competitive	actions	for	action	category	a.		A	firm	with	a	high	action	simplicity	score	favors	just	a	
few	action	types.	Conversely,	a	firm	with	a	low	action	simplicity	score	deploys	a	broad	range	of	
action	types	in	its	competitive	repertoire	(Ferrier	et	al.	1999).		

4.3.2.2

Market Share	Erosion	
Individual	market	shares	were	calculated	for	both	focal	and	industry	group	rivals	using	

total	sales	reported	in	the	business	and	geographic	segment	files	of	the	WRDS	Compustat	
database,	and	the	industry	segment	sales	from	the	U.S.	Census	website	
(http://www.census.gov).	We	use	the	method	of	Ferrier	et	al.	(1999)	of	calculating	market	
share	erosion	as	follows.		First,	we	calculated	the	market	share	for	each	firm	within	its	industry	
group	(as	denoted	by	the	six-digit	NAICS	code).	Next,	we	calculated	the	market	share	gap,	
which	is	the	difference	between	the	market	share	of	a	focal	firm	and	all	its	rivals	within	the	

84	

competing	retail	segment.		From	then,	we	calculated	the	market	share	erosion,	i.e.	the	
difference	in	market	share	from	year	; − 1	to	year	;.	Our	measure	of	relative	market	share	is	
derived	from	the	ratio	of	focal	firm’s	market	share	to	rival’s	market	share,	which	reduces	to	a	
market	share	difference	score	for	each	time	period	(Ferrier	et	al.,	1999)	
Gap = MS* −

MS2 ,	
23*

where	MS* 	represents	the	focal	firm’s	(	market	share	and	

23* MS2 	represents	the	market	share	

of	all	rivals	in	the	industry	group.		Next,	we	calculated	the	rate	of	erosion	of	the	market	share	
gap	as	the	change	in	the	market	share	gap	from	year	to	year	as	
Erosion = Gap+GH − Gap+ 	
where	positive	values	indicate	a	narrowing	gap	(market	share	erosion)	and	negative	values	
represent	a	widening	gap.			

4.4 Independent	Variables	
We	now	discuss	the	operationalization	of	the	independent	variables	in	our	model.	We	
begin	with	the	independent	variable	of	proactive	manipulation	of	institutional	logics.	Next,	we	
discuss	the	independent	variables	of	defensive	manipulation	of	political	logics	and	the	
independent	variables	pertaining	to	social	logics	(structural	embeddedness	and	positional	
embeddedness)	in	the	subsequent	subsections.	

85	

4.4.1 Proactive	manipulation	of	the	institutional	logics	by	focal	firm	
Based	on	our	rationale,	the	proactive	manipulation	of	institutional	logics	construct	
refers	to	innovative	actions	by	the	firm	that	diverge	from	the	prevailing	institutional	logics	of	
the	industry,	and	the	firm’s	effort	in	gaining	pragmatic	legitimacy	for	such	actions.	Accordingly,	
we	operationalize	proactive	manipulation	of	institutional	logics	as	the	number	of	active	
trademarks	filed	by	the	focal	firm	with	the	US	patents	and	trademarks	office	(http://uspto.gov).	
Extant	research	illustrates	trademarks	as	indicators	of	innovative	activity	in	service	
firms,	signaling	changes	in	strategy	or	changes	in	corporate	identity	as	well	as	a	means	of	
reinforcing	the	differentiation	of	their	product	and	service	offerings	(Schmoch	2003;	
Mendonca,	Pereira	and	Godinho	2004;	Hipp	and	Grupp	2005;	Gotsch	and	Hipp	2012).	Secondly,	
trademarks	involve	resource	commitments.	Unlike	patents	and	copyrights,	once	trademarks	are	
registered	they	need	to	be	renewed	indefinitely.	More	importantly,	firms	would	not	be	able	to	
justify	sustaining	a	trademark	(with	its	associated	costs	of	renewal	fees,	filing	legal	documents,	
etc.)	if	their	products	had	no	distinguishing	advantages	or	attributes	in	relation	to	other	
offerings	in	the	market	(Mendonca	et	al.,	2004).	Lastly,	institutional	theory	views	companies	
and	their	brands	as	embedded	in	the	institutional	environment,	which	comprises	of	the	cultural	
meanings,	ideals	and	accepted	social	norms	that	serve	as	guidelines	to	which	companies	must	
conform	in	order	to	maintain	legitimacy	with	key	audiences	(DiMaggio	and	Powell	1983;	
Suchman	1995).	The	registration	of	a	trademark	for	a	new	product	or	service	confers	pragmatic	
legitimacy	since	brands	facilitate	consumer	choice	and	frame	the	new	product	or	service	
offering	as	having	credibility	(Suchman	1995).			

86	

4.4.2 Defensive	manipulation	of	political	logics	by	focal	firm	
Defensive	manipulation	of	political	logics	was	operationalized	in	terms	of	the	sum	of	
total	dollars	spent	on	political	lobbying,	and	Political	Action	Committee	(PAC)	contributions	to	
federal	and	state	politics	by	individual	firms	(excluding	industry	group	PACs	or	industry	
association	lobbyists)	in	a	calendar	year.		Information	on	dollars	spent	on	campaign	finance	and	
lobbying	was	obtained	from	the	Sunlight	Foundation’s	Influence	Explorer	and	
TransparencyData	Project3,	The	Center	f0or	Responsive	Politics’	web	site	Opensecrets.org	(for	
federal	data),	and	Followthemoney.org	(for	state	and	local	data).	The	Followthemoney.org	
database	provides	historical	information	on	campaign	finance,	lobbying	and	includes	a	donor	
lookup	feature	to	track	individual	political	activity.	We	verified	the	data	with	the	Federal	
Election	Commission	(FEC)	online	database.		

4.4.3 Structural	Embeddedness	
On	the	basis	of	our	theoretical	rationale,	we	operationalized	structural	embeddedness	
for	the	focal	firm	using	three	measures:	1)	Total	number	of	board	directors	who	serve	on	
Fortune	1000	firms.	2)	Total	number	of	board	directors	serving	on	boards	of	capital	investment	
firms	such	as	banks,	venture	capitalists	and	insurance	companies	(Stearns	and	Miruchi	1993),	
and	3)	Focal	firm	indirect	ties	(indirect	board	interlocks)	with	firms	that	share	the	same	six-digit	
NAICS	codes	as	focal	firm.	For	example,	an	indirect	board	interlock	in	the	general	

3

	TransparencyData	was	a	resource	that	combined	data	from	OpenSecrets.org	and	
FollowTheMoney.org,	along	with	a	few	other	sources.	This	service	was	terminated	in	January	
2016.	Data	is	still	available	from	OpenSecrets.org	and	FollowTheMoney.org	websites.

87	

merchandising	strategic	group	(NAICS	code	452990),	occurs	when	a	board	member	from	
Walmart	and	a	board	director	from	Target	together	serve	on	the	board	of	The	Goldman	Sachs	
Group.	In	this	case,	both	Target	and	Walmart	are	said	to	be	in	an	indirect	interlock	(See	Figure 	
and	Figure 	for	an	illustration	of	indirect	board	interlocks	in	our	data).		

Figure	6:	Indirect	board	interlocks	among	general	merchandisers	in	2012.	Industry	rivals	
Walmart,	Target	and	Dollar	General	are	connected	through	at	least	one	indirect	board	interlock	
each.		Indirect	board	interlocks	also	occur	between	close	rivals-	Family	Dollar	and	Dollar	Tree.	

88	

Figure	7:	A	closer	look	at	the	two	indirect	board	interlocks	between	Target	and	Walmart	from	
the	year	2012.	The	first	indirect	board	interlock	occurs	through	Yahoo	(Target	board	director,	
Henrique	De	Castro	and	Walmart	board	director,	Marissa	Mayer	jointly	serve	on	Yahoo’s	board),	
and	the	second	indirect	board	interlock	occurs	through	Goldman	Sachs	(Target	board	director,	
James	A.	Johnson	and	Walmart	board	director,	H.	Lee	Scott,	Jr.		both	serve	on	the	Goldman	
Sachs	board).	

We	collected	information	on	the	board	interlocks	from	a	wide-range	of	resources,	with	
the	primary	resource	being	annual	proxy	statements	(Form	DEF	14A)	filed	by	companies	with	
the	Securities	and	Exchange	Commission	(SEC).		We	performed	multiple	checks	to	verify	the	
board	ties	of	individual	directors	using	multiple	sources,	starting	with	corporate	websites,	Lexis-

89	

Nexis’	Corporateaffiliations.com,	Bloomberg	executive	profiles,	LinkedIn	profiles	of	individual	
directors	(if	available),	or	personal	websites	of	directors.		
Information	on	the	Fortune	companies	was	obtained	from	the	archived	data	on	Fortune	
magazine’s	website.	We	verified	financial	institutions	using	Bloomberg	business	news	and	
company	websites.		

4.4.4 Positional	Embeddedness	
We	operationalized	positional	embeddedness	using	the	network	analysis	construct	of	
network	centrality.	Network	centrality	is	a	measure	of	how	many	ties	one	node	(firm)	has	to	
other	nodes	(firms).		Firms	that	are	connected	to	many	other	firms	via	board	interlocks	have	
access	to	multiple	sources	of	information.	Our	theory	argues	that	the	greater	access	to	a	large	
number	of	information	sources	enables	firms	to	launch	high-magnitude	competitive	actions	
against	rivals.		We	follow	the	method	used	by	extant	studies	that	measure	network	centrality	in	
terms	of	degree	centrality,	i.e.	by	counting	the	number	of	interlock	ties	to	other	firms	minus	the	
focal	firm	(Haunschild,	1993;	Haunschild	and	Beckman	1998;	Freeman	1979).	We	measured	
degree	centrality	using	the	igraph	package	in	R	program.	
Previously,	we	mentioned	that	board	directors	represent	a	special	type	of	network	
known	as	affiliation,	two-mode	or	bipartite	network	(Borgatti	and	Everett	1997;	Robins	and	
Alexander	2004).		Affiliation	networks	represent	two	distinct	sets	of	nodes	or	social	entities	
(corporate	boards	and	directors	in	our	case),	where	each	tie	represents	a	director’s	affiliation	
with	a	board	and	with	other	directors	serving	on	the	board	(Opsahl	2013).	The	duality	and	
intertwining	of	linkages	in	affiliation	structures	implies	that	neither	set	of	nodes	can	be	

90	

considered	in	isolation,	and	literature	on	the	bipartite	interlocking	directorate	network	stresses	
the	importance	of	considering	both	nodes	in	empirical	research	(Mizruchi,	1996;	Shropshire,	
2010;	Opsahl,	2013).	To	capture	the	two-mode	nature	of	interlocking	directors,	we	measured	
the	Gini	coefficient	of	board	interlocks	for	each	corporate	board.	The	Gini	coefficient	is	a	
measure	of	statistical	dispersion,	and	is	used	to	quantify	inequalities	in	the	distribution	of	
centrality	among	nodes	within	a	network,	including	informal	hierarchy	in	corporate	boards	(Gini	
1921;	Blau,	1977;	He	and	Huang,	2011;	Crucitti,	Latora	and	Porta,	2006).	In	our	study,	the	Gini	
coefficient	captured	the	inequality	in	the	distribution	of	board	ties	on	a	corporate	board	and	
presented	a	more	nuanced	view	of	positional	embeddedness	of	the	focal	firm.				
The	metric	of	inequality	was	calculated	per	year	and	firm,	and	was	applied	to	a	vector	of	
the	number	of	additional	boards	(ties)	that	each	board	director	was	a	part	of.	A	company	where	
one	board	director	is	on	many	boards	but	other	board	directors	are	on	few	boards	is	considered	
unequal.	The	Gini	coefficient	ranges	from	a	minimum	value	of	0.00,	where	all	directors	have	
equal	number	of	ties,	to	a	theoretical	maximum	of	1.00,	indicating	a	situation	of	absolute	
inequality,	where	every	board	director	with	the	exception	of	one	has	zero	board	ties.			
Gini	Formula:	Given	a	collection	of	values,	the	Gini	coefficient	can	be	interpreted	as	the	
mean	(average)	of	the	absolute	value	of	every	possible	pairwise	difference,	divided	by	the	
mean	of	the	original	collection	of	values.	If	one	can	order	the	collection	of	values	from	lowest	
to	highest	()* ,	the	number	of	interlocks	per	director	in	our	case)	the	formula	takes	the	following	
form,	
I=

2
1

L
*MH ( ⋅ )*
L
*MH )*

91	

−

1+1
1

where	I	denotes	the	Gini	coefficient;	1,	the	total	number	of	directors	on	the	board;	)* 	,	the	
number	of	interlocks	for	director	(;	and	factor	(	denotes	the	director’s	rank	in	the	number	of	
interlocks	among	all	board	members.	We	used	the	“Gini”	function	from	the	ineq	package	for	R.	

Descriptive	Statistics	
Conceptual	Variables	

Operationalized	
Variables	

Mean	

Std.	
Dev.	

Min.	

Max.	

Asymmetric	Rivalry	

Relative	Competitive	
Activity	
Market	Share	Erosion	a	

0.000	

128.1	

-508.0	

858.5

0.039	

0.297	

-0.848	

1.116

Action	Repertoire	
Simplicity	
Net	Sales	
Slack	Resources	
Debt	Ratio	
Citizens	United	vs.	FEC	
Board	Size	Total	
Industry	Concentration	
Industry	Growth	
Trademarks	

0.751	

0.245	

0.188	

1.000	

23.04E3	
0.661	
2.012	
0.300	
10.00	
3.891E6	
3.170	
24.21	

40.34E3	
0.522	
0.685	
0.459	
2.320	
8.298E6	
10.77	
31.20	

0.101E3	
-1.630	
0.676	
0.000	
4.000	
0.018E6	
-13.03	
0.000	

274.5E3	
2.284
4.163	
1.000	
17.00	
41.29E6	
197.0
188.0	

Political	Logics	

0.828E6	 2.339E6	 0.000	

20.76E6	

(Number	of)	Fortune	
1000	Directors		
(Number	of)	Financial	
Institution	Directors	
Indirect	Interlocks	
Degree	Centrality	

5.795	

4.438	

0.000	

20.00	

3.793	

2.648	

0.000	

15.00	

0.502	
17.278	

0.846	
9.371	

0.000	
1.000	

3.000	
48.00	

Gini	Coefficient	

0.427	

0.152	

0.125	

0.909	

Asymmetric	
Competitive	Analysis	

Control	Variables	

Proactive	Manipulation	
of	Institutional	Logics	
Defensive	Manipulation	
of	Political	Logics	
Structural	
Embeddedness	

Positional	
Embeddedness	

a	-	Includes	Amazon.com	Inc.	
Table	5:	Variable	descriptive	statistics.	

92	

Descriptive	statistics	are	presented	in	Table	5.	The	average	Gini	coefficient	of	centrality	
distribution	of	board	directors	is	0.43	(indicating	moderate	inequality	in	distribution	of	
centrality	for	a	particular	firm’s	board.	This	is	a	board	where	social	hierarchy	is	not	clearly	
obvious	(See	Figure	8	below).		The	maximum	Gini	coefficient	is	0.91	(showing	high	inequality	in	

6
Gini coef.: 0.5

Number of Interlocks

5
4
3
2

irr

Ba
x

e

0

lH
au

am

R

W
illi

.Y
za

n
Al
a

L.

gu

La
J.

m
s
lA
da
H

al

0

cy

.

re
A.
Te
r
ot
hy

D

or

on
D

Jr

ll

ty
C
ar
J.
d
al

el
M
ic
ha

W
.J
am
es

Fa
r

re

A.
M
ile
s

ll

0

Director Name
Figure	8:	Histogram	of	the	number	of	board	interlocks	for	each	director	serving	on	the	board	of	
Sears	department	stores	in	2004.	This	gives	an	idea	of	the	boardroom	structure	with	respect	to	
how	positional	embeddedness	(number	of	board	interlocks)	is	distributed	across	the	board.	In	
this	case,	positional	embeddedness	is	neither	concentrated	among	a	few	board	directors	who	
have	interlock	ties	to	other	companies,	nor	is	it	equally	distributed	across	all	board	members	
having	interlocks.	The	Gini	coefficient	at	0.5	indicates	that	there	is	no	clear	social	hierarchy	in	
the	boardroom	but	neither	is	it	perfectly	equitable.	

93	

the	distribution	of	centrality	scores	for	the	board).	This	is	a	board	where	only	few	directors	
have	multiple	interlocking	ties	and	there	is	an	obvious	social	hierarchy	in	the	boardroom	(See	
Figure	9	below).	

4

Number of Interlocks

Gini coef.: 0.91

w
ha

on

ns
re
E.
C

P.
J
W
illi

am

a

C

oh

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Ti
n

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ar
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N

0

lis

0

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ly

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uc

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ns

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rle

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n

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ki

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nk

Je
ol
ar
C

0

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ne

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so
ud
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ll
rri
Sh
e

0

tt

0

Director Name
Figure	9:	Histogram	from	Publix	chain	of	supermarket	stores	indicating	the	number	of	board	
interlocks	for	each	director	serving	on	the	board	of	Publix	in	2004.	In	this	case	we	see	a	
boardroom	that	has	an	extremely	lopsided	distribution	of	positional	embeddedness,	where	only	
one	board	member	(Sherrill	W.	Hudson)	has	interlocks	with	other	firms,	while	the	rest	have	
none.	The	Gini	coefficient	at	0.91	is	approaching	perfect	inequality	in	the	distribution	of	board	
interlocks	across	the	boardroom.	We	infer	from	the	Gini	coefficient	that	this	boardroom	has	a	
clear	social	hierarchy	situation	because	there	is	only	one	high	ranking	director	(with	four	
interlocks).	

94	

And	the	minimum	Gini	coefficient	is	0.12	(showing	low	inequality	of	centrality	of	
directors	on	a	board.	This	board	has	a	majority	of	directors	with	multiple	outside	ties	and	
denotes	an	equitable	and	nonexistent	boardroom	hierarchy	(See	Figure

).	These	values	

demonstrate	that	our	sampled	firms	had	a	considerable	range	in	the	distribution	of	director	
centrality.	

5
Gini coef.: 0.12

4

4

4

Number of Interlocks

4

3

3

n

y
as
m
Th
o

nc

e

M

.R

M
ur

ya

ra

an
re
Te
r

W
ya
av
id

D

ed
Al
fr

tt
D
or

re
J.
Ve
r

H
am
W
illi

m

cc
h

ia

ce
.J
oy

be
en
li

Z.
R

os

J.
ha
ic
R

Sh
e

rd

L.
n
ia
ar

M

rg

ift
Sw

ea
H

P.
G
er

rit
y

rd

2

as
m
Th
o

3

Director Name
Figure	10:	Histogram	of	number	of	board	interlocks	from	the	CVS	Pharmacy	Drugstores	in	2004.	
We	see	a	boardroom	that	has	an	almost	equitable	distribution	of	positional	embeddedness	
across	the	boardroom.	All	of	the	board	members	have	board	interlocks	and	the	relative	
difference	in	the	number	of	interlocks	from	one	director	to	another	is	not	that	extreme.		This	is	
demonstrated	by	the	very	low	Gini	coefficient	at	0.12	indicating	this	boardroom	has	high	
equality	(in	terms	of	number	of	board	interlocks	per	director).	The	Gini	coefficient	also	indicates	
that	there	is	no	social	hierarchy	in	the	boardroom.	This	appears	to	be	an	‘All	Stars’	boardroom.	

95	

4.5 Control	Variables	
We	used	control	variables	in	our	study	to	rule	out	alternative	interpretations	of	our	
results.		Control	variables	reduce	omitted	variable	bias	and	provide	a	more	conservative	test	of	
the	predicted	relationships.	The	selection	of	the	control	variables	in	our	empirical	models	was	
motivated	by	theoretical	evidence	found	in	the	extant	literature,	which	demonstrated	their	
influence	on	potentially	confounding	the	results	of	our	predicted	relationships.		

4.5.1 Firm	Size	
Prior	research	has	demonstrated	that	firm	size	influences	each	of	our	dependent	
variables	and	may	also	confound	the	effect	of	our	independent	variables	on	our	dependent	
variables.	Firm	size	as	measured	by	net	sales	is	said	to	influence	competitive	actions	since	large	
firms	have	the	capability	to	undertake	more	actions	(Smith,	Ferrier	and	Ndofor	2001;	Derfus	et	
al.	2008).	Large	firms	are	also	able	to	attract	well-connected	directors,	as	well	as	directors	who	
serve	on	prestigious	boards.	Larger	firms	are	also	likely	to	invest	in	protecting	their	turf,	and	are	
therefore	more	likely	to	spend	money	in	politics	to	protect	their	interests.		Additionally,	larger	
firms	are	more	likely	to	exhibit	action	repertoire	simplicity	as	a	form	of	competitive	inertia	
compared	to	small	firms	(Miller	and	Chen	1996;	Ferrier,	Smith	and	Grimm	1999).		Therefore,	we	
included	firm	size	as	control	variable	in	all	our	models.	Net	sales	were	obtained	from	the	WRDS	
Compustat	database	and	verified	with	company	annual	reports.	

96	

4.5.2 Slack	Resources	
In	line	with	previous	research	on	competitive	dynamics,	we	used	quick	ratio	as	a	
measure	for	slack	resources	due	to	its	effect	on	competitive	actions	(Derfus,	Maggitti,	Grimm	
and	Smith	2008).	Slack	resources	were	used	as	the	control	for	the	models	testing	the	effect	of	
institutional	logics	on	asymmetric	rivalry.	The	logic	behind	using	slack	resources	as	a	control	
variable	was	that	firms	with	excess	resources	are	more	likely	to	invest	in	developing	innovations	
that	get	trademarked	than	firms	with	low	slack	resources	(Nohria	and	Gulati	1997).		Quick	ratio	
was	measured	using	the	ratio	of	current	assets	(minus	inventories)	to	current	liabilities.	We	
obtained	raw	data	from	the	WRDS	Compustat	database	and	verified	it	with	company	annual	
reports.	

4.5.3 Citizens	United	v.	FEC	
The	2010	Citizens	United	Supreme	Court	decision	ruled	that	corporations	and	unions	
have	a	First	Amendment	right	to	spend	unlimited	funds	on	campaign	advertisements,	provided	
that	these	communications	are	not	formally	“coordinated”	with	any	candidate	(Citizens	United	
v. FEC,	130	S.	Ct.	876,	915,	2010).	Spending	by	corporations	and	outside	groups	has	swollen
during	the	three	federal	election	cycles	since	this	landmark	ruling	(Vandewalker	2015).	The	
Supreme	Court	decision	has	been	associated	with	competitive	advantage	and	firm	performance	
(Coates	2012;	Burns	and	Jindra	2014).		To	capture	the	effect	of	this	ruling,	we	used	a	dummy	
variable	coded	1	for	years	after	its	passage.	We	controlled	for	the	Supreme	Court	decision	in	all	
hypotheses	that	had	Political	Logics	as	the	independent	variable.	

97	

4.5.4 Debt	Ratio	
We	controlled	for	a	firm’s	debt	ratio	in	models	of	structural	embeddedness	because	
research	on	financial	representation	in	corporate	boards	indicates	that	a	firm’s	indebtedness	
reduces	the	likelihood	that	the	financial	representative	on	the	board	provides	a	legitimizing	role	
to	potential	lenders	of	the	firm.	Additionally,	financial	representatives	on	a	board	will	see	high	
debt	ratio	as	a	risk	that	needs	to	be	lowered	and	will	be	less	likely	to	lend	funds	to	highly	
leveraged	firms	(Stearns	and	Mizruchi	1993).		
Furthermore,	debt	imparts	discipline	upon	decision-makers	and	motivates	them	to	
engage	in	actions	that	are	in	the	best	interest	of	the	firm.		However,	the	presence	of	debt	also	
negatively	effects	action-taking	as	it	makes	decision	makers	less	likely	to	explore	new	business	
strategies	(Barnett	and	Salomon	2012).	Debt	ratio	was	measured	using	the	ratio	of	total	assets	
to	total	liabilities.	Raw	data	was	obtained	from	the	WRDS	Compustat	database	and	verified	
with	company	annual	reports.	

4.5.5 Board	Size	
We	controlled	for	board	size	in	our	models	of	positional	embeddedness	and	structural	
embeddedness	because	individual	directors	might	not	get	an	equal	opportunity	to	make	a	
contribution	to	decision-making	and	strategy	in	a	large	sized	group	(Carpenter	and	Westphal	
2001).	Filtering	vital	information	may	also	become	arduous	with	respect	to	board	size.	Larger	
boards	are	also	seen	as	being	less	cohesive	and	therefore	limited	in	their	ability	to	monitor	
decision-making	effectively	and	may	therefore	affect	action	taking	ability	(Johnson,	Hoskisson	
and	Hitt	1993).	

98	

4.5.6 Industry	Growth	
We	calculated	industry	growth	as	the	percentage	change	in	sales	from	the	previous	year	
to	the	current	year,	using	data	for	each	six-digit	NAICS	industry	subgroup4	(Ferrier,	Smith	and	
Grimm	1999;	Derfus,	Maggitti,	Grimm	and	Smith	2008).		Industry	growth	has	shown	to	
minimize	the	impact	of	Red	Queen	competition,	due	to	the	fact	that	in	an	environment	that	is	
marked	by	high	industry	demand,	firms	will	be	less	threatened	by	rival	actions	and	therefore	
less	likely	to	respond	to	competitive	threats	(Derfus,	Maggitti,	Grimm	and	Smith	2008).	
Additionally,	studies	show	that	high-growth	industries	experience	less	market	share	stability	
and	greater	market	share	erosion	than	low	growth	industries	(Gort,	1963;	Caves	and	Porter,	
1978;	Mueller,	1986;	Ferrier,	Smith	and	Grimm	1999).	We	controlled	for	industry	growth	in	
models	that	had	market	share	erosion	as	the	dependent	variable.	We	obtained	raw	data	on	
company	sales	from	the	WRDS	Compustat	database	and	the	industry	segment	sales	from	the	
U.S.	Census	website	(http://www.census.gov).	

4.5.7 Industry	Concentration	
Studies	have	shown	that	high	levels	of	industry	concentration	are	positively	correlated	
with	market	share	stability	among	leading	firms	(Gort	1963;	Caves	and	Porter	1978),	and	
competitive	actions	(Young,	Smith	and	Grimm	1996;	Ferrier,	Smith	and	Grimm	1999;	Derfus,	
Maggitti,	Grimm	and	Smith	2008).	We	calculated	industry	concentration	by	aggregating	the	
market	shares	of	the	two	largest	firms	in	each	industry	for	each	year.	We	obtained	raw	data	on	
company	sales	from	the	WRDS	Compustat	database	and	the	industry	segment	sales	from	the	
4

	Amazon.com	is	treated	as	its	own	industry	group.	
99	

U.S.	Census	website	(http://www.census.gov).	We	controlled	for	industry	concentration	in	
models	that	had	market	share	erosion	as	the	dependent	variable.	All	operationalized	
variables	are	summarized	in	

e

100	

Operationalization	of	Variables	

Dependent	

Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	

Asymmetric	
Rivalry	

Relative	
Competitive	
Activity	

Competitive	actions	used	in	
the	formula	were	obtained	
from	Infotrac’s	General	
Business	File	ASAP	database	

Asymmetric	rivalry	is	measured	by	subtracting	
a	focal	firm’s	!	total	number	of	actions	in	a	
given	year	"#! 	from	the	average	total	number	of	
actions	taken	by	all	competitors	in	a	focal	
industry	group.	
RCA#! = "#! −

)
*−)

"#+ ,	
+,!

RCA	is	the	Relative	Competitive	Activity	and	*	
is	the	total	number	of	firms	under	
consideration.	
Note:	Positive	values	indicate	asymmetric	
rivalry	(greater	number	of	actions	relative	to	
rival	others	in	industry	group).	
Asymmetric	
Competitive	
Analysis	

Action	
Repertoire	
Simplicity	

Herfindahl	action	concentration	index:	
.=
/

Table	6:	Summarization	of	all	operationalized	variables.	
101	

"/
*0

1

Competitive	actions	used	in	
the	formula	were	obtained	
from	Infotrac’s	General	
Business	File	ASAP	database	

Table 6
Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	

where	*0 	is	the	total	number	of	actions	(*0 =
/ "/ )	and	"/ /*0 	is	the	share	or	proportion	of	
competitive	actions	for	action	category	a.			
Note:	A	firm	with	a	high	action	simplicity	score	
favors	just	a	few	action	types.	Conversely,	a	
firm	with	a	low	action	simplicity	score	deploys	
a	broad	range	of	action	types	in	its	competitive	
repertoire.	
Asymmetric	
Competitive	
Analysis	

Market	Share	
Erosion	

Gap = MS! −

MS+ ,	
+,!

Where	MS! 	represents	the	focal	firm’s	!	market	
share	and	 +,! MS+ 	represents	the	market	
share	of	all	rivals	in	the	industry	group.			
Next,	calculate	the	rate	of	erosion	of	the	
market	share	gap	as	the	change	in	the	market	
share	gap	from	year	to	year	as:	
Erosion = Gap#>) − Gap# 	
Note:	Positive	values	indicate	a	narrowing	gap	
(market	share	erosion)	and	negative	values	
represent	a	widening	gap.			

102	

WRDS	Compustat	database.	
U.S.	Census	website.	

Table 6
Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	

Independent	 Proactive	
Trademarks	
Manipulation	
of	Institutional	
Logics.	

Number	of	active	trademarks	filed	by	the	focal	
firm	annually.	

US	Patents	and	Trademarks	
Office	website.	

Defensive	
Manipulation	
of	Political	
Logics	

Sum	of	total	dollars	spent	on	political	lobbying,	
and	Political	Action	Committee	(PAC)	
contributions	to	federal	and	state	politics	by	
individual	firms	(excluding	industry	group	PACs	
or	industry	association	lobbyists)	in	a	calendar	
year.			

Sunlight	Foundation’s	
Influence	Explorer	and	
TransparencyData	Project.	

Political	Logics	

The	Center	for	Responsive	
Politics’	web	site	
Opensecrets.org	(federal	
data).	
Followthemoney.org	(state	
and	local	data).	
Federal	Election	Commission	
online	data	base	(fec.gov)	

Structural	
Fortune	1000	
Embeddedness	 Directors	
Financial	
Institution	
Directors	

Total	number	of	board	directors	who	serve	on	
Fortune	1000	firms.	Updated	Annually.	

Fortune	Magazine	Website	
(Fortune.com)	

Total	number	of	board	directors	serving	on	
boards	of	capital	investment	firms	such	as	
banks,	venture	capitalists	and	insurance	
companies	for	each	year.	

Form	DEF	14A	annual	proxy	
statements.	

103	

Table 6
Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	
Securities	and	Exchange	
Commission	database	
(http://sec.gov).	
Lexis-Nexis	
(Corporateaffiliations.com)	
Bloomberg	executive	
profiles	(Bloomberg.com)	
LinkedIn	

Structural	
Indirect	
Embeddedness	 Interlocks	

Total	number	of	focal	firm	indirect	ties	(indirect	 ibid.	
board	interlocks)	with	firms	that	share	the	
same	six-digit	NAICS	codes	as	focal	firm	for	
each	year.	

Positional	
Degree	
Embeddedness	 Centrality	

Total	number	of	interlock	ties	to	other	firms,	
minus	the	focal	firm.	

Gini	Coefficient	

ibid.	

Given	a	collection	of	values,	the	Gini	coefficient	 ibid.	
can	be	interpreted	as	the	mean	(average)	of	
the	absolute	value	of	every	possible	pairwise	
difference,	divided	by	the	mean	of	the	original	
collection	of	values.	If	one	can	order	the	
collection	of	values	from	lowest	to	highest	("! ,	

104	

Table 6
Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	

the	number	of	interlocks	per	director	in	our	
case)	the	formula	takes	the	following	form,	
1
?=
*

*
!A) ! ⋅ "!
*
!A) "!

−

*+)
*

where	?	denotes	the	Gini	coefficient;	*,	the	
total	number	of	directors	on	the	board;	"! 	,	the	
number	of	interlocks	for	director	!;	and	factor	!	
denotes	the	director’s	rank	in	the	number	of	
interlocks	among	all	board	members.	
Note:	Gini	coefficients	closer	to	one	denote	
high	inequality	(clear	social	hierarchy)	in	the	
distribution	of	positional	embeddedness	in	the	
boardroom.	
Control	
Variables	

Size	

Net	Sales	

Total	net	sales	reported	annually.	

WRDS	Compustat	database	
Annual	reports	(Form	10-K)	

Slack	
Resources	

Quick	Ratio	

Quick	Ratio=	
				(Current	Assets	-	Inventories)	/	Current	
Liabilities	

ibid.	

Debt	Ratio	

Debt	Ratio	

Debt	Ratio	=	
				Total	Assets	/	Total	Liabilities.	

ibid.	

105	

Table 6
Conceptual	
Variable	

Operationalized	 Measure	
Variable	

Source	

Citizens	United	 Citizens	United	
vs.	FEC		
vs.	FEC	
Supreme	Court	
Ruling	

Dummy	variable	coded	1	for	years	after	its	
passage	in	2010.	

Board	Size	

Total	count	of	all	board	directors	serving	a	focal	 Form	DEF	14A	annual	proxy	
firm’s	board	for	each	year.	
statements.	

Board	Size	

Citizens	United	v.	FEC,	130	S.	
Ct.	876,	915,	2010	

Securities	and	Exchange	
Commission	database	
(sec.gov).	
Lexis-Nexis	
(Corporateaffiliations.com)	
Industry	
Concentration	

Industry	
Concentration	

Calculated	by	aggregating	the	market	shares	of	
the	two	largest	firms	in	each	industry	for	each	
year.	

Census.gov	
WRDS	Compustat	database	

Industry	
Growth	

Industry	
Growth	

Percentage	change	in	sales	from	the	previous	
year	to	the	current	year,	using	data	for	each	
six-digit	NAICS	industry	subgroup.	

ibid.	

106	

Chapter	5: Results	

We	analyzed	our	data	using	the	R	language	(ver.	3.2.2)	within	the	RStudio	(ver.	
0.99.473)	Integrated	Development	Environment	(IDE).	R	is	a	free	programming	language	and	
visualization	environment	for	statistical	computing	and	graphical	display	that	is	widely	used	by	
statisticians	and	data	analysts.	RStudio	is	a	free	and	open	source	IDE	for	R	language,	with	an	
interface	that	improves	ease	of	analysis	and	development	(RStudio	Team,	2015).	Furthermore,	
unlike	most	free	versions	of	statistical	software	packages	in	the	market,	R	allows	users	to	
perform	complex	data	analysis	without	any	restrictions	(R	Core	Team,	2015).	Table	13	in	
Appendix	B	shows	the	versioning	information	for	R,	R	Studio,	and	the	R	libraries	used	in	this	
work.	
Multicollinearity	in	our	variables	was	not	deemed	to	be	problematic	for	several	reasons.	
Correlations	between	independent	variables	were	less	than	the	standard	0.80	cut	off	point	
(Figure	11).	Furthermore,	the	inherent	structure	of	panel	data	reduces	multicollinearity	
problems	as	it	creates	more	variability	in	the	data,	combines	variation	across	micro	units	with	
variation	over	time,	and	enables	more	efficient	estimation	through	informative	data	(Kennedy	
2008;	Baltagi	2012).		

107	

0.08

Market Share Erosion

0.39 −0.11 −0.06 0.23
0.04 −0.06

0

Actions Repertoire
Simplicity −0.25 0.01
Net Sales

0

Gini Coefficient

Degree Centrality

Indirect Interlocks

(Number of)
Financial Institution Directors

(Number of)
Fortune 1000 Institutions

Political Logics

Institutional Logics
(Trademarks)

Industry Growth

Industry Concentration

1

−0.04 0.01 −0.16 −0.12 −0.14 −0.08 −0.16 0.01 −0.17 0.12

−0.01 −0.18 −0.04 0.02

0.02 −0.06 −0.04 −0.03 −0.02 −0.01

0

0.05

0.8

0.39 −0.04 −0.16 −0.09 −0.01 −0.18 −0.23 −0.22 −0.19 −0.2 −0.26 0.17

−0.2 −0.16 0.06

Slack Resources

Board Size Total

Citizens United vs. FEC

Debt Ratio

Slack Resources

Net Sales

Actions Repertoire
Simplicity

Market Share Erosion
Relative Competitive
Activity

0.3

0.51

0.06

0.08 −0.03 0.05

Debt Ratio

0

Citizens United vs. FEC

0.01

0.53

0.85

0.45

0.53

0.33

0.6

0.49 −0.08

0.07 −0.08 −0.12 −0.13 0.01 −0.11 −0.05 0.02

0.4

−0.18 −0.12 −0.08 −0.17 −0.09 −0.32 −0.19 −0.3 −0.26 0.45
0.02

Board Size Total

0

0.02 −0.02 0.12

0

0.2

−0.08 −0.01 −0.04 −0.12

0.23 −0.13 0.31

0.35

0.62

0.61

0.34

0.68 −0.23

Industry Concentration −0.09 0.05

0.06

0.31

0.13

0.02

0.29

0

Industry Growth

0.02

Institutional Logics
(Trademarks)

0.05 −0.08 −0.03 −0.04 −0.05

−0.3
0

0.41

0.43

0.38

0.27

0.41 −0.19

Political Logics

0.26

0.43

0.2

0.36 −0.04

0.5

0.44

0.66 −0.52

0.32

0.78 −0.21

Indirect Interlocks

0.41 −0.22

(Number of)
Fortune 1000 Institutions

(Number of)
Financial Institution Directors

−0.2

−0.4

−0.6

−0.8

Degree Centrality −0.45

−1

Figure	11:	Correlation	matrix.	Correlations	between	independent	variables	is	below	the	.80	
threshold.			

108	

5.1 Panel	data	analysis	procedure	
Panel	data	consists	of	observations	of	the	same	units	(firms)	over	several	time	periods.	
If	we	were	to	ignore	the	nature	of	panel	data	and	run	a	pooled	Ordinary	Least	Squares	(OLS)	
regression,	then	it	would	lead	to	biased	results	because	OLS	makes	unrealistic	assumptions	
about	panel	data.	
For	example,	pooled	OLS	assumes	that	all	unit-specific	heterogeneity	can	be	controlled	
for	by	the	independent	variables	in	the	model,	so	that	the	remaining	unexplained	variance	is	
simply	“white	noise”.		Panel	data	and	social	science	theories	rarely	fulfill	this	assumption.		
Therefore,	pooled	OLS	estimates	are	often	biased	because	of	correlated	unobserved	
heterogeneity.		Even	if	unobserved	heterogeneity	is	independent	of	the	variables	in	the	model,	
it	causes	the	error	term	to	be	serially	correlated,	and	therefore	OLS	standard	errors	will	
underestimate	the	true	standard	errors.		The	nature	of	panel	data	requires	special	estimation	
techniques	to	ensure	that	this	bias	is	eliminated	(Kennedy	2008).	
We	followed	Kennedy’s	procedure	for	panel	data	modeling	(Kennedy	2008)	as	seen	in	
Figure

.	We	began	by	running	Chow	tests	of	poolability	on	both	the	baseline	and	the	full	

model	hypotheses.	If	the	Chow	test	null	of	equal	intercepts	was	not	rejected,	then	we	pooled	
the	data	and	performed	ordinary	least	squares	regression	(Pooled	OLS)	on	those	models.	If	the	
null	of	the	Chow	test	was	rejected	then,	we	applied	a	Hausman	test	to	test	whether	the	random	
effects	estimator	was	unbiased.	If	this	null	was	not	rejected,	then	we	used	the	random	effects	
estimator.	If	this	null	was	rejected	then	we	used	the	fixed	effects	estimator	(Baltagi	1995).	The	
results	of	the	Chow	and	Hausman	tests	indicated	that	random-effects	models	were	preferred	in	
all	except	for	two	cases.	The	Chow	test	(

e 	for	Chow	test	results)	indicated	that	Hypothesis	
109	

5	(Positional	embeddedness	variables—Degree	centrality	and	Gini	Coefficient—as	independent	
variables,	and	Market	Share	Erosion	as	the	dependent	variable)	failed	to	reject	the	null	of	the	
Chow	test.	Therefore,	we	analyzed	hypothesis	5	using	pooled	OLS.		We	proceeded	with	the	
Hausman	test	for	the	rest	of	the	hypotheses.	The	Hausman	test	indicated	that	random-effects	
models	were	preferred	in	all	except	one	case	(

e 	for	Hausman	test	results).	Hypothesis	2	

(Structural	embeddedness	independent	variables	and	Relative	Competitive	Activity	as	the	
dependent	variable)	rejected	the	null	of	the	Hausman	test.	Thus,	hypothesis	2	was	analyzed	
using	a	fixed-effect	model.		

Perform Chow Test

Reject Null

NO

Pooled OLS
Model

YES

Perform Hausman Test

Reject Null

NO

an om ec s
Model

YES

e
ec s
Model

Figure	12:	Panel	data	procedure	

110	

Panel	Procedure	Test:	Chow	Test	of	Poolability
Hypotheses	
H1	
H2	
H3	
H3	&	H4	
H5	
H5	
H6	
H6	

F-Value		
8.35	
15.55	
8.39	
7.34	
3.20	
1.08	
3.92	
2.06	

	DF1		
160	
160	
200	
123	
200	
280	
160	
240	

	DF2		
246	
246	
205	
280	
205	
123	
246	
164	

	P-Value		
2.12E-48	
4.30E-75	
5.82E-45	
2.20E-16	
3.39E-16	
3.09E-01	
3.47E-22	
5.92E-07	

Reject	Null
YES	
YES	
YES	
YES	
YES	
NO	
YES	
YES	

Table	7:	Chow	test	of	poolability.	If	the	null	of	the	Chow	test	is	not	rejected,	then	proceed	to	
Pooled	OLS	Models.	If	null	of	Chow	test	is	rejected	then	proceed	to	Hausman	Test.	

Panel	Procedure	Test:	Hausman	Test	
Hypotheses	
H1	
H2	
H3	
H3	&H4	
H5	
H6	
H6	

	Chi.	Sq.	
1.082	
9.814	
1.497	
1.565	
0.852	
1.052	
3.888	

	DF	
3	
3	
4	
6	
4	
3	
5	

P-Value		
0.781	
0.020	
0.827	
0.955	
0.931	
0.789	
0.566	

Reject	Null
NO	
YES	
NO	
NO	
NO	
NO	
NO	

Table	8:	Hausman	test	results.	If	null	of	Hausman	test	is	not	rejected,	then	proceed	to	Random	
Effects	Models.	If	null	of	Hausman	test	is	rejected,	then	proceed	to	Fixed	Effect	Models.	

111	

5.2 Diagnostic	Checks	
We	also	ran	diagnostic	checks	to	test	for	heteroscedasticity	and	unit	root	in	our	panel	
data	(Croissant	and	Millo,	2008).	We	ran	the	Breusch-Pagan	test	against	heteroscedasticity	in	
R,	which	tests	the	null	of	homoscedasticity	(

e 	Breusch-Pagan	test	of	heteroscedasticity).	

We	detected	heteroscedasticity	in	our	models	for	Hypothesis	5	and	in	Hypothesis	6	(with	
Market	Share	Erosion	as	the	dependent	variable),	which	we	treated	using	robust	covariance	
matrix	estimators.		

Diagnostic	Checks:	Breusch-Pagan	Test	of	
Heteroscedasticity	
Hypotheses	
H1	
H2	
H3	
H3	&H4	
H5	
H5	
H6	
H6	

Breusch-Pagan	
1.955	
2.388	
1.554	
3.116	
11.055	
16.346	
5.063	
32.280	

DF	
3	
3	
4	
6	
4	
6	
3	
5	

Heteroscedasticity	
P-	Value Present	
0.582	
NO	
0.496	
NO	
0.817	
NO	
0.794	
NO	
0.026	
YES	
0.012	
YES	
0.167	
NO	
5.23E-06	 YES	

Table	9:	Breusch-Pagan	Test	of	Heteroscedasticity.	If	p-value	is	<	0.05	then	heteroscedasticity	is	
present.	

We	also	ran	the	Augmented	Dickey-Fuller	test	to	test	the	null	of	non-stationarity/unitroot	in	our	panel	data.	We	did	not	detect	unit	root	in	our	dependent	variables,	indicating	that	
our	data	was	stationary,	with	no	presence	of	stochastic	trends,	thereby	justifying	the	use	of	
our	panel	estimation	methods	in	our	paper	(

e
112	

	for	test	of	unit	root/stationarity).			

Testing	for	Unit	Roots/Stationarity	
Dependent	Variable	
Relative	Competitive	Activity	
Action	Repertoire	Simplicity	
Market	Share	Erosion	

Dickey-Fuller	
-7.37
-6.47
-11.44

Lag	order	
2	
2	
2	

P-Value		
0.01	
0.01	
0.01	

Unit	Root	Present
FALSE	
FALSE	
FALSE	

Table	10:	Results	of	Augmented	Dickey-Fuller	test	for	Unit	Roots/Stationarity.	If	p-value	is	<	0.05	
then	no	unit	roots	present.	

5.3 Tests	of	Hypotheses-	Asymmetric	Rivalry	
We	report	the	results	for	the	hypotheses	on	asymmetric	rivalry	in	

e

5

.	Hypothesis	

1	states	that	the	proactive	manipulation	of	the	institutional	logics	is	positively	related	to	focal	
firm	asymmetric	rivalry	with	rival	firms.	We	tested	hypothesis	1	by	entering	trademarks	as	the	
predictor	for	relative	competitive	activity,	and	slack	resources	and	firm	size	as	our	control	
variables.	The	results	of	our	random	effects	model	for	Hypothesis	1	were	not	significant.		We	
also	conducted	Wald	tests	of	model	fit	comparison	to	confirm	that	the	inclusion	of	the	
predictor	variables	significantly	improved	the	model	fit	with	only	control	variables.	The	results	
showed	that	inclusion	of	predictor	variables	did	not	have	a	significantly	better	fit	than	the	
model	with	only	control	variables.	
Hypothesis	2	states	that	the	defensive	manipulation	of	political	logics	is	positively	
related	to	focal	firm	asymmetric	rivalry	with	rival	firms.	Model	2	reports	the	results	from	the	

5

	We	report	the	results	for	the	hypotheses	without	Amazon.com	in	Appendix	III	and	IV.		The	
minor	differences	in	the	calculated	values	do	not	change	the	conclusions	of	our	study.		

113	

fixed-effects	regression	for	Hypothesis	2.	We	test	the	effect	of	the	defensive	manipulation	of	
political	logics	on	relative	competitive	activity	of	the	focal	firm,	while	controlling	for	Firm	Size	
and	the	Citizens	United	v.	FEC	Supreme	Court	decision.	Hypothesis	2	is	supported	(! < .05)	
with	a	positive	sign	for	the	Political	Logics	coefficient	(& = 9.570).	The	results	of	the	Wald	test	
confirmed	that	the	inclusion	of	the	predictor	variables	significantly	improved	the	model	fit	(p	<	
.001)	compared	to	model	fit	with	only	control	variables.	In	terms	of	effect	size	(& = 9.570),	the	
results	suggest	that,	all	else	being	equal,	on	average,	a	firm	that	spends	a	million	dollars	on	the	
defensive	manipulation	of	political	logics	increases	its	relative	competitive	activity	by	about	9	
actions.	In	other	words,	all	else	being	equal,	on	average	spending	a	million	dollars	on	campaign	
finance	and	lobbying	combined,	enables	the	focal	firm	to	carry	out	approximately	9	more	
competitive	actions	relative	to	its	industry	group	rivals.		
Hypothesis	3	states	that	structural	embeddedness	is	positively	related	to	focal	firm	
asymmetric	rivalry	with	rival	firms.	Structural	embeddedness	was	tested	using	Models	3	and	4	
in	

e

.	We	first	ran	random-effects	regression	of	financial	institution	representation	on	

relative	competitive	activity,	with	debt	ratio,	board	size	and	firm	size	as	control	variables.	As	
seen	in	Model	3,	we	did	not	find	support	(! < .05)	for	financial	institution	representation	on	
boards	(! < .10),	however	the	slope	for	financial	institution	representation	was	in	the	opposite	
sign	of	the	proposed	hypothesis	(& = −4.550).	The	results	also	showed	the	Wald	test	statistic	
to	not	be	significant.	We	tested	the	remaining	two	variables	of	structural	embeddedness:	
representation	of	Fortune	1000	firms	and	the	number	of	indirect	interlocks	with	industry	group	
rivals,	and	Hypothesis	4	using	Model	4.		

114	

Hypothesis	4	states	that	positional	embeddedness	is	positively	related	to	focal	firm	
asymmetric	rivalry	with	rival	firms.	Model	4	includes	degree	centrality	and	Gini	coefficient	as	
proxies	for	positional	embeddedness,	and	Fortune	1000	firms	and	indirect	interlocks	as	our	
predictors	for	positional	embeddedness	in	our	model.	We	controlled	for	board	size	and	firm	
size	in	the	random	effects	regression	of	Model	4.	The	results	of	Model	4	showed	no	support	for	
the	structural	embeddedness	variables	in	the	model,	i.e.	the	presence	of	Fortune	1000	
directors	on	the	focal	firm’s	board,	indirect	interlocks	and	relative	competitive	activity.		
Therefore,	hypothesis	3	was	not	supported.	
Model	4	supports	our	hypothesis	for	positional	embeddedness;	both	degree	centrality	
(p	<	.001,	β	=	-4.236),	and	Gini	coefficient	(p	<	.05,	β=100.14),	were	significantly	related	to	focal	
firm’s	relative	competitive	actions.	The	Wald	test	statistic	was	significantly	better	(p	<	.001)	
with	the	inclusion	of	the	predictor	variables	to	the	model.	The	slope	coefficient	for	degree	
centrality	was	in	the	opposite	sign	of	the	predicted	relationship,	however	as	predicted	the	Gini	
coefficient	of	the	board’s	distribution	of	positional	embeddedness	was	positively	related	to	
relative	competitive	activity.		The	effect	size	for	the	Gini	coefficient	(β=100.14)	indicates	that	all	
else	being	equal,	on	average	for	every	one	percent	increase	in	the	inequality	of	positional	
embeddedness	(degree	centrality)	distribution	in	the	boardroom,	relative	competitive	activity	
for	focal	firm	increases	by	about	100	more	competitive	actions	compared	to	other	firms.	In	
other	words,	all	else	being	equal,	on	average	for	every	one	percentage	increase	in	the	
inequality	of	director	ties	for	a	given	firm’s	board,	the	focal	firm	is	able	to	carry	out	about	100	
more	competitive	actions	compared	to	its	industry	group	rivals.	

115	

Results	of	Panel	Data	Random-Effects	Analyses	for	Asymmetric	Rivalry	(Relative	Competitive	Activity)	
N	=	410	

Model	2	(H2)c	
Coef.	
Std.	Err	

Model	3	(H3)	
Coef.	
Std.	Err.	

2.835E-4	

-1.129E-3*	 0.606E-3

1.072E-5 29.48E-5	 5.527E-5	

29.26E-5	

Slack	Resources	 -1.293
Citizens	United	
-	
vs.	FEC	

10.35	

-	

-	

-	

-	

-	

-	

-	

0.450	

7.160	

-	

-	

-	

-	

Debt	Ratio	
Board	Size	

-	
-	

-	
-	

-	
-	

-	
-	

26.29***	 9.835	
1.922	
2.916	

7.214**	

3.276	

Trademarks	

-0.127

0.168	

-	

-	

-	

-	

-	

-	

-

-	

9.570**	

4.011	

-	

-	

-	

-	

-	

-	

-	

-	

-	

-	

3.387*	

1.983	

-	

-	

-	

-	

-4.550*

2.538	

-

-	

Category	

Variables	

Model	1	(H1)	
Coef.	
Std.	Err.	

Control	
Variables	

Firm	Size	

-1.206E-4

Proactive	
Institutional	
Logics	
Defensive	
Political	Logics	

Political	Logics	

Structural	
Fortune	1000	
Embeddedness	 Directors	
Financial	
Institution	
Directors	

Model	4	(H3/H4)a	
Coef.	
Std.	Err.	

Table 11: Panel data results for random-effects models of asymmetric rivalry (relative competitive activity). Data includes Amazon.com.
Variables that are not present in a particular model are indicated by a dash (-). Hypothesis numbers are listed after the model number in
parenthesis, e.g. H1, H2, H3, or H3/H4.

116	

Table	11:	(cont’d)	
Indirect	
Interlocks	

-	

-	

-	

-	

-	

-	

5.649	

Positional	
Degree	
-	
Embeddedness	 Centrality	
Gini	Coefficient	 -

-	

-	

-	

-	

-	

-4.236****	 1.004

-	

-	

-	

-	

-	

100.1**	

43.38	

Constant	
Wald	Test	ChiSquare	
a	
c	
*
**	
***	
****	

5.857	

Value	 Std.	Err.	

Value	

Std.	Err.	 Value	

Std.	Err.	

Value	

Std.	Err.	

6.697	 20.55	
0.565	 -	

-	
5.694****	

-	

36.06	
-	

-65.47*
25.26****	

36.41	
-

-55.11
3.214	

Model	4	combines	Hypotheses	3	and	4.	See	Results	section	for	additional	information.	
Fixed	effects	model	 	
p	<	0.10
p	<	0.05	
p	<	0.01	
p	<	0.001	

117	

5.4 Tests	of	Hypotheses-	Asymmetric	Competitive	Analysis	
Hypothesis	5	states	that	positional	embeddedness	is	positively	related	to	asymmetric	
competitive	analysis	in	focal	firm.	Models	1	and	3	in	

e

	show	the	effect	of	positional	

embeddedness	on	the	dependent	variables,	action	repertoire	simplicity	and	market	share	
erosion	respectively.	We	ran	a	random-effects	model	to	test	the	effect	of	positional	
embeddedness	variables	on	action	repertoire	simplicity,	while	controlling	for	board	size	and	
firm	size.	As	seen	in	Model	1,	the	Gini	coefficient	is	significantly	related	to	action	repertoire	
simplicity	(p	<	.05,	β	=	0.240).	An	increase	in	the	inequality	of	director	ties	for	a	given	firm’s	
board	increases	the	likelihood	of	engaging	in	only	a	few	types	of	actions.	A	change	in	Gini	index	
equal	to	the	change	in	Gini	index	between	companies	with	a	low	inequality	in	ties	to	a	company	
with	a	high	inequality	in	ties	leads	to	a	change	in	action	repertoire	simplicity	of	9%,	everything	
else	being	equal.6		The	Wald	test	statistic	for	Model	1	was	also	significantly	better	(p	<	.05)	after	
the	inclusion	of	the	predictor	variables	to	the	model.	The	pooled	OLS	results	of	Model	3	were	
not	significant,	we	did	not	find	support	for	positional	embeddedness	and	market	share	erosion,	
controlling	for	firm	size,	board	size,	industry	concentration	and	industry	growth.	
Hypothesis	6	states	that	defensive	manipulation	of	political	logics	is	positively	related	to	
asymmetric	competitive	analysis	in	focal	firm.		Models	2	and	4	in	

e

	show	the	effect	of	

money	spent	on	manipulating	political	logics	on	action	repertoire	simplicity	and	market	share	

6

	A	difference	of	0.30	in	the	Gini	index,	as	measured	by	the	difference	of	one	standard	deviation	
above	and	below	the	mean	Gini	index)	leads	to	a	change	in	Action	Repertoire	Simplicity	(ARS)	of	
0.072.	This	change	in	ARS	is	9%	of	the	full	ARS	scale	(As	seen	in	Table	12,	the	ARS	goes	from	
0.188	to	1.000,	yielding	a	range	of	0.812).	
118	

erosion,	respectively.	The	random	effects	model	(as	seen	in	Model	2)	tests	the	effect	of	Political	
Logics	on	Action	Repertoire	Simplicity,	controlling	for	Firm	Size	and	the	Citizen’s	United	v.	FEC	
ruling.	This	relationship	was	not	significant.			
As	seen	in	Model	4,	the	random-effects	model	tests	the	effect	of	Political	logics	on	Focal	
firm	market	share	erosion,	while	controlling	for	firm	size,	the	Citizen’s	United	v.	FEC	ruling,	
industry	concentration,	and	industry	growth,	was	significant	(p	<	.05).		The	Wald	test	statistic	
confirmed	that	adding	the	predictor	variables	significantly	improved	(p	<	.05)	the	model	fit	
compared	to	when	the	model	only	had	control	variables.	To	illustrate	the	effect	of	the	slope	
coefficient	in	Model	4	(β	=	0.019),	on	average,	a	million	dollars	spent	on	defensive	manipulation	
of	political	logics	leads	to	an	increase	in	market	share	erosion	in	focal	firm	by	0.02	percent	
(effect	size	of	0.019	rounded	up	to	two	decimal	places).	This	is	substantial,	considering	that	the	
average	market	share	erosion	in	our	sample	is	nearly	0.04	percent.	It	implies	that	a	company	
with	an	average	market	share	erosion	rate	of	0.04	percent	would	increase	their	rate	of	market	
share	erosion	by	50	percent,	if	they	were	to	spend	a	million	dollars	to	pursue	political	interests.	

119	

Results	of	Panel	Data	Random-Effects	Analyses	for	Asymmetric	Competitive	Analysis	
N=410	

Action	Repertoire	Simplicity	

Market	Share	Erosion	

Model	1	(H5)	
Coef.	
Std.	Err.	

Model	2	(H6)	
Coef.	
Std.	Err.	

Model	3	(H5)c	
Coef.	
Std.	Err.	

Model	4	(H6)	
Coef.	
Std.	Err.	

-1.227E-6
***	
1.007E-2	

0.443E-6	

-9.662E-7	

8.415E-7

-3.856E-7 4.350E-7	

Board	Size	
Citizens	United	
-	
VS.	FEC	
Industry	
Concentration	
Industry	
-	
Growth	

6.982E-2	

-	

-	

4.443E-3	

8.952E-2	

-1.125E-6
****	
-	

-	

-1.133E-2	

1.911E-2

-

-	

-0.127	****	 0.038

-	

-	

-	

1.09E-9	

1.87E-9	

6.180E-10	

11.50E-10	

-	

-	

-	

5.861E-3	
****	

1.360E-3	

5.539E-3	
****	

0.500E-3	

Positional	
Embeddedness	

Degree	
Centrality	
Gini	
Coefficient	

-3.184E-3

2.790E-3	

-	

-	

2.196E-3	

2.394E-3	

-	

-	

0.240**	

0.120	

-	

-	

0.112	

0.110	

-	

-	

Defensive	
Political	
Logics	

Political	Logics	

-	

-	

-5.313E-3	

8.995E-3

-

-	

0.019**	

0.008	

Category	

Variables	

Control	
Variables	

Firm	Size	

0.329E-6	
-	

Table 12: Panel data results for random-effects regression of asymmetric competitive analysis. Data includes Amazon.com. Variables that are
not present in a particular model are indicated by a dash (-). Hypothesis numbers are listed after the model number in parentheses, e.g. H5 or
H6.

120	
	

Table	12:	(cont’d)	

Constant	
Wald-Test	ChiSquare	
c	
*
**	
***	
****	

Value	

Std.	Err	

Value	

Std.	Err	

Value	

0.631****	

0.103	

0.782****	

0.031	

-1.672E-2 9.231E-2	

6.682E-2**	 2.925E-2	

6.849**	

-	

0.353	

-

1.353

6.468**	

Pooled	panel	model	analysis	
p	<	0.10
p	<	0.05	
p	<	0.01	
p	<	0.001	

121	
	

Std.	Err.	
-	

Value	

Std.	Err.	
-	

Chapter	6: Discussion	and	Conclusions	
The	literature	on	asymmetric	competition	has	largely	portrayed	asymmetry	to	be	a	
competitive	advantage.	However,	this	assertion	has	been	largely	made	on	the	basis	of	theories	
that	assume	perfect	rationality	in	firms.	In	reality,	we	know	that	firms	have	bounded	rationality	
and	have	inferential	limitations.	Additionally,	decision-makers	have	tacit	beliefs	regarding	
interpersonal	interaction	that	constrains	learning	behavior	and	cause	firms	to	be	ineffective.			
These	inefficiencies	create	blind	spots	in	firms,	making	them	vulnerable	to	competitive	
attacks	from	rivals.	Therefore,	when	we	assume	these	inherent	limitations	in	the	cognition	of	
firms	and	the	way	it	informs	their	competitive	analysis,	the	favorability	of	asymmetry	that	
dominates	extant	research	becomes	questionable.	Our	study	explores	these	contradicting	
implications	of	asymmetric	competition	and	provide	a	contextual	explanation	of	why	
asymmetry	leads	to	favorable	consequences	and	why	it	can	also	lead	to	unfavorable	
consequences.	But	so	far,	the	explanation	and	acknowledgement	of	this	paradox	had	been	
lacking.		
Using	data	from	41	publicly	traded	firms	in	the	U.S.	retail	industry	observed	over	a	tenyear	period,	we	examined	the	paradoxical	nature	of	asymmetric	competition.	At	a	general	level,	
our	study	contributes	to	the	literature	on	competitive	asymmetry	by	extending	the	Red	Queen	
theory	to	understand	why	strategic	adaptations	to	the	context’s	logic	of	competition	lead	to	
favorable	asymmetric	rivalries	for	the	focal	firm,	and	why	some	of	the	same	strategic	
adaptations	lead	simultaneously	to	unfavorable	asymmetric	competitive	analysis	in	the	focal	
firm.		Understanding	the	implications	of	this	paradox	of	asymmetry	is	critical	from	the	

122	
	

standpoint	of	decision-makers	who	seek	to	evaluate	existing	strategic	programs	and	assess	
future	competitive	threats.	

6.1 Discussion	
This	study	began	with	the	aim	to	addresses	the	gap	in	extant	literature	pertaining	to	
how	cognitive	limitations	of	firms	effect	competitive	encounters	that	are	asymmetric	in	nature.		
We	chose	to	highlight	this	particular	gap	in	the	literature	which	assumes	competitive	analysis	to	
be	a	rational	choice,	where	firms	have	perfect	information	to	make	the	most	optimal	decisions	
regarding	competitive	analysis.	
However,	insights	from	organizational	learning	theory	point	to	the	fact	that	firms	tend	
to	fall	into	the	so-called	traps	of	learning—managers	tend	to	satisfice	as	opposed	to	choosing	
the	most	optimal	course	of	action.	We	consider	the	impact	of	this	assumption	regarding	the	
limits	of	decision-making	ability	in	the	case	of	a	specific	situation	that	confronts	firms	almost	
universally,	that	of	asymmetric	competition.		
As	scholars	have	noted,	creating	asymmetric	rivalries	with	industry	rivals	is	a	
competitive	advantage	because	it	enables	the	focal	firm	to	engage	in	a	wide	latitude	of	actions	
with	minimal	retaliation	from	other	firms	(Chen	1996).	Extant	studies	in	competitive	dynamics	
have	limited	the	role	of	firm’s	agency	to	the	action	and	response	aspect	of	competitive	
dynamics.	However,	the	literature	has	not	extended	the	agency	of	firms	with	respect	to	their	
task	environment.		

123	
	

Using	the	framework	of	the	Red	Queen	theory,	with	its	roots	in	organizational	learning	
and	emphasis	on	the	context-specificity	of	competition,	we	were	able	to	address	these	gaps	in	
the	literature.		Specifically,	we	were	able	to	establish	that	strategic	adaptation	to	the	context’s	
logic	of	competition	leads	to	favorable	consequences	in	terms	of	asymmetric	rivalry.	The	same	
adaptations,	however,	also	lead	to	the	disastrous	consequence	of	asymmetric	competitive	
analysis	in	firms.				
In	particular,	we	examined	how	defensive	manipulation	of	the	political	logics	of	the	
context’s	logic	of	competition	influenced	asymmetric	rivalry	and	asymmetric	competitive	
analysis.		Asymmetric	rivalry,	which	was	defined	in	terms	of	the	total	number	of	actions	carried	
out	by	the	focal	firm	relative	rivals,	increased	when	firms	engaged	in	the	defensive	
manipulation	of	political	logics	using	specific	tactics	such	as	government	lobbying	and	campaign	
finance.	Our	finding	supports	theoretical	evidence	from	resource-dependence	theory	and	
exchange	theory,	which	argue	that	firms	engage	in	tactics	such	as	lobbying	and	campaign	
finance	to	reduce	dependence	on	the	task	environment,	and	gain	power	to	improve	their	
competitive	advantage	relative	to	other	firms	that	are	less	politically	involved	(Hillman	and	Hitt	
1999;	Capron	and	Chatain	2008).		Our	study	also	supports	the	long-standing	view	held	by	
scholars	that	firms	engage	in	political	activity	because	they	believe	that	influencing	public	
officials	is	a	pathway	to	gain	competitive	advantage	over	others	(Stigler	1971;	Peltzman	1976).	
We	make	this	inference	based	on	the	increased	competitive	activity	of	firms	and	also	on	the	
basis	of	the	strong	upward	trend	seen	in	firms	engaging	in	influencing	political	logics,	marked	
by	the	sharp	increase	post	the	supreme	court	ruling	in	2008,	which	relaxed	laws	related	to	
political	contributions	by	corporations	(Figure	13).	
124	
	

40

12
20

11
20

10
20

09
20

08
20

07
20

06
20

05
20

20

20

04

20

03

Total Political Logics (in millions)

60

Year

Figure	13:	Money	spent	by	firms	on	campaign	contributions	and	lobbying.	The	amount	rose	
sharply	during	the	2008	election	year	and	continued	to	rise	after	the	Citizens	United	ruling	in	
2010,	spiking	once	again	during	the	election	year	of	2012.		

This	sustained	commitment	of	resources	to	the	strategy	of	political	logics	and	the	
resulting	enhancement	of	relative	competitive	activity	provides	additional	support	to	Porter’s	
(1980)	argument,	and	Chen,	Smith	and	Grimm’s	(1992)	finding	regarding	strategic	actions	that	
have	a	high	resource	commitment,	and	their	associated	immunity	to	other	competitors.		Our	
results	also	support	theoretical	studies	which	have	argued	that	strategic	political	management	
by	firms	leads	to	improved	performance	(Hillman	and	Hitt	1999;	Oliver	and	Holzinger	2008;	
Capron	and	Chatain	2008).		
Our	study	also	found	the	competitive	advantage	gained	from	the	defensive	
manipulation	of	political	logics	to	be	unsustainable.	The	same	strategy	leads	to	asymmetric	
competitive	analysis	in	the	focal	firm.		Our	results	also	enhance	(by	inference)	the	
125	
	

understanding	of	how	asymmetric	competitive	analysis	occurs	in	firms.		Weitz	(1985)	noted	
that	studies	of	competitive	interaction	have	paid	insufficient	attention	to	how	contextual	
factors	affect	cognition	in	firms.	Asymmetric	competitive	analysis	has	been	attributed	to	
reduced	awareness	due	to	firms	ignoring	rivals	who	are	not	similar	in	resources,	or	do	not	share	
common	markets	(Chen	1996;	Upson,	Ketchen,	Connelly	and	Ranft	2012).		However,	in	our	
study	we	found	compelling	evidence	of	asymmetric	competitive	analysis	in	firms	that	were	
similar	in	resources	and	had	sufficient	market	overlap,	demonstrating	that	these	factors	were	
not	sufficient	determinants	of	asymmetric	competitive	analysis.		
We	discussed	that	by	reducing	their	dependence	on	political	logics,	firms	gain	the	ability	
to	impose	their	actions,	products,	services	and	strategies	on	the	market	instead	of	adapting	to	
an	exogenous	environment.	Organizational	learning	theory	argues	that	the	power	of	being	able	
to	influence	the	environment	puts	a	firm	into	a	power	trap,	because	it	prevents	the	firm	from	
developing	alternative	strategies,	as	it	is	more	convenient	to	manipulate	the	political	logics	
instead.	Power	also	reduces	the	incentive	to	develop	adaptive	capabilities,	to	learn	and	counter	
rival	strategies.		Firms	in	a	power	trap	are	characterized	by	complacency	and	are	caught	off	
guard	by	a	changing	competitive	landscape	(Levinthal	and	March	1993).	We	infer	a	power	trap	
in	our	study	on	the	basis	of	the	high	rate	of	market	share	erosion,	a	surrogate	measure	of	
impaired	awareness	that	is	characteristic	of	asymmetric	competitive	analysis	in	the	firms	that	
defensively	manipulated	political	logics	(Ferrier	et	al.	1999;	Levinthal	and	March	1993).	
Our	observation	of	asymmetric	competitive	analysis	in	firms	that	manipulate	political	
logics	also	confirms	what	several	recent	empirical	studies	have	found	regarding	the	negative	
effects	of	political	activity	on	performance	(Hadani	and	Schuler	2013;	Aggarwal,	Meschke	and	
126	
	

Wang,	2012;	Coates,	2012;	Faccio	2006).	Specifically,	those	studies	have	found	corporate	
political	activity	reduced	share	value	and	profitability	in	firms.		Our	study	extends	these	findings	
to	show	that	political	strategy	also	leads	to	the	adverse	effect	of	asymmetric	competitive	
analysis	in	firms.	This	contradicts	theory	and	empirical	evidence	from	previous	studies	that	
show	corporate	lobbying	and	campaign	finance	to	have	a	favorable	effect	on	a	firm’s	outcome,	
and	demonstrates	that	the	relationship	between	a	firm’s	efforts	at	influencing	the	political	
logics	of	their	environment	and	its	outcomes	to	be	complex	and	insufficiently	understood.	
Our	theoretical	framework	of	Red	Queen	competition	may	answer	why	the	relationship	
between	political	activity	and	outcomes	is	contradictory.		Previous	studies	that	established	a	
positive	relationship	between	political	activity	by	firms	and	financial	returns	relied	on	the	
theory	of	industrial-organization	economics	and	resource-dependence	theory	as	the	primary	
lens	for	explaining	how	corporate	political	activity	affects	firms	(Hadani	and	Schuler	2013).	Both	
of	these	theories	assume	rational	choice	on	the	part	of	firms,	and	argue	that	decision-makers	
within	firms	have	perfect	information	and	are	able	to	scan	and	gather	information	to	make	the	
most	optimal	and	rational	decisions.		However,	we	argue	that	the	theoretical	assumption	of	
rationality	is	tenuous	due	to	the	reason	that	key	decision-makers	often	lack	intimate	knowledge	
of	government	policy	and	the	political	process	that	government	affairs	representatives	and	
lobbyists	are	aware	of.	At	the	same	time,	decision-makers	are	the	only	ones	who	are	
knowledgeable	about	the	firm’s	strategic	course,	but	may	face	political	pressure	within	the	firm	
to	pursue	political	activity	(Hart	2004).	By	explicitly	modelling	the	bounded	rationality	and	
learning	limitations	of	firms	in	our	study,	we	may	offer	a	better	understanding	of	this	complex	
relationship	between	corporate	political	strategy	and	firm’s	outcomes.	
127	
	

Our	results	also	found	evidence	regarding	the	Red	Queen	theory’s	hypothesis	that	
strategic	adaptation	to	the	social	logics	of	competition	leads	to	asymmetric	advantage.	We	
modelled	the	strategic	adaptation	to	the	social	logics	of	competition	in	terms	of	the	network	
construct	of	degree	centrality	to	denote	positional	embeddedness	of	firms	in	the	
intercorporate	network.		Our	rationale	was	that	positional	embeddedness	leads	to	asymmetric	
rivalry	in	the	focal	firm,	because	network	centrality	via	interlock	ties	enhanced	the	focal	firm’s	
access	to	information	(a	form	of	competitive	advantage),	which	improved	the	action	taking	
ability	by	the	focal	firm	(Davis	1991).	
Previous	research	has	indicated	that	the	flow	of	information	is	a	definitive	competitive	
advantage	and	leads	to	improved	action-taking	by	firms	(Gnyawali	and	Madhavan	2001).		We	
followed	the	previous	research	to	demonstrate	the	occurrence	of	positional	embeddedness	at	
the	inter-firm	level	of	network	centrality	by	using	the	proxy	of	interlocked	ties	of	the	focal	firm	
to	other	firms	(Shrophshire	2011;	Mizruchi	1996).	This	proxy	of	board	interlocks	allowed	us	to	
take	a	more	nuanced	look	at	positional	embeddedness	with	respect	to	asymmetric	
competition.	
In	network	research	a	distinction	is	made	between	two	node	sets	based	on	which	node	
set	is	more	responsible	for	the	embeddedness	of	the	firm	than	the	other.		In	the	case	of	board	
interlocks	both	nodes,	directors	and	corporate	boards,	are	considered	primary	nodes.	
Therefore,	we	reckoned	that	positional	embedeness	at	each	node	level	had	to	be	modelled	to	
offer	a	better	idea	of	how	it	affected	asymmetric	competition.		Theory	from	group	dynamics	
literature	argues	that	the	distribution	of	positional	embeddedness	on	the	focal	firm’s	board	
affects	decision-making	and	consequently	its	competitiveness	(He	and	Huang	2011).		Previous	
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research	has	demonstrated	that	directors	with	high	centrality	(those	who	hold	multiple	
directorships)	command	high	respect	and	authority	from	peers	in	boardrooms.	Consequently,	
when	a	boardroom	is	dominated	by	a	handful	of	board	members	with	high	centrality,	there	is	a	
clear	informal	hierarchy	in	the	boardroom	that	facilitates	interactions.	In	line	with	previous	
research,	we	modeled	this	phenomenon	using	the	Gini	coefficient,	and	found	some	interesting	
results	(He	and	Huang	2011).		Specifically,	we	found	that	high	inequality	in	the	distribution	of	
positional	embeddedness	of	the	focal	firm’s	board	lead	to	greater	asymmetric	rivalry,	enabling	
the	firm	to	carry	out	more	actions	compared	to	rivals.	This	result	finds	support	for	the	Red	
Queen	theory’s	hypothesis	that	strategic	adaptation	to	the	social	logics	of	competition	leads	to	
asymmetric	advantage.	In	terms	of	research	on	corporate	governance,	our	finding	lends	further	
evidence	to	extant	research	on	the	effects	of	boardroom	composition	on	performance	(He	and	
Huang	2011).	More	importantly,	it	attests	the	value	of	recognizing	the	human	aspect	of	
corporations	and	the	importance	of	studying	how	group	dynamics	of	decision-makers	affects	
firm	outcomes	(Beckman	et	al.	2014,	He	and	Huang	2011).		
However,	with	regard	to	the	overall	positional	embeddedness	of	the	focal	firm,	we	
found	that	degree	centrality	(number	of	board	interlocks)	was	negatively	related	to	asymmetric	
rivalry,	which	was	counter	to	our	prediction.		We	found	that	greater	positional	embeddedness	
of	the	focal	firm	via	number	of	board	interlocks	reduced	its	asymmetric	rivalry	advantage.		We	
speculate	that	our	results	follow	Shropshire’s	(2010)	theory	that	over-embeddedness	in	the	
interlocking	directorate	reduces	the	firm’s	ability	to	utilize	information	in	an	efficient	manner.			
Our	results	also	indicate	that	positional	embeddedness	can	lead	to	asymmetric	
competitive	analysis	in	firms	that	have	a	lopsided	distribution	of	positional	embeddedness	in	
129	
	

their	boardrooms.		The	results	enrich	(by	inference)	understanding	of	how	group	dynamics	
among	key	decision-makers	lead	to	asymmetric	competitive	analysis.	This	finding	also	extends	
recent	research	on	the	informal	hierarchy	of	a	board	and	its	implication	on	the	firm’s	
effectiveness	due	to	the	underlying	social	mechanisms	that	guide	boardroom	interactions	(He	
and	Huang	2011).	Our	study	adds	further	support	for	research	that	explicitly	accounts	for	the	
social	mechanisms	of	a	corporation’s	actions,	and	addresses	the	gap	in	extant	studies	on	
competition	and	strategy	that	treat	corporations	as	impersonal	black	boxes.		Our	study	also	
finds	empirical	support	for	the	research	of	Beckman	et	al.	(2014),	who	theorized	that	firms	are	
not	able	to	draw	upon	the	full	expertise	and	experience	of	their	board	members	when	the	firm	
is	dominated	by	a	few	star	board	members	with	greater	positional	embeddedness	relative	to	
fellow	board	members.	Additionally,	our	finding	of	action	repertoire	simplicity	is	indicative	of	
the	competitive	inertia	associated	with	having	high	ranking	decision-makers,	who	are	afraid	of	
deviating	from	established	actions	to	protect	their	reputation	and	reliability	(Wong	and	Boh	
2010)	
	Each	of	these	findings,	paint	an	interesting	story	about	positional	embeddedness	and	
asymmetric	competition	when	taken	together.		As	positional	embeddedness	of	the	firm	
increases,	it	may	create	problems	related	to	information	overload,	with	the	firm	getting	
overwhelmed	with	the	large	amount	of	information	from	multiple	outside	firms.	However,	
having	an	informal	social	hierarchy	in	the	boardroom	(unequal	distribution	of	positional	
embeddedness	in	the	boardroom)	may	create	a	system	that	makes	decision-making	more	
efficient,	resulting	in	the	ability	to	undertake	greater	number	of	competitive	actions	in	the	focal	

130	
	

firm.	But,	an	improvement	in	efficiency	may	have	compromised	the	quality	of	actions	taken,	as	
shown	by	the	reduced	repertoire	of	actions	taken.	
To	our	knowledge,	none	of	the	previous	studies	in	the	corporate	governance	literature	
that	investigate	the	embeddedness	of	interlock	networks	have	taken	the	affiliation	two-mode	
network	structure	of	interlocking	directorates	into	consideration.		Extant	research	has	applied	a	
network	perspective	to	corporate	governance	by	examining	interlocked	firms’	ties	separately	
from	the	interpersonal	network	of	ties	among	directors	that	sit	on	the	same	board.	As	scholars	
have	argued,	the	type	of	cross	level	theory	and	methodology	employed	in	our	research	that	
studied	positional	embeddedness	at	both	nodes	of	a	two-mode	network	structure	avoids	the	
aggregation	errors	of	single-level	theory	and	methodology	(Klein	and	Kozlowski	2000;	Klein,	
Danserau	and	Hall,	1994;	Shropshire,	2010;	Opsahl	2013).	By	doing	so,	our	study	answers	the	
call	for	empirical	research	in	the	area	of	corporate	governance	that	accounts	for	variance	within	
the	group	(of	focal	board	directors)	based	on	individual	differences	in	ties	to	other	boards	
(Shropshire	2010;	Opsahl	2013).	
In	conclusion,	by	modeling	the	positive	and	negative	consequences	of	firms	in	a	Red	
Queen	we	address	the	gap	in	the	literature	that	either	ignored	the	competitive	interaction	
aspect	of	Red	Queen	process	or	left	out	the	truly	maladaptive	consequences	of	Red	Queen	
competition.	Unlike	previous	research	that	modeled	the	negative	impact	of	the	Red	Queen	only	
in	terms	of	lower	performance	due	to	rival	actions,	our	research	demonstrates	that	
maladaptive	consequences	of	the	Red	Queen	are	way	worse	than	just	a	temporary	reduction	in	
performance.	By	assuming	cognitive	limitations	of	decision-makers,	we	were	able	to	

131	
	

demonstrate	how	coping	strategies	to	the	logics	of	competition	lead	to	a	truly	dangerous	
situation	of	impaired	awareness	in	firms.	
Our	research	also	contributes	to	the	network	literature	by	drawing	a	theoretical	link	
between	the	concept	of	structural	embeddedness	and	asymmetric	rivalry.		Specifically,	we	
theorized	that	firms	will	strategically	alter	the	configuration	of	network	ties	in	such	a	way	that	
their	structural	embeddedness	leads	to	asymmetric	rivalry.	We	argued	that	firms	employ	a	
strategy	of	structural	embeddedness	to	overcome	network	constraints	of	the	social	logics	of	
competition	that	prevent	firms	from	gaining	access	to	asset	and	status	benefits,	and	in	turn	
acquire	competitive	advantage	terms	of	asymmetric	rivalry	(Burt	1992;	Burt	2010;	Mizruchi,	
1996).	However,	our	results	showed	structurally	embedding	network	constraints	to	be	
unrelated	to	asymmetric	competitive	advantage	for	the	firm.		We	measured	structural	
embeddedness	in	terms	of	weak	ties	(indirect	interlocks),	representation	of	financial	
instututions	and	Fortune	firm	directors	on	the	board	but	found	no	relationship	to	focal	firm	
asymmetric	rivalry.	
The	failure	to	find	a	significant	relationship	between	weak	ties	and	asymmetric	rivalry	
for	the	focal	firm	also	extends	McEvily	and	Zaheer’s	(1999)	study,	which	was	unable	to	detect	a	
relationship	between	embedded	weak	ties	and	competitive	capabilities.	Perhaps,	the	lack	of	
association	between	indirect	interlocks	among	industry	rivals	and	relative	competitive	activity	
indicates	mutual	forbearance	between	industry	rivals	since	scholars	in	the	board	interlock	
literature	have	argued	that	the	presence	of	indirect	interlocks	between	industry	rivals	serve	as	
weak	ties	for	signaling	collusive	behavior	to	restrict	competition	(Mizruchi	1996).			

132	
	

Our	results	also	echo	research	on	board	interlock	studies	that	have	generally	failed	to	
find	a	significant	relationship	between	representation	of	financial	institutions	on	corporate	
boards	and	performance	(Mizruchi	1996).			On	the	basis	of	Mizruchi’s	(1996)	argument,	we	
consider	the	possible	ambiguity	of	the	causal	ordering	of	structural	embeddedness	constructs	
of	financial	representation	on	the	board,	the	high-status	Fortune	director	appointments	and	
their	effect	on	asymmetric	advantage.	This	is	because	having	high-status	directors	and	bankers	
on	the	board	may	lead	to	competitive	advantage,	but	it	could	also	be	argued	that	high-status	
directors	and	bankers	are	attracted	to	firms	that	are	successful	to	begin	with	(Mizruchi	1996).	
Therefore,	before	concluding	that	structural	embeddedness	is	not	related	to	asymmetric	rivalry	
in	firms,	we	encourage	future	research	to	investigate	other	possibilities.		For	example,	using	a	
lagged	dependent	variable	to	establish	that	the	structural	embeddedness	initiatives	by	the	firm	
precedes	the	firm’s	outcome	measures	(Mizruchi	1996).		

6.2 Managerial	and	Policy	Implications	
Our	study	joins	several	recent	studies	that	have	empirically	demonstrated	the	adverse	
impact	of	corporate	political	activity.		More	importantly,	unlike	previous	studies,	our	study	
illustrates	the	extent	of	damage	engaging	in	defensive	manipulation	of	political	logics	can	be	to	
the	firm.	While	engaging	in	political	logics	may	have	enabled	the	focal	firm	to	be	relatively	more	
competitive	compared	to	its	rivals,	this	improvement	was	modest	at	best.	Firms	that	heavily	
engaged	in	political	logics	were	likely	to	lose	their	market	share	at	a	significantly	higher	rate	
than	firms	that	did	not	participate	in	political	activity.	This	finding	has	several	implications	for	
133	
	

managers	and	government	policy.	First,	managers	need	to	realize	that	engaging	in	political	
activity	may	not	necessarily	lead	to	the	gains	they	hope	to.	Even	if	this	is	undertaken	to	keep	up	
with	the	mimetic	pressures	of	other	rival	firms	that	are	engaging	in	the	political	activity,	firms	
need	to	be	wary	of	having	a	core	strategy	that	is	deeply	focused	on	the	political	process.	As	our	
results	suggest,	this	is	likely	to	reduce	your	relative	competitive	standing,	and	blind	you	from	
rival	actions.	Restricting	rivals	from	competing	by	intervening	in	the	political	markets	is	also	
likely	to	impair	competitive	analysis	because	it	may	create	superstitious	learning	in	the	focal	
firm	(March,	Sproull	and	Tamuz	1991).	Playing	politics	may	also	be	a	generally	bad	idea	for	
reasons	other	than	generating	asymmetric	competitive	analysis	in	the	focal	firm.	Since	retailing	
is	a	service	industry,	the	brand	image	of	retailers	is	inextricably	intertwined	with	their	store	
image,	as	there	is	no	clear	separation	between	the	products	offered	by	the	retailer	and	the	
store’s	image	(Manjeshwar	and	Sternquist,	2008).	Although	politics	in	the	United	States	has	
gotten	increasingly	partisan	over	the	years,	the	majority	of	Americans	remain	solidly	united	in	
their	contempt	of	corporate	money	in	politics	(Stohr	2015).	Therefore,	engaging	in	political	
activity	may	hurt	retailers	more	adversely	compared	to	non-service	based	industries	(TorresSpelliscy	2016).		
Our	research	also	found	evidence	pertaining	to	the	impact	of	positional	embeddedness	
on	both	asymmetric	rivalry	and	asymmetric	competitive	analysis.	The	implications	for	managers	
follow	in	conjunction	from	the	global	measure	of	the	firm’s	overall	positional	embeddedness	
and	the	local	measure	of	distribution	of	positional	embeddedness	on	the	firm’s	board.	This	
means	that	if	we	plot	the	dimensions	of	these	global	and	local	measures	of	embeddedness	on	
an	x	and	y	axis	then,	per	our	results,	most	firms	need	to	strive	for	Quadrant	I	(Figure
134	
	

).	The	

most	optimal	board	structure	from	an	efficiency	standpoint	is	one	where	the	firm	is	not	overembedded	in	the	intercorporate	network,	and	the	type	of	boardroom	structure	where	
positional	embeddedness	of	directors	on	the	board	is	highly	unequal,	having	only	a	few	
directors	who	have	many	outside	ties	to	firms	(Quadrant	I	in	Figure	14).		

HIGH

ma oar
om os on

“Superstar” Board
om os on

Clear Social Hierarchy
in the Boardroom
+
Low Overall
Positional Embeddedness

Clear Social Hierarchy
in the Boardroom
+
High Overall
Positional Embeddedness

(II)

(I)

ne a
n os ona
m e e ness s r
on
(Boardroom)

“Insular” Board
om os on
Unclear Social Hierarchy
in the Boardroom
+
Low Overall
Positional Embeddedness
(IV)

LOW
LOW

“All Stars” Board
om os on
Unclear Social Hierarchy
in the Boardroom
+
High Overall
Positional Embeddedness
(III)

os ona m e e ness
(Firm-Firm Ties)

HIGH

Figure	14:	Board	Composition	based	on	the	distribution	of	positional	embeddedness	in	the	
boardroom	and	the	overall	positional	embeddedness	of	the	firm	in	the	intercorporate	board	
network.		

135	
	

Based	on	our	results,	a	boardroom	where	only	a	few	directors	have	positional	
embeddedness	also	leads	to	simple	repertoire	of	actions.	The	latter	is	expected	because	the	
presence	of	a	social	hierarchy	is	to	be	expected	in	any	social	group,	and	the	corporate	board	is	
not	an	exception	to	that	norm	(Magee	and	Galinsky	2008).	And	such	hierarchy	discourages	
lower	ranked	team	members	from	contributing	novel	ideas	and	encourages	the	tendency	to	
repeat	information	that	is	commonly	held	by	the	group	(Stewart	an	Stasser	1995;	Wittenbaum	
2000).	Information	possessed	by	lower	status	individuals	is	also	more	likely	to	be	viewed	with	
skepticism.		Additionally,	highly	central	directors	are	less	likely	to	endorse	or	propose	strategic	
actions	that	deviate	from	their	social	identity.		The	challenge	for	firms	then,	is	to	be	able	to	
combine	the	best	of	both	situations	without	sacrificing	the	quality	and	creativity	of	decisions	
made.		To	that	end,	we	recommend	that	the	chairman	of	the	board	take	an	active	leadership	
role	as	the	facilitator	of	boardroom	discussion,	and	keep	powerful	board	members	from	
dominating	decisions,	encourage	equal	participation	from	all	members	of	the	board,	and	
actively	facilitate	a	group	culture	of	mutual	respect,	empathy	and	interpersonal	trust.	These	
attributes	are	collectively	aspects	of	“psychological	safety”,	a	group	culture	which	is	defined	as	
“a	shared	belief	that	the	team	is	safe	for	interpersonal	risk	taking	(Edmondson,	1999,	P.	354).	
Psychological	safety	is	distinct	from	group	cohesiveness,	which	encourages	groupthink,	and	is	
also	separate	from	a	dysfunctional	culture	that	permits	a	laissez-faire	group	dynamic.	It	refers	
to	a	climate	that	fosters	confidence	in	group	members	that	their	ideas	will	not	be	ridiculed	or	
that	they	will	not	be	punished	for	speaking	up	(Edmondson	1999).	This	confidence	that	stems	
from	mutual	respect	and	trust	has	been	shown	to	be	critical	for	the	functioning	of	high	

136	
	

achieving	groups,	and	has	been	successfully	applied	at	companies	such	as	Google	Inc.	(Duhigg	
2016).	

6.3 Limitations	and	Future	Research	
This	study	has	some	limitations	worth	mentioning.	First,	we	sampled	firms	from	the	U.S.	
retail	industry.	Limiting	our	study	to	a	single	industry	has	allowed	us	to	perform	a	pure	test	for	
our	theoretical	model,	and	reduced	the	need	to	control	for	variation	in	the	strategic	adaptation	
to	different	logics	of	competition	operating	in	a	multi-industry	data	set.	However,	the	retail	
industry	does	have	certain	distinctive	characteristics	that	may	explain	why	we	were	not	able	to	
find	a	significant	relationship	for	our	hypothesis	regarding	the	proactive	manipulation	of	
institutional	logics	and	asymmetric	rivalry.	Being	a	service-based	industry,	the	retail	sector	does	
not	actively	file	patents	nor	does	it	heavily	invest	in	research	and	development	(R&D)	(Gotsch	
and	Hipp,	2012).	It	may	be	that	trademarks	may	not	have	adequately	captured	the	construct	of	
institutional	logics	because	they	do	not	measure	the	internal	process	of	institutional	
entrepreneurship	in	firms,	which	is	key	to	the	manipulation	of	institutional	logics.	Future	
studies	should	supplement	trademarks	with	survey	instruments	to	capture	the	manipulation	of	
institutional	logics	by	firms.		
Secondly,	although	the	methodology	of	recording	competitive	actions	by	citing	counts	
of	actions	found	in	press	articles	is	an	established	procedure,	similar	to	previous	studies,	our	
study	captures	only	observable	competitive	actions	that	have	been	reported	in	the	media	
(Jauch,	Osborn	and	Martin	1980;	Smith	et	al.,	1992;	Young,	Smith	and	Grimm	1996;	Derfus	et	al.	
137	
	

2008).		It	is	plausible	that	not	all	firms	believe	in	announcing	competitive	threats	to	rivals,	and	
some	may	even	prefer	to	attack	with	stealth	instead.	
Thirdly,	it	is	worth	noting	that	the	2010	Citizens	United	Supreme	Court	decision	fails	to	
take	into	account	the	existing	FEC	rules	regarding	disclosure,	and	allows	major	political	
spenders	to	contribute	undisclosed	sums	of	money,	anonymously.	This	means	that	some	of	the	
money	poured	by	the	firms	in	our	study	may	be	unaccounted	for.	There	is	also	the	possibility	
that	other	firms	that	are	not	actively	spending	money	in	politics	are	benefiting	by	riding	on	the	
coat	tails	of	those	firms	that	do.	Our	study	was	not	able	to	capture	this	possibility.			
Lastly,	while	we	did	discover	an	interesting	paradox,	we	have	no	way	of	verifying	the	
causal	mechanisms	that	linked	positional	embeddedness	to	the	action	repertoire	simplicity	
measure	of	asymmetric	competitive	analysis,	because	we	were	unable	to	analyze	the	content	
and	quality	of	actual	boardroom	interactions.	Although	we	developed	a	theoretical	argument	
based	on	logical	inference	to	arrive	at	the	causal	mechanism	that	linked	distribution	of	
positional	embeddedness	on	the	focal	firm’s	board	and	boardroom	interaction,	and	
consequently	removed	the	possibility	of	alternative	explanations,	we	believe	that	future	
research	will	benefit	from	the	capturing	actual	boardroom	interactions	of	firms	where	
positional	embeddedness	of	directors	constitutes	social	hierarchy.		

6.4 Conclusions	
In	his	seminal	essay	on	what	makes	a	particular	theory	interesting,	Davis	(1971,	p.309)	
differentiates	between	theories	that	are	interesting	from	theories	that	are	not	interesting	by	
138	
	

declaring	interesting	theories	as	those	which	deny	certain	assumptions	of	their	audience	(1971,	
p. 309).	Adapting	to	the	context’s	logic	of	competition	is	generally	assumed	to	result	into	a
favorable	consequence	for	the	firm.	However,	our	extension	of	the	Red	Queen	theory	to	the	
phenomenon	of	asymmetric	competition	denies	this	assumption	of	traditional	theories	of	
competition	to	reveal	an	interesting	paradox.	Strategic	adaptations	to	the	logics	of	competition	
prevailing	in	the	political	and	social	context	of	the	market	structure	lead	to	asymmetric	rivalries	
with	competitors.	However,	the	same	strategic	adaptations	lead	to	the	unintentional	but	
deleterious	consequence	of	asymmetric	competitive	analysis	in	the	focal	firm.		The	study	of	
asymmetric	competition	has	attracted	scant	research	attention	over	the	past	three	decades,	
but	our	results	indicate	that	asymmetric	competitive	behavior	as	seen	from	the	level	of	
rivalrous	action	and	from	the	level	of	competitive	analysis	bear	important	implications	for	
firms.	We	hope	that	our	effort	to	solve	the	paradoxical	nature	of	asymmetric	competition	will	
stimulate	further	investigation	into	this	important	area	of	research.	

139	
	

APPENDICES	

140	
	

Appendix	A:	Content	Analysis	Schedule	
Company	Name:	________________________________________________________________	

Date	of	Article:	____________________	Publication	Source:	___________________________	

Retailer	Actions:	The	following	list	describes	actions	typically	undertaken	by	retailers.	Please	
read	the	following	descriptions	and	place	a	checkmark	(✓)	in	the	column	at	the	left	to	indicate	
whether	the	article	captures	these	actions	for	the	firm.	If	an	article	lists	multiple	company	
actions	then	please	use	a	separate	sheet	for	each	company	listed	in	the	article.	

Category	

✓

Description

Actions	pertaining	to	the	depth	of	merchandise	carried-	the	
Assortment	Mix	 _____	 number	of	different	SKUs	(Stock	Keeping	Unit)	within	a	category.	
Actions	pertaining	to	the	variety	of	merchandise	categories	
Assortment	Mix	 _____	 offered.	
Actions	pertaining	to	the	complementarity	of	the	merchandise	
Assortment	Mix	 _____	 across	categories.	
Actions	pertaining	to	visual	merchandising,	presentation	of	the	
retail	space,	the	physical	characteristics	of	the	store	or	website	
Assortment	Mix	 _____	 layout	that	displays	the	merchandise.7	
Actions	pertaining	to	the	depth	of	merchandise	carried-	the	
Assortment	Mix	 _____	 number	of	different	SKUs	(Stock	Keeping	Unit)	within	a	category.	

7

	Note:	Do	not	include	actions	pertaining	to	displaying	special	merchandise,	seasonal	

merchandise	or	markdown	merchandise	that	is	being	promoted	to	attract	customers.	For	e.g.	
“Wal-Mart	debuts	a	more	contemporary	design:	revamped	signage	and	functional	flooring	
highlight	the	changes”	
141	
	

Category	

✓

Description

Actions	pertaining	to	inventory	management,	i.e.	acquiring	and	
Assortment	Mix	 _____	 maintaining	proper	assortment	of	merchandise.	
Actions	pertaining	to	licensed	brands,	an	exclusive	contractual	
agreement	with	a	well-known	brand	to	produce	or	carry	licensed	
Assortment	Mix	 _____	 merchandise.	
Actions	pertaining	to	private	label	brands,	products	developed	
and	marketed	by	the	retailer,	only	available	for	sale	at	the	
Assortment	Mix	 _____	 retailer.		
Actions	pertaining	to	premium	branding	strategy	that	offers	the	
consumer	a	private	label	at	a	comparable	manufacturer	brand	
Assortment	Mix	 _____	 quality.	

Promotion	

Actions	pertaining	to	displaying	merchandise	that	is	being	
promoted.	Do	not	include	actions	pertaining	to	visual	
merchandising	that	pertains	to	store	design/website	layout	
_____	 designed	to	facilitate	shopping.	

Promotion	

_____	 Actions	pertaining	to	promoting	seasonal	merchandise.	

Promotion	

_____	 Actions	pertaining	to	promoting	special	merchandise.	

Promotion	

Actions	pertaining	to	promoting	markdowns	aimed	at	generating	
sales	and	increasing	customer	flow.	Does	not	include	clearance	
markdowns	aimed	at	disposing	merchandise	or	end-of-season	
_____	 markdowns	aimed	at	price	sensitive	customers.	

Promotion	

Actions	pertaining	to	generating	publicity	for	the	retailer.	Includes	
exposure	in	any	new	media	that	boosts	sales	that	is	not	paid	for	
by	the	retailer.	For	e.g.	a	news	feature	about	a	retailer	sponsoring	
_____	 students	for	college	scholarships.	

Promotion	

Actions	pertaining	to	writing	a	newspaper	article,	magazine	
_____	 column	or	an	electronic	newsletter	on	timely	or	relevant	topics.	

Promotion	

_____	 Sponsorship	of	events,	tournaments,	television	shows.	

Promotion	

Hiring	a	celebrity	spokesperson	to	promote	the	retailer’s	products	
_____	 or	services.	

Promotion	

Actions	pertaining	to	sponsorship	of	community	project,	
_____	 nonprofit	or	charity	organization.	

Promotion	

_____	 Actions	pertaining	to	promoting	a	cause	and	generating	publicity.	

Promotion	

Actions	pertaining	to	sales	efforts	of	salespersons,	including	
_____	 training	of	expert	and	knowledgeable	sales	persons.	
142	
	

Category	

✓

Description

Promotion	

Actions	that	encourage	consumer	trials	through	samples,	in-store	
_____	 demonstrations,	online	videos	and	product	testimonials.	

Promotion	

Marketing	activities	that	include	TV,	print,	and	radio	commercials,	
_____	 including	paid	product	placement.	

Promotion	

Viral	marketing	activities	that	are	geared	towards	creating	a	
_____	 “buzz”	about	the	retailer.	

Promotion	

Activities	to	promote	word-of-mouth	advertising	that	includes,	
_____	 rewarding	customers	for	referrals	and	positive	reviews.	

Promotion	

Direct	mailing-	store	flyers,	catalogues,	postcards,	brochures,	and	
_____	 coupon	booklets.	

Pricing	Strategy	 _____	 Discounts,	sales,	mark-downs,	mark-ups.	
Pricing	Strategy	 _____	 Gifts	with	purchase,	free.	
Pricing	Strategy	 _____	 Odd	pricing-	setting	prices	that	end	in	odd	numbers.	
Pricing	Strategy	 _____	 Pricelining-	refers	to	when	merchandise	is	sold	in	price	ranges	
low,	medium,	high	(premium)	pricing.	
Pricing	Strategy	 _____	 Dynamic	pricing-	First-time	customers	are	charged	a	lower	
promotional	price.	
Pricing	Strategy	 _____	 Leader	pricing-	discounting	a	popular	item	to	encourage	and	
incentivize	purchase	on	non-discounted	items.	
Pricing	Strategy	 _____	 Low	price	guarantee	policy-	a	policy	that	guarantees	that	the	
retailer	will	have	the	item	for	the	lowest	possible	price	and	a	
promise	to	match	or	better	any	lower	price	found	at	a	competing	
store.	
Pricing	Strategy	 _____	 Multiple	unit	pricing-	offering	discounts	on	bulk	quantity	(	five	for	
two	dollars).	
Pricing	Strategy	 _____	 Bundling-discount	on	purchasing	multiple	items	together.	
Pricing	Strategy	 _____	 Every	Day	Low	Pricing	(EDLP)-	pricing	strategy	between	regular	
non	sale	prices	and	deep	discount	sale	price	of	competitors.	
Activities	and	programs	designed	by	the	retailer	to	make	the	
Service	Strategy	 _____	 shopping	experience	convenient	and	rewarding	for	the	customer.	
Activities	that	include	instrumental	support	to	deliver	service	to	
customers	through	call	centers,	online	chatting	with	support,	instore	kiosks	to	order	out	of	stock	items	or	website	that	allows	
Service	Strategy	 _____	 customer	to	look	up	availability	of	an	item	at	a	particular	location.	
143	
	

Category	

✓

Description

Using	technology	to	maintain	a	database	of	customer	
Service	Strategy	 _____	 information.	
Actions	geared	towards	giving	personalized	service	to	customers	
based	on	information	collected	from	the	customer	loyalty	
Service	Strategy	 _____	 database.	
Engaging	in	customer	relationship	management	through	surveys,	
Service	Strategy	 _____	 feedback	and	interviews	with	customers.	
Engaging	in	a	customer	loyalty	program	to	reward	customers	with	
incentives	such	as	discounts	on	purchases,	free	items,	gifts,	
Service	Strategy	 _____	 special	privileges	for	their	repeated	business.	
Proprietary	store	credit	card	system	that	allows	customers	to	
Service	Strategy	 _____	 charge	purchases	to	retailer’s	store	credit	cards.	
Opening	store	formats	that	cater	to	a	particular	market	segment.	
For	e.g.,	smaller	versions	of	retailer’s	big	box	stores	in	urban	
Service	Strategy	 _____	 areas.	
Service	Strategy	 _____	 Acceptance	of	different	form	of	payment	methods.	
Service	Strategy	 _____	 Alteration	or	customization	service	for	merchandise.	
Service	Strategy	 _____	 Gift	registry	for	special	occasions.	
Processing	of	financial	services-	cashing	checks,	loans,	insurance	
Service	Strategy	 _____	 etc.	
Service	Strategy	 _____	 Delivery	or	shipping	service.	
Service	Strategy	 _____	 Fitting	rooms.	
Easy	returns	and/or	easy	pick-up	from	websites	and	physical	
Service	Strategy	 _____	 stores.	
Service	Strategy	 _____	 Extended	store	hours.	
Service	Strategy	 _____	 Free	shipping	and	handling.	
Service	Strategy	 _____	 Gift-wrapping	service.	
Service	Strategy	 _____	 Layaway	plans.	
Service	Strategy	 _____	 Personal	shopping	assistance	in	selecting	merchandise.	
Service	Strategy	 _____	 Shopping	carts.	
Service	Strategy	 _____	 In	store	cafeteria	or	rest	areas.	
Service	Strategy	 _____	 Valet	parking	or	designated	parking	spots.	
144	
	

Category	

✓

Description

Supply	Chain	
Management	

The	set	of	activities	employed	by	firms	to	seamlessly	and	
efficiently	integrate	their	suppliers,	warehouses,	stores	and	
transportation	intermediaries	to	deliver	the	right	quantity	of	
_____	 merchandise,	at	the	right	time	and	right	location.	

Supply	Chain	
Management	

Electronic	Data	Interchange	systems-	data	exchange	systems	that	
are	developed	and	used	by	firms	for	the	purpose	of	exchanging	
_____	 information	with	suppliers.	

Supply	Chain	
Management	

_____	 Public	warehouses	that	are	owned	by	a	third	party.	

Supply	Chain	
Management	

Distribution	centers	for	holding	merchandise	that	is	unloaded	
_____	 from	trucks.	

Supply	Chain	
Management	

Drop	shipping:	direct	delivery	by	manufacturers	to	customers	
_____	 made	at	the	retailer’s	request.	

Supply	Chain	
Management	

Quick	response	delivery	systems	designed	to	reduce	the	lead-time	
_____	 for	receiving	merchandise.	Includes	just	in	time	delivery.	

145	
	

Appendix	B:	Software	Version	Information	

Software	/	
Library	

Version	

R	Studio	

0.99.473	 Integrated	Development	Environment	(IDE)	for	script	and	
software	development	
3.2.2	
The	R	programming	language	
1.5-12	
Linear	models	for	panel	data	
1.0.1	
Network	analysis	and	graphics	
0.14	
Moments,	cumulants,	skewness,	kurtosis,	and	related	tests	

R	Language	
plm	library	
igraph	library	
moments	
library	
corrplot	library	
dplyr	library	
tidyr	library	

Description	

0.73	
0.4.3	

Visualization	of	correlation	matrices	
Data	manipulation	tools	

1.0-7	

Data	formatting	tools	

Table	13:	Versioning	information	for	R,	IDE,	and	R	libraries	used	in	this	work.	

146	
	

Appendix	C:	Results of Panel Data Random-Effects Analyses for Asymmetric Rivalry (Relative Competitive
Activity)
(No Amazon.com; N = 400)
Category	

Variables	

Control	
Variables	

Firm	Size	
Slack	Resources	
Citizens	United	vs.	
FEC	
Debt	Ratio	
Board	Size	
Trademarks	

Proactive	
Institutional	
Logics	
Defensive	
Political	Logics	
Political	Logics	
Structural	
Fortune	1000	
Embeddedness	 Directors	
Financial	Institution	
Directors	
Indirect	Interlocks	
Positional	
Degree	Centrality	
Embeddedness	 Gini	Coefficient	

Model	1	(H1)	
Coef.	
Std.	Err.	
-1.50E-4 2.80E-4	

Model	3	(H3)	
Model	4	(H3/H4)a	
Coef.	
Std.	Err.	 Coef.	
Std.	Err.	
-0.077E-4 2.93E-4	 0.238E-4	
2.88E-4	
-	

-	

-	

-	

0.22	

10.35	

Model	2	(H2)	
Coef.	
Std.	Err	
-9.27E-4 4.17E-4	
**
-	

-	

-	

-0.054

7.12	

-	

-	

-	

-	

-	
-	

-	
-	

-	
-	

-	
-	

22.65**	
1.79	

9.914	
2.93	

6.75*	

3.29	

-0.16

0.171	

-	

-	

-	

-	

-	

-	

-	

-	

8.62**	

3.56	

-	

-	

-	

-	

-	

-	

-	

-	

-	

-	

3.39*	

1.98	

-	

-	

-	

-	

-4.75*

2.55	

-	

-	

-	
-	

-	
-	
-	

-	
-	
-	

-	
-	
-	

-	
-	
-	

-	
-	
-	

5.03	
-4.06****	
98.87**	

5.89	
1.00
43.60	

Table 14: Panel data results for random-effects models of asymmetric rivalry (relative competitive activity). Data excludes
Amazon.com. Variables that are not present in a particular model are indicated by a dash (-). Hypothesis numbers are listed after the model
number in parenthesis, e.g. H1, H2, H3, or H3/H4.
147	
	

Table	14:	(cont’d)	

Constant	
Wald	Test	ChiSquare	
a	
*
**	
***	
****	

Value	

Std.	Err.	

Value	

Std.	Err.	

Value	

13.82	
0.87	

19.78	
-	

21.42	
5.83**	

18.56	
-	

-39.06
3.48	

Std.	
Err.	
36.06	
-	

Model	4	combines	Hypotheses	3	and	4.	See	Results	section	for	additional	information.	
p	<	0.10
p	<	0.05	
p	<	0.01	
p	<	0.001	

148	
	

Value	

Std.	Err.	

-65.47*
23.81****	

36.41	
-

Appendix D: Results of Panel Data Random-Effects Analyses for Asymmetric Competitive Analysis
	

(No	Amazon.com;	N=400)	

Category	

Variables	

Control	
Variables	

Firm	Size	

Board	Size	
Citizens	United	
VS.	FEC	
Industry	
Concentration	
Industry	
Growth	
Positional	
Degree	
Embeddedness	 Centrality	
Gini	Coefficient	
Defensive	
Political	Logics	
Political	Logics	

Action	Repertoire	Simplicity	

Market	Share	Erosion	

Model	1	(H5)	
Coef.	
Std.	Err.	
-1.18E-6** 0.59E-6	

Model	2	(H6)	
Coef.	
Std.	Err.	
-1.04E-6	
0.83E-6

1.02E-2	

0.85E-2	

-	

-	

Model	3	(H5)c	
Model	4	(H6)	
Coef.	
Std.	Err.	 Coef.	
Std.	Err.	
-6.45-7
4.29E-7	 -1.22E-6 0.35E-6	
****
3.86E-3	
8.88E-2	 -	

-	

-	

-9.50E-3	

19.2E-3

-	

-	

-0.13***	 0.042

-

-	

-	

-	

1.23E-9	

1.84E-9	

1.14E-9	

1.09E-9	

-	

-	

-	

-	

3.46E-3	

2.28E-3	

-	

-	

2.36E-3	

8.83E-3	
***	
-	

3.05E-3	

-3.45E-3

6.96E-3	
**	
2.23E-3	

-	

0.22**	

0.12	

-	

-	

0.11	

0.11	

-	

-	

-

-	

-4.00E-3	

0.011E-3

-

-	

0.15**	

0.077	

Table 15: Panel data results for random-effects re ression of asymmetric analysis. Data excludes Amazon.com Inc. Variables that are not
present in a particular model are indicated by a dash (-). Hypothesis numbers are listed after the model number in parentheses,e.g. H5 or H6.

149	
	

Table	15:	(cont’d)	
Constant	
Wald-Test	ChiSquare	
c	
*
**	
***	
****	

Value	
0.63****	

Std.	Err	
0.11	

Value	
0.78	
****	

Std.	Err	
0.031	

Value	
-2.58E-2

Std.	Err.	 Value	
9.25E-2	 5.37E-2	
**	

Std.	Err.	
2.33E-2	

8.39**	

-	

0.20	

-

1.27

-	

-	

Pooled	panel	model	analysis	
p	<	0.10
p	<	0.05	
p	<	0.01	
p	<	0.001	

150	
	

3.97**	

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151	

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