INFORMATION SEARCH AND LEARNING ENVIRONMENT OF EMPLOYEES: THE
MODERATING EFFECTS OF EMPLOYEE EMPOWERMENT BASED ON
EMPLOYEES’ PERCEIVED MARKET STRATEGIES
By
So Jung Lee

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirement
For the degree of

DOCTOR OF PHILOSOPHY

Retailing

2011

ABSTRACT
INFORMATION SEARCH AND LEARNING ENVIRONMENT OF EMPLOYEES: THE
MODERATING EFFECTS OF EMPLOYEE EMPOWERMENT BASED ON
EMPLOYEES’ PERCEIVED MARKET STRATEGIES
By
So Jung Lee
New customers and emerging markets are continuously appearing in the marketplace,
firms’ efforts to search for creative/innovative ideas are critical to adapt to new market
conditions. For survival in an intensely competitive market, organizations need to have
competitive market strategies that assist in capturing current and future consumer trends quickly
and appropriate management strategies to enhance employee search of creative and/or new
market information and learning. In this dissertation, we focused on investigating the differential
effects of market orientation (MO) and entrepreneurship orientation (EO) strategies on employee
information search and information learning. Further we examined the moderating effects of
empowered employees, who are motivated to assume enhanced job responsibilities and are given
autonomy with their scope of work, are more likely to freely express their ideas and take risks
which are viewed as the foundation of creativity and innovation. Despite that both MO and EO
are widely studied, little is known about how each strategy influences the way firms integrate the
information, once it is collected. Thus, employing on the social network theory, this research
explained MO/EO’s group network ties and its impact on information search behaviors.
Organizations’ risk seeking and risk attenuated behaviors were also examined drawing on the
image theory perspective. Information search behavior and organizational learning environment
typologies were studied in relationship with market and management strategies. Specifically, we
developed scales to measure the three types of information search behaviors based on Grant’s

(1996a) conceptual constructs of knowledge integration types (efficiency, flexibility, and scope)
by conducting qualitative interviews with retail buyers.
Our study results revealed that employees with a strong market orientation not only
search for updates to existing information (efficient knowledge integration), they also extend
their search by configuring and refining old information and adding some new information
(flexible knowledge integration). Entrepreneurship oriented employees tended to engage in
flexible information search behavior but not in innovatively new information (scope knowledge
integration). Surprisingly, our study found that more empowered employees are less likely to use
flexibility and scope information search behaviors, which is contrary to previous studies.
Theoretical and practical implications were discussed and recommendations for future studies
were provided.

To my parents,
Kyung Koo Lee and Hyo Soon Na
and
Won Uong Lim and Hyun Sook Han

iv

ACKNOWLEDGEMENT

It has been a long journey with full of explorations, struggling, and excitement. It sure is
a hard job to accomplish it. I would like to express that this dissertation would not be complete
without the support of many individuals to whom I am most grateful.
First and foremost, I would like to give my endless thanks to my advisor, Professor Linda
Good, for her intelligent and thoughtful guidance, extreme patience, and constant encouragement.
Her steady guidance challenges me to think deeply and her emotional encouragement leads me to
conquer this dissertation. For the rest of my life, I will treasure everything I have learned from
Professor Good.
I would like to offer my sincerest appreciation to all my committee members: Dr. Patricia
Huddleston who helped me forward this dissertation with her tremendous mentoring, careful
review and constructive feedback, Dr. Brenda Sternquist who enlightened me with her invaluable
information and insights on my research model, and Dr. Mark Reckase who expanded my
research solving abilities with his technical advice. I deeply appreciate their valuable time,
precious comments, and guidance for this research.
I would also like to express my sincere appreciation to all my family members and
friends. Especially, I thank to my father Kyung Koo Lee and my mother Hyo Soon Na. They
encourage me to pursue my dreams with their deepest love, support, and endless prayers for me
along the long way. My appreciation also goes to my parents-in-law, Won Uong Lim and Hyun
Sook Han, who have been on my side with strong beliefs in me and helped me to finish this
dissertation. I still remember all my family members’ excitement about my accomplishment. My
special thanks go to my long time friends, either. Thank you, thank you.

v

Finally, this dissertation could not have been completed without my husband, In Kyu Lim
and my daughter Erin Lim. With their love, endurance, and spiritual support, I could put my foot
on the finish line. They are the inspiration for this dissertation.
Today, I just step forward to another full of adventures.

vi

TABLE OF CONTENTS

LIST OF TABLES ...................................................................................................... ix
LIST OF FIGURES ...................................................................................................... x
CHAPTER I
INTRODUCTION ........................................................................................................ 1
Significance of Study...................................................................................................................1
Statement of Problem...................................................................................................................2
Theoretical Frameworks ..............................................................................................................8
Social Network Theory: The Strength of Weak Ties...............................................................9
Image Theory .........................................................................................................................10
Typological Constructs ..............................................................................................................13
Information Search Behavior Thpology ................................................................................13
Organizational Learning Environment Typology..................................................................16

CHAPTER II
LITERATURE REVIEW ........................................................................................... 20
Market Orientation vs. Entrepreneurship Orientation ...............................................................21
Market Orientation vs. Entrepreneurship Orientation: How Does Each Strategy Influence
Employee Information Search Behavior?..................................................................................26
Employee Empowerment: Does It Moderate the Relationship between Market Strategy and
Information Search Behavior? ...................................................................................................33
Information Search Behavior and Its Effects on Organizational Learning................................38

CHAPTER III
RESEARCH METHODS ........................................................................................... 45
Rresearch Design .......................................................................................................................45
Sample Selection and Data Collection Procedures ....................................................................47
Measurements ............................................................................................................................48
Market Orientation.................................................................................................................49
Entrepreneurship Orientation.................................................................................................50
Employee Empowerment.......................................................................................................51
Information Search Behavior.................................................................................................51
Organizational Learning ........................................................................................................54
Data Analysis .............................................................................................................................54

CHAPTER IV
RESULTS AND DISCUSSION .................................................................................. 56
Demographic Characteristics of Sample....................................................................................56
Data Preparation and Screening.................................................................................................60
Reliability Tests .........................................................................................................................61
The Information Search Behavior Scale Validation ..................................................................70
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Confirmatory Factor Analysis: Convergent and Discriminant Validation ............................... 74
Structural Equation Model: Hypotheses Testing .......................................................................79
Market Orientation vs. Entrepreneurship Orientation ...........................................................80
Employees’ Information Search Behavior.............................................................................80
Employee Empowerment: Moderating Effect on Information Search Behavior...................83
Organizational Learning ........................................................................................................88

CHAPTER V
SUMMARY AND CONCLUSIONS .......................................................................... 95
Summary of Research Objectives, Findings, and Contributions ...............................................95
Managerial Implications ............................................................................................................98
Limitations and Recommendations for Future Studies............................................................101

APPENDICES ..........................................................................................................103
APPENDIX I: QUESTIONSNAIRE.......................................................................................104
APPENDIX II: COVER LETTERS ........................................................................................112
APPENDIX III: MISSING DATA ANALYSIS .....................................................................118
APPENDIX IV: DATA NORMALITY ANALYSES ............................................................132
APPENDIX V: CORRELATION MATRIX, MEANS, & STANDARD DEVIATIONS FOR
FULL MODEL: WHOLE GROUP, HIGH GROUP, & LESS HIGH GROUP......................140
APPENDIX VI: CORRELATION MATRIX, MEANS, & STANDARD DEVIATIONS FOR
FULL MODEL: WHOLE GROUP, HIGH GROUP, & LESS HIGH GROUP......................142

REFERENCES .........................................................................................................149

viii

LIST OF TABLES

Table 1 Sample Profile..................................................................................................................59
Table 2 Measurement Reliability..................................................................................................63
Table 3 Results for Higher-Order Factor Model: Information Search Behavior..........................73
Table 4 Confirmation Factor Analysis: Results for the Measurement Model ..............................77
Table 5 Structural Equation Model: Results for the Hypotheses..................................................92
Table 6 Moderating Effects: Chi-Square Difference Tests...........................................................94
Table A1 Univariate Statistics of Missing Data .........................................................................122
Table A2 Missing Items (More Than 5% of Respondents) ........................................................124
Table A3 Testing for Missing Completely at Random: EM Estimated Means ..........................125
Table A4 Separate-Variance t-Tests: Missing Value Analysis...................................................126
Table A5 Distributional Characteristics and Test of Normality .................................................136
Table A6 Assessment of Multivariate Normality .......................................................................139
Table A7 Correlation Matrix, Means and Standard Deviations for 2nd Order Factor Analysis of
Infformation Search Behavior......................................................................................................141
Table A8 Correlation Matrix, Means & Standard Deviations for Whole Group........................143
Table A9 Correlation Matrix, Means & Standard Deviations for High Group ..........................145
Table A10 Correlation Matrix, Means & Standard Deviation for Less High Group .................147

ix

LIST OF FIGURES

Figure 1 Conceptual Flow Model .................................................................................................19
Figure 2 The Relationships of Market Strategies (MO/EO) on Information Search and Learning
with Employee Empowerment.......................................................................................................44
Figure 3 Higher-Order Factor Model: Information Search Behavior...........................................72
Figure 4 Results of Structural Equation Model ............................................................................91

x

CHAPTER I

INTRODUCTION

Significance of Study
The value of human factors, such as each employees’ ideas or expertise is an important
focus of researchers in organizational studies since the 1960s; firms are more aware of the value
of their employees when they compare the investment they make in hiring, training, and
development with the cost of employee turnover and layoffs, (Altinay, Altinay, and Gannon,
2008; Huselid, 1995; Nauman, Khan, and Ehsan, 2010). Recent attention to the quality of
employee relationships as a potential source of competitive advantage helps explain the growing
interest in this topic. To enhance employees’ value in their work environment, organizations
focus on how management can facilitate employees’ concept of self-efficacy, an intrinsic
motivator, and how the organization can efficiently and effectively use the knowledge generated
through employees. Employees, who are motivated to assume enhanced job responsibilities and
are given autonomy with their scope of work, are more likely to freely express their ideas and
take risks without fear of being punished. These activities are viewed as the foundation of
creativity and innovation (Chan, Taylor, and Markham, 2008); therefore, organizations that seek
to differentiate themselves try to find ways to empower their employees. Psychologically
empowered employees, who tend to work beyond expectations exhibit “extra role behavior”
(Organ, 1988). Unlike a traditional command and control work environment, a cooperative and
supportive work condition engenders trust in each other, and based on trust, empowered
employees are likely to proactively take new directions (Chan et al., 2008).

1

New customers and emerging markets are continuously appearing in the marketplace;
consequently, firms’ efforts to search for creative/innovative ideas are critical to adapt to new
market conditions quickly and to increase market competitiveness. The increased attention to the
importance of creative and/or new market information/knowledge gathering and sharing is
emphasized in the innovation literature. Specifically, study results indicate that a firm may have
knowledge inertia when employees rely on and share knowledge that is only generated within the
firm; thus when they use a wide variety of knowledge/information from outside, it strengthens
the firms’ ability to innovate through enabling employees to generate and implement with novel
ideas (Barringer & Bluedorm, 1999; Cohen & Levinthal, 1990; Kimble, Grenier, and GoglioPrimard, 2010). Due to the intensely competitive market environment, savvy organizations feel
compelled to acquire market information so the firm can develop unique and innovative
initiatives. Therefore, it is logical to focus on empowering employees, who will likely
concentrate on increasing productivity by gathering information from diverse and creative
sources and who will assume a risk taking posture.

Statement of Problem
Because retailers offer a mix of services, products, and facilities directly to consumers, to
remain as sustainable business operations, they need to have competitive market and
management strategies that respond appropriately and/or creatively to consumer trends.
Knowledge of business strategies and management aspects that assist in capturing current and
future markets and that become embedded in companies’ business operations can help retailers
explore new markets or maximize business performance in existing markets. Studies mention
that a market focused culture/strategy guides how employees learn about market information
which provides them with knowledge-related resources to understand and better respond to the

2

market environment. As a result, these firms function at a high performance level (e.g.,
Atuahene-Gima & Ko, 2001; Deshpande, Farley, and Webster, 1993; Zahra & Covin 1993).
Two of the most widely studied business strategies are market oriented (MO) strategy
and entrepreneurship orientation (EO) strategy. A market-oriented business strategy requires
company employees to identify market trends, customer needs, competitor strategies, and to
respond to market intelligence (Kohli & Jaworski, 1990). Based on understanding of customers’
needs, competitors’ actions, and environmental trends, a market-oriented strategy responds to the
marketplace in a timely manner and this leads to better business performance (Slater & Narver,
1994). Entrepreneurship orientation (EO) stems from individual’s entrepreneurial traits such as
need for achievement, internal locus of control, risk-taking propensity, tolerance for ambiguity,
autonomy, and independence. Transforming such individual characteristics to organizational
behavior that enhances on firms’ innovative and creative marketing strategies is considered as
entrepreneurship orientation (Slevin and Covin, 1990). That is, entrepreneurship orientation
requires a focus on innovations that meet emerging and unarticulated customer needs to extend
into new product lines or markets; it adopts entrepreneurial efforts in innovation, renewal, and
venturing product and market development.
Since MO and EO strategies have different market focuses, we expect that the
information collected through a market orientation perspective different than information
collected through an entrepreneurship orientation perspective. However, previous MO/EO
studies have not distinctly examined the how each strategy influences the way firms integrate the
information, once it is collected. There are some criticisms about how employees in each strategy
seek market information. While some researchers comment that market oriented companies that
mostly target current markets may lead to ignorance of emerging market needs (Christensen,

3

1997; Deshpande et al., 1993), other researchers mention that entrepreneurship oriented firms
that take too much risk and develop new technologies which customers do not understand also
frequently fail to meet customers’ needs (Olleros, 1986; Tushman, Anderson, & O’Reilly, 1997).
Excessive amounts of market driven information can have negative effect on innovation.
Levinthal and March (1993) and Martin (1995) comment that customer-focused firms may have
myopic views (ignorance of long-run survival in preference of immediate pay-off) on strategies
and that these limits lead to adaptive rather than disruptive product development. Thus, the
authors conclude that a firm’s current market trend focus is not sufficient to capture radical
market opportunities due to the attention that they devote to current market needs. Christensen
and Bower (1996) argue that when a firm allocates its resources based on predominately on
current customer demands while ignoring allocation of resources for innovation, the firm’s
likelihood of failure increases. These criticisms provide a platform for examining how MO/EO
are related to employees’ information search behavior (relative to each strategy) and, because
information alone does not automatically yield superior performance for a firm, how such
information is absorbed and used throughout a company. When firm employees collect new
information, the firm must internalize it; understanding the ability to translate that information
into knowledge helps us extend research about relationships between MO/EO strategies and
other organizational behaviors.
Based on the recent managerial focus on empowering employees to increase the
likelihood of successful outcome behaviors, we are interested in the effects of empowered
employees on information search. Several studies found that employees’ creative characteristics,
complexity of the work, and supportive supervision enhance employees’ creative performance
(Oldham & Cummings, 1996; Ramus & Steger, 2000; Zhou & George, 2003). Specifically,

4

employees’ search for creative information and ideas is enhanced when employees have
supportive and non-controlling supervision. Amabile (1997) mentions that supervisors’ clear
goal setting and feedback, allowance of autonomy, good communication, and enthusiastic
support all nurture subordinate creativity. Therefore, empowered employees’ decision making
autonomy should render them to be less fearful of making mistakes and more willing to seek new
and unique ideas/knowledge, enhancing their creative performance in competitive market
conditions. Since supervisory support consists of supportive supervision and a low level of
control of subordinates, supervisors essentially empower employees by transferring their
legitimate power and control to subordinates while continuing to support them. Therefore, in our
study, supportive supervision is conceptualized as empowering employees and we further
examine how empowered employees affect the collection of market information and knowledge
in relation to both MO/EO strategies.
Since researchers recognized the positive effects of ability to learn on firm performance,
the value of organizational learning in relation to executing market strategies and other
organizational outcomes has been examined extensively (Day, 1994; Deshpande & Webster,
1989; Dickson, 1996; Hanvanich, Sivakumar, & Hult, 2006; Levinthal & March, 1993; Matsuno,
Mentzer, & Ozsomer, 2002; Sinkula, 1994; Slater & Narver, 1995). Organizational learning is a
firm’s tendency to create and use new knowledge/information; firms value learning of new
knowledge for future survival (Hanvanich et al., 2006). On the other hand, memory is a firm’s
stored and (thus) familiar knowledge/information and firms use memory for interpretation and
guidance for actions (Hanvanich et al., 2006). Specifically, Hanvanich et al. (2006) examine the
differences of organizational learning and memory with organizational innovativeness and
performance when market and technological environments are turbulent. Their study results

5

reveal that organizational learning affects innovativeness and performance more strongly than it
affects organizational memory, when environmental turbulence is high. However, inverse
relationships are found that when the environmental turbulence is low; memory has a stronger
effect on innovativeness and performance than on learning (Hanvanich et al., 2006). The results
explain that although different market conditions require different usage of organizational
learning and memory, somewhat proactive learning (creation and usage of innovative
knowledge/information) is critical in preparing for emerging market needs. When targeting
emerging and unknown market segments, proactive information search and learning are
important.
However, there still is a gap in the empirical study of organizational learning as a
consequence of knowledge/information search behavior (including both information sources and
search methods). Since information search behaviors rely on both MO and EO market strategies,
the firms’ success depends on how efficiently and effectively the collected information is learned.
Several studies discuss differences in organizational learning styles; firms have different
approaches to generate ideas, such as internal or external, and the different ways of idea
generation exhibit different organizational learning styles (Cohen & Levinthal, 1990; March,
1991; Yeung, Ulrich, Nason, and Gilnow, 1999). For instance, while some firms generate new
ideas from their own experiences, information, and knowledge, other firms try to search for
external experiences, information, and knowledge. Obtaining new information that is already
present in a firm’s own domain of knowledge may enhance the firm to master the accumulated
knowledge/information. In this case, generating knowledge/information is associated with the
refinement and extension of existing knowledge, which is closely related with exploitative
organizational learning. On the other hand, gaining of diverse and innovative knowledge, which

6

is not in the present domain of the firm’s knowledge, may require new skills or new ways
(experimentation) to observe the information/knowledge. This approach is similarly related to
the explorative organizational learning.
For survival in an intensely competitive market, organizations need to build their own
competitive advantages and protect them against competitors. Organizational resources,
knowledge/information, capabilities of learning, and application of it are the principal sources of
sustainable competitive advantage (Culpan, 2008). Studying the effects of MO and EO along
with organizational search for new information and organizational effort to accumulate and
integrate this new information into its own knowledge helps explain how a company builds a
competitive advantage. While previous studies investigated the relationship of organizational
strategies to firm performance, understanding of how organizations search for new information
and how they learn from that information is needed to clarify the differences/or similarities
between the firms’ MO and EO. Moreover, our study explores the effect of employee
empowerment on the search for new information. If the search for new information is influenced
by support of supervisors or managers, such empowerment enhances employee motivation and
autonomy in their work-related decision making; this could lead them to search for market
information in self-determined and more creative manner. Although market strategies guide
employees in their search for market information, the effects of employee empowerment
influence information search behaviors at the same time. Due to limited resources, companies try
to gather and use new information efficiently and effectively, thereby concentrating on either
cost efficiency or product differentiation; therefore, examination of information search based on
company market strategy and employee empowerment is necessary.

7

The results will assist the retailers in allocating in their resources effectively based on
their prevailing orientation strategy. Development of products and markets that generate better
returns or differentiate firms for the sake of increased performance is a continuous business goal;
we expect that retail firms can use our study results to make strategic decisions about market
strategy and management styles. As we focus on investigating the differential effects of market
strategy (MO/EO) on employee information search and information learning, our study enhances
the understanding of relationships among those factors and provides insights about development
of appropriate strategic and management decisions in new products or market expansion. Since
there is increased attention to the effects of employee empowerment in the work environment,
our results contribute to understanding of empowerment in search of new market information.

Theoretical Frameworks
Before examining each variable/construct of interest and developing hypotheses about
relationships, we provide theoretical perspectives behind the variable relationships. In bridging
the market strategies, employee information/knowledge search (with moderating effects of
employee empowerment), and organizational learning behaviors, we integrate theories and
construct typologies such as image theory (Hollenbeck, Ilgen, Phillips, & Hedlund, 1994;
Mitchell & Beach, 1990), social network theory (Granovetter, 1973; Rowley, 1997), information
search behavior typology (Grant, 1996a), and organizational learning typology (Argyris & Schon,
1978; March, 1991; Yeung et al., 1999). In our study, we attempt to explain the relationship of
market strategy and information search behavior with social network theory and image theory
and between information search and organizational learning environment with image theory.
Understanding the relationship of the variables based on proper theoretical explanations is

8

necessary to advance our scientific knowledge (Doty and Glick, 1994). Integration of social
network and image theories and information search and learning behaviors helped organize our
thoughts about the process and in this way, we believe, we could generalize our comprehension
and knowledge of the employee’s behavior.

Social Network Theory: The Strength of Weak Ties
Individuals and/or firms exist in a social network and interpersonal communication
structures and communication flows are identified within interpersonal network ties. Particularly,
Granovetter (1973) studied the influence of the degree of social network ties in relationship with
informational diffusion. According to him, the strength of interpersonal ties is defined by a
combination of several characteristics such as time, area, emotional intensity, intimacy, and
reciprocal services. If an interpersonal network consists of close family, friends, or co-workers in
the same department, this is an example of a strong tie network, because of their long time spent
together, in a same place, similar emotional intensity, mutual confiding, and reciprocal services
they perform for each other. Studies mention that such a strong group network results in little
variation in group norms and leads to a clear image and expectations (Bienenstock, Bonacich, &
Oliver, 1990; Rowley, 1997), and efficient and accurate communication (Rowley, 1997; Uzzi,
1996). On the other hand, people also have weakly related acquaintances, who are less likely
involved with each other; they form a low-density network (less inter-relational). Instead of
having similar backgrounds, this group of people has diverse cultures and backgrounds and such
diversity provides different and new information.
Informational diffusion is explained differently based on the type of network ties.
According to Rogers (1976), if the communication environment consists of strong ties,
communication is familiar and becomes common knowledge within the group due to their shared
9

commonalities; therefore the communication is effective and efficient. However since they have
a strong shared group norm and interlocking network characteristics, this communication
environment prevents receiving new ideas from sources outside the network. Contrary to strong
ties, individuals in a weak ties network are less resistant to risky or non-standard activities; they
get information from diverse parts of the network and they are able to access new information
and practices more easily than in a strong ties network. Since innovation comes from the
diffusion of new information or practices, the weak ties of a group network act as a necessary
step to the diffusion of an innovation. We attempt to explain how business market strategies
influence employee information search behavior. We employ social network theory in order to
explain MO/EO’s group network ties and its impact on information search behaviors. We
presume that the characteristics of market orientation and entrepreneurship orientation may
provide different information search preferences (e.g. collection through already known group of
people/sources vs. collection from unknown/diverse information sources). Overall, we assume
that business market strategies lead employees to have different information search behavior.

Image Theory
Image theory is used to explain employees’ and organizational decision making through
information search and learning behaviors. According to the image theory, the decision maker
has three different types of informational images that guide the decision makers’ behavior
(Beach, 1990; Mitchell & Beach, 1990). Those three images are: the value image, the trajectory
image, and the strategic image. The value image is the most abstract level of image and consists
of the decision maker’s beliefs, values, ethics, and morals. These principles guide the decision
maker’s behavior, relative to what is proper and appropriate. The trajectory image is the decision

10

maker’s aspired future and goals; it is what he or she hopes to become and to attain. The strategic
image is the way in which the decision maker attempts to attain the various goals of the
trajectory image.
Based on value, trajectory, or strategic images, two different types of decisions (adoption
and progress decisions) are evaluated through two test methods (comparability and probability
tests). An adoption decision is related to selecting new goals and plans for the trajectory or
strategic images while a progress decision is an evaluation of whether current goals and plans are
comparable with the trajectory image’s goals and plans. The comparability test eliminates
unacceptable choices while the probability test selects the best alternative. That is, adoption of
new goals/plans is made by eliminating unacceptable choices by comparability test or selecting
the best alternatives by probability tests. Progress of current goals/plans is evaluated by
comparability tests based on comparing of current and future images. Since both in adoption and
progress decisions use comparability test to measure the fit of current and future goal images, our
study focuses on the fit of current and future images in explaining differences of information
search types and learning methods.
Derived from the image theory explanation, few studies examine how a decision maker
compares current business plans (strategic image) to the trajectory image based on comparability
test (Hollenbeck et al., 1994; Richmond, Bissell, & Beach, 1998). Richmond et al. (1998) found
that when the current status quo (images) of supervisor and customer behavior and ideal
(trajectory) images are compatible, decision makers have little motivation to change their actions
while incompatibility between images leads them to prefer a change in actions. Hollenbeck et al.
(1994) also examined participants’ decision making behavior in their experimental study and
found that when there was a specific goal, which allows for less variability in outcomes, a

11

decision maker demonstrates risk avoiding behavior. When goals are less clear, allowing for
variability of the future image, participants make riskier decisions.
With respect to goals compared to the current image (status quo), translating image
theory into the context of decision making, it seems clear that the level of risk one is willing to
tolerate constitutes part of one’s strategic image. As such, this nature of one’s future aspirations
and goals should guide one’s choices regarding risk. Specifically, a clear image should promote
risk aversion, because a specific goal minimizes the acceptability of variability around the image.
The lack of a clear image, on the other hand, would support the acceptability of wide variability
in outcomes that are associated with risky decisions. We apply this rationale that when
organizations have specific goals (image), firms would like to show risk attenuated behavior
while firms would have risk seeking behavior if they have a less clear image of future goals.
Since market orientation (MO) provides market related information continuously,
employees’ search of new information is not much different from the current information, thus,
the new information search is clear and comparable and these lead to risk attenuated decision
making about the new information search. Also when newly collected information is not much
different from the information they already have, the learning method of it is risk attenuated way
such as benchmarking the best. On the other hand, entrepreneurship orientation (EO) needs to
search for emerging and originative information, the goal for new information search is not
comparable to their current goal. Hence, the image for new information search goal is not clear
and this leads them to take more risks in their search behavior. Also new information is not
comparable to the company’s current information, the decision to choose the learning such
information is likely to take more risks such as experimentation of it. We will discuss more about

12

this image theory in relation with information search behavior and organizational learning
methods.

Typological Constructs
Unlike a theory, which has a logical rationale and testable relationships among a set of
concepts, constructs, and variables, typologies classify schemes and taxonomies that categorize a
set of phenomena into clustering groups (Bacharach, 1989; Doty & Glick, 1994). In developing
our model based on market strategies and its relationship with other organizational variables, we
apply two typological constructs such as information search, and organizational learning
environment, to categorize the concepts more specifically.

Information Search Behavior Typology
Organizational knowledge management has been recognized as an important component
of organizational capital for more than a decade and research addresses how to effectively utilize
explicit and tacit information and knowledge in new product development processes (Cooper,
Edgett, & Kleinschmidt, 2004; Nonaka & Takeuchi, 1995; Zahra, 1993). In order to heighten the
firms’ competitive advantage, researchers consider knowledge as the principle resource and
focus on how to search, transfer, and integrate the knowledge into organizational memory
(Almeida, Phene, & Grant, 2003; Barney, 1991). Grant (1996b), specifically, emphasizes the role
of knowledge integration in the firm and mentions that “How knowledge is integrated to form
organizational capability, and goes on to identify characteristics of capabilities which are
associated with creating an sustaining competitive advantage in dynamically-competitive
markets…is the heart of knowledge-based theory” (p. 375).

13

Grant (1996a) discusses the types of knowledge integration and differentiates among
them. He proposes that the integration of knowledge or information is a critical factor in
organizational capability and is formed in terms of benefits derived from different integration
methods: efficiency, scope, and flexibility. Efficiency of knowledge integration is measured by
how well firms manage the cost (economies of scale perspective) of collecting and identifying
knowledge. There are three factors affecting the efficiency of information integration: the level
of common knowledge, the frequency and variability of task performance, and organizational
structure. Common knowledge, vocabulary, and experience among employees increase
communication ease, which leads to efficient integration of knowledge (Monge, Rothman,
Eisenberg, Miller, & Kirste, 1985; Tushman & Katz, 1980). The efficiency of organizational
communication also depends on the frequency of the pattern of coordinated activity. While more
frequently repeated organizational routines increase the efficiency of information integration,
variation in routines reduces efficiency. Lastly, efficiency of information integration is related
with an organization’s coordinated structure and level of task difficulty. A more coordinated and
centralized organizational structure increases the efficiency of information integration for simple
tasks, maximizing the affordable communication of knowledge, while a multidivisional
organizational structure increases efficiency of information integration for complex tasks,
achieving higher levels of coordination with lower levels of communication.
However, the pursuit of efficiency in information integration will reduce the diversity of
information; the knowledge of coworkers may be redundant (Granovetter, 1973; Granovetter,
1983; Szulanski, 1996; Teigland & Wasko, 2003). Grant (1996a) also warns that the use of
common knowledge and vocabulary derived from different specialists in order to communicate
would cause loss of some amount of specialty information. Thus, the knowledge available

14

through coworkers is likely to be limited and superfluous, and will likely decrease the ability to
develop new and creative ideas. The focus on efficiency of information integration through
common language and frequent interactions thus reduces the collection of creative and unique
information.
The scope of information integration refers to the different types of specialized
knowledge being integrated. The breadth of organizational knowledge will increase competitive
advantages due to the addition of specialized knowledge to the organization and the difficulty of
imitating the knowledge among competitors (Dierickx & Cool, 1989; Lippman & Rumelt, 1982).
Generally speaking, the wider the scope of information being integrated (and hence, the greater
the diversity of the individuals involved), the lower the level of common knowledge, and the
more inefficient the communication and integration of information.
Finally, flexibility of information integration is a firm’s capability to extend knowledge
while reconfiguring existing knowledge. That is, a firm’s competitive advantage comes from the
firm’s continuous renewal of knowledge. The flexibility of information integration is the way in
which a firm can access additional knowledge and reconfigure existing knowledge. This
flexibility can be understood by Low and MacMillan (1988)’s remark that, “It is useful to stop
occasionally, take inventory of the work that has been done, and identify new directions and
challenges for the future” (p.139). The process of introspective information search, such as
reconfiguring already used/collected information and integrating this prior information into new
information, which helps firms prepare for new development, is defined as flexibility of
information integration. Further, Grant (1996a) mentions that this flexible information
integration, either through continuous integration or constant reconfiguration of existing

15

information on product, process, or strategic involvement with other departments, leads to new
directions, which is defined as architectural or strategic innovation.
As Grant (1996a) explained, knowledge integration is associated with the firms’
capability or performance; we applied his rationale in that MO and EO’s information integration
is part of a firm’s specific capabilities and it impacts performance. Based on Grant’s (1996a & b)
knowledge integration, we could propose different information search behaviors for market
orientation and entrepreneurship orientation. In explaining the relationship between business
market strategies (MO and EO) and information search behaviors (Efficiency, Flexibility, and
Scope), we employ social network theory’s strong and weak interpersonal communication
networks ties and image theory’s explanation of the degree of goal specificity.

Organizational Learning Environment Typology
The original framework for organizational learning is derived from Cyert and March
(1963) and Argyris and Schon (1978). According to these two studies, organizational learning
takes place when there is a gap between performance expectations and actual performance. If
performance is lower than expectation, companies try to learn new knowledge in order to
increase their competence. Organizational learning consists of two types: single-loop (adaptive)
and double-loop (generative). If the learning process and new information changes only activities
within existing company norms or policies, then it is said to be single-loop or adaptive learning.
Single-loop learning adds knowledge without changing the organizational nature. The changes
precipitated by single-loop learning result in sequential improvement (Dodgson, 1993; Slater &
Narver, 1995). Single-loop learning is lower-level (Fiol & Lyles, 1985), adaptive (Senge, 1990),
and non-strategic (Mason, 1993) learning. However, if the learning changes organizational

16

norms or policies, and raises questions about the firm’s mission and market strategies, then it is
considered double-loop or generative (Argyris & Schon, 1978; Cyert & March, 1963; Sinkula,
1994; Slater & Narver, 1995).
In a more recent framework, March (1991, 1995) defines organizational learning in two
ways: as exploitation and as exploration. His view is that organizational learning, in relation to
allocation of resources, increases the competence of the firm. He states that exploitative learning
is the “refinement and extension of existing competences, technologies, and paradigms. Its
returns are positive, proximate, and predictable. The essence of exploration is experimentation
with new alternatives. Its returns are uncertain, distant, and often negative” (1991, p.85). In
consideration of exploitative learning’s focus on improvement of current competency and
explorative learning’s focus on new alternatives, most of which are new to the market, they are
similar to the previous views of adaptive and generative learning, respectively.
More recently, two exploitative and two explorative organizational learning environments
are discussed and measured by Yeung et al., (1999). The authors specifically focus on a
company’s approach to generate new ideas from diverse sources. Organizational learning takes
place when companies try to generalize newly created knowledge by codifying it and sharing, or
transferring it from one department or site to another. Those who use exploratory learning are
considered experimenters (innovators) and competent acquirers (skilled workers) while those
who use exploitative learning include benchmarkers (copiers), and continuous improvers
(experts).
Experimenters are those who create new ideas and learn them through their direct
controlled experimentation. Firms are able to learn state-of-the-art competencies through training
employees, recruitment of skilled employees, or through strategic alliances. In these two learning

17

typologies, companies’ new competitive advantages are created through innovative
product/process development. That is, through experimentation and new skill acquisition, which
are associated with originality, innovation, and uniqueness, companies try to meet the emerging
needs of customers and to explore the new possibilities.
The benchmarking and continuous improvers are the other two different learning
typologies that organizations use to discover new knowledge and ideas. Through benchmarking,
organizations learn by adopting and/or adapting the best practices into the organizations’ own
knowledge base. Organizations also learn by continuous improvement of existing practices. In
discovering of new opportunities, this learning type emphasizes mastering of existing
competences and experiences. Exploitation of external and internal existing practices is another
way organizations approach learning of market trends. In our study, we argue that organizational
learning differences are due to the firm’s depth and diversity of information as March (1991) and
Cohen and Levinthal (1990) mentioned. We use image theory to explain how firms choose their
learning environment appropriately as exploitative or explorative based on their
information/knowledge sources.
In the next section, using theoretical and typological underpinnings of social network
theory, image theory, information search behaviors, and organizational learning types, we
examine the organizations’ two market strategies: market orientation and entrepreneurship
orientation. In discussion of employees’ search of information, we try to differentiate employee
search effort based on employee empowerment; employees are more motivated and show
proactive behaviors when they are empowered. In their information search, we argue that the
more empowered employees search for the more diverse and innovative information. With use of
the theories and typologies, we believe, we could provide organized thoughts and views of firm

18

effort (through their employees) to increase competitive advantages in the marketplace through
proper implementation of market strategies and information search and learning styles. A model
for the conceptual flow is given in Figure 1.

Figure 1. Conceptual Flow Model

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CHAPTER II

LITERATURE REVIEW

In this chapter, we review the literatures which are relevant to our study. In order to
explore the relevance of orientation strategy (MO or EO) in the retail industry, our first focus is
to review previous MO and EO studies. The conceptual development of MO and EO studies and
how these strategies have been used in relation to other organizational variables are discussed in
the first section. Next, we examine the effects of MO and EO on retailers’ information search
behaviors (efficiency, flexibility, and scope) when they develop new product lines and new
markets. According to social network theory and image theory, the collection of diverse
information/knowledge is accomplished through a weakly tied social network system with a less
specified future image (goal/s); collection of a deeper form of existing information is
accomplished via a strongly tied group with a specifically well-defined future image (goal/s).
Thus, we discuss that how organizational social network ties are related to MO/EO and how
network ties influence the depth and breadth of information search when developing new
markets and product lines. Then, we review the moderating effects of employee empowerment
on the relationship between market strategy and information search behavior in new
product/market development. We argue that the empowerment of employees moderates the
magnitude and direction of the relationship between market strategies and employee search
behavior. Empowered employees’ proactiveness in their work decision making is expected in
new information/knowledge search. In the last section of this chapter, we discuss about the

20

organizational learning environments (exploitative and explorative), and how they are related to
information search behaviors.
With regard to the two market strategies (MO and EO) and their effects on employees’
information search and learning behaviors, we examine how retail employees incorporate a MO
and/or EO in their work environment. Several studies confirm that the firms’ strategic postures
are reflected in the organization members’ internal practices and procedures (De Clercq & Rius,
2007; Tortosa, Moliner, & Sanchez, 2009; Ullrich & Van Dick, 2007); employees’ perceptions
of both strategies are analyzed in our study. Thus, we examine the employees’ perceptions of
market strategies (MO/EO) first. Based on the level of each type of market strategy (MO/EO) as
perceived by the employees, we test its relationship with information search behaviors and
organizational learning environments.

Market Orientation vs. Entrepreneurship Orientation
Market orientation is a firm’s orientation toward the collection, dissemination, and
response to market intelligence (Kohli & Jaworski, 1990); the firm’s market focused information
processing activities are essential to create superior products and services to meet customer
needs. Previous studies provide different interpretations and approaches about the market
orientation (MO) concept. The first group of studies views market orientation as an
organization’s market information processing activities; market-oriented companies generate
information about current customers and competitors, disseminate it, and respond to market
intelligence (Jaworski & Kohli 1993; Kohli & Jaworski, 1990). The second stream of studies
views MO as an organizational culture; a market-oriented organizational culture creates
behaviors among employees to generate market information and thus return superior value, such

21

as innovative products, to the customers (Narver & Slater, 1990; Slater & Narver, 1994). They
define that market orientation consists of three behavioral components (customer orientation,
competitor orientation, and interfunctional coordination) and two decision-making criteria (a
long-term focus and a profit focus) (Narver & Slater, 1990). The last stream of studies mentions
that customer orientation and innovative business strategies, which are well accorded in
corporate culture, have a measurable impact on business performance; these three factors
(customer orientation, organizational innovativeness, and organizational culture) are considered
as measures of MO (Deshpande et al., 1993).
While all streams of studies include organizations’ seeking of customer related
information as a major part of market orientation strategy, the second and the third group of
studies consider MO as the organizational culture which influences other firm behaviors. Despite
the fact that each scale represents the specific different perspectives of MO, there are
overlapping concepts among the scales and it is evident that the customer focused business
strategy is a common factor of any market orientation measure.
While different conceptualizations of MO exist, Deshpande and Farley (1998) compared
those three MO scales’ robustness and generalizability. In order to evaluate the MO scales’
standardized operationalizaton and its robustness across the different countries, the authors
compared 15 items from Narver and Slater (1990), 20 items from Kohli, Jaworski, and Kumar
(1993), and 9 items from Deshpande et al. (1993). They used a sample of 82 marketing
executives from three Marketing Science Institute (MSI) meetings, consisting of 19 American
and 8 European companies. Reliability of all three scales in different cultural settings, such as
industrialized or industrializing countries was acceptable (i.e. Cronbach’s α’s ranges from .67
to .90 for US, and .61 to .83 for Europe. They tested the generalizability of the three scales

22

across consumer-, industrial-, and service-type goods in predicting subjective performance
measures; there were no significant differences across industry types on any of the three MO
scales. These results support that no substantive differences exist among the three MO scales and
support the generalizability of MO in different cultures and in different industries.
Based on the development of the MO construct, overall, market orientation enhances
employee commitment and team sprit to satisfy customer needs, market oriented firms constantly
monitor the market environment and they fulfill the needs of target customers with appropriate
products. That is, market orientation yields higher performance by unifying employees and
organizations, leading to the efficient gathering of market information and responding to market
needs (e.g., Jaworski & Kohli, 1993; Lumpkin & Dess, 1996; Matsuno, Mentzer, & Rentz, 2005;
Pelham, 1999; Slater & Narver, 1994). In particular, studies show that top management’s
emphasis on a market oriented mind, interdepartmental connectedness, organization’s nonauthoritative structure, and market-based reward systems are significant antecedent factors of
MO (Day, 1994; Kirca, Jayachandran, & Bearden, 2005; Matsuno et al., 2002). As the degree of
market orientation increases, customer perception of products and services quality (Brady &
Cronin, 2001), implementation of new products and ideas (Han, Kim, & Srivastava, 1998; Hult
& Ketchen, 2001), and employee commitment to the firms (Siguaw, Brown, & Widing, 1994)
are all improved.
Like market orientation, the effect of entrepreneurship and firm behaviors has been
examined vigorously. Entrepreneurship orientation consists of three components: innovativeness,
risk-taking, and proactiveness (Covin & Slevin, 1989; Miller, 1983). Miller (1983) comments
that entrepreneurs seek innovation and new opportunities and that these tendencies lead to
constructive risk-taking. Lumpkin and Dess (1996) define EO as “the processes, practices, and

23

decision making activities that lead to new entry (p. 136).” In addition, a company with an EO
engages in “product-market innovation, undertakes somewhat risky ventures, and is first to come
up with 'proactive' innovations, beating competitors (Miller, 1983, p. 771).” In an entrepreneurial
organization, employees value autonomy and freedom which enable them to pursue creativity,
risk taking, and exploration in the development of innovative ideas. To obtain competitive
advantages, the entrepreneurial culture encourages employees to look for innovative changes in
their product and/or process development. They define entrepreneurial spirit as “pursuing new
opportunities by participating in emerging markets” (Lumpkin & Dess, 1996, p.146). That is,
organizations’ entrepreneurial orientation encourages employees to pursue creative and new
projects (which are believed to be risky) more frequently than companies without an
entrepreneurial orientation. We could assume that entrepreneurship orientation results in creative
decision-making, especially in the search for new opportunities.
In Miller’s (1983) conceptualization, the three entrepreneurial characteristics
(innovativeness, risk taking, and proactiveness) are considered as antecedents of an
organizational structure to find new business opportunities, to create superior customer value,
and to improve business performance (e.g., Barringer & Bluedorn, 1999; Covin, 1991; Covin &
Slevin, 1989, 1991; Deshpande et al., 1993; Slater & Narver, 1995) . Many studies find that
market- and entrepreneurship-oriented strategies are closely related in their positive influence on
company performance (ex., Atuahene-Gima, 1996; Matsuno et al., 2002; Morris, Coombes,
Schindehutte, & Allen, 2007; Slater & Narver, 1995). Researchers view that the orientations are
somewhat associated since they engage in proactive market sensing activities (anticipating
customers’ current and future needs and responding to those needs proactively) and they
positively influence firm performance. Morris et al. (2007) find a weak but positive correlation

24

coefficient (r = 0.18, p < .05) between client focused market orientation and entrepreneurship
orientation in non-profit organizations. The authors explain that the market oriented effort of
continuous search helps the company serve clients better and yields better performance, but at
the same time, an innovative entrepreneurial nature enhances non profit organizations’ ability to
anticipate unmet customer needs, leading to proactive opportunity identification.
Matsuno et al., (2002) also examined the relationship between MO and EO with
marketing executives of 1300 U.S. manufacturing companies. In their study, however, they argue
that entrepreneurial characteristics facilitate employees’ willingness to search and learn about the
market information. Especially, innovativeness, as one of entrepreneurial firm characteristics,
requires extensive collection of information, because innovativeness is shaped by the diffusion of
new information and this is accomplished by new information sharing and utilization (MO
characteristics). They found significant and positive path coefficient (.468) for direct relationship
from entrepreneurial proclivity to market orientation; the authors conclude a causal effect of
EO’s characteristics leading on MO’s information generation, dissemination, and responses. In
order to complete a causal effect, however, counterfactual dependence must be confirmed (Pearl,
2009) such as without entrepreneurial characteristics, there is no market oriented information
search behaviors. However, as MO studies argue that MO alone has its own tendency to search
for market information and organization-wide sharing, thus, the causation relationship between
EO and MO is weak and this relationship is still needed more investigation.
Entrepreneurial orientation tends to engage in a great deal of information scanning
activities with innovative, proactive, and risk-taking while market orientation constantly gathers
market trends and consequently shares and utilizes the information. Although MO and EO may
guide employees to search differently for new/innovative information, the efforts in seeking

25

unmet customer needs, renewing the company products appropriately, and providing superior
customer value are viewed as shared characteristics of both MO and EO. Therefore, we
hypothesize that;
H1:

Employees’ perceived market orientation and employees’ perceived entrepreneurship
orientation are directly related.

Market Orientation vs. Entrepreneurship Orientation: How Does Each Strategy Influence
Employee Information Search Behavior?
MO studies view that better organizational performance results when an organization
collects market information, disseminates it through employees in various departments, and
develops a market-oriented culture. Market orientation provides a unifying focus for the
employees and the organization and leads to efficient gathering of and responding to market
intelligence. To understand the effect of market orientation on a company’s performance, Day
(1994) emphasizes the value of information, which is collected as part of a company’s marketoriented strategy. He says “a market driven culture supports the value of thorough market
intelligence and the necessity of functionally coordinated actions directed at gaining a
competitive advantage” (Day, 1994, p.43). Day views market-driven companies as well
positioned to anticipate and respond to customers’ needs, thus giving them a competitive
advantage.
However, because market-oriented companies focus on current customer needs, their
ability to prepare for new market opportunities has been challenged. Since market orientation
focuses on current customers and competitors, researchers argue that market-oriented
companies’ targeting only current markets may lead to ignorance of latent and/or emerging
market needs (Argyris, 1994; Christensen, 1997; Deshpande et al., 1993; Grewal & Tansuhaj,

26

2001; Hamel & Prahalad, 1994). Likewise, Grewal and Tansuhaj (2001) state that “highly
attuned market orientation would cause firms to lock into a standard mode of cognition and
response, thereby building inertia instead of the creative thinking” (p.70). In contrast to the
generally asserted fact about positive effects of customer oriented market implementation, these
studies argue the negative side of customer-oriented market information in the development of
new products/markets.
EO studies consistently mention the value of information in the execution of
innovativeness. Employees with a high level of EO are aggressively willing to tackle high-risk
projects which usually have a high uncertainty of success. EO firms tend to have a futuristic,
unidentified market focus, and to gather research on innovative products/markets, they need
extensive information to make decisions (Lumpkin and Dess, 1996; Shane, 2000). A focus on
future markets and innovations directs employees to be concerned with emerging and
unarticulated customer needs and maintain a competitive advantage through extension of new
product lines and/or market development (Barringer & Bluedorn, 1999; Covin & Slevin, 1989;
Lumpkin & Dess, 1996; Zahra, 1993). We believe that organizational information/knowledge
search in the development of new products/market is implemented through the firm’s market
strategy or strategic goals. Drawing on social network theory (strong and weak ties), we presume
that MO’s organization-wide focus on market demands and its response to the market needs
builds a strong group norm leading employees to collect, disseminate, and respond to market
information in an effective manner; the seamless and efficient information exchange within the
firm guide employees to work in unison. On the other hand, because entrepreneurship orientation
involves being innovative, proactive, and risk-taking, we assume that the entrepreneurship
oriented work environment is generally less structured and less resistant to obtaining radically

27

innovative information. Consistent with our assumption, researchers suggest that entrepreneurial
firm employees, who are seeking unknown market information, tend to search market
information more broadly than market oriented firm employees, who know what to search (they
have the prior market information). This demonstrates the entrepreneurial firm’s diverse market
opportunity search, when they are searching for unfamiliar information (Chandra, Styles, &
Wilkinson, 2009; Short, Ketchen, Shook, & Ireland, 2010). Especially, Chandra et al. (2009)
interviewed chief executive officers, owners, founders, and directors from eight small mediumsized enterprises (SMEs) in Australia and examined the process of opportunity recognition, firm
characteristics, and success and obstacle factors in international operations. The results show that
the search activities of entrepreneurs, who do not have prior knowledge about the market, are
more like a discovery than a deliberate search and they found entrepreneurial characteristics are
important success factors. In a study of Short et al., (2010), they review previous
entrepreneurship research and attempt to identify “new entrepreneurial opportunity” and its
antecedents and outcomes. As antecedents of new entrepreneurial opportunity, they mention that
opportunity finders think more deeply, engage in more search activities, and use different
analysis of data than non-finders/nonentrepreneurs. In order to explain such differences, the
authors cited Dyer, Gregersen, & Christensen’s (2008) study that firms’ different abilities of
questioning, observing, experimenting, and idea networking lead them to generate outcomes in a
variety of ways. Thus, our investigation of EO’s market information search behavior compared
to that of MO’s will add richness to this field.
Now we discuss in detail that how market strategy directs employees to search for
specific market information. Due to different characteristics of MO and EO, especially in their
different target markets and risk taking tendencies, collection of market information would be

28

different. MO employees collect information from current customers, competitors, and others in
the market environment (such as distributers, manufacturers, etc.). While market orientation
responds to expressed customer demands, entrepreneurship orientation is proactively involved
with innovative market information which may be risky (e.g., Deshpande et al., 1993; Tushman
et al., 1997). Thus, we expect that employees will search for different types of information to
develop new product lines and markets. EO’s search of information is likely about unexpressed
and emerging trends and their marketing goals and returns are relatively unknown compared to
that of MO.
Specific to our study’s focus on retail companies, retail organizations are aware of the
value of information in their product assortment decisions. For staple products, retailers
traditionally search the past season’s sales data when they prepare assortment plans for the next
season. They search for and collect explicit and codified information (e.g., sales, markdown
records) from their own operational system or from the vendors’ market trend analyses and they
consider that information in preparing assortment plans for the next season (Xu, 2003). However,
when more risk is involved for trendy products, employees seek diverse and/or different
information (Kogut & Zander 1992). In an effort to differentiate themselves, retailers engage in
product development and they gather all relevant information for their development decision
making . When developing a new product line or a new market, retailers collect implicit and tacit
information from personal and professional sources such as professional social networks, market
research, or trade publications.
Information from different sources is transferred throughout the organization at different
rates. According to Nonaka (1994), explicit and codified knowledge, such as computerized or
documented knowledge, is easily communicated among employees while tacit or contextual

29

knowledge, such as expertise, skills, or know-how, is more difficult and costly to transfer, so
firms integrate the information differently. However, while previous studies explain the
importance of human assets and how important they are to accessing such explicit and tacit
information through the knowledge management process (e.g., Barringer and Bluedorn, 1999), it
is not clear how different information is used in the new product development process, and how
it is influenced by market orientation and entrepreneurship orientation market strategies.
Grant’s (1996a) taxonomy of information integration and other knowledge management
studies, confirm that companies use different methods of information search and collection
(information integration) based on their market strategies. Since MO and EO have different
market approaches, the types of new information about the market they seek to enter would be
different. A market oriented strategy continuously senses and responds to market trends in a
timely manner, so the market information seems familiar. The information collected in this way
may be incremental new market information, but not radically different from previously
collected information, hence new product development goals can be clear and precise.
According to image theory, the decision maker has hierarchically arranged images, and
his decision making follows the image which is set from the higher image to the lower image.
That is, a decision maker has a current image for new projects/plans (strategic image) and
compares those to the aspired goals (trajectory image), and further to the values, beliefs, and
principles (value image) of the decision makers. While pursuing from the lower to the higher
goals, decision makers screen and evaluate the alternatives based on the compatibility and
profitability tests. With image theory, Hollenbeck et al. (1994) examined risk decision making.
They viewed that the risk taking decision depends on the degree of goal specificity. With a
specific goal (clear and precise goal), risk is attenuated due to less variability around the goal. On

30

the other hand, with a less specified and unclear goal, there is variability in outcome achievement
and a greater likelihood that risk taking is involved. Hollenbeck et al. (1994) found that
participants’ likelihood of making a high risk decision is greater when they are given a do-yourbest-goal than a specific goal. These results and logics lead us to explain why MO and EO
collect information differently.
In consideration of retailers’ market orientation perception as the value image, their
information search decision can be explained with image theory. The market oriented retailers’
trajectory image (goal) is responding to current market needs and wants, thus the goal is
somewhat specific and clear because they deal with similar customers and competitors due to the
continuous market sensing activities. As a strategic image, retailers search for new information
in a risk attenuated manner since the retailer has a specific goal in mind due to looking for
similar market information using similar search methods. Therefore, less risky information
collection is appropriate for market oriented retailers. We propose that efficient search for
information, such as looking for similar routines and/or watching for the best practices in
previous performance or in competitors’ activities, is positively related to market orientation
perception.
In contrast, entrepreneurship orientation (value image) motivates retailers to focus on the
future and unknown markets and emphasizes initiative and bold actions (trajectory image); the
information search decision (strategic image) more likely involves taking risks. Due to less
clearly specified goals, retailers are likely to accept a wide variety of outcomes which is
associated with risky decision making. Also, because of their entrepreneurial initiative and
preference for bold action, they seek broad/diverse sources of information which are not

31

conventionally used in market analysis. They may try to generate new and innovative ideas
through continual integration of new knowledge or reconfiguration of existing knowledge.
Overall, a market-oriented firms’ search for new information for new product/market
development is similar to Grant’s (1996a) efficiency information integration typology;
companies collect information in a cost efficient manner when their employees have common
knowledge, vocabulary, and experiences. On the other hand, an entrepreneurship-oriented focus
is on unknown and emerging future markets, so more differentiated and innovative information
is sought. EO companies have innovative, risk taking, and proactive product development
characteristics (Deshpande et al., 1993; Slater & Narver, 1995) that stimulate the quest for more
diverse information. Lumpkin and Dess (2001) compare EO’s proactiveness, which refers to
firms’ market opportunity seizing activities, and competitive aggressiveness, which refers to
firms’ reaction to current market competitive trends, examining with their impact on
performance outcomes. One hundred twenty four executive-level respondents from 94 firms
were selected from a commercial database of business marketing. The authors found that firms’
proactive search of market opportunities have positive and strong impacts on sales growth,
profitability, and return on sales while competitive aggressiveness is negatively related to sales
growth. These results imply that EO’s proactively searching for market opportunity is important
as opposed to simply responding to current market trends.
In relating two market strategies (MO and EO) and employee information search
behaviors, we argue that market orientation strategy’s continuous market sensing of current
market information is related to Grant’s (1996a) efficiency information search behavior.
Flexibility of information search behavior is reconfiguring existing information plus enlargement
with new information. On a continuum, flexibility of information integration falls between

32

efficiency and scope. Because flexibility involves refining and extending already existing
information while adding new information, we assume that such flexibility is related to both MO
and EO. MO is somewhat related to flexibility while EO is also somewhat related to flexible
information search behavior. Finally, entrepreneurship orientation’s diverse information search is
closely related to scope information search behavior. Thus, we hypothesized that:
H2:

There is a positive relationship between market orientation/entrepreneurship orientation
and the information search process.
H2a: Employees’ perception of market orientation is positively related to the
efficiency of information search.
H2b: Employees’ perception of market orientation is positively related to the
flexibility of information search.
H2c: Employees’ perception of entrepreneurship orientation is positively related
to the flexibility of information search.
H2d: Employees’ perception of entrepreneurship orientation is positively related
to the scope of information search.

Employee Empowerment: Does It Moderate the Relationship between Market Strategy and
Information Search Behavior?
Information is the core factor for development of a new product line or new market.
Gathering and analyzing information to assist in decision making is critical to a firm’s innovative
performance outcomes. Thus, understanding how firms enhance employee motivation to engage
in new information search activities may be important. Previous studies reveal that creative
employees have more innovative problem-solving styles. They are more self-driven, enthusiastic,
and willing to search for information sources in the new product development process (Amabile,
Conti, Coon, Lazenby, & Harron, 1996; Barron & Harrington, 1981; Cummings & Oldham,
1997). Employees with creative personalities are more attracted to complex jobs because these

33

jobs allow them to use a variety of knowledge and information, to have more freedom in
completing their work, and to have less supervisory control over their activities (Amabile &
Gryskiewicz, 1989; Baer, Oldham, and Cummings, 2003; Cummings & Oldham, 1997).
Therefore, employee creativity is strongly related to information search behavior and firm
management should focus on enhancing employee creativity.
The study of supportive leadership examines how supportive supervisors or team leaders
improve the performance of their subordinates (Bass, Avolio, Jung, & Berson, 2003; Gerstner &
Day 1997; Lynch, Eisenberger, & Armeli, 1999). Supportive supervisors are willing to help
subordinates. They focus on enhancing relationships with subordinates through participatory
decision making which reflects a strong perception of support to subordinates, and offers the
motivation that results in high quality work. Supportive supervisors show concern for
employees’ feelings and needs, encourage them to voice their own concerns, and provide
positive and informational feedback while cultivating subordinates’ skill development (Deci &
Ryan, 1987). Studies also find that when employees receive regular feedback on goal-related
performance, their performance rises (Locke & Latham, 2002; Ludwig & Geller, 1997). In sum,
supportive supervisors encourage subordinates’ self-determination and initiative at work,
increasing both their interest in work and their creativity.
Studies also examine how well supportive supervision works for different groups of
employees. Many studies suggest that supportive supervision is effective for creative employees.
Cummings (1978) suggests that a supportive leadership style is particularly effective for selfregulating work groups, such as R&D professionals (buyers/planners/product develop positions
use R&D skills in their jobs) who need freedom of choice and autonomy in work and an
atmosphere that encourages personal growth and development. Similarly, Glassman (1986)

34

comments that R&D managers are likely to set the direction for work, and consequently, are
likely to take risks; thus the supportive management style is especially important for this group.
Since supervisory support provides employees’ autonomy and self-confidence in their
work, the supervisors empower employees to be independent decision makers. Empowerment is
a social exchange-driven process which operates under a norm of reciprocity (Gouldner, 1960).It
requires managers to release some power and control to subordinates and use cooperation in
relating with their subordinates (Spreitzer, 1995). Due to delegated authority, subordinates feel
empowered and exhibit proactive and autonomous decision making (Mayer, Davis, &
Schoorman, 1995).
Spreitzer (1995) defined psychological empowerment in the workplace with 4 subfactors: meaning (how important the job is to them), self-determination (autonomous decision
making), competency (confidence to accomplish their job) and impact (their ability to influence
on other in the work unit). While the author validates the construct, he suggests two
consequences of empowerment, such as managerial effectiveness (the degree of a manager
fulfills or exceeds the work role expectation) and innovative behaviors. Thus, more empowered
employees have greater self-efficacy and they are likely to be innovative to achieve success.
Spreitzer’s (1995) study of mid- and low-level employees from industrial organizations found
the two consequence factors to be significant. Chan et al., (2008) examined the employee
empowerment process with five independent factors: two organizational factors such as
decentralized structure and innovative culture and three supervisory supports such as resource,
information, and social-political supports. They found that except for organizational structure,
these factors are significantly related to subordinates’ trust and this further leads to
empowerment and organizational citizenship behavior. These results show that supervisory

35

support is a good predictor of employee empowerment and once employees are empowered, it
motivates them to have extra roles for the organization beyond their responsibilities. Thus, while
market strategies have a specific impact on employees’ market information search, the degree of
employee empowerment would change this relationship. We hypothesized that:
H3:

There is a moderating effect of employee empowerment in the relationship between
market strategy (market orientation/entrepreneurship orientation) and types of
information search (efficiency/scope/flexibility).

Overall, empowered employees are more likely motivated to accept extra work and show
highly innovative and creative work performance. Previous studies have shown that supervisors’
supportive and non-controlling attitudes increase employees’ autonomy and confidence in their
work environment, because supportive supervisors show concern for employees’ feelings and
they give positive feedback on the subordinates’ work (Cummings & Oldham, 1997; Deci &
Ryan, 1987; Zhou, 2003). A supportive managerial style, which results in feelings of
empowerment, helps subordinates to have increased intrinsic task motivation in their work role.
As Spreitzer (1995) defined four factors of employee empowerment (meaning, competence, selfdetermination, and impact), empowered employees are proactive in their work responsibility.
Due to their competence and self-determination, empowered employees are more likely involved
with broad and diverse information search and to take risks in decision making. Feelings of
empowerment release them from feeling constrained to use the companies’ traditional rules or
traditional information sources. Based on these previous studies, our study examines the
importance of employee empowerment, in relation to retail buyers’ information search behavior
when developing new product lines or markets. While we propose that employees information
search behavior is influenced by market strategies, we further examine whether employee
empowerment strengthens/weakens the relationship between market strategies (MO/EO) and

36

information search behaviors (search in efficiency, breadth/diversity, flexibility). We now
discuss how employee empowerment affects the relationship between retail buyers’ information
search behavior, especially as they develop new product lines or markets.
Thus, we assume that employee empowerment strengthens the relationship between
entrepreneurship orientation and employees’ search for various and innovative information while
the relationship between market orientation and employees’ search for efficient and routinely
repeated information would be weakened by employee empowerment. Empowered employees,
who are encouraged to have more confidence, autonomy, and creativity through supervisors’
support, are likely to avoid repetitive traditional search methods or use of the same sources of
information. Thus, we hypothesized that:
H3a: The stronger the degree of employee empowerment, the weaker relationship
between market orientation and the efficiency dimension of employee search behavior.
H3b: The weaker the degree of employee empowerment, the stronger relationship
between market orientation and the efficiency dimension of employee search behavior.
H3c: The stronger the degree of employee empowerment, the stronger relationship
between market orientation and the flexibility dimension of employee search behavior.
H3d: The weaker the degree of employee empowerment, the weaker relationship
between market orientation and the flexibility dimension of employee search behavior.
H3e: The stronger the degree of employee empowerment, the stronger relationship
between entrepreneurship orientation and the flexibility dimension of employee search
behavior.
H3f: The weaker the degree of employee empowerment, the weaker relationship between
entrepreneurship orientation and the flexibility dimension of employee search behavior.
H3g: The stronger the degree of employee empowerment, the stronger relationship
between entrepreneurship orientation and the scope dimension of employee search
behavior.
H3h: The weaker the degree of employee empowerment, the weaker relationship
between entrepreneurship orientation and the scope dimension of employee search
behavior.

37

Information Search Behavior and Its Effects on Organizational Learning
Continuous learning and improvement are critical for company success. Since the goal
for any business is to create an organizational culture and/or structure that maximize its ability to
learn about a market, it is important to study how effectively a company learns about the
customers, competitors, and/or market trends. According to March (1991), organizational
learning starts for two different purposes: exploration of new possibilities or the exploitation of
old certainties. Based on the firm’s strategic purpose, the firm gathers information/knowledge
and the firm’s learning starts from that information. Researchers attempt to explain the
importance of a learning culture in market oriented companies by emphasizing its’ sustainable
competitive advantage to the firm (Day, 1994; Dickson, 1996). Learning is also emphasized in
the study of entrepreneurship orientation. Since entrepreneurship-oriented firms’ innovativeness
requires a continuous search for and extensive collection of information and knowledge, firms
must be committed to learning. Several authors mention that in the study of entrepreneurship
orientation and business performance, the way in which employees interpret and share newly
collected information must be considered (Sinkula, 1994; Slater & Narver, 1995). Therefore,
firms with an entrepreneurial competitive advantage also need to study employees’ learning
activities.
Among studies of employee learning activities, some scholars discuss the positive
relationships between market orientation and organizational learning (Greenley, 1995; Kumar,
Subramanian, & Yauger, 1998; Lockett & Littler, 1997). They state that market orientation is
essential in organizational effectiveness, for both longer-term marketing success, and for
superior business performance. A market oriented organizational culture leads to a higher level
of organizational learning due to its emphasis on developing intelligence about customers,

38

competitors, and other channel members. As a result of MO’s emphasis on the development of
market intelligence, a market orientation is inherently a learning orientation (Kumar et. al., 1998).
Some authors propose that synergy from both MO and EO behaviors would increase
companies’ performance outcomes, because the entrepreneurial antecedents, such as innovation,
proactive, and risk taking proclivity, have a positive relationship with market orientation, and
thus create a learning culture through information acquisition, dissemination, and shared
interpretation (Matsuno et al., 2002; Slater & Narver, 1995). The authors argue that companies
should have both a market- and entrepreneurial-oriented culture, and that the two should be
based on market needs and the firm’s ability to engage in market information learning and more
calculated risk taking activities. They added that strictly market oriented and current market
focused companies may miss emerging market opportunities, while strictly entrepreneurialoriented and technology development oriented companies may not maximize customer
satisfaction.
As we just discussed, previous researchers focused on the complementary relationship of
market orientation and entrepreneurial spirit on company performance, however, there is a lack
of empirical testing on how the two types of strategies (MO and EO) influence organization
learning behavior. To understand an organizations’ ability and/or its approach to learn market
trend information, we need to examine the relationship between firms’ shared practices in
information search methods and how the information helps firms to be capable of learning. Since
each orientation strategy has different information gathering behaviors, it is assumed that
employees interpret and learn from the collected information differently.
Yeung et al. (1999) explain that the rationales for companies’ different learning styles are
manifold, for example, there could be variation in business strategy and culture or variation in

39

resources and competitive constraints. The authors view that such diversity in rationale causes an
organization to gather and use information differently to solve problems and ultimately lead
them to develop their own competitive learning styles. Based on this logic, company learning
style is classified into four typologies; benchmarking, continuous improvement, competency
acquisition, and experimentation (Yeung et al., 1999). Companies try to meet customer needs by
copying and/or benchmarking what other companies have done. These companies try to generate
new ideas based on existing ideas or by discovering competitors’ best practices. Companies that
try to be experts or engage in continuous improvement seek to improve existing competencies;
thus, the mastery of previous knowledge and practices is important. These two learning styles
(benchmarking and continuous improvement) are involved in the refinement or extension of
existing opportunities and referred to as exploitative learning (March, 1991). Companies try to
explore new opportunities through experience such as learning new ideas, products, and
processes by their own experimentation or through experience of others such as acquiring new
competencies through training employees, borrowing competency through strategic alliances, or
buying competency through recruitment. These two learning styles (experimentation and
competency acquisition) are involved in exploring new possibilities to create a competitive
advantage and are called explorative learning.
Van den Bosch, Volberda, and Boer (1999) attempt to explain that company choice of
organizational learning style is defined by types of information collected and status of the market
environment. They argue that company learning of new information and knowledge can be
differentiated by Grant’s (1996a) three types of information integration (efficiency, scope, and
flexibility) and the status of market environments (stable/turbulent). They reasoned that firm
learning is based on the market environment. If a market is stable, a firm’s focus of knowledge

40

absorption is exploitative, such as refinement and extension of existing competency and in that
case, efficiency of knowledge absorption is required. On the other hand, when a market is
turbulent, the firm focuses on extensive absorption of knowledge (explorative) and the firm
needs to exhibit the scope and the flexibility dimensions of knowledge absorption.
Based on Van den Bosch et al.’s (1999) proposal of different types of organizational
learning in relationship with information types and market environment changes, we focus on the
learning and information relationship more in detail. Organizations’ collection of new
information/knowledge is the main impetus for organizational learning. When a firm develops
new products/markets based on the current market trends, the required information is not much
different from the previous season’s information. Use of previously existing and continuously
accumulated information would be appropriate. Similar segments of consumers’ needs and wants
may require use of previously existing or slightly modified information. According to the image
theory, risk averse decision making occurs where a decision maker’s goal is clear or not much
different from the current image. That is, if an organization’s new information/knowledge is
familiar to the company (employees), this leads the organization to have clear goals of their plan.
Further, clear goals guide the organization to engage in risk-averse decision making when
seeking new opportunities from the existing market (exploitative learning). In this case,
development of new products and expansion into new markets is through incremental progress
through benchmarking of others. To refine and extend existing knowledge, exploitative learning
is more likely to focus on current market trends, which lead the firm to have clear short-term
goals. Van den Bosch et al. (1999) comment that exploitative learning requires high efficiency
but is limited in scope and flexibility. Since exploitative learning is less likely to lead to change,
firms try to increase competitive advantage by turning their attention to increasing efficiency,

41

such as the standardization of procedures, heuristic problem solving, tighter control and
discipline, risk-aversion, and the benchmarking of best practices (Hunter, 2003).
Conversely, when a company’s strategic market/product development is targeting
innovative and emerging markets, the company collects information/knowledge, which is
radically different (diverse) from what they already have. As image theory explains, the image
and goals, which are ambiguous and not clearly defined, drive them to accept variability around
their future goals; thus, they prefer to take risky decision making to learn market information.
Companies prefer to learn the information/knowledge, which is collected through in diverse
manner, with the company’s own experimentation. Companies attempt to create an innovative
competitive advantage in this case.
Thus, we assume that efficiently updated information/knowledge, which is collected by
similar routines from similar sources, results in exploitative learning. In addition, information
collected in a flexible manner also results in exploitative learning. As discussed earlier, we
assume that flexibility is placed between efficiency and scope on a continuum of information
search behaviors. That is, in this intermediate position, flexible information search integrates
both existing and new information. Companies learn through benchmarking or adoption of best
practices in the marketplace and encourage employees to continuously improve their
competencies or skills. In contrast, organizational search for a breadth of information encourages
employees to take risks and leads to explorative organizational learning behavior. The flexible
dimension of information search is also related to explorative learning because through flexible
information search behavior, employees not only refine the existing information but also extend
their search to obtain new knowledge or new competencies. The content of the information is
new or sometimes information types are different than what they used previously. Companies

42

encourage employees to achieve new competitive skills through a company’s own
experimentation and training or companies could hire people who have such skills.
Organizations that try to create a competitive advantage through major breakthroughs in
competencies, technologies, and paradigms are engaged in explorative learning. Thus, we
hypothesize that:
H4: There is a relationship between information search types and information learning styles.
H4a: The efficiency of information search is positively related to the exploitative
organizational learning.
H4b: The flexibility of information search is positively related to the exploitative
organizational learning.
H4c: The flexibility of information search is positively related to the explorative
organizational learning.
H4d: The scope of information search is positively related to the explorative
organizational learning.

A detailed model describing relationships of constructs, which are discussed in our study,
is shown in Figure 2.

43

Figure 2. The Relationships of Market Strategies (MO/EO) on Information Search and Learning with Employee Empowerment

Employee
Empowerment

Market
Orientation

H3a,b
H2a

Efficiency

H3c,d

H4a
Exploitative
H4b
H2b

H1

Flexibility
H3e,f H2c
H4c
Explorative
H4d
Entrepreneur
-ship
Orientation

Market Strategy

H3g,h
H2d

Moderator:
Employee Empowerment

Scope

Organization’s
Information Search Types

44

Organization’s
Information Learning
Environment

CHAPTER III

RESEARCH METHODS

The model described in previous section has proposed 1) to identify the relationship
between two different market strategies (market orientation vs. entrepreneurship orientation) as
perceived by retail employees; 2) to examine the relationship of those market strategies and
information search types: efficiency, scope, and flexibility; 3) to test the moderating effects of
supervisor’s supportive supervision on the relationship between the perceived market strategies
and information search types; and 4) to examine the relationship of information search types and
different information learning types: exploitative vs. explorative.
In this section, we discuss the research design first, followed by a discussion of sample
selection and data collection. Then, we discuss the questionnaire items that measure each of the
major constructs in the proposed model. Finally, we describe how we analyze the data related to
the proposed model.

Research Design
For this study, we first completed a qualitative phase and then a quantitative phase. Since
there were no preexisting and validated measurements for one of the constructs in the proposed
model, information search behaviors, we needed to develop appropriate items to measure
information search types. Based on Grant’s (1996a) research and other literature that provide
fundamental ideas for information search (Allen, 1977; Gerstberger & Allen, 1968; Monge et al.,
1985; Tushman & Katz, 1980), three different information search types were selected: efficient

45

dimension of information search, flexibility dimension of information search, and scope
dimension of information search.
Therefore, in the qualitative phase, we conducted three one-on-one interviews with U.S.
retail buyers. Our research helped us develop a total of 13 items for information search types: 6
items of efficient dimension of information search, 4 items of flexibility dimension of
information search, and 3 items of scope dimension of information search. During the interviews,
we probed for general ideas about each information search behavior to determine whether our
items were appropriate and whether we overlooked additional items that would enhance the
measurement. Also during the interviews, the appropriateness of item wording was assessed and
revised if necessary. No additional measurement items were identified. After we revised the
information search items, based on the interviews, we confirmed the revised items with other
retail experts. More detailed information about development of the measures is discussed in the
measurement section.
For the quantitative phase, we used online methods for soliciting and obtaining data. We
used e-mail to relay the research process to potential respondents and a website for posting the
survey and responding to survey questionnaires. Online survey methods have become widely
accepted and, as shown by several studies, contain advantages over the traditional postal survey
including shorter response times and lower costs (Griffis, Goldsby, & Cooper, 2003; Ilieva,
Baron, & Healey, 2002; Philbrick, Smith, & Bart, 2010). Researchers also suggested that the use
of a specifically addressed, personalized cover letter increases both traditional mail and e-mail
response rates (Dillman, 1991; Schaefer & Dillamn, 1998).

46

Sample Selection and Data Collection Procedures
For this study, retail middle- to upper-level buyers, planners, or managers were contacted
as the target population. This level of management was necessary to be able to get accurate
responses to questions asking about the company’s future plans or information search behaviors
for developing new product lines. Several researchers (Atkinson, 1964; March & Shapira, 1987;
McClelland, 1961), however warn that using only upper-level managers to test outcomes of
organizational activities leads to biased results due to invariations in opinions and activities
among a single group of employees. To minimize these biases, we selected middle- and/or
upper-level managers from various departments in each organization, since middle level
employees would also have adequate experience to respond to our survey.
The convenience sampling method was used to test the hypotheses for this study. Due to
limited budget and time and considering the difficulty of compiling a sample group of middle- to
upper-level managers in the retail industry, random sampling was inappropriate for this study.
Even though the convenience sampling method maybe inappropriate for generating statistical
inference about the population, it is useful in developing theories/hypotheses or the discovering
of surrounding issues of the research subject (Schonlau, Fricker, & Elliott, 2002).
To request permission to conduct the survey with company employees, we collected the
names of retail companies’ corporate level human resource managers from one month’s use of
Hoover’s online database. We randomly selected sixty retail companies’ HR managers as our
first contact persons. Simultaneously, alumni from a Midwestern university, who hold retail
positions were contacted to refer us to their HR managers. When permission was secured, we
asked the HR managers to distribute an e-mail to the potential respondents who were middle- to

47

upper-level buyers/planners/managers. In the first e-mail, we briefly introduced the study, as
follows:
1.
2.
3.
4.
5.

An introduction to and explanation of the importance of the study.
A request for the respondent’s participation.
The website address of the online survey
(http://www.surveymonkey.com/s.asp?u=93604750067).
An offer to mail a hard copy version of the survey if the respondent prefers that
over the Web version.
A guarantee of confidentiality and pledge of anonymity during the data analysis
stage.

One week after the first e-mail was distributed, the second e-mail was sent to the
respondents to again solicit their assistance and encourage them to visit the website. The full
cover letters appear in Appendix II.

Measurements
This study investigates how retail employees collect information in their merchandising
operations, how they translate the information into company knowledge, and how the firms and
employees try to learn and execute the information in their product/market development. This
study assumes that these consequent activities would be different depending on whether the
company is market oriented or entrepreneurship oriented as perceived by the firms’ employees.
To measure each construct, appropriate measurement was selected based on the construct. To
identify demographic characteristics of our sample, at the end of the survey we asked
respondents about the following demographic characteristics: product category with which they
work, job title, number of years in retail business, number of years in buying/planning
department, education, salary, gender and age.

48

Market Orientation
To measure the degree of companies’ market orientation, we use a version of Jaworski
and Kohli’s (1993) scale. The authors describe market orientation as the process of
organizational effort to implement the marketing concept; they further divide the scale into the
three sub-scales of intelligence generation, intelligence dissemination and responsiveness.
However, since the scale was developed based on a manufacturing sample, it needs to be
modified for a retailing context. Generally, in a retail setting, generation and dissemination of
market information is accomplished via interactions between stores and suppliers rather than the
organizational unit. Also the retail buying department usually contacts customers and store
personnel to obtain consumer purchasing behaviors and market trends for new product
development in retailing while manufacturers do not have regular customer contacts. Huddleston
and Good (1999), modified Jaworski and Kohli’s (1993) items to appropriately fit a retailing
sample. For example, in the study of Jaworski & Kohli (1993), an item states “In this business
unit, we meet with customers at least once a year to find out products or services they will need
in the future.” Another item states “Individuals from our manufacturing department interact
directly with customers to learn how to serve them better.” Those items are reworded as “In this
store, we meet with customers at least once a year to find out products or services they will need
in the future.” and “Individuals from our buying department interact directly with customers to
learn how to serve them better.” Also one item from intelligence generation which is not relevant
to the retail industry is eliminated. The item is “We often talk with or survey those who can
influence our end users’ purchases (e.g., retailers, distributors)” (Huddleston & Good, 1999).
The market orientation scale consists of a series of items, using a seven-point Likert scale
(1 = strongly disagree, 7 = strongly agree). Intelligence generation, intelligence dissemination,

49

and responsiveness are measured by nine, eight, and fourteen items, respectively. Jaworski and
Kohli’s (1993) original study tested the scale and it was reliable and valid with coefficient alphas
ranging from .71 to .82. In the modified market orientation scale for the retail sample
(Huddleston & Good 1999), the coefficient alphas range from .66 to .88, demonstrating
acceptable reliability of this scale.

Entrepreneurship Orientation
To measure the degree of entrepreneurship orientation, we use the Covin and Slevin
(1989) entrepreneurship scale. This scale consists of three items each of innovation proclivity,
proactive orientation, and risk taking propensity using a 1 to 7 Likert-type scale (1 =
conservative, 7 = entrepreneurial). The three items of innovation proclivity and two of the items
of risk taking propensity were adopted from the studies of Miller and Friesen (1982) and
Khandwalla (1977), originally. Covin and Slevin (1989) tested factorial validity and found that
this scale is unidimensional (i.e., all items loaded above 0.5 on a single factor). They mention
that even though the scale measures three different aspects of the firms’ entrepreneurship posture,
the items are empirically related as one construct. Their unidimensional scale reliability was
good, with a coefficient alpha of .87. In our study, we use the 3 sub-factored entrepreneurship
orientation construct in order to measure the latent entrepreneurship orientation variable. Using
confirmatory factor analysis, we test the factorial validity of the entrepreneurship scale before
testing the structural model.

50

Employee Empowerment
To measure employee empowerment at work, we use Oldham and Cummings’ (1996) 8
items of supportive supervision and 4 items of non-controlling supervision. The items were rated
on a seven-point Likert-type scale which ranged from 1 (Strongly Disagree) to 7 (Strongly
Agree). Using two sets of manufacturing employees as their sample, they found acceptable
reliabilities of .86 for supportive supervision factor and .67 for non-controlling supervision. In
their study, the authors specifically found an interaction effect of creativity-relevant personal
characteristics, job complexity, employees’ creativity at work, and creative performance ratings.
When jobs were complex and supervisors showed supportive and non-controlling supervision,
employees’ creativity-relevant personal characteristics were strongly and positively related with
creative performance ratings.

Information Search Behavior
Since we found no measure of information search behavior, we developed items to
measure information integration based on Grant’s (1996a) typology of Knowledge Integration.
The author categorized three types of employee efforts to integrate new knowledge within an
organizational system. These types are efficiency, flexibility, and scope. In our study, we
developed employees’ knowledge integration to reflect retail buyers’ information integration in
the development of new product lines. Retail buyers searching for market information for
development of new product lines is considered as knowledge integration.
As Grant (1996a) and other authors (Allen, 1977; Gerstberger & Allen, 1968; Monge, et
al., 1985; Tushman & Katz, 1980) suggest, efficiency of knowledge integration is employees’
activities and organizational procedures which help employees to obtain knowledge in a cost

51

efficient manner. Grant (1996a) mentions several organizational characteristics that would
increase efficiency in obtaining market information such as the level of employees’ common
knowledge, the frequency of the coordinated activity, and the degree of coordinated task
performance. We developed items based on these previous studies’ propositions. Efficiency of
information search can be measured by employees’ good understanding of information,
departmental seamless coordination, employees’ frequent formal and/or informal meetings, and
use of communication technology; all factors would increase efficiency in information collection.
Also if a product development department searches for and uses the same sources of information
and the information is quickly dispersed among employees, increased efficiency of newly
collected information is demonstrated.
Since flexibility is defined as a firm’s capability to extend additional knowledge while
reconfiguring currently existing knowledge, we developed items asking about employees’ easy
access to old and new knowledge. For example, an item measuring flexibility probes about
departmental efforts of reconfiguring old information and using it along with new information in
the development of new products.
In obtaining new knowledge or information about the market, if employees put effort into
collecting it in an extensive and risky way, it is referred to as Scope. We developed items that
probed whether the product department looks for various and specialized information sources as
opposed to their traditional method; this effort would expand their information variety. In
addition, if the product department shares information with other departments, it will expand
their information scope.
Based on Grant’s typology (Grant, 1996a), we developed the information search scale.
During this process, we conducted one-on-one interviews with three retail buyers from different

52

retail organizations in order to clarify and verify the terms in the scale items based on their real
work practices.
In clarifying the efficiency items, the three interviewees mostly agreed with the terms
used in the items; however they suggested that we add some examples, to make some items
clearer. For example, one of the questions measuring efficiency was, “There is a seamless
coordination among the divisions that work together in developing/introducing a new product
line”; thus to clarify what is meant by product development divisions, at the end of the item, we
added “i.e., trend department, buying department, marketing department, etc.” referring the
different product line development divisions.
For items related to scope and flexibility, we specifically asked the interviewees what
their traditional information sources were and what they would look for if they wanted to add
totally new information. The three interviewees mentioned that they examine past sales records
first when they need to develop product lines for the next season. In addition, their in-house and
outside advertising staffs usually provide market analysis for the next season and they normally
use this when they develop new product lines. Also, they look for their competitors’ market
trends and the competitors’ buying power on regular basis. As new and specialized information
sources, interviewees mentioned trade and scholarly journals or market research analysis.
One specific question we asked buyers was about the ease of communication with other
divisions in the company. All three interviewees mentioned that they had difficulties in
communication with buyers of other product lines. One of them said, they rarely share their
information with other product lines developers. They discuss and brainstorm about market and
product line trends within their product line but it is not shared with other product line buyers.
Different buying divisions and buying offices make decisions separately. One of interviewees

53

felt that the lack of communication was problematic when developing lines for future markets.
Another interviewee said that most of the product line development was based on top-down
decision making; however, they have flexibility in discussing new trends and new product
development. Their supervisors and CEO understand trends and innovation, however, when they
actually develop new product lines, in the end, the decision mostly comes from the pressure from
the CEO.
We revised the 13 items of information search items based on the three interviews and
confirmed the revision with experts in retailing. As a result, we have 6 items of efficiency, 4
items of flexibility, and 3 items of scope to measure information search. The scales are 1 to 7
point Likert scales (1=Strongly Disagree, 7=Strongly Agree).

Organizational Learning
To measure organizational learning type, we use Yeung et al.’s (1999) scale.
Organizational learning is divided into two types: explorative and exploitative. This scale was a
six-point Likert scales (1 = to very little extent, 5 = to very large extent) with 0 for not applicable.
In Yeung et al.’s study, the coefficient alpha for explorative learning is .75 and exploitative
learning is .85, demonstrating scale reliability. In our study, to be consistent with other construct
measurement, we adapt the scale to 1 to 7 points (1 = to very little extent, 7 = to very large
extent).

Data Analysis
First, we examine demographic characteristics of the sample and reliability of each scale
to understand the data set. Next we test any missing data, outliers, skewness and kurtosis of the

54

data distribution to check the normality of data distribution. Generally, use of Structural
Equation Modeling (SEM) method requires random sampling of respondents and linearity of all
relationships. Thus, assurance of the randomness of the missing data patterns and distribution of
data normality check are required. Approaches to handle missing data and non-normal data are
discussed.
Once we understand the psychometric properties of the data, we test the proposed theory
with a structural equation model. We use Confirmatory Factor Analysis (CFA) to test the
measurement model. Since we developed the information search scales for this study, we need to
confirm convergent validity of those scales to establish whether the indicators specified to each
underlying factor are well related to the factors. At the same time, we test the distinctiveness of
the factors measured by different indicators by CFA. If CFA does not support the proposed
model, then a re-specified model is tested.
If the confirmatory factor analysis shows appropriate support for the proposed model,
then we test the structural equation model (SEM) (Anderson & Gerbing 1988, Kline 1998). In
addition to testing the SEM, we test the moderating factor of employee empowerment
(supportive supervision). Based on the means of employee empowerment, we divide the
respondents as high and low groups. Multigroup SEM analysis helps to test the moderating
effects by comparing constrained factor loading of both groups and unconstrained models. Chisquare difference tests are used for that. If overall fit indices of the unconstrained model are
significantly improved as compared to the fit indices of the constrained model, then we can
confirm that there is a significant effect of the moderator in this model.

55

CHAPTER IV

RESULTS AND DISCUSSION

The purpose of this chapter is to report the results of statistical analysis and hypothesis
testing, and to discuss the findings. We first describe the demographic characteristics of the
sample and discuss about the screening process of the collected data. Then we report the results
of reliability tests for questionnaire scales. We review the factor analysis results of employee
empowerment to determine whether the supportive supervision measure is appropriately defined
as we postulated. Since we developed the measures of information search behaviors (efficiency,
flexibility, and scope) for this study, we discuss the second-order structure of this construct,
information search behavior, as the higher order factor and its relationship with the three subfactors. Reliability and validity of all constructs are tested with confirmatory factor analysis.
Finally, we test the proposed structural equation model using AMOS maximum likelihood
estimation and present and discuss the findings.

Demographic Characteristics of Sample
The purpose of this study is to examine how retail companies’ market strategies (MO and
EO), as perceived by firm employees, affect employee information search and learning behaviors
in the development of new product lines. Generally, middle-to-upper level retail buyers, planners
and product developers make decisions about the seeking of new trend information for the
development of a new product line or a new market; therefore, we target this group of people for
this study.

56

Because company protocol mandated that we rely on their Human Resource professionals
to send the request for participation to appropriate employees at the designated positions, we
cannot calculate a response rate. Gaining participation of an industry sample is difficult due to
busy schedules and significant demands made on their time. In particular, retail consolidation
and the downward business trends in retailing in recent years made data collection difficult.
During the data collection period, there were mergers and acquisitions among retailers, which
were disruptive to companies and many companies decline to participate because HR
professionals were worried that morale might be low and stress might be high, affecting item
responses to reflect negatively on the firms.
We also contacted retailing alumni, at a university located in the Midwestern US, inviting
those who held the positions defined by our study to participate in the research. With all these
difficulties, we managed to collect a sample of one hundred six retail employees. However four
respondents failed to answer more than 50% of items, so we deleted those four, resulting in
usable data of one hundred two respondents. When we contacted HR professionals, we asked
them to distribute our questionnaire to the middle to upper level buyers, planners and product
developers; our sample represents this group of people in retail industry. We chose this group of
people for our study due to their knowledge about and involvement developing company
strategies, goals, and decision making, which are suitable for our study purpose.
The profile of the sample is shown in Table 1. A majority of respondents worked with an
apparel product category (45.1 percent) and about 5 percent of them worked in the automobile
industry and the rest of the respondents worked with different product categories such as
footwear, furniture, appliances, and media (books/music). Most (63.7 percent) of the respondents
were retail product buyers, planners and product developers who had experience in new

57

product/market development. This statistic, along with the fact that the questionnaires were only
distributed to employees with some experience with new products supports the appropriateness
of our sample for the study.
Over half (54.9%) the sample had worked in retailing for 5 to 10 years and 11.8 percent
worked up to 15 years and 15.7 percent worked less than 5 years. Close to half (45.1%) had
spent 5 to 10 years in the product buying, planning, developing areas and 38.2 percent had
worked in those areas for less than 5 years. Fifty one percent of respondents received the salary
range of $50,000 to $65,000, 14.7 percent received $65,001 to $80,000. In 2009, expected
median salary for merchandise buyer assistant in the US is $45,885 and a typical buyer III’s
median salary is $65,578 (“Salary Wizard,” November 2009). These figures demonstrate that our
sample’s income is similar to that of middle level buyers in the US, which shows the sample fits
the purpose of this study. Except not answered, females constituted about 75 percent of the
sample and the males are about 19 percent. More than 85 percent of respondents received at least
a university education. Most respondents’ (85.3 percent) were 20 to 39 years old.

58

Table 1. Sample Profile (N = 102)
N
%
N
Product Category
Education
Clothing
46
45.1
Grade school
.
Food
2
2.0
Some high school
.
Footwear
3
2.9
High school diploma
.
3
2.9
Furniture
Some business technical school
6
Appliances
3
2.9
College degree
73
Electronics
2
2.0
Master’s or higher degree
16
Others
21
20.6(a)
Missing
22
21.6
Missing
7
Total
102
100
Total
102
Job Title
Salary Range
Buyer
47
46.1
Below $50,000
13
Merchandise Planner
10
9.8
$50,000 - $65,000
52
Product Developer
8
7.8
$65,001 - $80,000
15
Others
9
8.8
$80,001 - $95,000
9
$95,001 - $110,000
1
Over $110,001
2
Missing
28
27.5
Missing
10
Total
102
100
Total
102
Total Years in Retail
Sex
16
15.7
Female
76
< 5years
≤ 5 to < 10 years
56
54.9
Male
19
≤ 10 to < 15 years
12
11.8
≤ 15 to < 20 years
3
2.9
> 20 years
6
5.9
Missing
9
8.8
Missing
7
Total
102
100
Total
102
Total Years in
Age Range
Under 20
.
Buying/Planning area
< 5years
39
38.2
20-29
60
≤ 5 to < 10 years
46
45.1
30-39
27
≤ 10 to < 15 years
5
4.9
40-49
5
≤ 15 to < 20 years
3
2.9
50-59
2
> 20 years
1
1.0
60 and over
.
Missing
8
7.8
Missing
8
Total
102
100
Total
102
(a) Respondents who answered as others listed categories such as automobile, media
(books/DVD/music), and health/beauty care products.

59

%
.
.
.
5.9
71.6
15.7
6.9
100
12.7
51.0
14.7
8.8
1.0
2.0
9.8
100
74.5
18.6

6.9
100
.
58.8
26.5
4.9
2.0
.
7.8
100

Data Preparation and Screening
Structural equation modeling (SEM) with the Maximum Likelihood (ML) estimation
examines the pattern of relationships across the study variables. The screening of data, especially
examining missing data and the normal data distribution is important because these can distort
correlation/covariance matrices, the main matrices used in SEM, and may produce the
misleading results. Thus, screening of the data before considering the theory-based model testing
is critical.
For missing data pattern analysis, we found that 20 items among our 81 items had about 5
to 7% of missing values while the rest had less than 5% of missing values. In order to check
whether the missing data pattern is at random, we ran Separate-Variance t-Tests to identify those
20 item’s missing pattern with the remaining item (61items) through comparison of mean value
of valid data and mean value of missing data. We found a small portion (only 8.8% out of those
20 items) of missing data yielded significant differences. Since it was tested at the .05 level, the
differences were expected to vary by 5%, 8.8% is not much different than that. Due to the small
difference, which could be the result of sampling variation, we determined this to be of marginal
concern (Reckase, 2010). With care, we consider that our missing observations are mostly
missing at random. Therefore, we employ the EM imputation method for further analysis. More
detailed analysis is given in Appendix III.
For data normality analysis, we found that most of our items were negatively skewed.
That is, our respondents answered on the higher end of the scale, we need to determine what
extent of non-normality is acceptable for obtaining less biased estimation of parameters, standard
errors, and chi-squared statistics. When the data are non-normal, there is overestimation of the
chi-square statistic and underestimation of standard errors, which can lead to false rejection of

60

the model and inflated significance statistics, respectively (Gao, Mokhtarian, & Johnston, 2008;
Muthén and Kaplan, 1985). However, Muthén and Kaplan (1985) and Hallow (1985) mention
that when univariate skewness and kurtosis are not severe, the data are not much distorted. Since
our data distribution is small to moderately non-normal, we assume that our non-normal data
distribution is not a big issue in using SEM method for testing our model. More detailed
explanation about analysis of data normality is given in Appendix IV.

Reliability Tests
In order to estimate the reliability of scales, we used Cronbach’s Alpha. As presented in
Table 2, the results of reliability tests indicated that 14 of the 15 sub-factors of the major
constructs with multiple items achieve acceptable reliability, which were between .60 to .89,
even though proactiveness (.65), employee authority (.66), efficiency (.66) and
copiers/benchmarkers (.60) are less than we preferred. While acceptable reliability cutoff value
of .60 (Nunnally, 1978) provides a minimum standard, a reliability of .70 is recommended as
“adequate” for measuring internal consistency of the constructs (Kline, 1998), therefore when we
deleted the most unreliable items in those four sub-constructs, we obtained .76 for proactiveness
(one sub-factor of EO) and .74 for efficiency (Information Search). However, item deletion is not
helpful to achieve adequate reliability for employee authority (Employee Empowerment), and
deleting one item only slightly improves the reliability of copier/benchmarker (Exploitative
Learning) to .63. The sub-construct with unacceptable reliability is innovativeness (EO), which
is .47. When we deleted the item that reflects an emphasis on traditional/established products and
services vs. technical/innovative products and services, the item reliability is increased to .62.
The deleted items for all constructs are given in the table 2 as italicized.

61

The scale for Employee Empowerment factor in our study is based on Oldham and
Cummings’ (1996) supervisory support measure. This scale consists of supportive supervision
and non-controlling supervision. However, as we discussed in the previous chapters, we assume
that supportive supervision enhances employee confidence and autonomy which ultimately
empowers employees in their decision making; we consider this factor as Employee
Empowerment.

62

Table 2. Measurement Reliability
Measurements

Mean

Market Orientation
Intelligence Generation (IG): 9 items
1 In this firm, we meet with customers at least once a year to find out what products
or services they will need in the future.
2 Individuals from our buying department interact directly with customers to learn
how to serve them better.
3 In this firm we do a lot of in-house market research.
4 We are slow to detect changes in our customers’ product preferences. ®
5 We poll customers at least once a year to assess the quality of our products and
services.
6 We collect industry information through informal means (e.g., lunch with sales
representatives, talks with trade partners).
7 In this firm, intelligence on our competitors is generated independently by several
departments.
8 We are slow to detect fundamental shifts in our industry (e.g., competition,
technology, regulation). ®
9 We periodically review the likely effect of changes in our business environment
(e.g., regulation) on customers.
Intelligence Dissemination (ID): 8 items
1 A lot of informal “hall talk” in this firm concerns our competitors’ tactics or
strategies.
2 We have meetings at least once a quarter to discuss market trends and
developments.
3 Marketing personnel in our firm spend time discussing customers’ future needs with
other functional departments.
4 Our firm periodically circulates documents (e.g., reports, newsletters) that provide
information on our customers.
5 When something important happens in the market the whole business unit knows

63

5.48

SD

Cronbach’s
α

0.96
.85

5.74

1.64

4.97

1.89

5.21
5.44
5.33

1.53
1.46
1.52

5.58

1.21

5.20

1.53

5.41

1.39

5.32

1.56

4.78

1.79

5.64

1.57

5.40

1.47

5.23

1.73

.87

α after
unreliable
item(s)
deleted

Table 2. Measurement Reliability (cont’d)
Measurements

Mean

about it in a short period.
6 Data on customer satisfaction are disseminated at all levels in this firm on a regular
basis.
7 There is minimal communication between marketing and buying departments
concerning market development. ®
8 When one department finds out something important about competitors, it is slow to
alert other departments. ®
Responsiveness(R): 14 items
1 It takes us forever to decide how to respond to our competitors’ price changes. ®
2 Principles of market segmentation drive new product selection efforts in this firm.
3 For one reason or another we tend to ignore changes in our customers’ product or
service needs. ®
4 We periodically review our product assortment to ensure that they are in line with
what customers want.
5 Our business plans are driven more by technological advances than by market
research. ®
6 Several departments get together periodically to plan a response to changes taking
place in our business environment.
7 The product lines we sell depend more on internal politics than real market needs. ®
8 If a major competitor were to launch an intensive campaign targeted at our
customers, we would implement a response immediately.
9 The activities of the different departments in this firm are well coordinated.
10 Customer complaints fall on deaf ears in this firm. ®
11 Even if we came up with a great marketing plan, we probably would not be able to
implement it in a timely fashion. ®
12 We are quick to respond to significant changes in our competitors’ pricing
structures.

64

SD

4.88

1.70

5.42

1.61

5.37

1.25

6.02
5.32
5.68

1.24
1.42
1.44

6.07

1.30

5.08

1.43

5.33

1.58

5.52
5.48

1.52
1.63

5.25
6.24
5.25

1.70
0.97
1.50

5.59

1.44

Cronbach’s
α

.89

α after
unreliable
item(s)
deleted

Table 2. Measurement Reliability (cont’d)
SD

13 When we find out that customers are unhappy with the quality of our service, we
take corrective action immediately.
14 When we find that customers would like us to modify product selection or service,
the departments involved make concerted efforts to do so.
Entrepreneurship Orientation
Innovativeness (I): 3 items
1 In general, the top managers of my firm favor…
A strong emphasis on the marketing of tried and true products or services (1)
A strong emphasis on R&D, technological leadership, and innovations (7)
2 How many new lines of products/services has your firm marketed in the past 5
years?
No new lines of products/services (1)
Very many new lines of products/services (7)
3 Describe the nature of the new lines of products/services that your firm marketed in
the past 5 years?
Changes in product or service lines have been mostly of a minor nature (1)
Changes in product or service lines have usually been quite dramatic (7)
Proactiveness (P): 3 items
1 In dealing with its competitors, my firm…
Typically responds to actions which competitors initiate (1)
Typically initiates actions which competitors then respond to (7)
2 In dealing with its competitors, my firm…
Is very seldom the first business to introduce new products/services (1)
Is very often the first business to introduce new products/services (7)
3 In dealing with its competitors, my firm…
Typically seeks to avoid competitive clashes, preferring a ‘live-and-let-live’ posture
(1)
Typically adopts a very competitive, ‘undo-the-competitors’ posture (7)

5.76

1.22

4.10

1.17

3.43

1.73

5.16

1.54

4.35

.76

1.17

5.64

.62

.65

Mean

α after
unreliable
item(s)
deleted

.47a

Measurements

Cronbach’s
α

2.03

65

4.24

1.88

3.90

1.73

4.36

1.45

Table 2. Measurement Reliability (cont’d)
Measurements

Mean

Risk-Taking Propensity (RP): 3 items
1 In general, the top managers of my firm have…
A strong proclivity for low-risk projects with normal and certain rates of return (1)
A strong proclivity for high-risk projects with chances of very high returns (7)
2 In general, the top managers of my firm believe that…
Owing to the nature of the environment, it is best to explore it gradually via timid,
incremental behavior (1)
Owing to the nature of the environment, bold, wide-ranging actions are necessary to
achieve the firm’s objectives (7)
3 When confronted with decision-making situations involving uncertainty, my firm…
Typically adopts a cautious, ‘wait-and-see’ posture in order to minimize the
probability of making costly decisions (1)
Typically adopts a bold, aggressive posture in order to maximize the probability of
exploiting potential opportunities (7)
Employee Empowerment (Supportive Supervision)
Supervisory Help (SH): 8 items
1 My supervisor helps me solve work-related problems.
2 My supervisor encourages me to develop new skills.
3 My supervisor keeps informed about how employees think and feel about things.
4 My supervisor encourages employees to participate in important decisions.
5 My supervisor praises good work.
6 My supervisor encourages employees to speak up when they disagree with a
decision.
7 My supervisor refuses to explain his or her actions. ®
8 My supervisor rewards me for good performance.

66

SD

3.74

1.54

4.19

1.70

3.90

1.79

5.40

Cronbach’s
α

0.65

.89

.85
5.80
5.72
4.68
5.65
5.52
5.53

1.19
1.20
1.41
1.22
1.21
1.10

5.83
3.67

1.13
1.64

α after
unreliable
item(s)
deleted

Table 2. Measurement Reliability (cont’d)
Measurements

.66

.66

Mean

Employee Authority (AU): 4 items
1 My supervisor always seems to be around checking on my work. ®
2 My supervisor tells me what shall be done and how it shall be done. ®
3 My supervisor never gives me a chance to make important decisions on my own. ®
4 My supervisor leaves it up to me to decide how to go about doing my job.
Information Search Behavior
Efficiency (EFF): 6 items
1 Your department employees have a good understanding of the information needed
to develop/introduce a new product line.
2 There is a seamless coordination among the divisions that work together in
developing/introducing a new product line (i.e., trend dept., buying dept., marketing
dept. etc.).
3 Your department typically uses the same sources of information each time when
developing/introducing a new product line.
4 Your department frequently holds formal and informal meetings when
developing/introducing a new product line.
5 Your department uses communication technology to reduce communication time
among all those involved in developing/introducing a new product line (i.e., email,
mobile phone, fax).
6 Information is quickly dispersed to all who are involved in developing/introducing a
new product line.
Scope (SCO): 3 items
1 Your department looks at specialized information when developing/introducing a
new product line (i.e., trade or scholarly journal, market research analysis).
2 Your department seeks different types of information compared to the traditional
buying process when developing/introducing a new product line (i.e., new vendor’s
history/merchandising supporting information, emerging market information, new
scientific knowledge, etc.).

67

SD

5.73
5.26
5.44
5.47
5.09

α after
unreliable
item(s)
deleted
No change

.74

Cronbach’s
α

1.15
1.40
1.64
1.01
0.89

5.28

1.12

4.64

1.49

4.69

1.21

5.30

1.08

6.12

1.02

4.49

1.55
.78

5.08

1.41

4.88

1.37

Table 2. Measurement Reliability (cont’d)
Measurements

Mean

SD

3 Your department shares with and uses information generated by other units as
necessary.
Flexibility (FLEX): 4 items
1 Your department regularly reconfigures your department’s old information (i.e.,
sales record, marketing analysis, etc.) to make it useful for development/introduction
of a new product line.
2 Your department regularly integrates new information (i.e., new market trends, new
product development, new sourcing methods) into your department’s old information
to make it useful for development/introduction of a new product line.
3 Your department regularly adopts new information for making decisions in
development/introduction of a new product line.
4 Your department has easy access to knowledge/information from other departments
that could be useful for development/introduction of a new product line.
Exploitative Organizational Learning
Copiers/Benchmarkers (CB): 4 items
1 Our department learns from others, entering a product or applying a process after it
has been fully tested.
2 Our department learns by broadly scanning what other companies do.
3 Our department learns by focusing our scanning on specific activities done by other
companies.
4 Our department primarily benchmarks competition, measuring progress against
competitors’ performance.
Experts/Continuous Improvers (EC): 4 items
1 Our department masters new ideas before moving on to the next round.
2 Our department upgrades the way that we do existing work until we have it right.
3 Our department wants to be known as the best technical experts in our industry.

5.40

1.46

5.43

1.32

4.91

1.41

5.05

1.51

4.95

0.99

4.44

1.36

4.66
5.07

1.28
1.22

4.40

1.25

4.52
4.77
4.85

1.40
1.38
1.46

α after
unreliable
item(s)
deleted

1.21

5.09

Cronbach’s
α

68

.83

.60

.87

.63

Table 2. Measurement Reliability (cont’d)
Measurements

Mean

4 Our department primarily benchmarks ourselves and measure progress against our
previous performance.
Explorative Organizational Learning
Experiments/Innovators (EI): 4 items
1 Our department constantly seeks new ideas, even before old ones are fully
implemented.
2 Our department constantly seeks new ways to do work.
3 Our department tries a lot of new ideas; we want to be known as experimenters
within our industry.
4 Our department wants to be the first in the market with a new process or product.
Competent Workers/Skill Acquirers(CS): 4 items
1 Our department encourages individuals to acquire new competencies.
2 Our department encourages teams to acquire competencies.
3 Our department learns by hiring people from other companies who have skills we
need.
4 Learning is a critical part of our department strategy.
a Cronbach’s alpha is less than .60
Italicized items were excluded.
® indicates the item was reverse code

69

SD

5.36

1.03

5.12

0.83

5.13

1.38

5.09
4.73

1.31
1.63

5.34

1.65

5.39
5.36
4.77

0.98
1.06
1.41

5.55

1.39

Cronbach’s
α

.81

.76

α after
unreliable
item(s)
deleted

The Information Search Behavior Scale Validation
In order to measure information search behavior, we developed the scale based on
Grant’s (1996a) three typologies of knowledge integration and conducted exploratory in-depth
one-on-one interviews with three retail buyers. From the exploratory research, we developed 13
items (6 for efficiency in information search, 3 for scope in information search, and 4 for
flexibility in information search) but deleted 1 from the efficiency information search behavior
due to unreliability of the item. To validate the scale, we test whether the lower-order factors
(efficiency, scope, and flexibility) measure the more abstract higher-order factor of information
search behavior. We examine the higher-order factor model for convergent validity and
discriminant validity in order to assess the unidimensionality of the scale (Anderson and Gerbing,
1988; Kline, 1998). In other words, each indicator should load on only one factor and the
measurement errors should be independent, and should not covary. The cross-loaded items were
deleted one by one until an acceptable model fit to the data was accomplished.
In the initial higher-order factor model fit indices for the information search behavior
scale show unacceptable fit (Chi-square = 159.28, df = 51, p < .001, CFI = .85, RMSEA = .15).
The error variances of EFF4 (Efficiency item) was cross loaded with the measurement error
terms of two items in Scope (SCO1, SCO2) and two other factor’s disturbance variances of
Scope (D2) and Flexibility (D3). This implies that there may be unobserved relationships
between Efficiency, Scope and Flexibility and that the model should be identified. However,
since the purpose of scale validation is to determine how well the items are measuring the related
latent factors, it is reasonable to delete the cross loaded items at this point, so we deleted this
item from further analysis. For the same reason, we deleted EFF1 due to its cross loadings with
the measurement errors of other item (FLX3) and disturbances (D1, D3). Also, when looking at

70

the error variances, E5 (Scope item) and E9 (Flexibility item) are highly correlated. Since these
two constructs were developed to measure a diversity and flexibility information search and we
also assume these two constructs are related similarly with other variables in the model, it is
reasonable to assume that these factors contributed to the common variance of the observed
variables. Thus, we measure the covariance of these error variances. The final model showed
improved model fits of higher-order factors (Chi-square = 39.15, df = 26, p < .05, CFI = .98,
RMSEA = .07). The Root Mean Square Error Approximation (RMSEA), (around or less than.08
is favorable), and the Comparative Fit Index (CFI) (greater than .90 is favorable) revealed an
acceptable fit for the model (Kline, 1998). It is reasonable to consider all three first-order factors
of the Information Search Behavior scale to be unidimensional and they measure the same
construct. Figure 3 and Table 3 show the results of the final model.

71

Figure 3. Higher-Order Factor Model: Information Search Behavior

72

Table 3. Results for Higher-Order Factor Model: Information Search Behavior
Standard
Critical Ratio
Estimate
Path Coefficient
Error Variance
Info Search → Efficiency
.71**
8.55
E1
Info Search → Flexibility
.99**
5.94
E2
Info Search → Scope
1.09**
8.10
E3
Efficiency → EFF2
1.03nt
E4
Efficiency → EFF5
.40**
4.42
E5
Efficiency → EFF6
.70**
7.48
E6
Flexibility → FLX1
.56nt
E7
Flexibility → FLX2
.85**
6.25
E8
Flexibility → FLX3
.82**
6.02
E9
Flexibility → FLX4
.78**
5.91
E10
Scope → SCO1
.68nt
Scope → SCO2
.77**
7.67
Disturbance Variance
Scope → SCO3
.77**
7.51
D1
D2
D3
Error Covariance
E3 ↔ E8
.29**
3.09
E9 ↔ E6
-.52**
-4.58
E3 ↔ E6
.39**
3.55
E8 ↔ E7
-.31**
-2.92
E2 ↔ E9
.37**
3.58
E2 ↔ E5
.38**
3.51
Goodness of Fit Summary
Chi-square = 39.15, df = 26, p < .05
CFI = 0.98, RMSEA = .07
nt: The parameter is not tested.
ns: The parameter estimate is not significant.
** p < .01, * p < .05

73

Standard
Estimate

Critical Ratio

-.06ns
0.84**
0.51**
0.69**
0.28**
0.33**
0.40**
0.54**
0.41**
0.40**

-.63
7.18
6.29
7.06
5.90
5.89
6.64
6.92
6.55
7.19

0.49**
0.01ns
-.19*

4.42
.083
-2.47

Confirmatory Factor Analysis: Convergent and Discriminant Validity
To evaluate the construct validity of measures before estimating the causal relationships
by the simultaneous equation model, we performed Confirmatory Factor Analysis (CFA).
Analysis of Moment Structure (AMOS) 17.0 was used for building the path diagram for
presenting relationships between variables of MO, EO, Efficiency, Scope, Flexibility,
Exploitative, and Explorative with their multiple observed variables. The Maximum Likelihood
(ML) method was used for the estimation of parameters because its estimators are asymptotically
unbiased, consistent, and efficient (Bollen, 1989).
The main purpose of CFA is to evaluate the relationship between the constructs and the
nomological network of the measurement model, thus, we examine convergent and discriminant
validities of 7 factors. The results of the CFA reveal a good fit of the model to the data (Chisquare = 183.37, df = 147, p < .05, CFI = .97, RMSEA = .05). The indicator parameters to
measure the presumed construct ranges from .45 to .94 (p < .01), which shows convergent
validity of each construct was achieved. In order to assess the discriminant validity, the estimated
correlations of the factors that are used different constructs should not be excessively high (r
< .85) (Kline, 1998). Although the CFA model fit was acceptable, the correlations between
factors show that the factors between Scope and Flexibility of information search behavior and
between Exploitative and Explorative organizational learning are poorly estimated but since they
were near 1, we decided to further analyze it (Reckase, 2010). High correlations greater than .85
indicate that the factors are not distinct enough to be considered as different variables and one
approach to solve this problem is considering the variables as one variable (Kline, 1998).
Another approach to solve the inadequate discriminant validity is to find a reasonable correlation
values from the previous literature and replace the correlation value in the CFA model and test

74

the model (Reckase, 2010). Since we have small sample size (N=102), it may not be large
enough to obtain adequate support for the distinctiveness of the estimation of parameters, we try
to find reasonable correlations between those factors.
For the Scope and Flexibility of information search behavior, there is no empirical study
examining the correlation of these variables. Instead these variables’ similar impacts in relation
with other organizational variables are presumed in previous studies. In the study of Van den
Bosch et al. (1999), the authors propose that organizational knowledge integration types are
enhanced differently by the organization’s infrastructural forms and stages of industrial
development. While scope and flexibility of information integration are usually used in an
innovative organization, efficiency of information integration is heightened in a functional
organization. In addition, Van den Bosch, Van Wijk, and Volberda (2003) distinguish the three
dimensions of knowledge/information based on the types of knowledge environment such as
stable and turbulent knowledge environment. Accordingly, in the emerging industrial
development stage, the knowledge environment is turbulent and scope and flexibility of
information integration is appropriate while efficiency of information integration is appropriate
for the mature industrial development stage where the knowledge environment is stable. These
studies reveal that scope and flexibility are more likely correlated than that of efficiency and
scope or efficiency and flexibility of information integration. Thus, we assume that the
correlation between the factors scope and flexibility is higher than that of scope and efficiency or
flexibility and efficiency. Our original results show the factor correlations between Efficiency ↔
Scope, Efficiency ↔ Flexibility, and Scope ↔ Flexibility are .83, .76 and 1.05 respectively, and
we approximate for the correlation between scope and flexibility to be .83 as high as that of
efficiency and scope.

75

For Exploitative and Explorative organizational learning, few studies (Hult, Ketchen Jr.,
& Nichols Jr., 2003; James, Brockbank, & Ulrich, 2001) have empirically tested its relationship
with firms’ antecedents (size, age, environment, structure, culture, and strategy) and
consequences (performances) and only one study found its standardized phi value of 0.5 between
organizational memory and organizational learning (Hanvanich et al., 2006). In their study, the
authors assert that when the market and product innovation process is radical, firms need to find
novel and innovative ideas and practice an explorative learning orientation. On the contrary,
when the innovation process is adaptive-driven, its innovation is incremental and the firms’ use
of knowledge comes from organizational memory of stocked knowledge and information. These
views are similar to organization’s explorative and exploitative learning. Thus, we replace the
factor correlation between exploitative and explorative as .50. Once we constrained those two
values, the new CFA results show a good fit (Chi-square = 227.54, df = 153, p < .001, CFI = .94,
RMSEA = .07). The results of CFA are shown in Table 4.

76

Table 4. Confirmatory Factor Analysis: Results for the Measurement Model

Standard
Estimate

Path Coefficient
Market Orientation (MO) → Information Generation (IG)
Market Orientation (MO) → Information Dissemination (ID)
Market Orientation (MO) → Responsiveness (R)
Entrepreneurship Orientation (EO) → Innovativeness (I)
Entrepreneurship Orientation (EO) → Proactiveness (P)
Entrepreneurship Orientation (EO) → Risk-Taking Propensity (RP)
Information Search Behavior: Efficiency → EFF2
Information Search Behavior: Efficiency → EFF5
Information Search Behavior: Efficiency → EFF6
Information Search Behavior: Scope → SCO1
Information Search Behavior: Scope → SCO2
Information Search Behavior: Scope → SCO3
Information Search Behavior: Flexibility → FLX1
Information Search Behavior: Flexibility → FLX2
Information Search Behavior: Flexibility → FLX3
Information Search Behavior: Flexibility → FLX4
Exploitative Organizational Learning → Copiers/Benchmarkers (CB)
Exploitative Organizational Learning → Experts/Continuous Improvers (EC)
Explorative Organizational Learning → Experiments/Innovators (EI)
Explorative Organizational Learning → Competent/Skill Acquirers (CS)
Correlation Coefficients
Market Orientation (MO) ↔ Entrepreneurship Orientation (EO)
Market Orientation (MO) ↔ Information Search B: Efficiency
Market Orientation (MO) ↔ Information Search B: Scope
Market Orientation (MO) ↔ Information Search B: Flexibility
Market Orientation (MO) ↔ Exploitative Org. Learning
Market Orientation (MO) ↔ Explorative Org. Learning
Entrepreneurship Orientation (EO) ↔ Information Search B: Efficiency

77

.90nt
.94**
.92**
.72nt
.77**
.80**
.97nt
.45**
.72**
.67nt
.79**
.78**
.58nt
.87**
.82**
.74**
.51nt
.40**
.79nt
.90**
.28*
.63**
.67**
.68**
.82**
.73**
.07ns

Critical
Ratio

Critical
Ratio
(new)

9.00

Standard
Estimate
(new)
.90nt
.93**
.92**
.73nt
.79**
.81**
.96nt
.45**
.73**
.72nt
.81**
.73**
.64nt
.90**
.82**
.74**
1.02nt
.20*
.81nt
.90**

2.24
4.99
4.46
4.20
4.01
4.89
0.64

.29*
.61**
.61**
.64**
.35**
.72**
.05ns

2.34
5.54
6.33
6.77
4.24
7.60
.46

15.28
14.57
6.06
6.75
4.61
8.28
7.49
7.30
6.51
6.23
5.91
3.75

15.23
14.57
6.07
6.75
4.67
9.00
11.13
9.46
13.35
11.07
9.48
2.42
11.63

Table 4. Confirmatory Factor Analysis: Results for the Measurement Model (cont’d)

Standard
Estimate

Path Coefficient

Critical
Ratio

Standard
Estimate
(new)
.02ns
.33**
.06ns
.38**
.80**
.68**
.30*
.45**
.83
.49**
.49**
.34**
.69**
.50
.41**
-.44**

Critical
Ratio
(new)
.18
2.72
.55
3.07
9.31
7.89
3.15
4.47
fixed
4.87
5.43
4.05
10.06
fixed
3.39
-3.33

Entrepreneurship Orientation (EO) ↔ Information Search B: Scope
.12ns
0.94
Entrepreneurship Orientation (EO) ↔ Information Search B: Flexibility
.26**
1.99
Entrepreneurship Orientation (EO) ↔ Exploitative Org. Learning
.17ns
0.87
Entrepreneurship Orientation (EO) ↔ Explorative Org. Learning
.34**
2.57
Information Search B: Efficiency ↔ Information Search B: Scope
.83**
5.05
Information Search B: Efficiency ↔ Information Search B: Flexibility
.76**
4.44
Information Search B: Efficiency ↔ Exploitative Org. Learning
.67**
3.45
Information Search B: Efficiency ↔ Explorative Org. Learning
.49**
3.81
Information Search B: Scope ↔ Information Search B: Flexibility
1.05**
4.47
Information Search B: Scope ↔ Exploitative Org. Learning
.83**
3.60
Information Search B: Scope ↔ Explorative Org. Learning
.61**
3.96
Information Search B: Flexibility ↔ Exploitative Org. Learning
.78**
3.30
Information Search B: Flexibility ↔ Explorative Org. Learning
.68**
4.00
Exploitative Org. Learning ↔ Explorative Org. Learning
1.10**
4.37
E9 ↔ E10
.37**
3.32
E11 ↔ E15
-.48**
-3.85
Goodness of Fit Summary
Chi-square = 183.37, df = 147, p < .05, CFI = 0.97, RMSEA = .05
Goodness of Fit Summary (Correlations between Scope and Flexibility and Exploitative and Explorative factors are fixed
as .83 and .50, respectively.)
Chi-square = 227.54, df = 153, p < .001, CFI = 0.94, RMSEA = .07
nt: The parameter is not tested. ** p < .01, * p < .05

78

Structural Equation Model: Hypotheses Testing
Our primary purpose was to determine how retail buyers’ perception of their company’s
level of market orientation and entrepreneurship orientation affects other organizational
behaviors leading to organizational learning. We hypothesized that these two market strategies
have different effects on other organizational behavioral variables due to the different market
foci of MO/EO. We conducted a hybrid model testing which consists of structural and
measurement components to test the causal relationship between the variables (MO, EO,
Efficiency, Scope, Flexibility, Exploitative Organizational Learning, and Explorative
Organizational Learning). (See Figure 4). Results of measurement, overall model fit, and
structural model tests are reported in Table 5. The results of SEM analyses for overall model fit
using Maximum Likelihood estimation showed that the proposed model had a significant chisquare value, but it was considered an acceptable fit to the data based on CFI and RMSEA (Chisquare = 260.60, df = 155, p < .001, CFI = .91, RMSEA = .08). The Chi-square (155) of 260.60
is significant at the .001 level, which indicates that the fit of this model is significantly worse
than if it were just identified. However, Chi-square statistic is sensitive to sample size, so we use
the less sensitive value of Chi-square/df (260.60/155 = 1.68); a value of less than 3 is acceptable
(Kline, 1998). The Comparative Fit Index (CFI), which indicates the relative overall fit of the
proposed model, is .91 which means it is 91% better than that of the null model estimated with
the same sample data. Root Mean Square Error of Approximation (RMSEA) indicates the
amount of unexplained variance of residual; a smaller value is favorable (<.08). All results are
significant except the factor loadings between entrepreneurship orientation (EO) and scope
information search behavior (standardized parameter = .18, n.s.) and between efficiency and
exploitative learning (standardized parameter = .23, n.s.).

79

Market Orientation vs. Entrepreneurship Orientation
H1:

Employees’ perceived market orientation and employees’ perceived entrepreneurship
orientation are directly related.
Each hypothesis was tested with the results of significance of each factor loading in the

SEM analyses. The SEM results reveal that market orientation is positively correlated with
entrepreneurship orientation (γ = .31, p < .05), thus Hypothesis 1 was supported. This result
indicates that retail employees perceive market orientation and entrepreneurship orientation as
somewhat related concepts. This is consistent with results of Matsuno et al. (2002) and Morris et
al. (2007), both of which are empirical studies that found .468 for the path between EO to MO
with a manufacturing firm sample and a correlation of 0.18 for the constructs with a non-profit
organization sample, respectively. Matsuno et al. (2002) explained that EO’s innovativeness
resulted from the diffusion of information that is accomplished by the MO’s organization-wide
information sharing and utilization activities. Consistent with the previous results, our results
demonstrate that both strategies’ continuous market search activities in the collection of
expressed and unexpressed market information, disseminating it, and responding to it, results in
competitive advantages to the firms.

Employees’ Information Search Behavior
H2:

There is a relationship between market orientation/entrepreneurship orientation and the
information search process.
H2a: Employees’ perception of market orientation is positively related to the
efficiency of information search.
H2b: Employees’ perception of market orientation is positively related to the
flexibility of information search.
H2c: Employees’ perception of entrepreneurship orientation is positively related
to the flexibility of information search.

80

H2d: Employees’ perception of entrepreneurship orientation is positively related
to the scope of information search.
H2a and H2b proposed that employees’ perception of market orientation has strong and
positive effects on employees’ efficiency and flexibility of information search behavior. As
predicted, the results indicated that MO has a positive and significant effect on efficiency in
information search behavior (standardized factor loading = .62, p < .01) and a positive and
significant effect on flexibility in information search behavior (.19, p< .05), although the
magnitude of the latter path is weaker than the former. Thus, hypotheses H2a, and H2b were
supported.
Since the information search behavior measures were newly developed in our study,
based on previous information integration literature (Grant, 1996a, b), there were no previous
empirical results with which to compare ours. However, we postulated based on previous studies,
such as Deshpande et al. (1993) and Tushman et al. (1997) which discuss MO’s focus on
expressed customer demands and our results support that market orientation is strongly related to
the efficiency dimension of information search behavior. Recall that efficient information search
behavior consists of familiarity with information/knowledge, frequent and coordinated pattern of
activities regarding market information search, and a seamless communication structure. Market
orientation’s continuous market sensing activities about customers, competitors, and overall
trends in the marketplace, yields employees who are familiar with the market information and
this familiarity leads them to search information efficiently. We assumed that market oriented
firms have clearly defined future plans/goals due to the firms’ continuous effort to monitoring
market needs. At the same time, market orientation’s focus of constant information sensing and
seamless communication structure provides increased common sense and common knowledge
about the market, this helps developing strong group ties. In drawing rationale from image theory

81

and social network theory, market orientation yields firms with well defined future goals
(images) and strong group ties, these lead the firm to avoid risk in their information search
decisions. With strong interpersonal ties, people have little variation in their expectations and
with clearly defined goals, risk taking is not desired.
A positive but less strong relationship was found between market orientation and the
flexibility dimension of information search. As Grant (1996 a, b) described the flexibility of
knowledge integration as a combination of old and new knowledge through refining,
reconfiguring, and extending the knowledge, we view such flexibility is an intermediate position
between search of existing information/knowledge and search of drastic new
information/knowledge. While MO strategy helps firms to gather information in efficiently
oriented manner, MO firms often bring new information to the firm; through consistent market
search, executives in market orientated firms encourage employees to capture not only the
expressed customer needs but also the unexpressed market needs, which may not be clearly
known to the firm and/or to the market. Thus, MO strategy guides companies to extend search of
new information and new knowledge through the flexible information search. Our results support
our assumption that while MO is strongly related to the efficiency dimension of information
search, market orientation also helps companies obtain flexible new market information. Market
orientation directs firms to reconfigure the existing information/knowledge while adding new
information.
H2c and H2d proposed that employees’ perception of entrepreneurship orientation has
positive effects on flexibility and scope in information search behavior. The results reveal that
the standardized factor loading of EO on flexibility is .24 (p < .05), which demonstrates that EO
has a positive effect on flexibility, while the standardized factor loading between EO and scope

82

is not significant (.18, n.s.). These show that retail entrepreneurs search for flexible dimension of
information in their product/market development but not in diverse or innovatively new
information. Hypothesis 2c was supported while H2d was not supported.
Previous studies (Deshpande et al., 1993; Lumpkin and Dess, 2001; Tushman et al.,
1997) argue that EO is proactively involved with innovative market information. Thus, we
hypothesized that entrepreneurship orientation’s innovativeness, proactiveness, and risk taking
preferences lead the firms to search for scope dimension of information which is diverse and
radically innovative. However, based on our study results, we discover that entrepreneurship
orientation’s search of new information relies on from the extension of new and the
reconfiguration of old information, which is the flexibility dimension of information search.
Gathering of really new information is not related to the entrepreneurship orientation. One
possible explanation is the characteristics of retail sample. Although retail employees in our
sample perceive the company strategy as entrepreneurship oriented, the search of radically new
and different sources of information (scope) may be unreliable or unrealistic.

Employee Empowerment: Moderating Effect on Information Search Behavior

H3:

There is a moderating effect of employee empowerment in the relationship between
market strategy (market orientation/entrepreneurship orientation) and types of
information search (efficiency/scope/flexibility).
H3a: The greater the degree of employee empowerment, the lesser the positive impact of
market orientation and the efficiency dimension of employee search behavior.
H3b: The lesser the degree of employee empowerment, the greater the positive impact of
market orientation and the efficiency dimension of employee search behavior.
To test the moderating effects of employee empowerment on employees’ information

search behavior, we conducted a two-group comparison SEM analysis. Previous studies suggest
that the group invariance of path coefficients should be tested by constraining the paths equal to

83

both groups then releasing a path constraint, one by one, to measure the group differences
(Myers, Calantone, Page, & Taylor, 2000; Reynolds & Kaiser, 1990). We compared the relative
fits with the Chi-square difference tests. If the fit of a model with equality-constrained loadings
is significantly worse than that of the unconstrained model, then individual factor loadings
should be compared across the groups to determine the extent that group membership moderates
the paths (Kline, 1998).
We first divide the sample into two groups based on the mean value of the employee
empowerment variable. The number of respondents in high employee empowerment group is 62
(the mean of employee empowerment ≥ 5.504) and the number of respondents in low employee
empowerment group is 40. However, since the mean of this empowerment is very high (based on
1 to 7 scale), it is not appropriate to call the group name as low empowerment group. Thus, we
call this group the less highly empowered group and compare differences of the high and less
highly empowered groups. Initially, the moderating effect is tested based on the acceptable
whole model with paths and measurements (Chi-square = 260.60, df = 155, p < .001, CFI = .91,
RMSEA = .08). Then, we tested a path model with latent variables with cross-group constraints.
All four path coefficients (MO→EFF, MO→FLEX, EO→SCOPE, and EO→FLEX) were
constrained in order to compare the two groups and these constraints were released one by one
based on Chi-square difference test results. Our results reveal that three constraints (MO→EFF,
EO→SCOPE, and EO→FLEX) contributed to the inequality of the two groups (See Table 6 and
Figure 4). Comparison of Chi-square differences in the four path constrained model and the
MO→EFF path released model statistics show Chi-square difference of 5.15 (523.88 – 518.73 =
5.15) with 1 degree of freedom (314-313 = 1). The Chi-square difference result is significant at
the .05 level. This shows employee empowerment moderates the effects between market

84

orientation and efficient search of information. Looking at the group differences between high
and less high employee empowerment groups, the high employee empowerment group factor
loading is .30 while the less high group factor loading is .81.
We hypothesized that the more empowered employees would have a weaker positive
relationship between market orientation and the efficiency information search. Highly
empowered employees’ proactive and self-determined confidence feeling in their information
search decision making are not fit to search of efficiency oriented information search. On the
other hand, less empowered employees would have a stronger positive relationship between
market orientation and the efficiency information search. That is, less highly empowered
employees are more prone to efficient information search. Thus, hypotheses H3a and H3b are
supported.

H3c: The greater the degree of employee empowerment, the greater the positive impact
of market orientation and the flexibility dimension of employee search behavior.
H3d: The lesser the degree of employee empowerment, the lesser the positive impact of
market orientation and the flexibility dimension of employee search behavior.
Likewise, the Chi-square difference test results of MO→FLEX with the moderating
effect of employee empowerment is 10.50 (1df, p < .01) indicating that the group difference path
released model is improved significantly from the model with four paths constrained. It shows
the employee empowerment moderating effect exists. However, examining the group differences
between highly and less highly empowered factor loadings, the high group is .08 while the less
high group is .17. These findings reveal that the magnitudes between the two groups are different
from our hypotheses. We assumed that the proactive and autonomous feelings of highly
empowered employees would have a greater effect (than less highly empowered employees) on
the relationship between MO and EO and their flexible search of old and new information.

85

Surprisingly, the results show an inverse relationship; the relationship between MO and flexible
information search is greater for less highly empowered employees than for highly empowered
employees. These results are not consistent with previous studies showing that empowered
employees are more likely proactive, innovative, and creative (Mayer et al., 1995; Spreitzer,
1995; Chan et al., 2008). While empowerment gives a self-determined feeling, this is usually
accompanied with additional responsibility. That is, due to delegated power, empowered
employees receive increased responsibility and perhaps they feel stress from the pressure to
demonstrate innovative and creative decision making (Hakimi, Van Knippenberg, and Giessner,
2010; Heathfield, 2010). Although the moderating magnitudes are different than we expected,
our results show employee empowerment to be a moderating effect between market orientation
and flexible information search. Thus, hypotheses H3c and H3d are partially supported.

H3e: The greater the degree of employee empowerment, the greater the positive impact
of entrepreneurship orientation and the flexibility dimension of employee search behavior.
H3f: The lesser the degree of employee empowerment, the lesser the positive impact of
entrepreneurship orientation and the flexibility dimension of employee search behavior.
The Chi-square difference test result of EO→FLEX is 3.42 (1df, ns), indicating that the
group difference path released model is not improved from the model with four paths constrained.
Employee empowerment does not moderate the relationship between entrepreneurship
orientation and flexible information search behavior. The H3e and H3f are not supported.
However, despite the fact that the high and less high employee empowerment groups are not
significantly different, the factor loading for high empowered group is .20 while the factor
loading for less high empowered group is .03, which is the same direction as we hypothesized;
empowered employees are more likely involved with scope information search behavior and to
take risks in decision making.

86

H3g: The greater the degree of employee empowerment, the greater the positive impact
of entrepreneurship orientation and the scope dimension of employee search behavior.
H3h: The lesser the degree of employee empowerment, the lesser the positive impact of
entrepreneurship orientation and the scope dimension of employee search behavior.
The Chi-square difference test result of EO→SCOPE is 5.62 (1df, p < .05), implying that
employee empowerment does moderate the relationship between entrepreneurship orientation
and diverse information search behavior (scope). However, the results were the inverse of what
we expected. The path coefficient for the highly empowered group was .05 while the less highly
empowered group was .30. We expected that the greater empowered employees with an
entrepreneurship oriented business perception would have a stronger relationship with
information search related to the scope dimension (diverse and innovatively new information
search), but the results show the EO-Scope relationship is stronger for less highly empowered
employees. Again, these results might be explained the accompanied responsibilities of
empowerment. Highly empowered employees might have stronger responsibility than less highly
empowered employees; this led them to search weakly in scope information dimension.
Hypotheses H3g and H3h are partially supported.
Overall, employee empowerment moderating effects are found in the following
relationships: 1) market orientation and efficiency, 2) market orientation and flexibility, and 3)
entrepreneurship orientation and flexibility. Surprisingly, in all three cases, high employee
empowerment provides less search of efficiency, flexibility information while less high
employee empowerment leads them to search more in efficiency and flexibility information
dimension. Appropriate employee empowerment is discussed in managerial implication in
conclusion.

87

Organizational Learning
H4: There is a relationship between information search types and information learning styles.
H4a: The efficiency of information search is positively related to the exploitative
organizational learning.
H4b: The flexibility of information search is positively related to the exploitative
organizational learning.
H4c: The flexibility of information search is positively related to the explorative
organizational learning.
H4d: The scope of information search is positively related to the explorative
organizational learning.
Hypotheses 4 proposed that variation in employees’ information search behavior leads to
different types of organizational learning. More specifically, H4a and H4b presumed that
organizations’ exploitative learning, which consists of firms’ routinized benchmarking and
continuous incremental learning is suitable for efficiently collected information (H4a).
Information collected in a flexible manner requires continuous learning and is also related to
exploitative learning (H4b). However, our results show that there is no significant relationship
between efficient information search behavior and exploitative organizational learning (.23, ns)
while the flexible information search dimension is positively and significantly related to the
exploitative organizational learning (.70, p < .01). Therefore, hypothesis H4a is not supported
while H4b is supported. These results indicate that efficient information search, such as familiar
information sources and routinized collection method, may not induce any organizational
learning. These findings are contrary to Van den Bosch et al.’s (1999) proposition that efficiency
in information search is closely related to exploitative learning’s focus on cost competitive
advantage. They mention that in absorbing (learning) of efficiency knowledge, routines and
procedures that firms use to identify are assimilate and thus cost saving (economies of scales)

88

could occur. Hunter (2003) and Yeung et al.’s (1999) discussion of benchmarking and
continuous improving learning environments as exploitative learning are not supported by our
results. Probably, information collected in efficient manner is already perceived to the employees
and such information does not require any learning effort. On the other hand, information
collected in flexible manner, which is a balance of modifying old information while adding new
information to that is appropriate to use of exploitative, continuous learning environment.
H4c and H4d hypothesized that explorative organizational learning, which consists of
innovative experimentation and acquisition of new skills, is positively related to the scope and
flexible information search dimensions. As we expected, scope (diverse and radically new)
information is significantly and positively related to explorative learning (2.73, p < .01), so
hypotheses H4d is supported; this supports Van den Bosch et al’s (1999) proposition that
company’s innovative breakthroughs are closely related with explorative learning. However,
contrary to our expectation and Van den Bosch et al.’s (1999) propositions, search of flexible
information dimension is significantly negatively related to the explorative organizational
learning (-2.29, p < .05). Since explorative learning is involved with risk and large variation in
its success and failure of implementation, we possibly view this as the reason that, flexible
information does not require such learning environment, thus, it is negatively related to the
explorative learning. Thus, H4c is not supported.
Overall, market information searched in efficient manner does not need any organization
learning since the information is familiar to the employees. Information collected in diverse and
innovatively search requires explorative organizational learning due to innovative and originality
of its information. Information searched in flexible manner is learned by exploitative learning
such as benchmarking or incremental learning methods. However information search in flexible

89

manner does not require the explorative learning which usually involves with the firms’ large
and risky investment may not compensate the learning of flexible information.

90

Figure 4. Results of Structural Equation Model

91

Table 5. Structural Equation Model: Results for the Hypotheses
Standard Estimate Critical Ratio
Standard Estimate Critical Ratio
Path Coefficient
Error Variance
MO → Efficiency
.62**
6.90
E1
0.19**
5.29
MO → Flexibility
.19*
2.59
E2
0.11**
3.66
EO → Scope
.18ns
1.43
E3
0.17**
4.88
EO → Flexibility
.24*
1.98
E4
0.49**
5.20
Efficiency → Exploitative
.23ns
1.06
E5
0.38**
3.78
Flexibility → Exploitative
.70**
2.71
E6
0.39**
3.99
Flexibility → Explorative
2.73**
2.67
E7
0.04ns
0.42
Scope → Explorative
-2.29*
-2.22
E8
0.80**
6.98
MO → IG
.90nt
E9
0.52**
6.02
MO → ID
.94**
15.24
E10
0.71**
6.89
MO → R
.91**
14.34
E11
0.22**
5.23
EO → I
.71nt
E12
0.39**
5.68
EO → P
.79**
5.90
E13
0.43**
6.37
EO → RP
.78**
6.71
E14
0.59**
6.45
EFF → EFF2
.98nt
E15
0.39**
5.58
EFF → EFF5
.44**
4.45
E16
0.35**
5.68
EFF → EFF6
.69**
7.20
E17
0.77**
5.00
FLEX → FLX1
.54nt
E18
0.87**
6.39
FLEX → FLX2
.88**
6.33
E19
0.42**
5.37
FLEX → FLX3
.78**
5.98
E20
0.22**
2.93
FLEX → FLX4
.75**
5.87
SCOPE → SCO1
.64nt
Disturbance Variances
SCOPE → SCO2
.78**
6.84
D1
0.62**
4.32
SCOPE → SCO3
.80**
6.82
D2
0.88**
2.93
EXI → CB
.48nt
D3
0.97**
3.55
EXI → EC
.36**
3.59
D4
0.41ns
.78
EXR → EI
.76nt
D5
0.33ns
1.63
EXR → CS
.88**
8.97

92

Table 5. Structural Equation Model: Results for the Hypotheses (cont’d)
Standard Estimate Critical Ratio
Factor Covariance
Error Variance
MO ↔ EO
.31**
2.42
E9 ↔ E14
E15 ↔ E12
E9 ↔ E12
E14 ↔ E12
Disturbance Covariance
D2 ↔ D3
D4 ↔ D5
Goodness of Fit Summary
Chi-square = 260.60, df = 155, p < .001
CFI = 0.91, RMSEA = .08
nt: The parameter is not tested.
ns: The parameter estimate is not significant.
** p < .01, * p < .05

93

Standard Estimate Critical Ratio
.43**
-.45**
.42**
.34**

3.68
-3.99
3.56
2.72

.99**
1.57**

4.12
3.44

Table 6. Moderating Effects: Chi-square Difference Tests
All 4 paths were constrained.
a1 (high ememp MO→EFF) =
a2 (high ememp MO→FLEX) =
a3 (high ememp EO→FLEX) =
a4 (high ememp EO→SCOPE) =

b1 (low ememp MO→EFF)
b2 (low ememp MO→FLEX)
b3 (low ememp EO→FLEX)
b4 (low ememp EO→SCOPE)

Chisquare
523.88

df
314

518.73

313

Chisquare
diff
(df=1)
5.15*

513.38

313

10.50**

MO → Flexibility

520.46

313

3.42ns

EO → Flexibility

518.25

313

5.63*

EO → Scope

Model equality was released.
(Releasing one path constraint while constraining the others)
H3a: a1(high ememp MO→EFF) ≠ b1 (low ememp MO→EFF)
a2=b2, a3=b3, a4=b4
H3b: a2(high ememp MO→FLEX) ≠ b2 (low ememp MO→FLEX)
a1=b1, a3=b3, a4=b4
H3c: a3(high ememp EO→FLEX) ≠ b3 (low ememp EO→FLEX)
a1=b1, a2=b2, a4=b4
H3d: a4(high ememp EO→SCOPE) ≠ b4(low ememp EO→SCOPE)
a1=b1, a2=b2, a3=b3
*p < .05, **p < .01
ns: Not Significant

94

Path Coefficient

Standard
Estimate

MO → Efficiency

High: .30
Low: .81
High: .08
Low: .17
High: .20
Low: .03
High: .05
Low: .30

CHAPTER V

SUMMARY AND CONCLUSIONS

The purpose of this chapter is to summarize the study objectives, findings and provide
implications from the research findings. First, we discuss the objectives of the study, findings,
and contributions. We present managerial implications next, followed by a discussion of the
study limitations and recommendations for future studies.

Summary of Research Objectives, Findings, and Contributions
Organizational strategy literature emphasizes the strong and positive impacts of market
orientation and entrepreneurship orientation on organizational outcomes. Market orientation
helps company employees continuously monitor market information and quickly react to market
trend changes in an effort to satisfy consumer needs and yield increased firm performance.
Entrepreneurship orientation encourages employees to use their proactive and risk-taking
tendencies to seek information for fast changing and emerging market product initiatives.
Previous research criticizes MO for only focusing on needs of current customers and using
efficient information search behaviors to meet those needs. Likewise, previous research
criticizes EO for focusing too heavily on radical information search behaviors that are not
complimentary with consumer’s needs. Therefore, our objective was to investigate more
thoroughly how MO and EO are related to information search behaviors.
Because empowered employees have enhanced authority and freedom in their decision
making, they are more likely than less empowered employees to search for diverse and

95

innovative information. Therefore, we tested whether employee empowerment moderated the
relationship between MO/EO and information search behavior. Finally, we investigated the types
of organizational learning environments that are related to information search behaviors. We
argue that in order to absorb market/product information, firms must have appropriate learning
environments based on the information types.
Our study findings have three important contributions to the literature in retail marketing
and management. First, in this study, we developed scales to measure the 3 types of information
search behavior by conducting a thorough literature search and conducting qualitative interviews
with retail buyers. Our scale development was based on Grant’s (1996a) conceptual constructs of
knowledge integration types (efficiency, flexibility, and scope) and we framed the items relative
to the search for market information in the development of new products and/or markets.
Second, we proposed and confirmed that there are differences between MO and EO in
their information search behaviors. Previous studies have been inconsistent about the types of
information searched for by MO and EO employees and the effects of market strategy on
performance. Our results show that employees with a market orientation are strongly related to
the efficiency dimension of information search behavior. MO employees also engage in
flexibility search behavior but the relationship is less strong. Therefore, employees with a strong
market orientation not only search for updates to existing information, they also extend their
search by configuring and refining old information and adding some new information. Our
results do not confirm the criticism that MO has a tendency toward information inertia (Grewal
and Tansuhaj, 2001). Market orientation strategy collects new information, even if it is not
radically or innovatively new; the information search does extend into new sources.

96

While previous research (Lumpkin and Dess, 1996) suggested that entrepreneurial
employees would likely search for radical and innovative new information, we found no
evidence of that tendency. As we also proposed, EO employees tend to engage in flexible
information search behavior. This may be due to our retail sample characteristics. Since our
sample did not consist of employees from any specialty or upscale stores, perhaps their target
market does not require truly unusual and innovative merchandise. Also, unlike manufacturers,
retailers have direct contact with customers and they need to satisfy customer needs in their
product and market development decision making (Jones and Reynolds, 2006). Since excessive
innovation is not always accepted by customers, our study confirms that there is no need for
employees (at least in our sample) to use the scope dimension of information search. Perhaps we
should not be too surprised that the scope dimension of information search was not significantly
accessed by EO employees because, during our qualitative interviews, customer demands in
relation to product/market development were discussed. One interviewee said that not all product
lines are developed innovatively. For instance, most product lines prepare for their new season
by updating previous products and only a small portion of the company’s product lines are
innovatively developed. Those innovatively developed lines usually require specialized
information focusing on a special line and special target market (such as advanced sportswear
product line or targeting young contemporary designer lines). This demonstrates that retail
buyers collect information based more on efficiency or flexibility factors than the scope factor.
Use of efficient information search method produces profits with less effort and lower budgets,
and it might be the reason of why retailers commonly search for information in efficient ways
(Abernathy, Dunlop, Hammond, and Weil, 1995). Also, use of flexible information search is
necessary for them to differentiate themselves in the marketplace.

97

Finally, we extended our understanding of the organizational learning environment based
on information types. Literatures in organizational learning discuss refinement and extension of
existing competencies as exploitative learning with predictable learning outcomes. As Yeung et
al. (1999) defined, we proposed that benchmarking of the company’s or competitors’’ best
practices or continuously improving their competencies are necessary to internalize/learn
information searched by using either efficient or flexible information search behaviors. We also
proposed that learning about innovative, new alternatives through experiment or acquiring new
competency is appropriate for flexible and scope information search behaviors. Our findings
show that exploitative learning occurs when flexible information search behavior is implemented
while explorative learning occurs when scope information search behavior is implemented.
However, exploitative learning is not significantly related to efficiently collected information.
Learning does not occur when employees routinely use existing information. Instead,
information obtained by refinement of previously existing knowledge plus incrementally adapted
(flexibility) information results in exploitative organizational learning. Although, our findings
only support the relationship between scope and explorative organizational learning based on
Van den Bosch et al. (1999) and Yeung et al.’s (1999) discussion, our efforts for connecting
information search behaviors to learning styles help firms develop an appropriate learning
environment. Firms need to think about how they can improve employee learning of dynamically
changing market trends and our study would be groundwork in that consideration.

Managerial Implications
From beginning of this study, we were aware of two criticisms, one related to marketing
orientation and the other related to entrepreneurship orientation: 1) does market orientation guide

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firms to develop knowledge/information myopia and ultimately render firms ignorant of
emerging market trends? 2) does entrepreneurship orientation drive firms to have radically
innovative products that do not match with customer expressed needs and wants and increase the
likelihood of firms to fail in the marketplace? In our study, especially in a retail setting, we found
the answers to be no.
Since retailers’ survival depends on responding to customers’ needs and wants, providing
a corresponding mix of products and services, and consequently increasing customers’
purchasing decision, retailers like those in our sample should collect information that is not
radically or innovatively new, but is incrementally or moderately new.
However, it should be noticed that although retailers’ market orientation is strongly
related to search of routine and incremental expansion of new information, routine information is
not related to organizational learning. If retailers do not learn about fast changing market trends,
they risk becoming obsolete. Thus, we suggest that search of efficient dimension of information
should be balanced with collection of flexible information, such as information from other
departments or new market research in order to meet actively changing market needs. Although,
efficient information yields cost savings, learning of flexible information provides adaptive
capabilities that are favorable in the rapidly changing marketplace.
Flexibly collected information requires benchmarking or continuously mastering
additional competencies. Therefore, firm executives should provide data about the strongest
competitors and/or data about the success cases within the company, in order to help employees
learn the collected market information in a compatible manner.
Even though our sample did not yield a significant relationship between EO and scope
information search behavior, scope was strongly related to explorative learning. If EO

99

employees would search for diverse and innovative information, which is really new to the
company, an explorative learning environment would help employees adopt a culture of being
industry leaders who continuously acquire new skills and competencies. If a company target an
unfamiliar market or developing a new product which is not previously available in the company,
perhaps market testing as an experiment is a suitable way to learn the market. Hiring skilled and
knowledgeable people or having a strategic alliance with a specialized company are the
necessary learning methods for obtaining radically innovative information.
Employee empowerment has been considered as an important concept in business due to
their proactive and creative performances, and managers try to support and encourage employees
by providing them more autonomous power and self-determined decision making in the work
environment. Surprisingly, our study found that more empowered employees are less likely to
use flexibility and scope information search behaviors, which is contrary to previous studies.
Empowered employees are less likely to search for diverse and innovative information than less
empowered employees. Perhaps empowered employees receive additional responsibilities due to
delegated power, leading to more pressure/stress for employees (Hakimi et al., 2010; Heathfield,
2010). As a result, they avoid the more risky search for diverse and innovative information.
Sharing the responsibilities with other employees may enhance the search of diverse information.
Networking with other people or team decision making in search of new information, may
reduce their own risks in obtaining radically new information. Offering a training class or
mentoring/role modeling system in the workplace would assist employees in knowing where and
how to search for innovative information.

100

Limitations and Recommendations for Future Studies
Generally small sample size causes issues with reliability of measurement, the shape of
population distribution, and statistical power in testing parameter estimates. However, small
sample size is often unavoidable, especially when collecting a corporate retail sample. A larger
sample size decreases standard errors and provides more precise statistical parameter estimates
for better results. Future retail studies should consider the possible sample size about how to
obtain an appropriately large enough sample size for the model testing and parameter estimates.
Compared to market orientation, our results demonstrate that information search behavior
is not clearly explained for entrepreneurship orientation. Since our sample was basically
collected from Hoover’s online database of retailers and contact through retail position alumni,
these retailers are not small and independent retailers. It would be interesting to examine how
small local retailers, who mostly work independently, behave in relation to information search
and learning. Thus, we suggest investigating our proposed model with different retail settings,
would bring more rich information to our understanding of the construct relationships.
Since the information search behavior scale is newly developed, based on previous
knowledge integration literatures, further investigation of the scale or appropriate modification of
the scale are required. Since our focus was retail buyers’ information search behavior,
information search in different industries or in different managerial positions may extend our
understanding of this construct. Further, investigation of this construct with firms’ other
variables, such as monetary performance or success rate of new product/market development
may enhance information search as a valuable construct in organizational study.
Along with market strategies, the moderating effects of employee empowerment on
information search behaviors may require further study. Unexpectedly, our results show that

101

employee empowerment is not related to the risk taking or creative information search behavior.
Instead more delegated power and freedom in decision making may burden employees and as a
result, they are less likely search for diverse information. Future studies should examine how the
added responsibility of empowerment affects other organizational variables.

102

APPENDICES

103

APPENDIX I: QUESTIONNAIRE

104

Please answer each question honestly and candidly. There are no right or wrong answers.
Please circle the number that reflects the most appropriate response for your company.
Section 1.
1. In this firm, we meet with customers at least once a year to find out what products or
services they will need in the future.
2. When we find out that customers are unhappy with the quality of our service, we take
corrective action immediately.
3. Marketing personnel in our firm spend time discussing customers’ future needs with
other functional departments.
4. Our firm periodically circulates documents (e.g., reports, newsletters) that provide
information on our customers.
5. The activities of the different departments in this firm are well coordinated.
6. We collect industry information through informal means (e.g., lunch with sales
representatives, talks with trade partners).
7. When we find that customers would like us to modify product selection or service, the
departments involved make concerted efforts to do so.
8. We are quick to respond to significant changes in our competitors’ pricing structures.
9. It takes us forever to decide how to respond to our competitors’ price changes.
10. Our business plans are driven more by technological advances than by market research.
11. We have meetings at least once a quarter to discuss market trends and developments.
12. We periodically review the likely effect of changes in our business environment (e.g.,
regulation) on customers.
Individuals from our buying department interact directly with customers to learn how to
13.
serve them better.
14. In this firm, intelligence on our competitors is generated independently by several
departments.
15. Principles of market segmentation drive new product selection efforts in this firm.
16. There is minimal communication between marketing and buying departments concerning
market development.
17. We are slow to detect changes in our customers’ product preferences.
18. The product lines we sell depend more on internal politics than real market needs.
19. Data on customer satisfaction are disseminated at all levels in this firm on a regular basis.

105

Strongly
Disagree
1
2
3

4

Strongly
Agree
5
6
7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1
1

2
2

3
3

4
4

5
5

6
6

7
7

1

2

3

4

5

6

7

1
1
1
1
1

2
2
2
2
2

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

6
6
6
6
6

7
7
7
7
7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1
1

2
2

3
3

4
4

5
5

6
6

7
7

1
1
1

2
2
2

3
3
3

4
4
4

5
5
5

6
6
6

7
7
7

20. For one reason or another we tend to ignore changes in our customers’ product or service
needs.
21. We periodically review our product assortment to ensure that they are in line with what
customers want.
22. A lot of informal “hall talk” in this firm concerns our competitors’ tactics or strategies.
23. Several departments get together periodically to plan a response to changes taking place
in our business environment.
24. In this firm we do a lot of in-house market research.
25. If a major competitor were to launch an intensive campaign targeted at our customers, we
would implement a response immediately.
26. We poll customers at least once a year to assess the quality of our products and services.
27. Customer complaints fall on deaf ears in this firm.
28. Even if we came up with a great marketing plan, we probably would not be able to
implement it in a timely fashion.
We are slow to detect fundamental shifts in our industry (e.g., competition, technology,
29.
regulation).
30. When one department finds out something important about competitors, it is slow to alert
other departments.
31. When something important happens in the market the whole business unit knows about it
in a short period.

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1
1

2
2

3
3

4
4

5
5

6
6

7
7

1
1

2
2

3
3

4
4

5
5

6
6

7
7

1
1
1

2
2
2

3
3
3

4
4
4

5
5
5

6
6
6

7
7
7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

Please read two different statements at each side and circle the number that reflects the most appropriate response for your
company. (i.e., In Section 2, question #32, 6 means that your company has a stronger emphasis than scale 1, 2, 3, 4, and 5
on R&D, technological leadership, and innovation but less emphasis than scale 7.)
Section 2.
32. In general, the top managers of my firm favor…
A strong emphasis on the marketing of tried
1 2 3 4 5 6 7 A strong emphasis on R&D, technological
and true products or services
leadership, and innovation.
33. In dealing with its competitors, my firm…
Typically responds to actions which
1 2 3 4 5 6 7 Typically initiates actions which competitors
competitors initiate
then respond
34. Describe the nature of the new lines of products/ services that your firm marketed in the past 5 years?
Changes in product or service lines have been
1 2 3 4 5 6 7 Changes in product or service lines have

106

35.
36.

37.

38.

39.

40.

mostly a minor nature
usually been quite dramatic
How many new lines of products/services has your firm marketed in the past 5 years?
No new lines of products/services
1 2 3 4 5 6 7 Very many new lines of products/services
In dealing with its competitors, my firm…
Is very seldom the first business to introduce
1 2 3 4 5 6 7 Is very often the first business to introduce
new products/services
new products/services
In general, the top managers of my firm believe that…
1 2 3 4 5 6 7 Owing to the nature of the environment,
Owing to the nature of the environment, it is
best to explore it gradually via timid,
bold, wide-ranging acts are necessary to
incremental behavior
achieve the firm’s objectives
In general, the top managers of my firm have…
A strong proclivity for low-risk projects with
1 2 3 4 5 6 7 A strong proclivity for high-risk projects
normal and certain rates of return
with chances of very high returns
In dealing with its competitors, my firm…
Typically seeks to avoid competitive clashes,
1 2 3 4 5 6 7 Typically adopt a very competitive, ‘undopreferring a ‘live-and-let-live’ posture
the-competitors’ posture
When confronted with decision-making situations involving uncertainty, my firm…
Typically adopts a cautious, ‘wait-and-see’
1 2 3 4 5 6 7 Typically adopts a bold, aggressive posture
posture in order to minimize the probability of
in order to maximize the probability of
making costly decisions
exploiting potential opportunities

Please circle the number that reflects the most appropriate response for your company.
Section 3.
41. My supervisor helps me solve work-related problems.
42. My supervisor tells me what shall be done and how it shall be done.
43. My supervisor keeps informed about how employees think and feel about things.
44. My supervisor encourages employees to participate in important decisions.
45. My supervisor leaves it up to me to decide how to go about doing my job.
46. My supervisor encourages employees to speak up when they disagree with a decision.
47. My supervisor refuses to explain his or her actions.
48. My supervisor rewards me for good performance.
49. My supervisor always seems to be around checking on my work.

107

Strongly
Disagree
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2

3
3
3
3
3
3
3
3
3

4
4
4
4
4
4
4
4
4

5
5
5
5
5
5
5
5
5

Strongly
Agree
6
7
6
7
6
7
6
7
6
7
6
7
6
7
6
7
6
7

50. My supervisor encourages me to develop new skills.
51. My supervisor never gives me a chance to make important decisions on my own.
52. My supervisor praises good work.

1
1
1

2
2
2

3
3
3

4
4
4

5
5
5

6
6
6

7
7
7

When you read the following questions, assume that you are involved in developing/introducing a new product line.
Strongly
Strongly
Section 4.
Disagree
Agree
1
2
3
4
5
6
7
53. Your department employees have a good understanding of the information needed to
develop/introducing a new product line.
1
2
3
4
5
6
7
54. Your department regularly reconfigures your department’s old information (i.e., sales
record, marketing analysis, etc.) to make it useful for development/introduction of a new
product line.
1
2
3
4
5
6
7
55. Your department typically uses the same sources of information each time when
developing/introducing a new product line.
1
2
3
4
5
6
7
56. Your department regularly adopts new information for making decisions in
development/introduction of a new product line.
2
3
4
5
6
7
57. Your department shares with and uses information generated by other units as necessary. 1
1
2
3
4
5
6
7
58. Your department regularly integrates new information (i.e., new market trends, new
product development, new sourcing methods) into your department’s old information to
make it useful for development/introduction of a new product line.
1
2
3
4
5
6
7
59. Your department has easy access to knowledge/information from other departments that
could be useful for development/introduction of a new product line.
1
2
3
4
5
6
7
60. When developing/introducing a new product line (i.e., new vendor’s
history/merchandising supporting information, emerging market information, new
scientific knowledge, etc.), your department seeks different types of information
compared to the traditional buying process.
1
2
3
4
5
6
7
61. Your department uses communication technology to reduce communication time among
all those involved in developing/introducing a new product line (i.e., email, mobile
phone, fax, etc.).
1
2
3
4
5
6
7
62. There is a seamless coordination among the divisions that work together in
developing/introducing a new product line (i.e., trend dept., buying dept., marketing
dept., etc.).

108

63. Information is quickly dispersed to all who are involved in developing/introducing a new
product line.
64. Your department frequently holds formal and informal meetings when
developing/introducing a new product line.
65. Your department looks at specialized information when developing/introducing a new
product line (i.e., trade or scholarly journal, market research analysis, etc.).

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

To very
little extent
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

4
4
4
4
4
4
4
4
4

To very
large extent
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7

1

2

3

4

5

6

7

1
1
1
1
1

2
2
2
2
2

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

6
6
6
6
6

7
7
7
7
7

To what extent do the following statements characterize your business?
Section 5.
66. We constantly seek new ideas, even before old ones are fully implemented.
67. We encourage individuals to acquire new competencies.
68. We try a lot of new ideas; we want to be known as experimenters within our industry.
69. We master new ideas before moving on to the next round.
70. We upgrade the way that we do existing work until we have it right.
71. We learn by broadly scanning what other companies do.
72. We want to be known as the best technical experts in our industry.
73. We want to be the first in the market with a new process or product.
74. We learn from others, entering a product or applying a process after it has been fully
tested.
75. We primarily benchmark competition, measuring progress against competitors’
performance.
76. We learn by focusing our scanning on specific activities done by other companies.
77. We encourage teams to acquire competencies.
78. Learning is a critical part of our business strategy.
79. We constantly seek new ways to do work.
80. We learn by hiring people from other companies who have skills we need.
81. We primarily benchmark ourselves and measure progress against our previous
performance.

109

Please check (√) your response.
Section 6.
82. Please indicate your company name. _______________
83. Please check the product category with which you work.
_____ clothing
_____ food
_____ footwear
_____ furniture
_____ appliances
_____ electronics
Other: Please specify _______________
84. What is your job title? _______________
85. How many total years in retail business? _______________
86. How many total years in buying/planning department? _______________
87. Please check highest education level attained.
_____ grade school
_____ some high school
_____ high school diploma
_____ some business college or technical school
_____ college degree
_____ Master’s or higher degree
88. Please check your salary range.
_____ below $50,000
_____ $50,000 - $65,000
_____ $65,001 - $80,000
_____ $80,001 - $95,000
_____ $95,001 - $110,000
_____ over $110,000
89. Please indicate your sex.
_____ female
_____ male

110

90. Please check your age range.
_____ under 20
_____ 20-29
_____ 30-39
_____ 40-49
_____ 50-59
_____ 60 and over

111

APPENDIX II: COVER LETTERS

112

Dear Participants:
We are cordially requesting your partnership in a study of retail organizations, conducted by
Michigan State University. The purpose of our survey is to gather information that will help
retailers plan effective market intelligence. More specifically, we are trying to identify linkages
between retailers’ market strategies (market orientation & entrepreneurship orientation) and
organizational risk taking and information assimilation. To achieve our goal, we need to collect
data from middle- to upper-level buyers/product managers/product developers. Participation is
voluntary, but your input is of great value to us. The questionnaire can usually be completed
about 15 minutes.
Click on this link to go directly to the survey. Your responses are anonymous.
http://www.surveymonkey.com/s.asp?A=55756698E27787
If you are interested in learning more about our study or any questions about the study, you may
contact Dr. Linda Good (goodl@msu.edu, 355-1282) or So Jung Lee (leesojun@msu.edu, 3533877) in Retailing, Michigan State University. If you have questions or concerns regarding your
rights as a study participant, or are dissatisfied at any time with any aspect of this research
project, you may contact – anonymously, if you wish – Peter Vasilenko, Ph.D., Chair of the
University Committee on Research Involving Human Subjects (UCRIHS) by phone: (517) 3552180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East
Lansing, MI 48824.
You indicate your voluntary participation in this study by submitting the completed
questionnaire online survey. Your assistance is greatly appreciated we thank your in advance for
your time and attention. Good luck on your retail management career!

Sincerely,

Linda K. Good, Professor - Retailing
Phone: 517-355-1282 Fax: 517-352-1058
E-mail: goodl@msu.edu
112 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

So Jung Lee, Ph.D. Candidate - Retailing
Phone: 519-393-9678
E-mail: leesojun@msu.edu
404 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

113

Dear MSU Merchandising Management Alumni,
I am writing this letter to remind you about a survey that Dr. Good and I asked your involvement
in completing a retailing survey about a week ago. If you have already completed the survey and
voluntarily agreed and gained permission from your HR for the later sampling (second phase
sampling), Thank you.
If you have not completed the survey, please go to the Internet survey link and complete the
survey as soon as possible. Also as we explained about our second phase sampling procedure,
please let us know if you would gain permission to conduct our second phase survey in your
company, from HR or whoever in your company is appropriate (or providing us the contact name
so we can contact them directly).
Click on this link to go directly to the survey. Your responses are anonymous.
http://www.surveymonkey.com/s.asp?A=55756698E27787
We count on your responses to help us in our study about retail corporate issues. We really
appreciate for your voluntary participation.
Sincerely,

Linda K. Good, Professor - Retailing
Phone: 517-355-1282 Fax: 517-352-1058
E-mail: goodl@msu.edu
112 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

So Jung Lee, Ph.D. Candidate - Retailing
Phone: 519-393-9678
E-mail: leesojun@msu.edu
404 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

114

Date
Director of Human Resources
Central Division
(store name)
(address)
Dear Mr. (name):
Please find enclosed the packet of information I described per our recent phone conversation.
This packet describes the study which examines the effects of organizational strategy and risk
taking behavior on an organization’s market information learning activities.
Please be assured that all data collected will be held in strictest confidence. (company name) will
never be referred to and no individual data will be analyzed or reported. All data will be entered
in the computer with numeric codes only and will be analyzed in aggregate with no personally
identifiable information.
We are seeking your permission to conduct the study and to collect data and follow-up from the
middle- to upper-level buyers in any division(s) of your company. The survey will take
approximately 15 to 20 minutes to complete and there will be no risks involved with the
participants. We are hopeful that results from this study can be incorporated into university
curricula so that future retailing career employees have a better understanding of the retail
organization environment. Likewise, study results will be provided to you so that they may be
incorporated as needed by (name of company). It is our hope that the results of this study will
provide appropriate information assimilation (integration and learning) for (company name)
when development/introduction of new product lines based on market strategy and risk
orientation. A better understanding of these corporate issues will hopefully have productive
results for your company.
The first page of the packet explains the purpose, objectives, and time frame of the study. The
rest of the packet consists of the questionnaire drafts to be used to collect the data. Please feel
free to suggest additional items to be included in the questionnaire.
If you are interested in learning more about our study or any questions about the study, you may
contact Dr. Linda Good in the Human Environment and Design Department (goodl@msu.edu,
355-1282) and So Jung Lee in Human Environment and Design (leesojun@msu.edu, 353-3877).
If you have questions or concerns regarding your rights as a study participant, or are dissatisfied
at any time with any aspect of this research project, you may contact – anonymously, if you wish
– Peter Vasilenko, Ph.D., Chair of the University Committee on Research Involving Human
Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu, or
regular mail: 202 Olds Hall, East Lansing, MI 48824. If you willing to participate our study
please sign this form and returned to Dr. Linda Good by December 15, 2004. Your participation
is greatly appreciated.

115

Your Signature: ______________________________

Linda K. Good, Professor - Retailing
Phone: 517-355-1282 Fax: 517-352-1058
E-mail: goodl@msu.edu
112 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

So Jung Lee, Ph.D. Candidate - Retailing
Phone: 519-393-9678
E-mail: leesojun@msu.edu
404 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

116

Dear (store name) Managers:
We are writing this letter to respectfully ask for your help in completing a MSU retailing
study. Participation is voluntary, but your input is of great value to us. However, you may
choose not to participate at all, or may discontinue in any time without penalty or loss of benefits.
The questionnaire can usually be completed within 15 or 20 minutes. When completed, this
research project will provide information to your HR and market planning departments about
how to optimize your work environment with the most-appropriate methods for information
gathering and learning activities when development/introduction of new product lines.
(Company name) is cooperating with us in conducting this research and has approved our contact
with you. However, no one in the company will see the forms that you fill out. We guarantee that
your responses are confidential and will be analyzed only as a group, not on an individual basis,
with no personally identifiable information. Your privacy will be protected to the maximum
extent allowable by law.
Your part can be easily completed through an Internet-based survey at
(http://www.surveymonkey.com/s.asp?A=55756698E27787). If you prefer to use an identical
printed survey we will promptly send one to you through the postal service. Simply reply to this
e-mail (leesojun@msu.edu) with the subject: Requesting paper questionnaire. Please include
your name, the name of your company, and the address to which you wish the questionnaire
to be sent.
If you are interested in learning more about our study or any questions about the study, you may
contact Dr. Linda Good in the Human Environment and Design Department (goodl@msu.edu,
355-1282) and So Jung Lee in Human Environment and Design (leesojun@msu.edu, 353-3877).
If you have questions or concerns regarding your rights as a study participant, or are dissatisfied
at any time with any aspect of this research project, you may contact – anonymously, if you wish
– Peter Vasilenko, Ph.D., Chair of the University Committee on Research Involving Human
Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu, or
regular mail: 202 Olds Hall, East Lansing, MI 48824.
You indicate your voluntary participation in this study by submitting the completed
questionnaire on-line or printed survey. Your assistance is greatly appreciated we thank your in
advance for your time and attention. Good luck on your retail management career!
Linda K. Good, Professor - Retailing
Phone: 517-355-1282 Fax: 517-352-1058
E-mail: goodl@msu.edu
112 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

So Jung Lee, Ph.D. Candidate - Retailing
Phone: 519-393-9678
E-mail: leesojun@msu.edu
404 Human Ecology Building
East Lansing, MI 48824-1030
Michigan State University

117

APPENDIX III: MISSING DATA ANALYSES

118

Randomness of missing data pattern is one of the assumptions in testing the structural
equation modeling (SEM). In order to determine whether the reasons underlying the missing data,
are random or systematic, it is recommended to check if cases with missing values are
systematically different from cases without missing values. To look at the extent of the missing
data pattern, we first look at the univariate statistics of the missing number in each variable (See
Table A1 in Appendix III).
The total number of continuous variables in our survey is 81 items from Q1 to Q81.
Among 102 respondents, the table shows how many respondents did not answer each item. Each
item’s mean and standard deviation without the missing values are given in the table. The largest
number of missing values in an item is 7 (6.9%), and there are 11 items which have 7 missing
values. Kline (1998) suggests that when a variable’s missing values is over 5%, the pattern of the
missing variable needs to be examined. Among our 81 items, 61 had less than 5% missing values,
which is acceptable. There were 20 items with missing values ranging from 5 to 7%, which
according to Kline (1998) triggers further examination. Interestingly, the number of missing
values increased as respondents reached the end of the survey. Those 20 items are items from
Q60 to Q81 (Q62 and Q66 had less than 5% missing values). Five of them are information
search behavior and the other 15 items are organizational learning (See the Table A2 in
Appendix III). We assume that the respondents did not know the answers so they skipped the
items. Or, perhaps the respondents were tired of answering the questionnaire at the end of the
survey and chose not to complete the entire questionnaire.
In order to check whether the 20 items’ (we call these indicator items) missing data
pattern is completely at random (MCAR), missing at random (MAR), or neither of these, we
conducted Little’s missing completely at random (MCAR) test. When the pattern of missing

119

values is not related to any other variables, it is defined as missing completely at random
(MCAR) and any data deletions or imputation methods can be used for missing values. At the
bottom of the expectation-maximization (EM) estimations, Little’s MCAR test results are given
(See Table A3 in Appendix III). The null hypothesis for Little’s MCAR test is rejected at the .05
level of significance (Chi-square = 1416.69, df = 1280, p = .004). The results show that the
missing data pattern of our sample is not completely random, which is not uncommon, and
further testing is required to determine whether it is missing at random (MAR) or not random at
all.
MAR means that missing values are not randomly distributed across all observations but
are randomly distributed within one or more subsamples such as missing more among females
than males or missing values differ from non-missing values only by chance, which is unrelated
to the respondents’ true status on that variable (Kline, 1998). If this is the case, imputing the
missing value with the estimated scores based on EM method is suggested (MarryAnn, 1997). In
order to check whether the missing data pattern is at random, we ran Separate-Variance t-Tests to
identify each indicator item’s missing pattern with the remaining item through comparison of
mean value of valid data and mean value of missing data.
The total number of individual t-Test results were summed to 1220 (20 missing indicator
items × rest 61 items) and 107 (8.8%) significant t-Test results showed that the mean values were
significantly different between valid and missing data (See Table A4 in Appendix III). For
example, the group of people who answered Q61 and Q15 are a valid group while the group of
people who skipped Q61 but answered Q15 are a missing group, so we compare the group
differences on item Q15. That is, the valid group’s mean value of Q15 is 5.41 while the missing

120

group’s mean value of Q15 is 3.80. The remaining items, Q15, Q22, Q45, Q48, and Q55 show tTest differences on more than 10 out of 20 indicators. Those items are;
Q15. Principles of market segmentation drive new product selection efforts in this firm.
Q22. A lot of informal “hall-talk” in this firm concerns our competitors’ tactics or strategies.
Q45. My supervisor leaves it up to me to decide how to go about doing my job.
Q48. My supervisor rewards me for good performance.
Q55. Your department typically used the same sources of information each time when
developing/introducing a new product line.
It reveals that the respondents who answered the lower values on these questions were
likely to skip the indicator items. It is possible that respondents might have difficulty or do not
know the answer in question Q55, so they might skip it. However, because we believe that the
questions ask about general ideas of business operations and supervisor behavior, the terms in
our questions should not cause any difficulty in our middle to upper level retail buyers, we
determined that there is no clear reason for the missing pattern. Response fatigue, which causes
low response rates due to time and effort involved in participating in a survey, may cause
participants to lose interest and have a higher likelihood of skipping the questions at the end of
the survey (Porter, Whitcomb, and Weitzer, 2004). Since the impact of the differences is
marginal as the number of cases with missing data on the indicator items are only six to seven
and differences were found in a small portion (8.8%) of missing values, thus, we conclude that
only a small portion of missing data yielded significant differences, making this of marginal
concern (Reckase, 2010). With care, we consider that our missing observations are mostly
missing at random. Therefore, we employ the EM imputation method for further analysis.

121

Table A1. Univariate Statistics of Missing Data
Univariate Statistics
Std.
Missing
No. of Extremes (a)
N
Mean
Deviation Count Percent Low
High
Q1
102 5.7353
1.64061
0
.0
9
0
Q2
102 5.7647
1.17040
0
.0
1
0
Q3
102 5.4020
1.47090
0
.0
6
0
Q4
101 5.2376
1.73867
1
1.0
0
0
Q5
102 5.2549
1.70431
0
.0
0
0
Q6
100 5.5900
1.20684
2
2.0
2
0
Q7
101 5.6436
1.22951
1
1.0
1
0
Q8
101 5.5941
1.45037
1
1.0
7
0
Q9
102 6.0196
1.24264
0
.0
14
0
Q10
102 5.0784
1.43290
0
.0
12
0
Q11
102 5.6373
1.57196
0
.0
8
0
Q12
100 5.3400
1.56489
2
2.0
14
0
Q13
102 4.9706
1.89034
0
.0
0
0
Q14
100 5.2100
1.52617
2
2.0
1
0
Q15
101 5.3366
1.41617
1
1.0
0
0
Q16
102 5.4216
1.61329
0
.0
10
0
Q17
102 5.4118
1.38845
0
.0
12
0
Q18
102 5.5196
1.52036
0
.0
5
0
Q19
102 4.8824
1.69620
0
.0
5
0
Q20
102 5.6765
1.44306
0
.0
6
0
Q21
101 6.0792
1.30907
1
1.0
9
0
Q22
102 4.7843
1.79453
0
.0
5
0
Q23
101 5.3465
1.58389
1
1.0
9
0
Q24
101 5.2178
1.53365
1
1.0
3
0
Q25
100 5.5100
1.62987
2
2.0
0
0
Q26
101 5.3366
1.53152
1
1.0
15
0
Q27
102 6.2353
.96653
0
.0
7
0
Q28
102 5.2549
1.50040
0
.0
3
0
Q29
102 5.4412
1.45951
0
.0
13
0
Q30
102 5.3725
1.25012
0
.0
13
0
Q31
102 5.5686
1.41078
0
.0
5
0
Q32
102 3.4314
1.72638
0
.0
0
0
Q33
98 4.2347
1.90929
4
3.9
0
0
Q34
99 4.3333
2.06032
3
2.9
0
0
Q35
99 5.1616
1.56301
3
2.9
1
0
Q36
99 3.8990
1.75252
3
2.9
0
0
Q37
98 4.1939
1.73893
4
3.9
0
0
Q38
99 3.7273
1.56374
3
2.9
0
0
Q39
97 4.3402
1.47822
5
4.9
0
0
Q40
99 3.8990
1.81544
3
2.9
0
0
Q41
100 5.8000
1.19764
2
2.0
3
0
Q42
100 5.2600
1.41863
2
2.0
13
0

122

Table A1. Univariate Statistics of Missing Data (cont’d)
Univariate Statistics
Std.
Missing
No. of Extremes (a)
N
Mean
Deviation Count Percent Low
High
Q43
100 4.6800
1.42049
2
2.0
4
0
Q44
100 5.6500
1.23399
2
2.0
6
0
Q45
100 5.4700
1.01956
2
2.0
2
0
Q46
100 5.5300
1.11423
2
2.0
5
0
Q47
100 5.8300
1.13756
2
2.0
2
0
Q48
100 3.6700
1.65178
2
2.0
0
0
Q49
100 5.7300
1.16216
2
2.0
2
0
Q50
100 5.7200
1.21506
2
2.0
7
0
Q51
100 5.4400
1.65950
2
2.0
11
0
Q52
100 5.5200
1.21838
2
2.0
8
0
Q53
99 5.2828
1.13426
3
2.9
5
0
Q54
99 5.0909
1.47835
3
2.9
3
0
Q55
98 4.6939
1.23862
4
3.9
1
0
Q56
98 4.8980
1.43225
4
3.9
1
0
Q57
98 5.3878
1.22376
4
3.9
7
0
Q58
98 5.4184
1.33874
4
3.9
7
0
Q59
98 5.0306
1.52946
4
3.9
3
0
96 4.8854
1.39827
6
2
0
Q60
5.9
96 6.1354
1.04246
6
8
0
Q61
5.9
Q62
97 4.6392
1.51510
5
4.9
4
0
96 4.5104
1.56941
6
0
0
Q63
5.9
96 5.3125
1.10799
6
2
0
Q64
5.9
96 5.0937
1.44425
6
1
0
Q65
5.9
Q66
97 5.1340
1.41884
5
4.9
1
0
95 5.4000
1.01478
7
6
0
Q67
6.9
96 4.7292
1.67633
6
0
0
Q68
5.9
95 4.5263
1.45018
7
0
0
Q69
6.9
95 4.7789
1.43051
7
1
0
Q70
6.9
95 4.6632
1.32577
7
2
0
Q71
6.9
95 4.8632
1.51314
7
3
0
Q72
6.9
96 5.3438
1.69723
6
0
0
Q73
5.9
95 4.4421
1.41207
7
0
0
Q74
6.9
95 5.0737
1.26527
7
11
0
Q75
6.9
95 4.4000
1.29154
7
11
0
Q76
6.9
96 5.3646
1.09660
6
3
0
Q77
5.9
95 5.5789
1.41104
7
5
0
Q78
6.9
95 5.0947
1.35328
7
0
0
Q79
6.9
95 4.7895
1.45057
7
3
0
Q80
6.9
96 5.3646
1.05750
6
5
0
Q81
5.9
a. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR)

123

Table A2. Missing Items (More Than 5% of Respondents)
Q60 When developing/introducing a new product line (i.e., new vendor’s
history/merchandising supporting information, emerging market information, new
scientific knowledge, etc.), your department seeks different types of information
compared to the traditional buying process.
Q61 Your department uses communication technology to reduce communication time among
all those involved in developing/introducing a new product line (i.e., email, mobile
phone, fax, etc.).
Q63 Information is quickly dispersed to all who are involved in developing/introducing a new
product line.
Q64 Your department frequently holds formal and informal meetings when
developing/introducing a new product line.
Q65 Your department looks at specialized information when developing/introducing a new
product line
(i.e., trade or scholarly journal, market research analysis, etc.).
Q67 We encourage individuals to acquire new competencies.
Q68 We try a lot of new ideas; we want to be known as experimenters within our industry.
Q69 We master new ideas before moving on to the next round.
Q70 We upgrade the way that we do existing work until we have it right.
Q71 We learn by broadly scanning what other companies do.
Q72 We want to be known as the best technical experts in our industry.
Q73 We want to be the first in the market with a new process or product.
Q74 We learn from others, entering a product or applying a process after it has been fully
tested.
Q75 We primarily benchmark competition, measuring progress against competitors’
performance.
Q76 We learn by focusing our scanning on specific activities done by other companies.
Q77 We encourage teams to acquire competencies.
Q78 Learning is a critical part of our business strategy.
Q79 We constantly seek new ways to do work.
Q80 We learn by hiring people from other companies who have skills we need.
Q81 We primarily benchmark ourselves and measure progress against our previous
performance.

124

Table A3. Testing for Missing Completely at Random: EM Estimated Means
EM Means (a,b)
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
5.7353 5.7647 5.4020 5.2612 5.2549 5.6695 5.6290 5.5804 6.0196 5.0784
Q11
Q12
5.6373 5.3326

Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
4.9706 5.2492 5.3192 5.4216 5.4118 5.5196 4.8824 5.6765

Q21
Q22
6.0962 4.7843

Q23
Q24
Q25
Q26
Q27
Q28
Q29
Q30
5.3509 5.2082 5.5070 5.3437 6.2353 5.2549 5.4412 5.3725

Q31
Q32
5.5686 3.4314

Q33
Q34
Q35
Q36
Q37
Q38
Q39
Q40
4.2239 4.4192 5.1810 3.9803 4.2773 3.7724 4.3538 3.9352

Q41
Q42
5.8223 5.2531

Q43
Q44
Q45
Q46
Q47
Q48
Q49
Q50
4.6981 5.6457 5.4439 5.5393 5.8132 3.7045 5.7292 5.7237

Q51
Q52
5.4371 5.5267

Q53
Q54
Q55
Q56
Q57
Q58
Q59
Q60
5.3254 5.1363 4.6390 4.8951 5.4040 5.4130 5.0412 4.8717

Q61
Q62
6.1140 4.6424

Q63
Q64
Q65
Q66
Q67
Q68
Q69
Q70
4.5031 5.3322 5.0511 5.1492 5.3714 4.8056 4.6442 4.8849

Q71
Q72
4.6044 5.0311

Q73
Q74
Q75
Q76
Q77
Q78
Q79
Q80
5.4121 4.4376 5.0277 4.4424 5.4017 5.6253 5.1813 4.7426

Q81
5.4223
a. Little's MCAR test: Chi-Square = 1416.686, DF = 1280, Sig. = .004
b. The EM algorithm failed to converge in 25 iterations.

125

Table A4. Separate-Variance t-Tests: Missing Value Analysis
Remaining Items
Q15. Principles of market segmentation drive new product selection
efforts in this firm.
Q22. A lot of informal “hall talk” in this firm concerns our competitors’
tactics or strategies.
Q25. If a major competitor were to launch an intensive campaign targeted at
our customers, we would implement a response immediately.
Q37. In general, the top managers of my firm believe that…
Owing to the nature of the environment, it is best to explore it gradually
via timid, incremental behavior (1)
Owing to the nature of the environment, bold, wide-ranging acts are
necessary to achieve the firm’s objectives (7)

Mean
M(V)
M(M)
M(V)
M(M)
M(V)
M(M)

Indicator Items
Q60 Q61 Q63 Q64 Q65
5.41
5.43 5.43 5.43
3.80
3.83 3.83 3.83
4.97 4.94 4.94 4.94 4.94
1.83 2.33 2.33 2.33 2.33
5.64 5.63 5.63 5.63
3.50 3.67 3.67 3.67

Q67
5.41
4.17
4.97
2.29

M(V)
M(M)

M(V)
M(M)
M(V)
Q45. My supervisor leaves it up to me to decide how to go about doing
M(M)
my job.
Q46. My supervisor encourages employees to speak up when they disagree
M(V)
with a decision.
M(M)
M(V)
Q48. My supervisor rewards me for good performance.
M(M)
Q53. Your department employees have a good understanding of the
M(V)
information needed to develop/introducing a new product line.
M(M)
M(V) 4.73
Q55. Your department typically uses the same sources of information
M(M) 3.00
each time when developing/introducing a new product line.
Q56. Your department regularly adopts new information for making decisions M(V)
4.92
in development/introduction of a new product line.
M(M)
4.00
Q57. Your department shares with and uses information generated by other
M(V)
5.40
units as necessary.
M(M)
5.00

4.26
3.17

Q42. My supervisor tells me what shall be done and how it shall be done.

126

Q68
5.41
4.17
4.93
2.50

5.42
6.40

3.55
6.00

4.74
3.33

5.22
6.25
5.43
6.50
5.49
6.50
3.58
5.75
5.24
6.67

Table A4. Separate-Variance t-Tests: Missing Value Analysis (cont’d)
Indicator Items
Mean Q60 Q61 Q63 Q64 Q65 Q67 Q68

Remaining Items

Q58. Your department regularly integrates new information (i.e., new market
trends, new product development, new sourcing methods) into your
M(V)
5.45 5.45 5.45 5.45
department’s old information to make it useful for
M(M)
4.00 4.00 4.00 4.00
development/introduction of a new product line.
Q62. There is a seamless coordination among the divisions that work together
M(V)
in developing/introducing a new product line (i.e., trend dept., buying
M(M)
dept., marketing dept., etc.).
Indicator Items: Missing Data ≥ 5 %.
M(V): Mean (Valid), M(M): Mean (Missing)
Bolded items show more than ten t-Test result differences between mean of valid group (M(V)) and mean of missing group (M(M)).
All reported mean values are significantly different at p < .05.

127

Table A4. Separate-Variance t-Tests: Missing Value Analysis (cont’d)
Remaining Items
Q15. Principles of market segmentation drive new product selection
efforts in this firm.
Q22. A lot of informal “hall talk” in this firm concerns our competitors’
tactics or strategies.
Q25. If a major competitor were to launch an intensive campaign targeted at
our customers, we would implement a response immediately.
Q37. In general, the top managers of my firm believe that…
Owing to the nature of the environment, it is best to explore it gradually
via timid, incremental behavior (1)
Owing to the nature of the environment, bold, wide-ranging acts are
necessary to achieve the firm’s objectives (7)

Mean
M(V)
M(M)
M(V)
M(M)
M(V)
M(M)

Q69
5.41
4.17
4.97
2.29

Q70
5.41
4.17
4.97
2.29

Indicator Items
Q71 Q72 Q73
5.41 5.41 5.41
4.17 4.17 4.17
4.97 4.97 4.93
2.29 2.29 2.50

M(V)
M(M)

5.22
6.25
5.43
6.50
5.49
6.50
3.58
5.75
5.24
6.67

Q75
5.41
4.17
4.97
2.29

4.26
3.17

M(V)
M(M)
M(V)
Q45. My supervisor leaves it up to me to decide how to go about doing
M(M)
my job.
Q46. My supervisor encourages employees to speak up when they disagree
M(V)
with a decision.
M(M)
M(V)
Q48. My supervisor rewards me for good performance.
M(M)
Q53. Your department employees have a good understanding of the
M(V)
information needed to develop/introducing a new product line.
M(M)
M(V)
Q55. Your department typically uses the same sources of information
M(M)
each time when developing/introducing a new product line.
Q56. Your department regularly adopts new information for making decisions M(V)
in development/introduction of a new product line.
M(M)
Q57. Your department shares with and uses information generated by other
M(V)
units as necessary.
M(M)

Q74
5.41
4.17
4.97
2.29

Q42. My supervisor tells me what shall be done and how it shall be done.

128

5.42 5.42 5.42
6.40 6.40 6.40

5.42
6.40

3.55 3.55 3.55
6.00 6.00 6.00

3.55
6.00

4.74 4.74 4.74
3.33 3.33 3.33

4.74
3.33

5.42 5.42
6.40 6.40

3.55 3.55
6.00 6.00

4.74 4.74
3.33 3.33

Table A4. Separate-Variance t-Tests: Missing Value Analysis (cont’d)
Indicator Items
Mean Q69 Q70 Q71 Q72 Q73 Q74 Q75

Remaining Items

Q58. Your department regularly integrates new information (i.e., new market
trends, new product development, new sourcing methods) into your
M(V)
department’s old information to make it useful for
M(M)
development/introduction of a new product line.
Q62. There is a seamless coordination among the divisions that work together
M(V)
in developing/introducing a new product line (i.e., trend dept., buying
M(M)
dept., marketing dept., etc.).
Indicator Items: Missing Data ≥ 5 %.
M(V): Mean (Valid), M(M): Mean (Missing)
Bolded items show more than ten t-Test result differences between mean of valid group (M(V)) and mean of missing group (M(M)).
All reported mean values are significantly different at p < .05.

129

Table A4. Separate-Variance t-Tests: Missing Value Analysis (cont’d)
Remaining Items
Q15. Principles of market segmentation drive new product selection
efforts in this firm.
Q22. A lot of informal “hall talk” in this firm concerns our competitors’
tactics or strategies.
Q25. If a major competitor were to launch an intensive campaign targeted at
our customers, we would implement a response immediately.
Q37. In general, the top managers of my firm believe that…
Owing to the nature of the environment, it is best to explore it gradually
via timid, incremental behavior (1)
Owing to the nature of the environment, bold, wide-ranging acts are
necessary to achieve the firm’s objectives (7)

Mean
M(V)
M(M)
M(V)
M(M)
M(V)
M(M)

Q76
5.41
4.17
4.97
2.29

M(V)
M(M)

M(V)
M(M)
M(V)
Q45. My supervisor leaves it up to me to decide how to go about doing
M(M)
my job.
Q46. My supervisor encourages employees to speak up when they disagree
M(V)
with a decision.
M(M)
M(V)
Q48. My supervisor rewards me for good performance.
M(M)
Q53. Your department employees have a good understanding of the
M(V)
information needed to develop/introducing a new product line.
M(M)
M(V)
Q55. Your department typically uses the same sources of information
M(M)
each time when developing/introducing a new product line.
Q56. Your department regularly adopts new information for making decisions M(V)
in development/introduction of a new product line.
M(M)
Q57. Your department shares with and uses information generated by other
M(V)
units as necessary.
M(M)

Indicator Items
Q78 Q79 Q80
5.45
5.41
3.86
4.17
4.94 4.93 4.97
2.71 2.86 2.29

4.26 4.30
3.17 2.86

Q42. My supervisor tells me what shall be done and how it shall be done.

130

Q77
5.41
4.17
4.93
2.50

5.42
6.40

3.55
6.00

4.74
3.33

5.22
6.25
5.43
6.50
5.49
6.50
3.58
5.75
5.24
6.67

4.27
3.14

5.42
6.40

3.61
4.80

Q81
5.41
4.17
4.93
2.50

Q76
5.41
4.17
4.97
2.29

4.26
3.17

5.42
6.40

3.57
5.60

3.55
6.00

4.74
3.33

4.74
3.33

5.22
6.25
5.43
6.50
5.49
6.50
3.58
5.75
5.24
6.67

5.42
6.40

3.55
6.00

4.74
3.33

Table A4. Separate-Variance t-Tests: Missing Value Analysis (cont’d)
Indicator Items
Mean Q76 Q77 Q78 Q79 Q80 Q81 Q76

Remaining Items

Q58. Your department regularly integrates new information (i.e., new market
trends, new product development, new sourcing methods) into your
M(V)
department’s old information to make it useful for
M(M)
development/introduction of a new product line.
Q62. There is a seamless coordination among the divisions that work together
M(V)
4.71 4.70
in developing/introducing a new product line (i.e., trend dept., buying
M(M)
2.33 2.67
dept., marketing dept., etc.).
Indicator Items: Missing Data ≥ 5 %.
M(V): Mean (Valid), M(M): Mean (Missing)
Bolded items show more than ten t-Test result differences between mean of valid group (M(V)) and mean of missing group (M(M)).
All reported mean values are significantly different at p < .05.

131

APPENDIX IV: DATA NORMALITY ANALYSES

132

Multivariate normality is another data assumption of Structural Equation Modeling.
Before examining multivariate normality, univariate normality must be assured. We test the
normality of each item (univariate normality) by examining the shape of the distribution using
skewness and kurtosis. Standardized statistics of skewness and kurtosis are calculated by
dividing the statistics of skewness and kurtosis by their standard errors.

Skew =

∑(X − M )

SE skew =

z skewness =

3

SD 3
6
N

Kurtosis =

SE kurt =

skewness
6
N

z kurtosis =

∑(X − M )

4

SD 4

24
N
kurtosis
24
N

N = total sample number (102)
For a normal distribution, skewness and kurtosis have means of 0 and a standard error of
skewness of

6
= .243 and a standard error of kurtosis of
N

24
= .485. If the statistical z value
N

is within a score range of ±1.96, the data are normally distributed with a level of 95% rejection
confidence interval (CI), which corresponds to a .05 rejection probability level. At the same time,
we conduct the Shapiro-Wilk’s statistical test (S-W test) for calculating the level of significance
for the differences from a normal distribution of data. Table A5 in Appendix IV shows skewness,
kurtosis, and S-W test results for all the items.
The results show that market orientation items are negatively skewed, indicating MO
scores tend to occur toward the upper end of the scale (Strongly Agree). Looking at the each
item’s z value of skewness, using a 95% of CI level, no items are normally distributed because
those z values are above ±1.96 level. For kurtosis, which indicates “peakedness” or “flatness” of
133

data distribution, we find 20 out of 31 MO items are in normal distribution range with a 95% of
CI. S-W normality test results show that all market orientation data are significantly different
from the normal distribution curve. Based on assessment of this set of test results (skewness,
kurtosis, and S-W test), we determined that market orientation data are not normally distributed.
Examining the distribution of entrepreneurship orientation data (see Table 2), all items
are negatively skewed except one item. Eight out of nine items’ skewnesses are normal at .05
rejection probability level. All EO kurtosis scores are negative showing flat distribution and the 6
out of 9 items are in normal distribution range. The S-W test results indicate all EO items are not
normally distributed. For employee empowerment (supportive supervision), except one item, all
items are negatively skewed and only 2 out of 12 items are in the normal distribution range at .05
rejection probability level. For kurtosis of employee empowerment, only 1 item is normal, the
value does not exceed ±1.96. S-W’s normality test results show that all supportive supervision
items are significantly different than normal distribution. For information integration, all items
are negatively skewed. Only 1 item is in normal distribution range. Ten items among 13 of
information integration kurtosis are normal. S-W normality results show that all information
integration items are significantly different than the normal distribution, therefore these items
need to be transformed. For the organizational learning items (total of 16 items), all items are
negatively skewed except one item. Four items are in the normal distribution range.
Overall, assessment of these sets of test results, such as skewness, kurtosis, and S-W test,
reveals that our data are negatively skewed except three items (Q32, Q48, and Q74).
Except those 3 items, all items are skewed to the higher end of the scale and this leads to nonnormal distributions. That is, our respondents answered on the higher end of the scale and such
univariate non-normality results in a multivariate non-normal distribution in general. In order to

134

apply a normal theory-based model and find parameter estimations, we need to determine what
extent of non-normality is acceptable for obtaining less biased estimation of parameters, standard
errors, and chi-squared statistics.
When the data are non-normal, there is overestimation of the chi-square statistic and
underestimation of standard errors, which can lead to false rejection of the model and inflated
significance statistics, respectively (Gao et al., 2008; Muthén and Kaplan, 1985). However,
Muthén and Kaplan (1985) find from their Monte Carlo simulation, that when univariate
skewness and kurtosis are not severe, the data are not much distorted. Similarly, Hallow (1985)
finds unbiased parameter estimates when univariate skewness and kurtosis and multivariate
Mardia’s kurtosis are between -1.25 < skewness < 2.0, -1.0 < kurtosis < 8.0, and -4.9 < Mardia’s
kurtosis < 49.1. These set of studies imply that when data distribution is small to moderately
non-normal, the parameter estimation method is still useful and since our data are in the range of
somewhat moderately non-normal (-2.152 to 0.466 for skewness and -1.335 to 5.141, and
Mardia’s kurtosis as 60.562), we assume that our non-normal data distribution is not a big issue
in using SEM method for testing our model. See table A6 in Appendix IV for Mardia’s kurtosis.

135

Table A5. Distributional Characteristics and Test of Normality
Market Orientation (31 items)
Skewness
Kurtosis
S-W Test of Normality
Item #
statistic
z value
statistic
z value
statistic significance
Q1
-1.293
-5.331
0.560
1.154
0.761
0.000
Q2
-0.926
-3.818
0.511
1.053
0.858
0.000
Q3
-0.784
-3.233
-0.187
-0.386
0.872
0.000
Q4
-0.863
-3.558
-0.225
-0.464
0.864
0.000
Q5
-0.824
-3.397
-0.285
-0.588
0.865
0.000
Q6
-0.931
-3.839
1.307
2.694
0.879
0.000
Q7
-0.700
-2.886
-0.185
-0.381
0.880
0.000
Q8
-1.275
-5.257
1.128
2.325
0.822
0.000
Q9
-1.648
-6.795
2.832
5.838
0.757
0.000
Q10
-1.149
-4.737
1.229
2.534
0.863
0.000
Q11
-1.265
-5.216
0.905
1.866
0.808
0.000
Q12
-1.030
-4.247
0.337
0.695
0.858
0.000
Q13
-0.495
-2.041
-1.050
-2.165
0.875
0.000
Q14
-0.763
-3.146
-0.084
-0.173
0.892
0.000
Q15
-0.551
-2.272
-0.526
-1.084
0.899
0.000
Q16
-0.945
-3.896
-0.243
-0.501
0.829
0.000
Q17
-1.251
-5.158
1.300
2.680
0.837
0.000
Q18
-0.986
-4.065
0.120
0.247
0.846
0.000
Q19
-0.768
-3.167
-0.338
-0.697
0.889
0.000
Q20
-1.329
-5.480
1.261
2.600
0.810
0.000
Q21
-2.152
-8.873
5.141
10.598
0.696
0.000
Q22
-0.581
-2.396
-0.745
-1.536
0.900
0.000
Q23
-0.921
-3.797
-0.046
-0.095
0.859
0.000
Q24
-0.888
-3.661
0.417
0.860
0.888
0.000
Q25
-0.995
-4.102
0.232
0.478
0.841
0.000
Q26
-0.928
-3.826
0.123
0.254
0.869
0.000
Q27
-1.766
-7.281
4.000
8.246
0.735
0.000
Q28
-1.093
-4.507
0.669
1.379
0.856
0.000
Q29
-1.704
-7.026
2.118
4.366
0.698
0.000
Q30
-1.332
-5.492
1.360
2.804
0.790
0.000
Q31
-1.049
-4.325
0.327
0.674
0.839
0.000

136

Table A5. Distributional Characteristics and Test of Normality (cont’d)
Entrepreneurship Orientation (9 items)
Skewness
Kurtosis
S-W Test of Normality
Item #
statistic
z value
statistic
z value
statistic significance
Q32
0.366
1.509
-0.968
-1.996
0.919
0.000
Q33
-0.248
-1.023
-1.247
-2.571
0.907
0.000
Q34
-0.157
-0.647
-1.335
-2.752
0.901
0.000
Q35
-0.528
-2.177
-0.705
-1.453
0.899
0.000
Q36
-0.042
-0.173
-0.721
-1.486
0.932
0.000
Q37
-0.284
-1.171
-0.726
-1.497
0.931
0.000
Q38
-0.197
-0.812
-0.522
-1.076
0.937
0.000
Q39
-0.388
-1.600
-0.756
-1.559
0.923
0.000
Q40
-0.124
-0.511
-0.932
-1.921
0.935
0.000

Table A5. Distributional Characteristics and Test of Normality (cont’d)
Supportive Supervision (12 items)
Skewness
Kurtosis
S-W Test of Normality
Item #
statistic
z value
statistic
z value
statistic significance
Q41
-1.199
-4.944
1.636
3.373
0.836
0.000
Q42
-1.222
-5.038
1.427
2.942
0.852
0.000
Q43
-0.475
-1.958
0.332
0.684
0.916
0.000
Q44
-1.354
-5.583
2.212
4.560
0.833
0.000
Q45
-0.888
-3.661
2.716
5.599
0.875
0.000
Q46
-1.725
-7.112
4.501
9.279
0.788
0.000
Q47
-1.436
-5.921
3.456
7.125
0.823
0.000
Q48
0.466
1.921
-0.996
-2.053
0.879
0.000
Q49
-1.357
-5.595
2.837
5.849
0.839
0.000
Q50
-1.804
-7.438
4.135
8.525
0.763
0.000
Q51
-1.353
-5.579
1.024
2.111
0.796
0.000
Q52
-1.204
-4.964
2.108
4.346
0.854
0.000

137

Table A5. Distributional Characteristics and Test of Normality (cont’d)
Information Integration (13 items)
Skewness
Kurtosis
S-W Test of Normality
Item #
statistic
z value
statistic
z value
statistic significance
Q53
-0.675
-2.783
1.348
2.779
0.871
0.000
Q54
-0.868
-3.579
0.696
1.435
0.896
0.000
Q55
-0.558
-2.301
0.199
0.410
0.922
0.000
Q56
-0.549
-2.264
-0.460
-0.948
0.913
0.000
Q57
-0.704
-2.903
0.443
0.913
0.902
0.000
Q58
-0.778
-3.208
0.306
0.631
0.889
0.000
Q59
-0.748
-3.084
0.071
0.146
0.903
0.000
Q60
-0.787
-3.245
0.322
0.664
0.907
0.000
Q61
-1.220
-5.030
1.127
2.323
0.797
0.000
Q62
-0.683
-2.816
-0.161
-0.332
0.911
0.000
Q63
-0.137
-0.565
-0.625
-1.288
0.948
0.000
Q64
-0.741
-3.055
1.792
3.694
0.892
0.000
Q65
-0.594
-2.449
-0.016
-0.033
0.914
0.000

Table A5. Distributional Characteristics and Test of Normality (cont’d)
Organizational Leraning (16 items)
Skewness
Kurtosis
S-W Test of Normality
Item #
statistic
z value
statistic
z value
statistic significance
Q66
-0.774
-3.191
0.372
0.767
0.905
0.000
Q67
-1.337
-5.513
2.231
4.599
0.816
0.000
Q68
-0.532
-2.193
-0.449
-0.926
0.924
0.000
Q69
-0.026
-0.107
-0.844
-1.740
0.942
0.000
Q70
-0.403
-1.662
-0.266
-0.548
0.940
0.000
Q71
-0.771
-3.179
0.247
0.509
0.906
0.000
Q72
-0.586
-2.416
0.064
0.132
0.929
0.000
Q73
-0.980
-4.041
-0.085
-0.175
0.841
0.000
Q74
0.019
0.078
-0.599
-1.235
0.904
0.000
Q75
-1.024
-4.222
1.613
3.325
0.881
0.000
Q76
-0.584
-2.408
-0.293
-0.604
0.904
0.000
Q77
-1.247
-5.142
3.008
6.201
0.854
0.000
Q78
-1.035
-4.267
0.887
1.829
0.868
0.000
Q79
-0.709
-2.923
-0.136
-0.280
0.896
0.000
Q80
-0.705
-2.907
0.221
0.456
0.914
0.000
Q81
-0.354
-1.460
-0.206
-0.425
0.913
0.000

138

Table A6. Assessment of Multivariate Normality
Variable
min
max
skew
c.r.
CS
2.250
6.750
-.874
-3.602
EI
1.250
7.000
-.450
-1.854
EC
2.750
7.000
.027
.110
CB
1.000
6.000
-1.365
-5.627
FLX4
1.000
7.000
-.737
-3.040
FLX3
1.000
7.000
-.541
-2.231
FLX2
2.000
7.000
-.767
-3.162
FLX1
1.000
7.000
-.855
-3.527
SCO3
2.000
7.000
-.693
-2.859
SCO2
1.000
7.000
-.776
-3.198
SCO1
1.000
7.000
-.585
-2.413
R
3.286
6.714
-.892
-3.677
ID
2.000
6.750
-.970
-4.000
IG
2.111
7.000
-1.032
-4.256
RP
1.000
7.000
-.293
-1.208
P
1.000
7.000
-.451
-1.859
I
1.500
7.000
-.368
-1.516
EF5
1.000
7.000
-.135
-.557
EF4
3.000
7.000
-1.202
-4.956
EF2
1.000
7.000
-.673
-2.774
Multivariate

139

kurtosis
.856
.039
-.625
2.243
.010
-.496
.232
.604
.364
.248
-.073
-.111
.020
.455
-.747
-.901
-.841
-.653
1.014
-.211
60.562

c.r.
1.764
.079
-1.288
4.624
.020
-1.022
.479
1.245
.749
.511
-.151
-.228
.041
.938
-1.539
-1.858
-1.735
-1.346
2.091
-.436
10.309

APPENDIX V: CORRELATION MATRIX, MEANS, & STANDARD DEVIATIONS FOR
SECOND-ORDER FACTOR ANALYSIS OF
INFORMATION SEARCH BEHAVIOR

140

Table A7. Correlation Matrix, Means and Standard Deviations for 2nd Order Factor Analysis of Information Search Behavior
Std.
Mean
FLX4 FLX3 FLX2 FLX1 SCO3 SCO2 SCO1 EF5 EF4 EF2
Deviation
FLX4
5.045
1.505
1.000
FLX3
4.913
1.411
.635 1.000
FLX2
5.433
1.319
.659 .692 1.000
FLX1
5.091
1.456
.434 .456 .474 1.000
SCO3
5.401
1.206
.654 .687 .713 .470 1.000
SCO2
4.876
1.370
.652 .493 .711 .469 .597 1.000
SCO1
5.078
1.415
.426 .600 .623 .411 .523 .521 1.000
EF5
4.488
1.545
.389 .568 .424 .280 .423 .422 .523 1.000
EF4
6.120
1.020
.222 .233 .425 .160 .242 .456 .211 .282 1.000
EF2
4.640
1.491
.570 .599 .621 .410 .620 .618 .542 .724 .413 1.000

141

APPENDIX VI: CORRELATION MATRIX, MEANS, & STANDARD DEVIATIONS
FOR FULL MODEL: WHOLE GROUP, HIGH GROUP, & LESS HIGH GROUP

142

Table A8. Correlation Matrix, Means & Standard Deviations for Whole Group
Std.
FLX FLX FLX
Mean
CS
EI
EC
CB
Deviation
4
3
2
CS
5.271
.932
1.000
EI
5.073
1.198
.709 1.000
EC
4.877
1.122
.426 .339 1.000
CB
4.707
.9447
.484 .461 .207 1.000
FLX4
5.045
1.506
.461 .315 .271 .338 1.000
FLX3
4.913
1.411
.475 .439 .217 .310 .589 1.000
FLX2
5.433
1.319
.576 .452 .222 .397 .682 .730 1.000
FLX1
5.091
1.456
.356 .220 .201 .185 .440 .436 .473
SCO3
5.401
1.206
.432 .333 .244 .335 .696 .685 .715
SCO2
4.876
1.370
.483 .332 .231 .403 .625 .495 .699
SCO1
5.078
1.415
.365 .375 .188 .388 .415 .645 .583
EF5
4.488
1.545
.400 .368 .222 .170 .294 .530 .430
EF4
6.120
1.020
.291 .230 .074 .260 .223 .235 .458
EF2
4.640
1.491
.418 .335 .290 .316 .540 .542 .640
RP
3.945
1.523
.205 .229 .032 .064 .072 .120 .160
P
4.075
1.617
.325 .366 .130 .149 .157 .249 .267
I
4.756
1.536
.103 .121 -.067 -.008 .073 .160 .170
R
5.589
.895
.623 .535 .385 .342 .490 .491 .516
ID
5.287
1.148
.595 .520 .346 .341 .494 .555 .545
IG
5.356
1.031
.587 .520 .350 .367 .435 .497 .538

143

FLX SCO SCO SCO
1
3
2
1

1.000
.523
.531
.340
.442
.240
.514
.070
.124
.094
.409
.339
.319

1.000
.551
.500
.379
.225
.568
.111
.127
.118
.474
.465
.432

1.000
.593
.494
.486
.665
-.012
.075
.052
.504
.475
.464

1.000
.615
.287
.590
.029
.142
.052
.458
.454
.480

EF5

EF4

EF2

1.000
.235
.713
.078
.201
.017
.450
.471
.396

1.000
.434
-.120
.021
.031
.312
.364
.370

1.000
-.016
.198
-.038
.519
.598
.524

Table A8. Correlation Matrix, Means & Standard Deviations for Whole Group (cont’d)
RP
P
I
R
ID
IG
RP
1.000
P
.608 1.000
I
.604 .542 1.000
R
.134 .298 .094 1.000
ID
.100 .335 .132 .857 1.000
IG
.149 .388 .139 .825 .843 1.000

144

Table A9. Correlation Matrix, Means & Standard Deviations for High Group
Std.
Mean
CS
EI
EC
CB FLX4 FLX3
Deviation
CS
5.522
.940
1.000
EI
5.343
1.085
.622 1.000
EC
5.028
1.115
.398 .325 1.000
CB
4.847
.890
.565 .624 .332 1.000
FLX4
5.412
1.340
.395 .164 .160 .336 1.000
FLX3
5.084
1.411
.434 .239 .195 .245 .631 1.000
FLX2
5.802
1.180
.503 .206 .266 .387 .811 .692
FLX1
5.277
1.320
.372 .075 .272 .252 .471 .399
SCO3
5.637
1.152
.395 .130 .331 .267 .756 .685
SCO2
5.159
1.246
.435 .143 .385 .308 .673 .426
SCO1
5.223
1.378
.316 .247 .250 .265 .327 .609
EF5
4.750
1.499
.316 .102 .255 .119 .011 .390
EF4
6.390
.820
.126 .102 .073 .156 .286 .313
EF2
5.038
1.327
.257 -.043 .288 .210 .319 .406
RP
4.003
1.567
.275 .300 .012 .216 .014 .120
P
4.309
1.653
.340 .309 .045 .252 .032 .150
I
4.847
1.530
.143 .063 -.120 .081 .080 .103
R
5.802
.857
.585 .381 .422 .380 .426 .494
ID
5.621
1.040
.525 .290 .298 .297 .375 .522
IG
5.644
.958
.503 .368 .353 .341 .333 .460

145

FLX2 FLX1 SCO3 SCO2 SCO1 EF5

EF4

EF2

1.000
.495
.733
.658
.436
.236
.357
.522
.124
.126
.070
.449
.405
.426

1.000
.505
-.151
.016
-.041
.340
.377
.360

1.000
-.101
.026
-.153
.338
.365
.313

1.000
.490
.603
.260
.294
.337
.463
.003
.088
.092
.337
.214
.252

1.000
.531
.409
.194
.147
.440
.041
.005
.033
.407
.365
.357

1.000
.445
.349
.427
.614
-.072
-.081
-.071
.481
.418
.384

1.000
.485
.166
.463
-.005
-.019
-.046
.387
.324
.411

1.000
.186
.621
.093
.043
-.055
.277
.267
.199

Table A9. Correlation Matrix, Means & Standard Deviations for High Group (cont’d)
RP
P
I
R
ID
IG
RP
1.000
P
.659 1.000
I
.546 .606 1.000
R
.046 .157 .061 1.000
ID
.104 .249 .180 .863 1.000
IG
.163 .336 .199 .812 .807 1.000

146

Table A10. Correlation Matrix, Means & Standard Deviations for Less High Group
Std.
Mean
CS
EI
EC
CB FLX4 FLX3 FLX2
Deviation
CS
4.882
.782
1.000
EI
4.653
1.256
.796 1.000
EC
4.641
1.107
.400 .287 1.000
CB
4.491
.997
.287 .204 -.029 1.000
FLX4
4.476
1.585
.422 .348 .334 .260 1.000
FLX3
4.648
1.387
.501 .668 .200 .356 .509 1.000
FLX2
4.862
1.334
.561 .617 .051 .329 .450 .790 1.000
FLX1
4.802
1.621
.269 .306 .063 .057 .358 .459 .402
SCO3
5.035
1.210
.369 .481 .032 .355 .571 .664 .641
SCO2
4.437
1.452
.450 .437 -.057 .453 .503 .551 .689
SCO1
4.853
1.459
.402 .496 .051 .518 .487 .682 .766
EF5
4.082
1.548
.438 .625 .099 .161 .549 .708 .594
EF4
5.702
1.161
.318 .199 -.040 .276 -.004 .077 .421
EF2
4.022
1.535
.498 .626 .202 .353 .682 .698 .678
RP
3.854
1.468
.055 .120 .046 -.181 .126 .104 .200
P
3.712
1.509
.174 .374 .202 -.081 .221 .364 .370
I
4.614
1.555
-.026 .160 -.021 -.163 .018 .227 .270
R
5.257
.859
.581 .644 .252 .203 .460 .437 .479
ID
4.770
1.127
.574 .689 .327 .303 .502 .574 .572
IG
4.909
.991
.594 .601 .258 .313 .414 .511 .538

147

FLX1 SCO3 SCO2 SCO1 EF5

EF4

EF2

1.000
.527
.416
.409
.586
.079
.530
.147
.116
.073
.447
.414
.327

1.000
.232
-.144
-.114
.059
.131
.180
.218

1.000
.062
.331
.047
.641
.765
.655

1.000
.509
.599
.562
.173
.652
.201
.221
.205
.472
.486
.416

1.000
.763
.619
.455
.660
.042
.194
.173
.442
.429
.452

1.000
.780
.367
.743
.068
.349
.171
.529
.600
.551

1.000
.175
.800
.032
.379
.085
.624
.666
.577

Table A10. Correlation Matrix, Means & Standard Deviations for Less High Group (cont’d)
RP
P
I
R
ID
IG
RP
1.000
P
.521 1.000
I
.697 .428 1.000
R
.258 .436 .099 1.000
ID
.065 .369 .018 .812 1.000
IG
.107 .380 .005 .798 .838 1.000

148

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