Impact of National Culture on Knowledge Management: A Comparative Analysis of Italian
and American Perspectives
In today’s competitive global marketplace it is imperative that organizations manage not
only tangible resources but also intangible ones. This realization has produced great interest
among the business and academic communities in developing ways for capturing, creating,
integrating, applying, communicating, assessing, and evolving the intellectual capital
available to the organization by implementing and sustaining knowledge management (KM)
programs.
Extensive literature has been published to attest to the value of knowledge as an
organization’s best source for obtaining and retaining competitive advantage (Nonaka and
Takeuchi 1995; Davenport and Prusak 1998; Drucker 2001; Nishiguchi and Nonaka
2001; Desouza and Evaristo 2003). According to a survey by KPMG Consulting (2000)
knowledge management programs provide organizations with the following benefits:
Better decision making, better customer handling, faster response to key business issues,
improved employee skills, improved productivity, increased profits, the sharing of best
practices, reduced costs, new ways of working, increased market share, creation of additional
business opportunities, improved new product development, better staff attraction and retention,
and increased share price (KPMG 2000).
Knowledge management has reached maturity and organizations should be pursuing KM in
some way (Mann 2007).
We have reached a new phase of social development where the new economic currency is
human capital. Intangible assets are replacing natural (land and unskilled labor) and
tangible assets (buildings, equipment machinery, and finance) as the main source of
wealth in industrial societies (Dunning 2000). We have entered a knowledge-based
economy, where an organization’s business and technical know-how are directly or
indirectly shown on a corporation’s financial statements and are becoming one of the main
determinants for mergers, acquisitions and strategic alliances.
1
In its early implementations, knowledge management was often primarily focused on
information technology (IT). Today, practitioners realize that knowledge management is a
multi-disciplinary science where technology can be an enabler, but not the main driver.
Since knowledge is socially produced and reproduced, greater focus is being placed on
understanding the activities necessary for managing knowledge resources and managing
knowledge in the context of social activity. In particular, knowledge manipulating
processes are constrained by the social and cultural contexts in which they are embedded
(Abou-Zeid 2003).
Recent research suggests that organizational culture can be a major barrier to leveraging
knowledge (Davenport and Prusak 1998; KPMG 2000), but only limited research
attention has been given to cross-cultural issues (Diemers 2000; Holden 2002; Desouza
and Evaristo 2003; Ford and Chain 2003; Pauleen 2007). As noted by Hofstede (1980) an
organization’s culture is nested within a national culture. Today’s global business
environment, of multinational corporations and international networking, has created a
more complex multicultural workforce. Understanding the interrelation between national
specific values and the adoption of knowledge management initiatives plays a critical role
for selecting KM tools and practices that will improve the organization’s performance.
Our research compares KM beliefs, expectations, and practices in Italy and the United
States. These two countries have relatively small cultural gaps, and hence may have similar
perceptions about KM; the deviations are expected to be smaller than previous similar studies
where countries with large cultural gaps were analyzed.
Research Initiative Motivation
My research motivations emerged from my business interactions, contacts, and experience
with Italian industry and my experience in the United States. In April of
2006, I co-founded the Confederation of Italian Entrepreneurs Worldwide
(Confederazione degli Imprenditori Italiani nel Mondo) (CIIM), a non-profit organization
which aggregates Italian entrepreneurs to find synergies between Italy and abroad, and I have
served as CIIM’s Vice-President for the USA.
2
CIIM is the by-product of the first Convention of Italian Entrepreneurs Worldwide, held in
Rome in October of 2003, and hosted by the Italian Minister of Foreign Affairs and the
Minister for Italians Living Abroad. The institutional character of the organization has
given me the privilege of collaborating with Italian businesses, government and business
advocacy associations at the highest levels to promote learning and develop a global
business network for Italian entrepreneurs.
Since its inception, CIIM has achieved significant progress towards its mission of
developing a global business network linking Italian business communities abroad with
businesses operating in Italy. The organization has reached over one-thousand members
throughout the North American chapters of Washington D.C., New York, Cleveland, San
Francisco, Toronto and Vancouver. CIIM established a web portal allowing its members to
promote their company and share business opportunities, and organized various events
such as roundtables discussions, workshops and seminars to promote networking and
exchange of ideas. Today the CIIM is an important reference for businesses and for
regional and national government initiatives.
My involvement with CIIM and the Italian business community has enabled me to understand
the overlap between the business cultures of Italian industry and that of the
U.S. Holding dual citizenship from both Italy and the United States, I have worked and
studied in the United States for the last seventeen years, and have been in a management
role of web, data management, and emerging technologies for institutions of higher
education institutions for the last twelve years. During this tenure, I have witnessed and
participated in the development and implementation of new business processes and practices
to enhance the flow and use of information.
Because of the opportunities offered by KM, and my ongoing experience with both Italian
and American organizations, it is of great interest to me to analyze the similarities and
differences between the KM perceptions of Italian and American organizations, to
understand how to effectively use those perceptions to enhance the organization.
3
“If you have an apple and I have an apple and we exchange apples then you and I will
still each have one apple. But if you have an idea and I have an idea and we exchange
these ideas, then each of us will have two ideas.”
George Bernard Shaw
Knowledge Economy, Technology and Globalization
The world has undergone an important socioeconomic transformation; from adding value by
producing things, which is ultimately limited, to adding value by creating and utilizing
knowledge, which can grow indefinitely. In fact, while most other resources depreciate over
time, knowledge increases in value with use; once discovered and made public, knowledge
expands, defying the “law” of scarcity that governs most commodity markets. Since
knowledge investments are characterized by increasing (rather than decreasing) returns,
they are essential to long-term economic growth (OECD 1996).
Today knowledge is the primary wealth-producing asset for a country’s economy
(Drucker 1969; Drucker 2001). International organizations, such as the World Bank, the
International Monetary Fund, the European Commission, and the Organization for
Economic Cooperation and Development (OECD), are paying close attention to assessing
a country’s investment in knowledge and developing economic theories and models to
relate such investments to economic performance (See OECD 2001; OECD 2007; Revilak
2006; World Bank 2008). In particular, econometric tests by the World Bank reveal a
statistically significant causal relationship between a country’s knowledge economy index
and future economic growth (World Bank 2008).
Technology and globalization have produced new demands and opportunities for the
knowledge economy. The ability to create, distribute and exploit knowledge is increasingly
central to achieving and maintaining competitive advantage, wealth creation, and better
standards of living.
In most advanced and developed countries, technological advances have decreased the need
for workers who are skilled with their hands and work in repetitive and sometimes
4
physically demanding jobs. Information and communication technologies allow for more
rapid knowledge creation and distribution, which in turn increases the rate of innovation,
but also changes the competitive scenario. In fact, any advantage of one company can be
eliminated by competitive improvements overnight. To compete in this new scenario,
companies must fine tune their processes of knowledge management and innovation;
combining market and technology know-how with the creative talents of knowledge
workers, their ideas, concepts and information. National governments are addressing this
changing economic trend with new public policies, from subsidies and procurement to
alternative instruments such as research and development tax relief and reinforcement of
industry-science linkages (cooperation between firms and universities).
In addition to technological advances, globalization has an important role in the
development of the knowledge economy. The continued globalization of trade and
investments has increased flows of knowledge from within and across national borders, as
shown by growing co-operation in science and innovation and greater international
mobility of highly-skilled workers (OECD 2001; Friedman 2007). For most of human
history, an individual’s daily life remained close to his or her birthplace. Money did not
move far. Most people lived the majority of their lives within a radius of one hour of
whatever the dominant mode of transportation was. Hence when the mode of
transportation was by foot the radius extended to only a couple of miles, when it was on
horseback or by a horse-drawn carriage the radius expanded, and so on.
As the mode of transportation evolved (automobiles, trains and planes), people’s mobility
increased linearly, while their know-how increased exponentially. In the last few decades,
with the advent of new information and communication technologies the mobility of
people’s knowledge has grown at unprecedented rates. If we think of globalization as
mobility and flows of people, goods, information, and money, historically people have
been the least mobile. The sudden acceleration in mobility of people and their knowledge
(experiences, ideas, concepts, and information) has brought new and exciting possibilities
for innovation and growth.
5
“We need diversity of thought in the world to face the new challenges.”
Tim Berners
Lee
Global Diversity
Today’s global business environment is increasingly complex. Companies not only have to
deal with language differences and varied political, economic and regulatory systems, but
also with the cultural values, attitudes, beliefs, and norms shared at the national level by the
individuals of a country.
Understanding the cultural differences that exist between countries is an important
prerequisite for companies that want to successfully introduce their products and services
in foreign markets, effectively coordinate operations with foreign subsidiaries and
business partners, establish effective multinational business networks, and leverage the
overall capabilities of their workforce (Gundling, Zanchettin et al. 2007). Such cultural
differences are embedded in “the collective programming of the mind which distinguishes
the members of one human group from another” (Hofstede 1980); the set of shared
beliefs and values that distinguish people of one nationality from those of another.
According to Trompenaars and Hampden-Turner, “understanding our own culture and our
own assumptions and expectations about how people ‘should’ think and act is the basis
for success” (Trompenaars 1994).
Many management theories are no longer universally applicable; but rather, need to be
understood and evaluated in the cultural context in which they are to be implemented
(Trompenaars 1994). For example, participative schemas like Management by
Objectives (MBO) will not work in countries where there is a large power distance,
because it presupposes some form of negotiation between superiors and subordinates with
which neither party will feel at ease (Hofstede 2005). Moreover, management in an
individualist society requires attention to the individual (e.g. incentives and bonuses
should be linked to an individual’s performance); on the other hand, management in a
collectivist society will focus more on the group (Hampden-Turner, Trompenaars et al.
2000; Hofstede 2005). Consequently, global managers must take into consideration all
aspects of their business and acclimate to the culture of a particular country, which may
6
require different practices from what they are accustomed in their home country. They
must develop a much broader perspective when implementing management practices,
which includes estimating how local policies, actions, or changes may influence intended
outcomes (Ferraro 2002).
Culture Implications
Organizational culture, which describes the attitudes, experiences, beliefs and values of an
organization, has been shown to influence the successful adoption of knowledge
management practices (Davenport, De Long et al. 1998; Davenport and Prusak 1998;
Ribière 2001; Holsapple 2003; Stankosky 2005). According to Hofstede (1980; 2005)
organizational culture is not independent of national culture. Therefore, knowledge
management tools and practices that are appropriate in one culture may not work in
another (Holden 2002; Stankosky 2005; Pauleen 2007; Stary, Barachini et al. 2007). In
fact, “research using a variety of frameworks has shown that national cultural values are
related to workplace behaviors, attitudes and other organizational outcomes.” (Kirkman,
Lowe et al. 2006; Wei et al. 2008).
Perhaps the most influential and most cited framework for evaluating national culture is
that of Geert Hofstede, which proposes five main cultural dimensions: (1) individualism
vs. collectivism, (2) uncertainty avoidance, (3) power distance, (4) masculinity vs.
femininity, and (5) long-term vs. short-term orientation. These dimensions have since
been associated with outcomes in various management domains, including: change
management, conflict management, decision making, human resource management,
leadership, work-related attitudes, business negotiations, reward allocation, social
networks, foreign direct investments, and business joint ventures and alliances (Kirkman,
Lowe et al. 2006). Only limited research has been conducted to find relationships between
cross-cultural factors and knowledge management (Diemers 2000; Choo and Bontis 2002;
Desouza and Evaristo 2003; Ford and Chan 2003; Wang 2004).
The World Wide Web
Technologies in general and the World Wide Web in particular have greatly contributed to
the agility of an organization to rapidly gather and share information and knowledge
within and across the enterprise. Some examples follow:
7
•Wikis, Web pages designed to enable anyone to contribute or modify content, have
revolutionized the way in which companies document policies, processes and
procedures and enable corporate knowledge exchange processes (Muller,
Meuthrath et al. 2008; Andriole 2009).
•Blogs, either used internally to enhance the communication and culture in a
corporation or externally for marketing, branding or public relations purposes
provide an efficient way to vet ideas, strategies, projects, and programs (Andriole
2009). Blogs help capture, annotate, organize and exchange personal knowledge
and receive feedback and form communities of practices and interests (Lytras,
Tennyson et al. 2009).
•Folksonomies, the practice and method of collaboratively creating and managing
tags to annotate and categorize content, allow non-expert users to collectively
classify and find information. Folksonomies allow for the annotation of web
content; recording how the data relates to real world objects within a given
knowledge domain (Al-Khalifa and Davis 2007). Users tag interesting information
for each other and find people with similar interests (Lytras, Tennyson et al. 2009).
•Podcasts, a series of audio or video digital-media files that can be syndicated,
subscribed to, and downloaded automatically when new content is added, can
contribute to institutional memory of the organization (Andriole 2009).
•RSS, a family of standardized Web feed formats used to publish frequently updated
works (such as blog entries, news headlines, audio, and video) can be used to
facilitate information flows to employees, customers, suppliers, and partners
(Andriole 2009).
•Crowdsourcing, the act of taking a task traditionally performed by an employee or
contractor, and outsourcing it to an undefined, generally large group of people.
Through the contribution of the crowd, an organization can tap a wider range of
talent than might be present in its own organization and reduce costs associated
with exploring new ideas and concepts (Howe 2008).
•Corporate Portals, a framework for integrating information, people and processes
across the enterprise, can integrate and make otherwise disperse applications
integrated and interoperable via a single point of access. Corporate portals can
provide information and knowledge personalized to the specific needs and access
rights of the knowledge worker.
8
•Service Oriented Architecture (SOA), a group of reusable and interoperable
methods for systems development and integration is rapidly becoming a standard
approach for enterprise information systems (Andriole 2009). SOA allows for
connecting and sharing resources and data in a flexible and standard manner. Web
services (XML, SOAP, WSDL, UDDI) make use of the SOA approach to integrate
heterogeneous and distributed business functions and services (e.g. enterprise
resource planning, business intelligence/reporting systems, social software, Blogs,
Wikis, web-based training) and hence surface and organize existing information
and knowledge (Abou-Zeid 2008).
Today, Web 2.0, the concept of the proliferation of interconnectivity and interactivity of
web-based content and its supporting technologies (which include social-networking sites,
video sharing sites, wikis, blogs, and folksonomies) have led to the development and
evolution of organic communities of knowledge. With the same spirit of Web 2.0,
companies are embracing the concept of Enterprise Social Software, also known as
Enterprise 2.0, to allow for spontaneous, knowledge-based collaboration within and
across the enterprise (Mcafee 2006).
Many of the above mentioned technologies foster bottom-up community building. To
embrace the new paradigm brought by these technologies in the context of knowledge sharing,
companies must foster a participatory and trusting culture that allows knowledge to flow
(Lytras, Tennyson et al. 2009); it is necessary to create a receptive culture in order to prepare
the way for these new practices (Mcafee 2006). Hodgson (Allen 2008) describes several
studies where a positive correlation between social networking and Hofstede’s cultural
dimensions can be observed and measured.
9
“An investment in knowledge always pays the best interest.”
Benjamin Franklin
Chapter 2 Literature Review
This chapter presents selected summaries of literature reviews on knowledge management,
national culture, and relevant comparative data and information on economic and cultural
profiles for Italy and the United States. As such it augments the literature reviewed and
cited in Chapter 1.
Knowledge Management
We are now an information society in a knowledge economy where knowledge
management is essential. While the realization of the need for a systematic strategy for
knowledge management may be relatively new, the concept may be as old as mankind.
Companies have always managed knowledge (Davenport and Prusak 1997). The creativity
and innovation required in today’s competitive global environment, the increasing
employee turnover (resulting in higher knowledge loss), and the expansion of markets of
knowledge (patenting, licensing, intellectual property), make the need for formalizing KM
far more urgent.
To understand why managing knowledge provides sustainable advantage, we must first
understand what knowledge is, and how knowledge management differs from information
management.
10
Knowledge
What constitutes “knowledge” has been debated since the early philosophers and
continues to be a matter of on-going debate in the field of epistemology (Wallace 2007).
For the purpose of this study we cite Davenport and Prusak (1998), who define
knowledge as “a fluid mix of framed experiences, values, contextual information, and
expert insight that provides a framework for evaluating and incorporating new
experiences and information.”
Authors have provided distinctions and relationships between data, information, and
knowledge. Some placed knowledge on top of the data-information-knowledge chain, and
in some cases added wisdom on top of knowledge (Wallace 2007).
Figure 1: The Data-Information-Knowledge-Wisdom value chain
Data is the raw material to be interpreted with information (e.g. contextual and
transactional facts), which when combined with other information, insights, experience and
reasoning produces knowledge. The accumulated knowledge provides wisdom for
addressing future actions (Kavouras and Kokla 2008). We provide an example of the data-
information-knowledge-wisdom relationship in Table 1.
Table 1: Example of the data, information, knowledge, and wisdom relationship (Similar to the
example provided by Kavouras and Kokla 2008)
Data = Facts as symbols without additional meaning
P1=(38 14 81N, 15 65 36E) P2=( 38 12 46N, 15 65 36E)
Information = Data + Meaning
Latitude and longitude coordinates, describing the position of two points on
earth, expressed in meters.
Knowledge = Information + other information + insights, experience and reasoning
Using the Haversine formula we calculate the distance between the two points
to be 4.788 kilometers. The calculated distance P2 is south of P1. There is no
information on the elevation of these points, or an estimate of the accuracy of
this position.
Wisdom = Accumulated knowledge for addressing future actions
I need the altitude of the two points to calculate the exact distance between the
two points. The coordinates of the two points can be converted to and plotted on
my custom map reference system. Limitations include the earth curvature.
11
An individual or an organization can have different types of knowledge: tacit or explicit.
Explicit knowledge is defined as factual, objective knowledge that can readily be stored,
accessed, understood within its contexts, and more easily transmitted (Nonaka 1994). It
can readily be stored electronically (e.g. in databases, manuals, documents and
procedures) and transmitted to others. Tacit knowledge, on the other hand, is subjective
and since it resides within the individual it is more difficult to articulate and codify. In
order to transfer tacit knowledge effectively, personal contact and trust are necessary
(Ribière 2001). Tacit knowledge represents internalized knowledge that an individual
may not be consciously aware of as he or she performs a task. For an organization to be
successful, it must capitalize on individual knowledge and turn it as much as possible into
organizational knowledge. This includes not only converting internalized tacit knowledge
into explicit knowledge in order to share, but also permitting individuals to internalize
codified knowledge and make it personally meaningful.
Tacit knowledge To Explicit knowledge
Tacit
Knowledge Socialization Externalization
From
Internalization Combination
Explicit
knowledge
Figure 2: Four modes of knowledge conversion (Nonaka and Takeuchi 1995)
Nonaka and Takeuchi (1995) describe four modes of knowledge conversion ( Figure 2):
socialization (from individual tacit knowledge to another individual tacit knowledge),
externalization (from tacit knowledge to explicit knowledge), combination (the exchange
and combining of different bodies of explicit knowledge; from explicit knowledge to
systemic explicit knowledge), and internalization (from explicit knowledge to tacit
knowledge).
12
Knowledge management practices try to close the gap between what the individual knows
and what the organization knows.
But, what is knowledge management more precisely?
Knowledge Management defined
There are many definitions of knowledge management; we selected the following three, which
provide important insights:
“Knowledge management (KM) covers any intentional and systematic process or
practice of acquiring, capturing, sharing and using productive knowledge, wherever
it resides, to enhance learning and performance in organizations.” (OECD 2003)
“Knowledge management (KM) is a discipline that promotes an integrated
approach to identifying, capturing, retrieving, sharing and evaluating an
enterprise's information assets. These information assets may include databases,
documents, policies and procedures as well as the uncaptured, tacit expertise and
experience resident in individual workers.” (James Bair 1996)
“Knowledge management is a framework and tool set for improving the
organization’s knowledge infrastructure, aimed at getting the right knowledge to the
right people in the right form at the right time.” (Schreiber 2000)
KM must be a deliberate and conscious strategy by an organization to follow knowledge
supporting activities - with a systems thinking approach. The activities described in Table
2 are suggested by various authors (Schreiber 2000):
Table 2: Knowledge management activities and associated knowledge-value chain (Schreiber 2000)
KM Activity Description
Identify Identify internally and externally existing knowledge
Plan Plan what knowledge will be needed in the future
13
Acquire
Develop
Acquire and/or develop needed knowledge
Distribute Distribute knowledge to where it is needed
Foster
Use
Foster the application of knowledge in the business processes of the
organization
Maintain
Control quality
Control the quality of knowledge and maintain it
Dispose Dispose of knowledge when it is no longer needed
Knowledge management frameworks have been developed to sustain the activities
described in Table 2. In early 1999, Stankosky proposed a framework where various
elements are grouped in four principal areas; the four pillars of KM (see Table 3 and Figure
3): (1) Leadership/management, (2) Organization, (3) Technology, and (4) Learning, and
(4) Technology. Calabrese (Calabrese 2000) validated this framework and the key
elements defining effective KM programs.
14
Table 3: Description of the four KM pillars (Stankosky 2005)
KM Pillar Description
Leadership/
management
Concerns the environment, strategic, and enterprise level
decisionmaking process involving the values, objectives, knowledge
requirements, knowledge sources, prioritization, and resource
allocation of an organization’s knowledge assets. It stresses the need
for integrative management principles and techniques, primarily based
on systems thinking and approaches.
Organization Concerns the operational aspects of knowledge assets, including
functions, processes, formal and informal organizational structures,
control measures and metrics, process improvement, and business
process reengineering. Underlying this pillar are system engineering
principles and techniques to ensure a flow down, tracking, and
optimum utilization of all an organization’s knowledge assets.
Technology
Concerns the various information technologies peculiar to supporting
and/or enabling KM strategies and operations. One taxonomy used
relates to technologies that support the collaboration and codification
of KM strategies and functions.
Learning Concerns organizational behavioral aspects and social engineering.
The learning pillar focuses on the principles and practices to ensure that
individuals collaborate and share knowledge to the maximum.
Emphasis is given to identifying and applying the attributes necessary
for evolving and institutionalizing robust “learning organizations”.
Note. Description of pillars of KM taken from Stankosky 2005 (pp. 4-5).
15
Figure 3: The Four pillars of Knowledge Management (Stankosky, Calabrese, Baldanza 1999)
Note. Figure reproduced based on Figure 1-4 in Stankosky 2005 (p. 6).
The four pillars created a theoretical construct and a research platform for examining
correlations between and among key factors, elements and dozens of disciplines. The four
pillar framework has matured, evolved and retained its relevance throughout the decade
since its creation.
Country Profiles: Italy and the United States
This section compares Italy and the United States on various statistical, economic, business
and knowledge related aspects.
16
Economic Climates
Despite the recent financial crisis, the United States continues to be the most competitive
economy in the world (World Economic Forum 2008). The United States has the largest
and most technologically powerful economy and enjoys greater flexibility than its
counterparts in Italy in decisions to raise capital, to create physical facilities, to hire and
lay off workers, and to develop new products. Conversely, over the years, Italy has
promoted and firmly established its “Made in Italy” trademark as a synonym for
highquality, good taste and innovation; initially focused on the food, fashion, furniture,
and luxury vehicles, but now the recognition has encompassed other industries, including
high precision machinery, as well as aerospace and defense technologies.
Table 4: Italy and US ranks in economic statistics (CIA 2008)
Italy United States
Largest GDP 7th 1st
Largest GDP per capita (PPP) 38th 10th
Largest public debt (% of GDP) 7th 27th
Largest Exporter 6th 5th
Largest Importer 7th 1st
Table 4 and 5 report some general statistical data (CIA 2008). Italy has a large public debt,
the 7th largest in the world as a percentage of GDP.
Table 5: Statistical comparison between Italy and the United States (CIA 2008)
Italy United States
Land Area 301,230 sq km
(116,305 sq miles)
9,826,630 sq km
(3,794,083 sq miles)
Population 58,145,320 (July 2008 est.) 303,824,640 (July 2008 est.)
Italy United States
GDP $2.105 trillion (2007 est.) $13.84 trillion (2007 est.)
GDP per capita (PPP) $30,900 (2007 est.) $45,800 (2007 est.)
Public debt 104% of GDP (2007 est.) 60.8% of GDP (2007 est.)
Exports $502.4 billion (2007 est.) $1.148 trillion (2007 est.)
Imports $498.1 billion (2007 est.) $1.968 trillion (2007 est.)
17
The Global Competitiveness Report 2008-2009 of the World Economic Forum (2008)
ranked the United States as the most competitive worldwide, while Italy ranked 49th, down
by three places since 2007. In the same report, Italy is ranked 21st for its business
sophistication; it produces high value goods with the latest production processes. On the
other hand Italy continues to lack in efficiency and flexibility in its labor market; ranking
129th out of 134 countries, creating a large hindrance to job creation. A survey included
in the Global Competitiveness Report shows that respondents from Italy and the United
States perceive the inefficient government bureaucracy, tax rates and tax regulations as the
most problematic factors for doing business in their country.
The World Bank’s Doing Business (2008) report is closely correlated to the Global
Competitiveness Report and further highlights the areas of necessary improvements.
Table 6 provides comparative data for Italy and the United States.
Table 6: Ease of Doing Business rank (out of 181 economies) for Italy and United States
Ease of… Italy United States
Doing Business 65 3
Starting a Business 53 6
Dealing with Construction Permits 83 26
Employing Workers 75 1
Registering Property 58 12
Getting Credit 84 5
Protecting Investors 53 5
Paying Taxes 128 46
Ease of… Italy United States
Trading Across Borders 60 15
Enforcing Contracts 156 6
Closing a Business 27 15
The above rankings show net differences in the ease of doing business in Italy versus the
United States. In particular, the ease of employing workers, getting credit, protecting
investors, trading across borders, and enforcing contracts are optimal in the United States,
but very disadvantageous in Italy. On a positive note, Renato Brunetta, Italian Minister for
18
Public Administration and Innovation, has vowed to modernize government offices and
procedures. Moreover, the current Berlusconi government has dedicated a (noncabinet)
Minister for Legislative Simplification to review and simplify government bureaucracies.
These initiatives, if carried out successfully, may improve the ease of doing business,
which will allow for more agile entrepreneurship and a more business friendly
environment for attracting foreign investors.
The economic and global competitive challenges described above require that countries assess
their readiness for the knowledge economy and design initiatives, policies and practices to
shape and capitalize their knowledge advantage.
In the next section, we will review factors that positively influence a country's ability to
generate, adopt and diffuse knowledge and present a comparative analysis between Italy and
the United States.
Knowledge Economy Index (KEI)
The World Bank Institute’s Knowledge for Development Program has created the
Knowledge Assessment Methodology (KAM), an interactive benchmarking tool that reports
the strengths and weaknesses, and relative performance of countries on the knowledge
economy (World Bank 2008). The KAM evaluates countries against a fourpillar
knowledge-economy framework made up of 12 knowledge indicators (Table 7).
Table 7: Mapping of the four knowledge economy pillars to the 12 knowledge indicators (World Bank
2008)
Pillar Indicators
Economic and institutional regime
The country’s economic and institutional regime
must provide incentives for the efficient use of
existing and new knowledge and the flourishing of
entrepreneurship.
•Tariff and non-tariff barriers
•Regulatory quality
•Rule of law
Education and skill of population • Adult literacy rate
• Gross secondary enrollment rate
19
The country’s people need education and skills that • Gross tertiary enrollment rate enable
them to create and share, and use it well.
Information infrastructure
A dynamic information infrastructure is needed to
facilitate the effective communication,
dissemination, and processing of information.
•Telephones per 1,000 people
•Computers per 1,000 people
•Internet users per 1,000 people
Innovation System
The country’s innovation system – firms, research
centers, universities, think tanks, consultants, and
other organizations – must be capable of tapping the
growing stock of global knowledge, assimilating
and adapting it to local needs, and creating new
technology.
•Royalty payments and receipts, US$ per person
•Technical journal articles per million people
•Patents granted to nationals by the U.S. Patent
and Trademark Office per million people
Note. Table adapted from Figure 1 and Table 1 of the World Bank KAM Report 2008 (p. 1, 3).
The KAM includes two additional indicators for overall economic growth: (1) Annual GDP
growth and (2) Human Development Index1 (HDI).
Comparative data from the KAM between Italy and the United States on the knowledge indexes
and relative pillars is reported in Table 8.
Table 8: Comparative data between Italy and United States (World Bank 2008)
Variable Italy United States
actual normalized actual normalized
Annual GDP Growth (%), 2002-2006 0.66 0.36 3.00 2.45
Human Development Index, 2005 0.94 8.62 0.95 9.20
Tariff & Non tariff Barriers, 2008 81.00 6.67 86.80 9.56
Regulatory Quality, 2006 0.84 7.50 1.47 9.07
Rule of Law, 2006 0.37 6.36 1.57 8.86
Economic Incentive and Institutional Regime 6.84 9.16
Total Royalty Payments and receipts (US$/pop.) 52.43 7.50 296.25 9.29
1 HDI provides information on the human development aspect of economic growth. The HDI is based on
three indicators: longevity, as measured by life expectancy at birth; educational attainment, as measured by a
combination of adult literacy rate and the combined gross primary, secondary and tertiary school enrollment
ratio; and standard of living, as measured by GDP per capita
20
Scientific and Technical Journal Articles / Mil.
People, 2005
420.51 8.35 692.46 9.06
Patents Granted by USPTO / Mil. People, avg
2002-2006
32.97 8.29 324.12 10.00
Innovation System 8.04 9.45
Adult Literacy Rate (% age 15 and above), 2007 98.87 6.88 100.00 10.00
Gross Secondary Enrollment, 2006 100.28 8.09 93.89 6.69
Gross Tertiary Enrollment, 2006 66.99 8.60 81.77 9.61
Education and skill of population 7.86 8.77
Total Telephones per 1,000 People, 2006 1,650.00 9.50 1,350.00 7.64
Computers per 1,000 People, 2005 370.00 8.12 760.00 9.64
Internet Users per 1000 People, 2006 490.00 8.43 690.00 9.50
Information infrastructure 8.68 8.93
Note. Table adapted from Table 2 of the World Bank KAM Report 2008 (p. 1, 3). Data
from Table 8 is plotted in Figure 4.
Figure 4: Knowledge indexes comparison between Italy and the United States (World Bank 2008)
Note. Figure obtained from Cross-country Comparison (KAM 2009) at www.worldbank.org/KAM.
From the comparative data of the KAM it is evident that the United States is investing more
than Italy in human capital, effective institutions, information and communication
technologies, and innovative and competitive enterprises.
21
Table 9 provides the country ranking on the four pillars of the knowledge economy for Italy
and the United States among 140 countries evaluated by the KAM.
Table 9: Ranking of Italy and the United States among the 140 countries evaluated by the KAM
(World Bank 2008)
Italy United States
Economic Incentive and Institutional Regime 47 14
Innovation System 27 7
Education and skill of population 31 13
Information infrastructure 18 13
Knowledge Economy Index (KEI) 27 9
The United States is ranked as ninth, preceded from first to eighth by Denmark, Sweden,
Finland, Netherlands, Norway, Canada, Switzerland, and the United Kingdom. The
Knowledge Economy Index ranking for Italy and the United States relative to the averages of the
G7 countries, Western Europe, high income countries, and the world, is reported in Figure 5.
Here the United States is shown to be above average compared to said country groupings, while
Italy performs below average for all listed world subsets.
Figure 5: Knowledge Economy Index Comparison between Italy, the United States, G7 Countries,
Western Europe, high income countries, and the World (World Bank 2008).
Note. Figure adapted from Cross-country Comparison (KAM 2009) at www.worldbank.org/KAM.
22
Investments in Knowledge
A recent study by the Organization for Economic Cooperation and Development2 (OECD
2007), reports that in 2004 the ratio of investment in knowledge to GDP was 4.18
percentage points higher in the United States than in Italy; the US invested 2.75 times more
as a percentage of its GDP towards R&D, higher education, and investment in software
(See Table 10). Moreover, Italy’s ratio of investment in knowledge to GDP is considerably
lower than the average from the European Union and OECD member countries,
respectively 1.23 and 2.52 percentage points; Italy spends one third of the European Union
and half of the OECD countries averages.
Table 10: Investment in knowledge, as a percentage of GDP, 2004 (OECD 2007)
R&D Software Higher
Education
Investment in
Knowledge
United States 2.74 1.46 2.36 6.56
Sweden1 3.98 1.54 0.93 6.44
Finland1 3.49 1.31 1.11 5.92
Japan 3.31 1.19 0.83 5.33
Denmark1 2.58 1.36 1.16 5.10
OECD2 2.41 1.08 1.42 4.91
Canada 2.02 0.83 1.60 4.45
France 2.20 1.16 0.95 4.31
Australia 1.81 0.99 1.14 3.94
Germany 2.54 0.64 0.73 3.90
Netherlands1 1.84 1.10 0.80 3.75
EU3 2.02 0.80 0.79 3.62
United Kingdom1 1.80 1.01 0.70 3.50
Austria1 2.21 0.67 0.54 3.43
Belgium1 1.92 0.59 0.90 3.41
Spain 1.12 0.64 0.92 2.68
Italy1 1.14 0.57 0.68 2.38
2 Organization for Economic Cooperation and Development (OECD) is an international organization of
thirty countries that share the principles of democracy and free-markets. Most members are high-income
economies.
23
Ireland1 1.19 0.19 0.89 2.27
Greece1 0.64 0.26 0.96 1.85
Portugal1 0.78 0.13 0.83 1.74
Note: For all countries, investment on educations refers to 2003. For Belgium, Australia and Austria the
period of reference is 1998-2003.
1. Data from 2003
2. OECD excludes Greece, Australia and Austria from the group of reporting countries.
3. EU: excludes Greece from the group of reporting countries.
The investment in knowledge is the sum of the percentages in GDP expenditures in R&D,
software, and higher education. For ease of visualization of the data from Table 10 see
Figure 6.
Note: For all countries, investment on education refers to 2003. For Belgium, Australia and Austria the period
of reference is 1998-2003.
1. Data from 2003
2. OECD excludes Greece, Australia and Austria from the group of reporting countries.
3. EU: excludes Greece from the group of reporting countries.
Figure 6: Investment in knowledge, as a percentage of GDP, 2004 (OECD 2007)
24
An OECD policy brief describing the significance of knowledge management in the
business sector reported that “knowledge management practices matter for innovation and
productivity performance (OECD 2004). The authors presented a French study of 5,500
companies and demonstrated that of these, the ones that had knowledge management
policies in place innovated more and filed more patents than those which did not. The
same study revealed that companies that actively promoted a culture of knowledge
sharing had higher labor productivity levels than the ones which did not.
To be successful, knowledge management policies and practices must take into
consideration the culture of the organization and the culture of the country. In a field study
of 31 knowledge management efforts in 24 global companies, Davenport et al. (1998)
identified eight key aspects for creating, sharing and using knowledge effectively,
including culture and process, developing a common purpose, and a clear purpose and
common language.
National Cultures
As argued in the Culture Implications section of Chapter 1, national culture influences
human resource practices and organizational behavior. When considering the adoption of
management practices that have proven to be successful in a different country, Hofstede
(1984) warns that “there are no universal solutions to organization and management
problems”. He goes on to say, though, that countries can and should learn from each other;
in fact, “looking across the border is one of the most effective ways of getting new ideas in
the area of management, organization, or politics”, but this must be done with “prudence
and judgment”.
Hofstede’s National Culture Dimensions
Hoftede’s taxonomy is based on his survey at IBM, which was held in 40 countries, once
in 1968 and once in 1972, producing a total of over 116,000 responses, and on an
additional survey (Bond’s Chinese Value Survey) held in 23 countries (not including
Italy) in the 1980s, which validated the results from the previous study and added the fifth
dimension (Hofstede 1984). The five dimensions of culture in his study of national work
related values are presented in Table 11.
25
Table 11: Hofstede's five cultural dimensions
Dimension Description
Power
Distance
(PDI)
Power Distance Index (PDI) represents the degree of inequality among
people, which the population of a country accepts and expects; from
relatively equal to unequal.
Individualism
versus
Collectivism
(IDV)
Individualism (IDV) on the one side versus its opposite, collectivism, is the
degree to which individuals in a country prefer to act as individuals or as
members of groups. On the individualist side we find societies in which
the ties between individuals are loose; everyone is expected to look after
him/herself. On the collectivist side, we find societies in which people are
integrated into groups, which continue protecting them in exchange for
unquestioning loyalty.
Masculinity
versus
Femininity
(MAS)
Masculinity (MAS) versus its opposite, femininity refers to the degree to
which individuals in a country exhibit very assertive and competitive
behaviors (generally associated with men), versus the more gentle values
like quality of life, maintaining good relationships and solidarity
(generally associated with women). The IBM studies revealed that women
in feminine countries have the same modest, caring values as the men; in
the masculine countries they are somewhat assertive and competitive, but
not as much as the men. Therefore these countries show a gap between
men’s values and women’s values.
Uncertainty
Avoidance
(UAI)
Uncertainty Avoidance Index (UAI) deals with a society’s tolerance for
uncertainty and ambiguity. It indicates to what extent an individual feels
either comfortable or uncomfortable in unstructured situations.
Unstructured situations contain unknowns and surprises that some cultures
will try to mitigate through strict rules and regulations, while other
cultures that are more tolerant to novelty and change will have as few rules
as possible.
Long term/
short term
(Confucian
Dynamism)
Long-Term Orientation (LTO) versus short-term orientation represents a
country’s preference to plan ahead and practice long term future-oriented
values (e.g. persistence, thrift, and having a sense of shame), versus
shortterm past-oriented values (respect for tradition, fulfilling social
obligations, and protecting one’s face). Both the positively and the
negatively rated values of this dimension are found in the teachings of
Confucius, the most influential Chinese philosopher who lived around 500
B.C.; however, the dimension also applies to countries without a
Confucian heritage.
Note. Description of cultural dimensions are direct citations from Hofstede’s website ww.geerthofstede.com
The indices derived from Hofstede’s research represent aggregate values at the national level;
corporate culture was kept constant and professional culture can be considered a random
variable due to the large sample population of his study (Hofstede 1984). Hofstede’s
26
pioneering attempt to create quantifiable dimensions with both direction and intensity (low
vs. high scores) allowed for detailed quantitative analysis of the cultural dimensions.
Replications of Hofstede’s study over the years have confirmed his findings
(Søndergaard 1994). Moreover, several studies have been developed not as replications, but
along similar lines, to test the relevancy of Hofstede’s research methodology (Søndergaard
1994).
Despite some criticism of Hofstede’s methodology and the fact that results may have been
sensitive to the specific timeframe of the survey, Hofstede’s cultural dimensions remain
the most widely known, accepted, improved upon, and used approach for comparing
cross-cultural influences (Pauleen 2007). Consequently, this research also utilizes
Hofstede’s methodology and country specific data for Italy and the United States as its
baseline.
“To know that we know what we know, and to know that we do not know what we do not
know, that is true knowledge.”
Nicolaus Copernicus
Chapter 3 Research Method
In this chapter we present three hypotheses for determining if the cultural attributes of Italy
and the USA influence the respective perceptions of knowledge management.
Hofstede’s country data for Italy and the USA are reviewed and a case is made for comparing
results between our study to Wang’s (2004) findings, which found a positive relationship
between large gaps in national culture values and knowledge management.
27
Finally, we present our survey instrument and research analysis methodology.
Question to be Answered
The primary question is:
“Do Italian and American cultural attributes influence the respective beliefs, expectations
and practices of their citizens regarding Knowledge Management?”
This primary question will be answered by measuring the differences between Italian and
American beliefs, expectations, and practices about knowledge management, as stated in
the following hypotheses:
H1: There is a positive relationship between the cultural dimensions of
Italian and US respondents and their beliefs about factors
influencing successful knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their expectations about the
benefits of knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their knowledge management
practices.
H2:
H3:
Our hypotheses have been stipulated based on literature review and previous research
contributions by Po-Jeng Wang (2004) who compared the knowledge management beliefs,
expectations and practices of Taiwanese and Americans. While the gaps between
Taiwanese and American national culture dimensions are considerably large for the
dimensions of individualism and long-term orientation indexes, the gaps on all available
dimensions between Italian and American indexes are relatively small (See Table 12).
Table 12: Hofstede's national culture dimension index values and rankings for Italy and the USA
(Hofstede 1997; 2001)
Country Power
Distance
Individualism Masculinity Uncertainty
Avoidance
Confucian
Dynamism
28
Index Rank Index Rank Index Rank Index Rank Index Rank
Italy 50 34 76 7 70 4-5 75 23 NA NA
USA 40 38 91 1 62 15 46 43 29 14
Taiwan 58 29-30 17 44 45 32-33 69 26 87 2
The similarities between Italy and the USA in the scores and ranks are reflected in
Hofstede’s country clusters framework (Figure 7). Italy and the USA are part of two
adjacent country clusters with relatively low cluster distance; by comparison Taiwan and the
USA belong to two clusters that have the greatest cluster distance.
29
Figure 7: Hofstede's Country Clusters dendrogram diagram (Hofstede 1984)
Various studies have demonstrated that clustering of countries using an integration of
cultural dimensions allows for greater predictability of which management practices may or
may not be effective across national borders (Ronen and Shenkar 1985).
The largest difference between Italian and American dimensions is that of uncertainty
avoidance (see Table 13). Hofstede’s distribution of the index values for the uncertainty
avoidance dimension suggests a mean of 65 and a standard deviation of 24 (Hofstede 2001).
30
TAI
Therefore, the difference in uncertainty avoidance scores of 29 between Italy and the USA
is above a full standard deviation, and could potentially account for country effects on
knowledge management perceptions.
Table 13: Culture dimension gaps between Italy and the USA, and data set mean and standard
deviation (Hofstede 2001).
Dimension Gap Mean* Standard
Deviation*
Italy USA
Power Distance 10 (10) 57 22
Individualism (15) 15 43 25
Masculinity 12 (12) 49 18
Uncertainty Avoidance 29 (29) 65 24
* Based on 53 countries
Method
We have surveyed a sample of US and Italian nationals to assess the differences and similarities
between their knowledge management beliefs, expectations and practices.
The results were then to be correlated to the indexes of Hofstede’s culture dimensions.
The survey was administered in English for US nationals and in Italian for Italian
nationals. The adopted questionnaire is based on Po-Jeng Wang’s (2004) survey, which
used the Lickert five-point scale to evaluate an individual’s agreement or disagreement on
a set of attitude statements. This survey uses previously validated measures from Dr.
Charles Bixler (2000), and Dr. Francesco Calabrese (2000). The survey instrument
contains four parts:
1. Demographics: to capture the organization profile and the respondent profile.
2. Belief: an individual’s rating of critical factors for developing and sustaining
successful knowledge management.
3. Expectations: an individual’s rating of expected benefits obtained by an
organization through knowledge management.
31
4. Practices: an assessment of knowledge management practices within an individual’s
organization.
The survey was translated into Italian for use with the Italian sample population. To validate
the translation, a third party reverse translated the Italian version back to English and later
verified that the meaning of the questions matched that of the original survey. Both the
English and Italian questionnaires were tested on a small group of individuals to make sure
all questions were properly understood and interpreted in the intended context. Furthermore,
such test runs revealed that the survey could be completed in 10 minutes or less.
The survey tool and its planned implementation were then submitted to the Institutional
Review Board (IRB), which given the nature of the study and the anonymity of the survey
tool, approved its execution as exempt from IRB committee review.
The survey was then published online on our research website at www.km-research.com. The
survey was programmed to default to the English version except for users whose web browser
language was set to Italian. Users had the option to toggle between languages.
To prevent duplicate submissions, the survey application required a Survey Key, which
was individually assigned to prospective participants (one key per participant tied to
his/her email address). Prospective respondents received a personalized email
invitation containing the assigned Survey Key (appended to the web URL address – e.g.
http://www.km-research.com/?KEY=12345ABCDE67890FGHIJ). Additionally,
prospective participants who did not have an individually assigned key had the option to
request one online; upon submission of a valid email address, the system
instantaneously assigned and emailed a key to the requestor’s email address.
The survey application recorded the time it took for each respondent to complete each of
the four parts; response records included a start and end date and time. This allowed us to
later see if participants responded according to an expected time period; hence reduce the
possibility of bogus submissions.
To assure anonymity of responses, survey answers were automatically disassociated from
the participant’s key upon survey submission; keys were managed in a separate database
32
table where each record was updated to indicate survey completion and to prevent multiple
submissions by the same key. Therefore, from the time of submission there was no way to
match keys with survey responses.
Subjects
Participation in our study was intended for employees and managers that were expected to
be involved in knowledge management initiatives at any level, and within companies and
organizations where an IT infrastructure was in place, and their employees were capable
of using it. We did not restrict our study to a specific industry or company profile. But,
company demographics were collected to allow for data analysis on homogeneous
company profiles and to provide additional comparative analysis.
Participation in our study was solicited through a number of direct and indirect channels to
individuals who had the above mentioned profile. Channels included:
1. Friends and colleagues.
2. Referrals from friends and colleagues.
3. Postings on Knowledge Management related web forums and electronic distribution
lists.
4. Direct emails to members of Knowledge Management and business related
communities of interest.
5. Search engine marketing (paid placements through Google’s advertising program).
6. Email referrals from individuals who completed the online questionnaire. Upon
survey submission participants were asked to refer friends and colleagues for a chance
to win a prize. Upon receipt, we reviewed each referral and sent a customized
electronic invitation.
Data Analysis
We replicated Po-Jeng-Wang’s (2004) approach using descriptive statistics and analysis of
variance (ANOVAs) for analyzing the survey data; allowing for a direct comparison of
our research results with Wang’s. We used PASW Statistics version 18 by SPSS (an IBM
company) for our analyses.
33
Limitations
The following are limitations of the research method:
•Data collected represents the perception of the sample population, as opposed to
an objective measurement of data.
•The population of this study is composed of employees and managers, which were
expected to be involved in knowledge management initiatives at any level. We did
not restrict our study to a specific industry or company profile. But, company
information was collected to focus our data analysis on homogeneous company
profiles and to provide additional comparative analysis.
•The US and Italian sample populations is not a probability sample because the
sample is voluntary and hence it may have a self-selection bias.
Expected contributions to the body of knowledge
Our research identifies cross-cultural aspects between Italian and American respondents
that influence the perceived beliefs, expectations, and practices about knowledge
management (KM) initiatives. More broadly, this research is part of a multi-country study
undertaken at The George Washington University designed to help companies or units
within a company select KM tools and practices that are more likely to succeed in the
national culture setting in which these are to be implemented. Moreover, our research will
aid future cross-cultural comparative research in the field of KM.
34
“Measure what is measurable, and make measurable what is not so.”
Galileo Galilei
Chapter 4 Survey Results
This chapter describes our data collection methodology and provides quantitative analysis of
our survey responses.
Data Collection
All surveys were completed and recorded online. Participation in our study was solicited
via email, referrals by participants who completed the survey, postings on KM related web
forums and electronic distribution lists, and search engine marketing (US and Italian
regional paid placements through Google’s advertising program – see Appendix 3).
Survey participation solicitations can be divided into two types: direct and indirect. Direct
solicitations are those where the participant received via email an individual access key
directly from us (or from us through the referral system). Indirect solicitations are those
where a prospective participant reached our website by following links on KM related web
forums, electronic distribution lists, and search engine paid advertisements.
The distribution of direct and indirect access keys is summarized in Table 14.
Table 14: Survey Access Keys Generated by the Survey Application
Italian
Language
English
Language
Total
35
Access Keys Sent Out
Direct
Indirect
867
659
208
1068
902
76
1,935
1,651
284
Since the survey was accessible to anyone from anywhere, we received responses also
from countries other than Italy and the USA. Table 15 reports the survey response rates.
Note that to mitigate the effect of population variances inequality in our analysis of
variance (ANOVA), which is most pronounced when the sample sizes are unequal, we
selected responses until the two country groups reached equal numbers of responses.
Since our study compares residents of Italy and the USA, responses from individuals
resident in other countries were not accepted. All usable responses were completed within
a reasonable timeframe; the minimum time of completion recorded by the survey
application was six minutes, which based on our test runs was an acceptable timeframe.
Table 15: Survey Response Rates
Respondents’ Country of Residence
Italy USA Other Total
Total Responses 312 265 60 637
Incomplete Responses 28 28 3 59
Usable Responses 284 237 0 521
Selected Responses 237 237 0 474
The following sections provide an analysis of all responses; aggregate responses from the Italian
and the US sample populations.
Frequencies of Respondents for All Usable Responses
The first part of our survey collected demographic information, including country data, job
position level, and company industry type, size and business focus. Additionally, the
survey collected information about the stage of Knowledge Management initiatives within
the organization, the organizational level that promotes KM, and which departmental or
functional budget contributes the most to KM.
36
Country of Organization Headquarters
In addition to the respondent’s country of residence, the survey recorded the country of
organization’s headquarters. As shown in Table 16, almost 95% of the responses included
companies whose headquarters was either in the USA or in Italy. This high percentage
further homogenizes the population samples; reducing the possibility of nonItalian or non-
US organization national culture influences.
Table 16: Country of Organization Headquarters
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid USA 258 54.4 54.4 54.4
Italy 192 40.5 40.5 94.9
Germany 5 1.1 1.1 96.0
United Kingdom 5 1.1 1.1 97.0
Switzerland 2 .4 .4 97.5
France 2 .4 .4 97.9
Japan 2 .4 .4 98.3
Sweden 2 .4 .4 98.7
Spain 1 .2 .2 98.9
Ireland 1 .2 .2 99.2
Iran 1 .2 .2 99.4
Macedonia 1 .2 .2 99.6
Netherlands 1 .2 .2 99.8
San Marino 1 .2 .2 100.0
Total 474 100.0 100.0
Participant’s Country of Nationality
The survey included two questions about the participant’s country of nationality, one about
the current nationality and one about the nationality at birth. As shown in Table 17
95.4% of the responses included individuals whose country of nationality was either the USA
or Italy. The high percentage of Italian and US nationals in our study further homogenizes
our population sample.
37
Table 17: Participant’s Country of Nationality
Valid Cumulative
Frequency Percent Percent Percent
38
Valid Italy 250 52.7 52.7 52.7
USA 202 42.6 42.6 95.4
India 3 .6 .6 96.0
France 2 .4 .4 96.4
Russia 2 .4 .4 96.8
Switzerland 1 .2 .2 97.0
China 1 .2 .2 97.3
Czech Republic 1 .2 .2 97.5
Germany 1 .2 .2 97.7
Dominican Republic 1 .2 .2 97.9
Jamaica 1 .2 .2 98.1
South Korea 1 .2 .2 98.3
Macedonia 1 .2 .2 98.5
Netherlands 1 .2 .2 98.7
Peru 1 .2 .2 98.9
Pakistan 1 .2 .2 99.2
Puerto Rico 1 .2 .2 99.4
Turkey 1 .2 .2 99.6
Vietnam 1 .2 .2 99.8
South Africa 1 .2 .2 100.0
Total 474 100.0 100.0
As shown in Table 18, as much as 90.7% of the participants indicated that their country of
nationality at birth was either Italy or the USA.
Table 18: Participants’ Country of Nationality at Birth
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Italy 250 52.7 52.7 52.7
USA 180 38.0 38.0 90.7
India 5 1.1 1.1 91.8
Germany 3 .6 .6 92.4
Russia 3 .6 .6 93.0
United Kingdom 3 .6 .6 93.7
Vietnam 3 .6 .6 94.3
France 2 .4 .4 94.7
Argentina 1 .2 .2 94.9
Botswana 1 .2 .2 95.1
Belarus 1 .2 .2 95.4
Switzerland 1 .2 .2 95.6
China 1 .2 .2 95.8
Dominican Republic 1 .2 .2 96.0
Ecuador 1 .2 .2 96.2
Ethiopia 1 .2 .2 96.4
Greece 1 .2 .2 96.6
Ireland 1 .2 .2 96.8
Israel 1 .2 .2 97.0
Iran 1 .2 .2 97.3
Jamaica 1 .2 .2 97.5
Japan 1 .2 .2 97.7
South Korea 1 .2 .2 97.9
Macedonia 1 .2 .2 98.1
Malaysia 1 .2 .2 98.3
Nigeria 1 .2 .2 98.5
Netherlands 1 .2 .2 98.7
Peru 1 .2 .2 98.9
Pakistan 1 .2 .2 99.2
Puerto Rico 1 .2 .2 99.4
Syria 1 .2 .2 99.6
Turkey 1 .2 .2 99.8
South Africa 1 .2 .2 100.0
Total 474 100.0 100.0
39
Considering the fact that the USA attracts professional talent from all over the world, the
percentage of participants who indicated other nationalities at birth is relatively low and further
indicates a homogeneous nationality within our population sample.
Percent of Responses by Number of Employees
Table 19, also depicted in Chart 1, shows the frequency of responses by the number of
employees. We notice that almost one quarter of the respondents indicated a very large
organization (more than 10,000 employees) and slightly more than one quarter belonged to
a very small firm (less than 100 employees). The remaining in between categories shared
about one sixth each.
Table 19: Percent of Responses by Number of Employees
Cumulative
Frequency Percent Valid Percent Percent
Chart 1: Percent of Responses by Number of Employees
40
Valid <100 120 25.3 25.3 25.3
100-999 90 19.0 19.0 44.3
1,000-5,000 79 16.7 16.7 61.0
5,000-10,000 70 14.8 14.8 75.7
>10,000 115 24.3 24.3 100.0
Total 474 100.0 100.0
Percent of Responses by Annual Business Revenue
Table 20 and Chart 2, show the frequency of responses by annual revenue. Almost 44% of
the respondents indicated annual revenues greater than 250 million USD, about 24%
between 25 and 250 million USD, and almost 32% less than 25 million USD.
Table 20: Company Annual Business by Revenue
Frequency Percent Valid Percent
Cumulative
Percent
Valid >250M 208 43.9 43.9 43.9
<25M 151 31.9 31.9 75.7
25-250M 115 24.3 24.3 100.0
Total 474 100.0 100.0
Chart 2: Company Annual Business by Revenue
41
Percent of Responses by Business Activity
According to Table 21 and Chart 3, 83.3% of the responses came from individuals that
worked for a service-focused organization or organizations that focused both on products
and services. Product focused organizations made up only 16.7% of the sample population.
Table 21: Percent of Responses by Business Activity
Frequency Percent Valid Percent
Cumulative
Percent
Valid Services 295 62.2 62.2 62.2
Products & Services 100 21.1 21.1 83.3
Products 79 16.7 16.7 100.0
Total 474 100.0 100.0
Chart 3: Percent of Responses by Business Activity
42
Percent of Responses by Industry Type
According to Table 22, also depicted in Chart 5, 18.4% of the responses came from
individuals that worked for education, 8.6% for government and most of the rest for various
business industries.
Table 22: Percent of Responses by Industry Type
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid IT/Telecommunications 97 20.5 20.5 20.5
Education 87 18.4 18.4 38.8
Other 79 16.7 16.7 55.5
Consulting 67 14.1 14.1 69.6
Government 41 8.6 8.6 78.3
Manufacturing & Process Industries 37 7.8 7.8 86.1
Software Development 28 5.9 5.9 92.0
Financial/Banking/Accounting 21 4.4 4.4 96.4
Healthcare/Pharmaceutical 9 1.9 1.9 98.3
Constructions/Architecture/Engineering 8 1.7 1.7 100.0
Total 474 100.0 100.0
Chart 4: Percent of Responses by Industry Type
43
In Table 23, also depicted in Chart 5, we consolidate all business related industry types into
a single business category. This will allow us later in Chapter 5 to use industry type as a
control variable with meaningful frequencies.
Table 23: Percent of Responses by Industry Type (business consolidated in a single category)
Frequency Percent Valid Percent
Cumulative
Percent
Valid Business 267 56.3 56.3 56.3
Education 87 18.4 18.4 74.7
Other 79 16.7 16.7 91.4
Government 41 8.6 8.6 100.0
Total 474 100.0 100.0
Chart 5: Percent of Responses by Industry Type (business consolidated in a single category)
44
Percent of Responses by Job Position Level
According to Table 24, also depicted in Chart 6, 37.1% of the participants were managers or
directors and 12% were executives, for a total of 39.1%. Technical and support staff were
respectively 26.8% and 11.2% of the sample population.
Table 24: Percent of Responses by Job Position Level
Cumulative
Frequency Percent Valid Percent Percent
Chart 6: Percent of Responses by Job Position Level
45
Valid Manager/Director 176 37.1 37.1 37.1
Technical Staff 127 26.8 26.8 63.9
Other 61 12.9 12.9 76.8
Executive 57 12.0 12.0 88.8
Support Staff 53 11.2 11.2 100.0
Total 474 100.0 100.0
Percent of Responses by KM Stage
According to Table 25, also depicted in Chart 7, over 71% of the participants’ organizations
were involved at different stages of KM implementations (40.1% had a KM program in
place, 15.8% were in the process of setting up KM, and 15.2 were examining the need for a
KM program). Slightly over 18% of the respondents indicated that their organization either
had considered and decided against a KM program or had no KM program. Only four of
the 474 respondents (0.8%) stated that they had considered and decided against setting up a
KM program.
Table 25: Percent of Responses by KM Stage within the Organization
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Knowledge Management program is in place 190 40.1 40.1 40.1
No program/Not considered one 82 17.3 17.3 57.4
Currently setting up such program 75 15.8 15.8 73.2
Examining need for such program 72 15.2 15.2 88.4
Do not know 51 10.8 10.8 99.2
Considered and decided against program 4 .8 .8 100.0
Total 474 100.0 100.0
Chart 7: Percent of Responses by KM Stage within the Organization
46
10.8% of the respondents indicated that they did not know if their organization had a KM
program in place.
Percent of Responses by Organizational Level That Promotes KM
According to Table 26, also depicted in Chart 8, over half of the participants (51.3%)
indicated that KM is promoted primarily by senior (25.9%) and middle (25.3%) management.
8% of KM initiatives resulted to be mandated at the board level.
Table 26: Percent of Responses by Organizational Level That Promotes KM
Cumulative
Frequency Percent Valid Percent Percent
47
Valid Senior Management 123 25.9 25.9 25.9
Middle management 120 25.3 25.3 51.3
Do not know 93 19.6 19.6 70.9
Grass roots/employees 54 11.4 11.4 82.3
Across the spectrum 46 9.7 9.7 92.0
Board level 38 8.0 8.0 100.0
Total 474 100.0 100.0
Chart 8: Percent of Responses by Organizational Level That Promotes KM
Over one-fifth of the population sample indicated that KM is primarily promoted at the
grassroots (11.4%) level or “across the spectrum” (9.7%). Those who did not know which
level in the organization promoted the most the use of KM accounted for 19.6%.
Percent of Responses by Departmental or Functional Budget that Contributes
the Most to KM
According to Table 27, also depicted in Chart 9, almost one third of the respondents
(31%) indicated that the IT budget contributed the most to KM initiatives. Other
participants indicated Training, learning and development (8.4%), Operations (7.8%),
Human Resources (7.6%), Research and Development (4.6%), Marketing (4%),
Consumer Services Sales (1.9%), Finance (0.8%), and others (7.4%). Over one-quarter of
the population sample (26.4%) did not know which departmental or functional budget
contributed the most to KM programs.
Table 27: Percent of responses by departmental/functional budget that contributes the most to KM
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid IT 147 31.0 31.0 31.0
Do not know 125 26.4 26.4 57.4
48
Training, learning & development 40 8.4 8.4 65.8
Operations 37 7.8 7.8 73.6
Human Resources 36 7.6 7.6 81.2
Others 35 7.4 7.4 88.6
R&D 22 4.6 4.6 93.2
Marketing 19 4.0 4.0 97.3
Consumer Services Sales 9 1.9 1.9 99.2
Finance 4 .8 .8 100.0
Total 474 100.0 100.0
Chart 9: Percent of responses by departmental/functional budget that contributes the most to KM
Frequencies of Respondents for KM Beliefs
In the second part of the survey, participants were asked to indicate the extent to which they
agreed or disagreed that the statements reported in Table 28 are critical factors for
developing successful knowledge management within the enterprise.
Table 28: Factors of Successful KM (KMF) Variables
1. Improvements in IT infrastructure
2. Organizational buy-in and support
3. Leadership involvement, support, and advocating
49
4. Rewards system based on employee KM participation and support
5. Climate of openness and thinking "outside the box"
6. Continuous education of employees
7. KM advocates and champions within the enterprise
8. Identify enterprise core competencies and necessary knowledge domains to
support those core competencies
9. Gathering and formalizing existing internal enterprise knowledge
10. Gathering and formalizing existing external enterprise knowledge
11. Developing an enterprise repository and database of information and knowledge
12. Allocating resources to manage enterprise knowledge as to relevance, accuracy
and value to the enterprise - ability to eliminate old, outdated, incorrect, or
unnecessary information and knowledge
13. Effective and efficient methodology of distributing knowledge to employees
(automating information and knowledge to be easily accessible to employees)
14. Developing and promoting employee sharing and collaboration
Each attitude statement was measured on a five-point Lickert scale: (1) strongly disagree,
(2) disagree, (3) neutral, (4) agree, and (5) strongly agree.
We will now look at the combined US and Italian responses for each of the variables in Table
28.
Improvements in IT infrastructure
According to Table 29, whose values are also represented in Chart 10, three quarters of
the participants either agreed (51.1%) or strongly agreed (24.1%) that improvements in IT
infrastructure are critical to the success of KM. The remaining quarter of the respondents
were either neutral (18.6%), disagreed (5.7%) or strongly disagreed (0.6%).
Table 29: Frequency of Responses: Improvements in IT infrastructure
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 242 51.1 51.1 51.1
Strongly Agree 114 24.1 24.1 75.1
Neutral 88 18.6 18.6 93.7
Disagree 27 5.7 5.7 99.4
Strongly Disagree 3 .6 .6 100.0
Total 474 100.0 100.0
50
Chart 10: Frequency of Responses: Improvements in IT infrastructure
Organizational buy-in and support
According to Table 30, whose values are also represented in Chart 11, almost 92% of the
participants either strongly agreed (55.5%) or agreed (36.3%) that organizational buy-in and
support are critical to the success of KM. The other responders were either neutral (6.8%),
disagreed (1.3%) or strongly disagreed (0.2%).
Table 30: Organizational buy-in and support
Frequency Percent Valid Percent
Cumulative
Percent
Valid Strongly Agree 263 55.5 55.5 55.5
Agree 172 36.3 36.3 91.8
Neutral 32 6.8 6.8 98.5
Disagree 6 1.3 1.3 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
51
Leadership involvement, support, and advocating
According to Table 31, whose values are also represented in Chart 12, more than 93% of the
participants either strongly agreed (59.7) or agreed (33.8%) that leadership involvement,
support, and advocating are critical to the success of KM. The other responders were either
neutral (5.7%), disagreed (0.6%) or strongly disagreed (0.2%).
Table 31: Leadership involvement, support, and advocating
Frequency Percent Valid Percent
Cumulative
Percent
Valid Strongly Agree 283 59.7 59.7 59.7
Agree 160 33.8 33.8 93.5
Neutral 27 5.7 5.7 99.2
Disagree 3 .6 .6 99.8
Strongly Agree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 12: Leadership involvement, support, and advocating
52
Rewards system based on employee KM participation and support
According to Table 32, whose values are also represented in Chart 13, almost 60% of the
participants either agreed (41.8) or strongly agreed (17.3%) that rewards system based on
employee KM participation and support are critical to the success of KM initiatives. A
considerable percentage (31.6%) expressed neutrality on this topic. The other respondents
either disagreed (8.2%) or strongly disagreed (1.1%).
Table 32: Rewards system based on employee KM participation and support
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 198 41.8 41.8 41.8
Neutral 150 31.6 31.6 73.4
Strongly Agree 82 17.3 17.3 90.7
Disagree 39 8.2 8.2 98.9
Strongly Disagree 5 1.1 1.1 100.0
Total 474 100.0 100.0
Chart 13: Rewards system based on employee KM participation and support
53
Climate of openness and thinking "outside the box"
According to Table 33, whose values are also represented in Chart 14, over 82% of the
participants either strongly agreed (42.2%) or agreed (40.3%) that a climate of openness
and thinking "outside the box" is critical to the success of KM. The remaining respondents
were either neutral (15.2%), disagreed (1.7%) or strongly disagreed (0.6%).
Table 33: Climate of openness and thinking "outside the box"
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 200 42.2 42.2 42.2
Strongly Agree 191 40.3 40.3 82.5
Neutral 72 15.2 15.2 97.7
Disagree 8 1.7 1.7 99.4
Strongly Disagree 3 .6 .6 100.0
Total 474 100.0 100.0
Chart 14: Climate of openness and thinking "outside the box"
54
Continuous education of employees
According to Table 34, whose values are also represented in Chart 1, almost 87% of the
participants either agreed (49.6%) or strongly agreed (37.1%) that continuous education of
employees is critical to the success of KM. The remaining respondents were either
neutral (11.6%) or disagreed (1.7%).
Table 34: Continuous education of employees
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 235 49.6 49.6 49.6
Strongly Agree 176 37.1 37.1 86.7
Neutral 55 11.6 11.6 98.3
Disagree 8 1.7 1.7 100.0
Total 474 100.0 100.0
55
KM advocates and champions within the enterprise
According to Table 35, whose values are also represented in Chart 16, 80% of the participants
either agreed (43.7%) or strongly agreed (36.3%) that KM advocates and champions within
the enterprise are critical to the success of KM. The remaining respondents were either
neutral (18.8%), disagreed (0.8%) or strongly disagreed (0.4%).
Table 35: KM advocates and champions within the enterprise
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 207 43.7 43.7 43.7
Strongly Agree 172 36.3 36.3 80.0
Neutral 89 18.8 18.8 98.7
Agree 4 .8 .8 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 16: KM advocates and champions within the enterprise
56
Identify enterprise core competencies and necessary knowledge domains to
support those core competencies
According to Table 36, whose values are also represented in Chart 17, more than 83% of
the participants either agreed (54.9%) or strongly agreed (28.5%) that identifying enterprise
core competencies and necessary knowledge domains to support those core competencies
are critical to the success of KM. The remaining respondents were either neutral (14.3%),
disagreed (2.1%) or strongly disagreed (0.2%).
Table 36: Identify enterprise core competencies and necessary knowledge domains to support those
core competencies
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 260 54.9 54.9 54.9
Strongly Agree 135 28.5 28.5 83.3
Neutral 68 14.3 14.3 97.7
Disagree 10 2.1 2.1 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 17: Identify enterprise core competencies and necessary knowledge domains to support those
core competencies
57
Gathering and formalizing existing internal enterprise knowledge
According to Table 37, whose values are also represented in Chart 18, more than 86% of the
participants either agreed (50.2%) or strongly agreed (36.3%) that gathering and
formalizing existing internal enterprise knowledge are critical to the success of KM. The
remaining respondents were either neutral (11.4%) or disagreed (2.1%).
Table 37: Gathering and formalizing existing internal enterprise knowledge
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 238 50.2 50.2 50.2
Strongly Agree 172 36.3 36.3 86.5
Neutral 54 11.4 11.4 97.9
Disagree 10 2.1 2.1 100.0
Total 474 100.0 100.0
Chart 18: Gathering and formalizing existing internal enterprise knowledge
58
Gathering and formalizing existing external enterprise knowledge
According to Table 38, whose values are also represented in Chart 19, more than 66% of the
participants either agreed (49.8.9%) or strongly agreed (16.5%) that gathering and
formalizing existing external enterprise knowledge are critical to the success of KM. The
remaining respondents were either neutral (28.3%) or disagreed (5.5%).
Table 38: Gathering and formalizing existing external enterprise knowledge
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 236 49.8 49.8 49.8
Neutral 134 28.3 28.3 78.1
Strongly Agree 78 16.5 16.5 94.5
Disagree 26 5.5 5.5 100.0
Total 474 100.0 100.0
Chart 19: Gathering and formalizing existing external enterprise knowledge
59
Developing an enterprise repository and database of information and
knowledge
According to Table 39, whose values are also represented in Chart 20, more than 85% of
the participants either agreed (46.6%) or strongly agreed (38.8%) that developing an
enterprise repository and database of information and knowledge is critical to the success of
KM. The remaining respondents were either neutral (10.8%), disagreed (3.4%) or strongly
disagreed (0.4%).
Table 39: Developing an enterprise repository and database of information and knowledge
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 221 46.6 46.6 46.6
Strongly Agree 184 38.8 38.8 85.4
Neutral 51 10.8 10.8 96.2
Disagree 16 3.4 3.4 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 20: Developing an enterprise repository and database of information and knowledge
60
Allocating resources to manage enterprise knowledge
According to Table 40, whose values are also represented in Chart 21, more than 80% of
the participants either agreed (42.4%) or strongly agreed (38.4%) that allocating resources
to manage enterprise knowledge as to relevance, accuracy and value to the enterprise
(ability to eliminate old, outdated, incorrect, or unnecessary information and knowledge)
is critical to the success of KM. The remaining respondents were either neutral (14.3%),
disagreed (4.4%) or strongly disagreed (0.4%).
Table 40: Allocating resources to manage enterprise knowledge as to relevance, accuracy and value to
the enterprise - ability to eliminate old, outdated, incorrect, or unnecessary information and
knowledge
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 201 42.4 42.4 42.4
Strongly Agree 182 38.4 38.4 80.8
Neutral 68 14.3 14.3 95.1
Disagree 21 4.4 4.4 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 21: Allocating resources to manage enterprise knowledge as to relevance, accuracy and value to
the enterprise - ability to eliminate old, outdated, incorrect, or unnecessary information and
knowledge
61
Effective and efficient methodology of distributing knowledge to employees
According to Table 41, whose values are also represented in Chart 22, more than 87% of
the participants either agreed (45.6%) or strongly agreed (41.8%) that effective and
efficient methodology of distributing knowledge to employees (automating information and
knowledge to be easily accessible to employees) is critical to the success of KM.
The remaining respondents were either neutral (11.6%), disagreed (0.8%) or strongly disagreed
(0.2%).
Table 41: Effective and efficient methodology of distributing knowledge to employees
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 216 45.6 45.6 45.6
Strongly Agree 198 41.8 41.8 87.3
Neutral 55 11.6 11.6 98.9
Disagree 4 .8 .8 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 22: Effective and efficient methodology of distributing knowledge to employees
62
Developing and promoting employee sharing and collaboration
According to Table 42, whose values are also represented in Chart 23, more than 94% of
the participants either strongly agreed (56.3%) or agreed (37.8%) that developing and
promoting employee knowledge sharing and collaboration is critical to the success of KM.
The remaining respondents were either neutral (5.1%), disagreed (0.4%) or strongly
disagreed (0.4%).
Table 42: Developing and promoting employee sharing and collaboration
Frequency Percent Valid Percent
Cumulative
Percent
Valid Strongly Agree 267 56.3 56.3 56.3
Agree 179 37.8 37.8 94.1
Neutral 24 5.1 5.1 99.2
Disagree 2 .4 .4 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 23: Developing and promoting employee sharing and collaboration
63
Frequencies of Respondents for KM Expectations
In the third part of the survey, participants were asked to indicate the extent to which they agreed
or disagreed that the statements reported in Table 43 are expected benefits to their enterprise
from knowledge management.
Table 43: Knowledge Management Expectations (KME) Variables
1. Stimulation and motivation of employees
2. Formalized knowledge transfer system established (best practices, lessons learned)
3. Better on-the-job training of employees
4. Enhanced enterprise innovation and creativity
5. Improved overall enterprise performance
6. Enhanced client relations - better client interaction
7. Development of an entrepreneurial culture for enterprise growth and success
8. Improved employee retention
9. Improved ability to sustain a competitive advantage
10. Enhanced transfer of knowledge from one employee to another
11. Means to identify industry best practices
12. Better methods for enterprise-wide problem solving
13. Enhance the development of business strategies
14. Enhance business development and the creation of enterprise opportunities
15. Enhanced and streamlined internal administrative processes
Each attitude statement was measured on a five-point Lickert scale: (1) strongly disagree,
(2) disagree, (3) neutral, (4) agree, and (5) strongly agree.
64
We will now look at the combined US and Italian responses for each of the variables in Table
43.
Stimulation and motivation of employees
According to Table 44, whose values are also represented in Chart 24, almost 60% of the
participants either agreed (49.6.1%) or strongly agreed (10.1%) that stimulation and
motivation of employees are expected benefits of KM. The remaining respondents were either
neutral (32.5%), disagreed (7.2%) or strongly disagreed (0.6%).
Table 44: Stimulation and motivation of employees
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 235 49.6 49.6 49.6
Neutral 154 32.5 32.5 82.1
Strongly Agree 48 10.1 10.1 92.2
Disagree 34 7.2 7.2 99.4
Strongly Disagree 3 .6 .6 100.0
Total 474 100.0 100.0
Chart 24: Stimulation and motivation of employees
65
Formalized knowledge transfer system established
According to Table 45, whose values are also represented in Chart 25, 85% of the
participants either agreed (55.3.1%) or strongly agreed (29.7%) that having formalized
knowledge transfer system established (best practices, lessons learned) is an expected
benefits of KM. The remaining respondents were either neutral (12.7%), disagreed (2.1%)
or strongly disagreed (0.2%).
Table 45: Formalized knowledge transfer system established (best practices, lessons learned)
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 262 55.3 55.3 55.3
Strongly Agree 141 29.7 29.7 85.0
Neutral 60 12.7 12.7 97.7
Disagree 10 2.1 2.1 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 25: Formalized knowledge transfer system established (best practices, lessons learned)
66
Better on-the-job training of employees
According to Table 45, whose values are also represented in Chart 25, almost 80% of the
participants either agreed (53.6%) or strongly agreed (25.7%) that better on-the-job training
of employees is an expected benefit of KM. The remaining respondents were either neutral
(18.4%) or disagreed (2.3%).
Table 46: Better on-the-job training of employees
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 254 53.6 53.6 53.6
Strongly Agree 122 25.7 25.7 79.3
Neutral 87 18.4 18.4 97.7
Disagree 11 2.3 2.3 100.0
Total 474 100.0 100.0
Enhanced enterprise innovation and creativity
According to Table 47, whose values are also represented in Chart 27, almost 75% of the
participants either agreed (46.8%) or strongly agreed (27.8%) that enhanced enterprise
innovation and creativity is an expected benefit of KM. The remaining respondents were either
neutral (21.5%), disagreed (3.2%), or strongly disagreed (0.6%).
67
Table 47: Enhanced enterprise innovation and creativity
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 222 46.8 46.8 46.8
Strongly Agree 132 27.8 27.8 74.7
Neutral 102 21.5 21.5 96.2
Disagree 15 3.2 3.2 99.4
Strongly Disagree 3 .6 .6 100.0
Total 474 100.0 100.0
Chart 27: Enhanced enterprise innovation and creativity
Improved overall enterprise performance
According to Table 48, whose values are also represented in Chart 28, more than 84% of the
participants either agreed (54.6%) or strongly agreed (29.7%) that improved overall
enterprise performance is an expected benefit of KM. The remaining respondents were
either neutral (14.1%), disagreed (1.1%), or strongly disagreed (0.4%).
Table 48: Improved overall enterprise performance
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 259 54.6 54.6 54.6
Strongly Agree 141 29.7 29.7 84.4
68
Neutral 67 14.1 14.1 98.5
Disagree 5 1.1 1.1 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 28: Improved overall enterprise performance
Enhanced client relations - better client interaction
According to Table 49, whose values are also represented in Chart 29, more than 68% of the
participants either agreed (48.1%) or strongly agreed (20.3%) that enhanced client relations
(better client interaction) are expected benefits of KM. The remaining respondents were
either neutral (25.9%), disagreed (5.3%), or strongly disagreed (0.4%).
Table 49: Enhanced client relations - better client interaction
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 228 48.1 48.1 48.1
Neutral 123 25.9 25.9 74.1
Strongly Agree 96 20.3 20.3 94.3
Disagree 25 5.3 5.3 99.6
Strongly Disagree 2 .4 .4 100.0
69
Total 474 100.0 100.0
Chart 29: Enhanced client relations - better client interaction
Development of an entrepreneurial culture for enterprise growth and success
According to Table 50, whose values are also represented in Chart 30, more than 58% of the
participants either agreed (39.9%) or strongly agreed (18.6%) that development of an
entrepreneurial culture for enterprise growth and success is an expected benefit of KM. The
remaining respondents were either neutral (32.7%), disagreed (7.8%), or strongly disagreed
(1.1%).
Table 50: Development of an entrepreneurial culture for enterprise growth and success
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 189 39.9 39.9 39.9
Neutral 155 32.7 32.7 72.6
Strongly Agree 88 18.6 18.6 91.1
Disagree 37 7.8 7.8 98.9
Strongly Disagree 5 1.1 1.1 100.0
Total 474 100.0 100.0
Chart 30: Development of an entrepreneurial culture for enterprise growth and success
70
Improved employee retention
According to Table 51, whose values are also represented in Chart 31, just over 40% of the
participants either agreed (29.1%) or strongly agreed (11%) that improved employee
retention is an expected benefit of KM. The remaining respondents were either neutral
(the highest at 44.5%), disagreed (13.7%), or strongly disagreed (1.7%).
Table 51: Improved employee retention
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 211 44.5 44.5 44.5
Agree 138 29.1 29.1 73.6
Disagree 65 13.7 13.7 87.3
Strongly Agree 52 11.0 11.0 98.3
Strongly Disagree 8 1.7 1.7 100.0
Total 474 100.0 100.0
71
Improved ability to sustain a competitive advantage
According to Table 52, whose values are also represented in Chart 32, more than 79% of
the participants either agreed (55.3%) or strongly agreed (24.1%) that improved ability to
sustain a competitive advantage is an expected benefit of KM. The remaining respondents
were either neutral (17.3%), disagreed (3.2%), or strongly disagreed (0.2%).
Table 52: Improved ability to sustain a competitive advantage
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 262 55.3 55.3 55.3
Strongly Agree 114 24.1 24.1 79.3
Neutral 82 17.3 17.3 96.6
Disagree 15 3.2 3.2 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 32: Improved ability to sustain a competitive advantage
72
Enhanced transfer of knowledge from one employee to another
According to Table 53, whose values are also represented in Chart 33, more than 87% of
the participants either agreed (49.4%) or strongly agreed (38.2%) that enhanced transfer of
knowledge from one employee to another is an expected benefit of KM. The remaining
respondents were either neutral (10.3%), disagreed (1.7%), or strongly disagreed (0.4%).
Table 53: Enhanced transfer of knowledge from one employee to another
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 234 49.4 49.4 49.4
Strongly Agree 181 38.2 38.2 87.6
Neutral 49 10.3 10.3 97.9
Disagree 8 1.7 1.7 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 33: Enhanced transfer of knowledge from one employee to another
73
Means to identify industry best practices
According to Table 54, whose values are also represented in Chart 34, more than 77% of the
participants either agreed (53%) or strongly agreed (24.5%) that acquiring means to identify
industry best practices is an expected benefit of KM. The remaining respondents were
either neutral (19.4%), disagreed (2.7%), or strongly disagreed (0.4%).
Table 54: Means to identify industry best practices
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 251 53.0 53.0 53.0
Strongly Agree 116 24.5 24.5 77.4
Neutral 92 19.4 19.4 96.8
Disagree 13 2.7 2.7 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 34: Means to identify industry best practices
74
Better methods for enterprise-wide problem solving
According to Table 55, whose values are also represented in Chart 35, 80% of the
participants either agreed (51.3%) or strongly agreed (28.7%) that better methods for
enterprise-wide problem solving is an expected benefit of KM. The remaining respondents
were either neutral (17.1%), or disagreed (3%).
Table 55: Better methods for enterprise-wide problem solving
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 243 51.3 51.3 51.3
Strongly Agree 136 28.7 28.7 80.0
Neutral 81 17.1 17.1 97.0
Disagree 14 3.0 3.0 100.0
Total 474 100.0 100.0
Chart 35: Better methods for enterprise-wide problem solving
75
Enhance the development of business strategies
According to Table 56, whose values are also represented in Chart 36, 66% of the
participants either agreed (49.8%) or strongly agreed (16.2%) that enhanced development
of business strategies is an expected benefit of KM. The remaining respondents were either
neutral (28.3%), disagreed (5.3%), or strongly disagreed (0.4%).
Table 56: Enhance the development of business strategies
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 236 49.8 49.8 49.8
Neutral 134 28.3 28.3 78.1
Strongly Agree 77 16.2 16.2 94.3
Disagree 25 5.3 5.3 99.6
Strongly Disagree 2 .4 .4 100.0
Total 474 100.0 100.0
Chart 36: Enhance the development of business strategies
76
Enhance business development and the creation of enterprise opportunities
According to Table 57, whose values are also represented in Chart 37, over 60% of the
participants either agreed (48.9%) or strongly agreed (15.2%) that enhanced business
development and the creation of enterprise opportunities are expected benefits of KM. The
remaining respondents were either neutral (30.8%), disagreed (4.9%), or strongly
disagreed (0.2%).
Table 57: Enhance business development and the creation of enterprise opportunities
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 232 48.9 48.9 48.9
Neutral 146 30.8 30.8 79.7
Strongly Agree 72 15.2 15.2 94.9
Disagree 23 4.9 4.9 99.8
Strongly Disagree 1 .2 .2 100.0
Total 474 100.0 100.0
Chart 37: Enhance business development and the creation of enterprise opportunities
77
Enhanced and streamlined internal administrative processes
According to Table 57, whose values are also represented in Chart 37, over 54% of the
participants either agreed (40.3%) or strongly agreed (13.9%) that enhanced and
streamlined internal administrative processes are expected benefits of KM. The remaining
respondents were either neutral (36.1%), disagreed (9.1%), or strongly disagreed (0.6%).
Table 58: Enhanced and streamlined internal administrative processes
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 191 40.3 40.3 40.3
Neutral 171 36.1 36.1 76.4
Strongly Agree 66 13.9 13.9 90.3
Disagree 43 9.1 9.1 99.4
Strongly Disagree 3 .6 .6 100.0
Total 474 100.0 100.0
Chart 38: Enhanced and streamlined internal administrative processes
78
Frequencies of Respondents for KM Practices
In the fourth and last part of the survey, participants were asked to indicate the extent to
which they agreed or disagreed that the knowledge management practices reported in Table
59 were followed by their organization.
Table 59: Knowledge Management Practices (KMP) Variables
1. The organizational benefits of a knowledge-centric organization are clearly
understood by everyone in our organization
2. Knowledge management is a top priority in our organization
3. Our organization has a clear and strong commitment to knowledge management
initiatives from senior management
4. Our organization has sufficient financial resources to support knowledge
management initiatives
5. Our organizational culture encourages knowledge sharing
6. People in our organization have the time to share information
7. Teamwork is a critical component of our organization's culture, structure and
processes
8. Our organizational strategies, structures, policies, procedures, processes and
reward systems focus on long-term growth
9. Our organization has evolved from a rigid hierarchical structure to a
processoriented structure
10. Our organization has invested in knowledge management technologies (i.e.
intranet, database, email and digital libraries)
11. Our organization has the human resources to support our information technology
systems, software and network
12. People in our organization are often rewarded for continuous learning or
79
knowledge sharing
Each attitude statement was measured on a five-point Lickert scale; (1) strongly disagree,
(2) strongly agree, (3) neutral, (4) agree, and (5) strongly agree.
We will now look at the combined US and Italian responses for each of the variables in Table
59.
The organizational benefits of a knowledge-centric organization are clearly
understood by everyone in our organization
According to Table 60, whose values are also represented in Chart 39, only little over 26%
of the participants either agreed (21.5%) or strongly agreed (5.3%) that everyone in their
organization clearly understands the benefits of knowledge-centric organization. The
remaining respondents were either neutral (21.9%), disagreed (with the greatest number of
responses at 39.5%) or strongly disagreed (11.8%).
Table 60: The organizational benefits of a knowledge-centric organization are clearly understood by
everyone in our organization
Frequency Percent Valid Percent
Cumulative
Percent
Valid Disagree 187 39.5 39.5 39.5
Neutral 104 21.9 21.9 61.4
Agree 102 21.5 21.5 82.9
Strongly Disagree 56 11.8 11.8 94.7
Strongly Agree 25 5.3 5.3 100.0
Total 474 100.0 100.0
Chart 39: 1.The organizational benefits of a knowledge-centric organization are clearly understood
by everyone in our organization
80
Knowledge management is a top priority in our organization
According to Table 61, whose values are also represented in Chart 40, only little over 27%
of the participants either agreed (21.3%) or strongly agreed (5.9%) that knowledge
management is a top priority in their organization. The remaining respondents were either
neutral (30.6%), disagreed (29.1%) or strongly disagreed (13.1%).
Table 61: Knowledge management is a top priority in our organization
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 145 30.6 30.6 30.6
Disagree 138 29.1 29.1 59.7
Agree 101 21.3 21.3 81.0
Strongly Disagree 62 13.1 13.1 94.1
Strongly Agree 28 5.9 5.9 100.0
Total 474 100.0 100.0
Chart 40: Knowledge management is a top priority in our organization
81
Our organization has a clear and strong commitment to knowledge
management initiatives from senior management
According to Table 62, whose values are also represented in Chart 41, only little over 29%
of the participants either agreed (24.5%) or strongly agreed (5.3%) that their organizations
have a clear and strong senior management commitment to knowledge management
initiatives. The remaining respondents were either neutral (31.4%), disagreed (25.5%) or
strongly disagreed (13.3%).
Table 62: Our organization has a clear and strong commitment to knowledge management initiatives
from senior management
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 149 31.4 31.4 31.4
Disagree 121 25.5 25.5 57.0
Agree 116 24.5 24.5 81.4
Strongly Disagree 63 13.3 13.3 94.7
Strongly Agree 25 5.3 5.3 100.0
Total 474 100.0 100.0
Chart 41: Our organization has a clear and strong commitment to knowledge management initiatives
from senior management
82
Our organization has sufficient financial resources to support knowledge
management initiatives
According to Table 63, whose values are also represented in Chart 42, little over 47% of the
participants either agreed (32.9%) or strongly agreed (10.5%) that their organizations have
sufficient financial resources to support knowledge management initiatives. The remaining
respondents were either neutral (30.4%), disagreed (21.5%) or strongly disagreed (4.6%).
Table 63: Our organization has sufficient financial resources to support knowledge management
initiatives
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 156 32.9 32.9 32.9
Neutral 144 30.4 30.4 63.3
Disagree 102 21.5 21.5 84.8
Strongly Agree 50 10.5 10.5 95.4
Strongly Disagree 22 4.6 4.6 100.0
Total 474 100.0 100.0
Chart 42: Our organization has sufficient financial resources to support knowledge management
initiatives
83
Our organizational culture encourages knowledge sharing
According to Table 64, whose values are also represented in Chart 43, little less than 47% of
the participants either agreed (35.7%) or strongly agreed (11.2%) that their organizations’
culture encourages knowledge sharing. The remaining respondents were either neutral
(24.5%), disagreed (20.3%) or strongly disagreed (8.4%).
Table 64: Our organizational culture encourages knowledge sharing
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 169 35.7 35.7 35.7
Neutral 116 24.5 24.5 60.1
Disagree 96 20.3 20.3 80.4
Strongly Agree 53 11.2 11.2 91.6
Strongly Disagree 40 8.4 8.4 100.0
Total 474 100.0 100.0
Chart 43: Our organizational culture encourages knowledge sharing
84
People in our organization have the time to share information
According to Table 65, whose values are also represented in Chart 44, just over 32% of the
participants either agreed (29.3%) or strongly agreed (3.2%) that people in their
organization have the time to share information. The remaining respondents were either
neutral (30.2%), disagreed (30%) or strongly disagreed (7.4%).
Table 65: People in our organization have the time to share information
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 143 30.2 30.2 30.2
Disagree 142 30.0 30.0 60.1
Agree 139 29.3 29.3 89.5
Strongly Disagree 35 7.4 7.4 96.8
Strongly Agree 15 3.2 3.2 100.0
Total 474 100.0 100.0
Chart 44: People in our organization have the time to share information
85
Teamwork is a critical component of our organization's culture, structure and
processes
According to Table 66, whose values are also represented in Chart 45, over 69% of the
participants either agreed (43.9%) or strongly agreed (25.5%) that teamwork is a critical
component of their organization's culture, structure and processes. The remaining respondents
were either neutral (15.2%), disagreed (11.2%) or strongly disagreed (4.2%).
Table 66: Teamwork is a critical component of our organization's culture, structure and processes
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 208 43.9 43.9 43.9
Strongly Agree 121 25.5 25.5 69.4
Neutral 72 15.2 15.2 84.6
Disagree 53 11.2 11.2 95.8
Strongly Disagree 20 4.2 4.2 100.0
Total 474 100.0 100.0
Chart 45: Teamwork is a critical component of our organization's culture, structure and processes
86
Our organizational strategies, structures, policies, procedures, processes and
reward systems focus on long-term growth
According to Table 67, whose values are also represented in Chart 46, little less than 44%
of the participants either agreed (34.4%) or strongly agreed (9.5%) that their organizational
strategies, structures, policies, procedures, processes and reward systems focus on long-
term growth. The remaining respondents were either neutral (31.2%), disagreed (16.5%)
or strongly disagreed (8.4%).
Table 67: Our organizational strategies, structures, policies, procedures, processes and reward
systems focus on long-term growth
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 163 34.4 34.4 34.4
Neutral 148 31.2 31.2 65.6
Disagree 78 16.5 16.5 82.1
Strongly Agree 45 9.5 9.5 91.6
Strongly Disagree 40 8.4 8.4 100.0
Total 474 100.0 100.0
Chart 46: Our organizational strategies, structures, policies, procedures, processes and reward
systems focus on long-term growth
87
Our organization has evolved from a rigid hierarchical structure to a
processoriented structure
According to Table 68, whose values are also represented in Chart 47, little over 30% of the
participants either agreed (24.7%) or strongly agreed (5.7%) that their organizations
evolved from a rigid hierarchical structure to a process oriented structure. The remaining
respondents were either neutral (36.3%), disagreed (24.1%) or strongly disagreed (9.3%).
Table 68: Our organization has evolved from a rigid hierarchical structure to a process-oriented
structure
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 172 36.3 36.3 36.3
Agree 117 24.7 24.7 61.0
Disagree 114 24.1 24.1 85.0
Strongly Disagree 44 9.3 9.3 94.3
Strongly Agree 27 5.7 5.7 100.0
Total 474 100.0 100.0
Chart 47: Our organization has evolved from a rigid hierarchical structure to a process-oriented
structure
88
Our organization has invested in knowledge management technologies
According to Table 69, whose values are also represented in Chart 48, 64% of the
participants either agreed (47.5%) or strongly agreed (16.5%) that their organizations have
invested in knowledge management technologies (e.g. intranet, database, email and digital
libraries). The remaining respondents were either neutral (20.7%), disagreed (11.4%) or
strongly disagreed (4%).
Table 69: Our organization has invested in knowledge management technologies
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 225 47.5 47.5 47.5
Neutral 98 20.7 20.7 68.1
Strongly Agree 78 16.5 16.5 84.6
Disagree 54 11.4 11.4 96.0
Strongly Disagree 19 4.0 4.0 100.0
Total 474 100.0 100.0
Chart 48: Our organization has invested in knowledge management technologies
89
Our organization has the human resources to support our information
technology systems, software and network
According to Table 70, whose values are also represented in Chart 49, almost 62% of the
participants either agreed (45.1%) or strongly agreed (16.7%) that their organizations have
the human resources necessary to support their information technology systems, software
and network. The remaining respondents were either neutral (20.3%), disagreed (13.7%)
or strongly disagreed (4.2%).
Table 70: Our organization has the human resources to support our information technology systems,
software and network
Frequency Percent Valid Percent
Cumulative
Percent
Valid Agree 214 45.1 45.1 45.1
Neutral 96 20.3 20.3 65.4
Strongly Agree 79 16.7 16.7 82.1
Disagree 65 13.7 13.7 95.8
Strongly Disagree 20 4.2 4.2 100.0
Total 474 100.0 100.0
Chart 49: Our organization has the human resources to support our information technology systems,
software and network
90
People in our organization are often rewarded for continuous learning or
knowledge sharing
According to Table 71, whose values are also represented in Chart 50, little over 29% of
the participants either agreed (24.5) or strongly agreed (4.9%) that people in their
organizations are often rewarded for continuous learning or knowledge sharing. The
remaining respondents were either neutral (31%), disagreed (25.7%) or strongly
disagreed (13.9%).
Table 71: People in our organization are often rewarded for continuous learning or knowledge
sharing
Frequency Percent Valid Percent
Cumulative
Percent
Valid Neutral 147 31.0 31.0 31.0
Disagree 122 25.7 25.7 56.8
Agree 116 24.5 24.5 81.2
Strongly Disagree 66 13.9 13.9 95.1
Strongly Agree 23 4.9 4.9 100.0
Total 474 100.0 100.0
Chart 50: People in our organization are often rewarded for continuous learning or knowledge
sharing
91
Data Collection Summary
In summary, we accepted 474 responses, half of which came from individuals resident in Italy
and the other half from individuals resident in the USA. 95.4% of the respondents’ country of
nationality was either the USA or Italy; 90.7% since birth.
Almost 95% of the responses included companies whose headquarters were either in the
USA or in Italy. Almost half of the responses were from individuals within small
(24.3%) and very large (25.3%) organizations. 83.3% of the responses came from individuals
that worked for a service-focused organization or organizations that focused both on products and
services. 18.4% of the responses came from individuals that worked for education, 8.6% for
government, and most of the rest for various business industries. 37.1% of the participants were
managers or directors and 12% were executives, for a total of 39.1%. Technical and support staff
were respectively 26.8% and
11.2% of the sample population.
Over 71% of the participants indicated that their organizations were involved at different
stages of KM implementations. More than half of the participants (51.3%) indicated that
KM is promoted primarily by senior (25.9%) and middle (25.3%) management, but also at
the grassroots (11.4%) level and “across the spectrum” (9.7%). Almost one third of the
respondents (31%) indicated that the IT budget contributed the most to KM initiatives.
92
Most participants agreed or strongly agreed with the KM success factors (KMF) and KM
expected benefits (KME). The results about the state of KM practices (KMP) within the
participant’s organization show that not all organizations follow best practices.
In the next chapter we will compare the means of the KMF, KME, and KMP variables
between Italian and US participants. Also, descriptive statistics, analysis of variance to test
our hypotheses, and linear regression will be performed on the data.
“It is not the strongest of the species that survive, nor the most
intelligent, but the one most responsive to change.”
Charles Darwin
Chapter 5 Analysis of Results & Hypotheses
Testing
In this chapter we will provide descriptive statistics for all factors of successful KM (KMF),
KM expectations (KME), and KM practices (KMP). In order to quantify trends, we will
develop a statistical index for each of the three factors: a single number derived from the
average of the individual variables statistics.
Descriptive Statistics of KM Variables for Entire Sample
We now provide aggregate descriptive statistics for the entire sample.
Table 72: Ranked mean values of KMF variables for US and Italian responses
N Mean
Std.
Deviation
1st Leadership involvement, support, and advocating 474 4.52 .654
93
2nd Developing and promoting employee sharing and collaboration 474 4.49 .654
3rd Organizational buy-in and support 474 4.46 .697
4th Effective and efficient methodology of distributing knowledge to
employees (automating information and knowledge to be easily accessible to
employees)
474 4.28 .714
5th Continuous education of employees 474 4.22 .712
6th Gathering and formalizing existing internal enterprise knowledge 474 4.21 .721
7th Developing an enterprise repository and database of information and
knowledge
474 4.20 .794
8th Climate of openness and thinking "outside the box" 474 4.20 .801
9th KM advocates and champions within the enterprise 474 4.15 .776
10th Allocating resources to manage enterprise knowledge as to relevance,
accuracy and value to the enterprise - ability to eliminate old, outdated,
incorrect, or unnecessary information and knowledge
474 4.14 .851
11th Identify enterprise core competencies and necessary knowledge domains
to support those core competencies
474 4.09 .724
12th Improvements in IT infrastructure 474 3.92 .840
13th Gathering and formalizing existing external enterprise knowledge 474 3.77 .785
14th Rewards system based on employee KM participation and support 474 3.66 .894
Valid N (listwise) 474
As shown in Table 72 the three most important factors for successful KM are (1)
leadership involvement, support, and advocating, (2) developing and promoting employee
sharing and collaboration, and (3) organizational buy-in and support. On the other hand,
the least important factors are (1) rewards systems, (2) acquisition of external enterprise
knowledge, and (3) improvements in IT infrastructure.
Overall respondents give a relatively high ranking to all variables, with a range of 3.66 to
4.52 in a scale of 1 to 5. Moreover, the low standard deviation in each variable indicates that
the responses tended to be close to the mean.
Table 73: Ranked mean values of KME variables for US and Italian responses
N Mean
Std.
Deviation
1st Enhanced transfer of knowledge from one employee to another 474 4.23 .733
2nd Formalized knowledge transfer system established (best practices,
lessons learned)
474 4.12 .717
3rd Improved overall enterprise performance 474 4.12 .711
4th Better methods for enterprise-wide problem solving 474 4.06 .758
5th Better on-the-job training of employees 474 4.03 .731
94
6th Improved ability to sustain a competitive advantage 474 4.00 .748
7th Means to identify industry best practices 474 3.98 .766
8th Enhanced enterprise innovation and creativity 474 3.98 .824
9th Enhanced client relations - better client interaction 474 3.82 .826
10th Enhance the development of business strategies 474 3.76 .799
11th Enhance business development and the creation of enterprise
opportunities
474 3.74 .779
12th Development of an entrepreneurial culture for enterprise growth and
success
474 3.67 .902
13th Stimulation and motivation of employees 474 3.61 .789
14th Enhanced and streamlined internal administrative processes 474 3.58 .862
15th Improved employee retention 474 3.34 .906
Valid N (listwise) 474
As shown in Table 73 the three most important benefits of knowledge management are (1)
enhanced knowledge transfer from one employee to the other, (2) formalized knowledge
transfer (best practices, lessons learned), and (3) improved overall enterprise performance.
On the other hand, the least benefits are (1) improved employee retention, (2) enhanced
and streamlined internal administrative processes, and (3) stimulation and motivation of
employee.
Respondents gave lower rankings to KME compared to KMF, but overall we find
relatively high ranking for all variables, with a range of 3.34 to 4.23 (scale of 1 to 5).
Moreover, we also find a low standard deviation in each variable which indicates that also
for KME variables responses tended to be close to the mean.
Table 74: Ranked mean values of KMP variables for US and Italian responses
N Mean
Std.
Deviation
1st Teamwork is a critical component of our organization's culture, structure
and processes
474 3.75 1.084
2nd Our organization has invested in knowledge management technologies
(i.e. intranet, database, email and digital libraries)
474 3.61 1.019
3rd Our organization has the human resources to support our information
technology systems, software and network
474 3.56 1.053
4th Our organization has sufficient financial resources to support knowledge
management initiatives
474 3.23 1.049
5th Our organizational culture encourages knowledge sharing 474 3.21 1.141
95
6th Our organizational strategies, structures, policies, procedures, processes
and reward systems focus on long-term growth
474 3.20 1.090
7th Our organization has evolved from a rigid hierarchical structure to a
process-oriented structure
474 2.93 1.041
8th People in our organization have the time to share information 474 2.91 1.004
9th Our organization has a clear and strong commitment to knowledge
management initiatives from senior management
474 2.83 1.103
10th People in our organization are often rewarded for continuous learning or
knowledge sharing
474 2.81 1.104
11th Knowledge management is a top priority in our organization 474 2.78 1.103
12th The organizational benefits of a knowledge-centric organization are
clearly understood by everyone in our organization
474 2.69 1.095
Valid N (listwise) 474
As shown in Table 74, respondents indicated that within their organizations the three most
followed KM practices are (1) teamwork, (2) KM technology investments, and (3) human
resources for supporting technology. On the other hand, the least followed practices are
(1) making sure all within the firm understand the benefits of a knowledge centric
organization, (2) making KM a top priority for the organization, and (3) rewarding for
continuous learning or knowledge sharing.
Respondents gave the lowest rankings to KMP compared to KME and KMF, with a range of
2.69 to 3.75 (scale of 1 to 5). The standard deviation is higher than in the previous two
cases, indicating a larger spread out over a larger range of values.
Descriptive Statistics of KM Variables by Country
Here we provide descriptive statistics grouped by country for all KM variables. Table 75,
Table 76, and Table 77 provide a quick reference for comparing scores between the Italian
and US sample populations.
Table 75: Means of KMF variables for Italian and US respondents
Improvements in IT infrastructure Italy 4.02 237 .683
USA 3.83 237 .965
Total 3.92 474 .840
Organizational buy-in and support Italy 4.39 237 .690
USA 4.52 237 .699
96
Mean N
Std.
Deviation
Total 4.46 474 .697
Leadership involvement, support, and advocating Italy 4.45 237 .666
USA 4.59 237 .635
Total 4.52 474 .654
Rewards system based on employee KM participation and support Italy 3.64 237 .835
USA 3.68 237 .951
Total 3.66 474 .894
Climate of openness and thinking "outside the box" Italy 4.31 237 .709
USA 4.09 237 .871
Total 4.20 474 .801
Continuous education of employees Italy 4.23 237 .712
USA 4.22 237 .713
Total 4.22 474 .712
KM advocates and champions within the enterprise Italy 3.98 237 .739
USA 4.31 237 .778
Total 4.15 474 .776
Identify enterprise core competencies and necessary knowledge domains to
support those core competenciesdimension0
Italy
USA
4.08
4.11
237
237
.666
.779
Total 4.09 474 .724
Gathering and formalizing existing internal enterprise knowledge Italy 4.17 237 .725
USA 4.24 237 .717
Total 4.21 474 .721
Gathering and formalizing existing external enterprise knowledge Italy 3.66 237 .810
USA 3.88 237 .744
Total 3.77 474 .785
Developing an enterprise repository and database of information and knowledge Italy 4.17 237 .811
USA 4.23 237 .776
Total 4.20 474 .794
Allocating resources to manage enterprise knowledge as to relevance, accuracy
and value to the enterprise - ability to eliminate old, outdated, incorrect, or
unnecessary information and knowledge
Italy
USA
Total
4.01
4.27
4.14
237
237
474
.892
.788
.851
Effective and efficient methodology of distributing knowledge to employees
(automating information and knowledge to be easily accessible to employees)
Italy
USA
4.19
4.37
237
237
.738
.680
Total 4.28 474 .714
Developing and promoting employee sharing and collaboration Italy 4.54 237 .600
USA 4.44 237 .703
Total 4.49 474 .654
Table 76: Means of KMF variables for Italian and US respondents
Stimulation and motivation of employees Italy 3.66 237 .752
97
Mean N
Std.
Deviation
USA 3.57 237 .824
Total 3.61 474 .789
Formalized knowledge transfer system established (best practices, lessons
learned)
Italy
USA
4.13
4.11
237
237
.660
.770
Total 4.12 474 .717
Better on-the-job training of employees Italy 4.03 237 .703
USA 4.03 237 .759
Total 4.03 474 .731
Enhanced enterprise innovation and creativity Italy 4.08 237 .791
USA 3.88 237 .845
Total 3.98 474 .824
Improved overall enterprise performance Italy 4.15 237 .676
USA 4.10 237 .744
Total 4.12 474 .711
Enhanced client relations - better client interaction Italy 3.85 237 .843
USA 3.80 237 .809
Total 3.82 474 .826
Development of an entrepreneurial culture for enterprise growth and success Italy 3.78 237 .855
USA 3.56 237 .935
Total 3.67 474 .902
Improved employee retention Italy 3.27 237 .891
USA 3.41 237 .919
Total 3.34 474 .906
Improved ability to sustain a competitive advantage Italy 4.01 237 .710
USA 3.98 237 .786
Total 4.00 474 .748
Enhanced transfer of knowledge from one employee to another Italy 4.20 237 .724
USA 4.27 237 .743
Total 4.23 474 .733
Means to identify industry best practices Italy 4.12 237 .699
USA 3.84 237 .806
Total 3.98 474 .766
Better methods for enterprise-wide problem solving Italy 4.01 237 .719
USA 4.11 237 .793
Total 4.06 474 .758
Enhance the development of business strategies Italy 3.71 237 .777
98
USA 3.81 237 .819
Total 3.76 474 .799
Enhance business development and the creation of enterprise opportunities Italy 3.72 237 .752
USA 3.76 237 .806
Total 3.74 474 .779
Enhanced and streamlined internal administrative processes Italy 3.34 237 .805
USA 3.82 237 .852
Total 3.58 474 .862
Table 77: Means of KMP variables for Italian and US respondents
Mean N
Std.
Deviation
The organizational benefits of a knowledge-centric organization are clearly
understood by everyone in our organization
Italy
USA
2.90
2.48
237
237
1.051
1.099
Total 2.69 474 1.095
Knowledge management is a top priority in our organization Italy 2.92 237 1.106
USA 2.63 237 1.084
Total 2.78 474 1.103
Our organization has a clear and strong commitment to knowledge management
initiatives from senior management
Italy
USA
2.82
2.84
237
237
1.084
1.124
Total 2.83 474 1.103
Our organization has sufficient financial resources to support knowledge
management initiatives
Italy
USA
3.16
3.30
237
237
1.045
1.050
Total 3.23 474 1.049
Our organizational culture encourages knowledge sharing Italy 3.19 237 1.160
USA 3.23 237 1.124
Total 3.21 474 1.141
People in our organization have the time to share information Italy 2.90 237 .982
USA 2.92 237 1.028
Total 2.91 474 1.004
Teamwork is a critical component of our organization's culture, structure and
processes
Italy
USA
3.76
3.74
237
237
1.071
1.099
Total 3.75 474 1.084
Our organizational strategies, structures, policies, procedures, processes and
reward systems focus on long-term growth
Italy
USA
3.29
3.11
237
237
1.099
1.076
Total 3.20 474 1.090
Our organization has evolved from a rigid hierarchical structure to a
processoriented structure
Italy
USA
3.08
2.78
237
237
1.078 .9
83
Total 2.93 474 1.041
99
Our organization has invested in knowledge management technologies (i.e.
intranet, database, email and digital libraries)
Italy
USA
3.49
3.73
237
237
1.060 .9
62
Total 3.61 474 1.019
Our organization has the human resources to support our information technology
systems, software and network
Italy
USA
3.67
3.46
237
237
1.062
1.035
Total 3.56 474 1.053
People in our organization are often rewarded for continuous learning or
knowledge sharing
Italy
USA
2.60
3.01
237
237
1.087
1.085
Total 2.81 474 1.104
Descriptive Statistics of KM Indexes
For the purpose of providing a quantitative measure of the relative importance each country
group gives to KM factors, expectations and practices we compute individual index scores
by averaging the means of the responses for each variable within each category. The
descriptive statistics for such indexes are reported in Table 78.
Table 78: Means of KMF, KME, and KMP Indexes for Italian and US respondents
95% Confidence Interval for
To
validate our assumption that the variables scored together within each category measure
the same construct, we calculated Cronbach's Alpha to determine the internal consistency
(average correlation) of the three sets of variables. The higher the Cronbach’s Alpha
value, the more reliable the generated index is. Our alpha values reported in Table 78 are
greater than 0.7, which is considered to be an acceptable reliability coefficient (Nunnally
100
N Mean
Std.
Deviation
Std. Error
Mean
Lower Bound Upper Bound
KMF Index
Cronbach’s Alpha=0.798
USA
Italy
237
237
4.1983
4.1311
.42966
.36542
.02791
.02374
4.1433
4.0843
4.2533
4.1779
Total 474 4.1647 .39983 .01836 4.1286 4.2008
KME Index
Cronbach’s Alpha=0.887
USA
Italy
237
237
3.8692
3.8712
.54515
.43489
.03541
.02825
3.7994
3.8155
3.9390
3.9268
Total 474 3.8702 .49259 .02263 3.8257 3.9146
KMP Index
Cronbach’s Alpha=0.866
USA
Italy
237
237
3.1034
3.1491
.69981
.66820
.04546
.04340
3.0138
3.0636
3.1929
3.2346
Total 474 3.1262 .68385 .03141 3.0645 3.1880
1981); therefore the derived indexes can be considered to be representative of the KME,
KMF and KMP constructs.
In the next section, we will perform analysis of variance (ANOVA) to test the hypothesis that
several means within each index are equal. This type of analysis will provide a statistical test
of whether the means of the two country groups are all equal.
Hypotheses Testing
In this section we will test our research hypotheses using ANOVA.
An important first step in the analysis of variance is establishing that the variances of the
groups are equivalent. To test this assumption, we plot the means and standard errors and
perform a Levene's homogeneity-of-variance test for each of the three indexes. According
to Chart 51, Chart 52, and Chart 53, not only the means, but also variations in scores
between the two country groups are very close for all three indexes.
Chart 51: KMF Index Homogeneity of Variance
Chart 52: KME Index Homogeneity of Variance
101
Chart 53: KMP Index Homogeneity of Variance
We report test results of Levene’s homogeneity of variance test in Table 79 where we note
that homogeneity of variance can be assumed for the KMF Index (0.056>0.05) and for the
KMP Index (0.449>0.05), but not for the KME Index (0.003<0.05). Nevertheless,
ANOVA is robust to this violation since the group sample size used in the test is equal; the
F statistic is robust to unequal variances when sample sizes are equal or nearly equal
(Cardinal 2006).
102
Table 79: LeveneTest of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
KMFIndex 3.682 1 472 .056
KMEIndex 9.113 1 472 .003
KMPIndex .575 1 472 .449
Restatement of the Hypotheses
As discussed previously, the Italian and U.S. cultures belong to adjacent country clusters (See
Figure 7 on page 35); Hofstede’s culture dimensions scores are relatively similar.
Since dissimilar cultures have shown significant differences in beliefs, expectations, and practices
of knowledge management indexes (Wang 2004), we anticipated that the similar cultures of Italy
and the USA would show similarities, as stated in our hypotheses:
H1: There is a positive relationship between the cultural dimensions of
Italian and US respondents and their beliefs about factors
influencing successful knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their expectations about the
benefits of knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their knowledge management
practices.
H2:
H3:
Beliefs are quantified by the KMF Index, a computed value of averaged scores on all KM
factor variables. Expectations are quantified by the KME Index, a computed value of
averaged scores on all KM expectation variables. And practices are quantified by the KMP
Index, a computed value of averaged scores on all KM factor variables.
The results of our analysis of variance fail to reject our hypotheses. The results factored by
country shown in Table 80 do not indicate significant differences (p < 0.05) in beliefs,
expectations, and practices of knowledge management indexes between Italian and
American respondents.
103
Table 80: ANOVA of KM Indexes Between Country Group
Hypothesis KM Index F Sig.
H1 KMF Index 3.365 .067
H2 KME Index .002 .965
H3 KMP Index .529 .467
Hypothesis 1 Finding
With p = 0.067 > 0.05, we accept the hypothesis that there is a positive relationship between
the cultural dimensions of Italian and US respondents and their beliefs about factors
influencing successful knowledge management.
Hypothesis 2 Finding
With p = 0.965 > 0.05, we accept the hypothesis that there is a positive relationship
between the cultural dimensions of Italian and US respondents and their expectations about
the benefits of knowledge management.
Hypothesis 3 Finding
With p = 0.529 > 0.05, we accept the hypothesis that there is a positive relationship between
the cultural dimensions of Italian and US respondents and their knowledge management
practices.
Exceptions to our Hypotheses Findings
Although the data within the aggregate values of our indexes fails to reject our research
hypotheses, some variables when looked at individually are statistically different. Table 81,
Table 82, and Table 83 present the ANOVA results for the KMF, KME, and KMP variables
respectively. Variables that are significantly different between country groups are marked
in bold.
Table 81: ANOVA of KMF Variables by Country
Sum of
Squares df
Mean
Square F Sig.
Improvements in IT infrastructure Between Groups 4.272 1 4.272 6.113 .014*
Within Groups 329.840 472 .699
104
Total 334.112 473
Organizational buy-in and support Between Groups 1.899 1 1.899 3.936 .048*
Within Groups 227.671 472 .482
Total 229.570 473
Leadership involvement, support, and advocating Between Groups 2.297 1 2.297 5.422 .020*
Within Groups 199.992 472 .424
Total 202.289 473
Rewards system based on employee KM participation
and support
Between Groups
Within Groups
.171
378.143
1
472
.171
.801
.213
.644
Total 378.314 473
Climate of openness and thinking "outside the box" Between Groups 5.705 1 5.705 9.046 .003*
Within Groups 297.654 472 .631
Total 303.359 473
Continuous education of employees Between Groups .019 1 .019 .037 .847
Within Groups 239.722 472 .508
Total 239.741 473
KM advocates and champions within the enterprise Between Groups 13.167 1 13.167 22.866 .000*
Within Groups 271.789 472 .576
Total 284.956 473
Identify enterprise core competencies and necessary Between Groups
knowledge domains to support those core Within Groups
competencies Total
.135
247.781
247.916
1
472
473
.135
.525
.257
.6
12
Gathering and formalizing existing internal enterprise Between Groups
knowledge Within Groups
.540
245.198
1
472
.540
.519
1.040
.308
Total 245.738 473
Gathering and formalizing existing external enterprise Between Groups
knowledge Within Groups
5.705
285.688
1
472
5.705
.605
9.425
.002*
Total 291.392 473
Developing an enterprise repository and database of Between Groups
information and knowledge Within Groups
.475
297.485
1
472
.475
.630
.753
.386
Total 297.960 473
Allocating resources to manage enterprise knowledge Between Groups
as to relevance, accuracy and value to the enterprise - Within Groups
ability to eliminate old, outdated, incorrect, or Total unnecessary
information and knowledge
8.110
334.700
342.810
1
472
473
8.110 .7
09
11.436
.001*
Effective and efficient methodology of distributing Between Groups
knowledge to employees (automating information and Within Groups
knowledge to be easily accessible to employees) Total
3.722
237.519
241.241
1
472
473
3.722
.503
7.395
.007*
Developing and promoting employee sharing and Between Groups
collaboration Within Groups
1.116
201.350
1
472
1.116
.427
2.616 .106
Total 202.466 473
* Significant at p < 0.05
105
Table 82: ANOVA of KME Variables by Country
Sum of
Squares df
Mean
Square F Sig.
Stimulation and motivation of employees Between Groups .930 1 .930 1.497 .222
Within Groups 293.418 472 .622
Total 294.348 473
Formalized knowledge transfer system established
(best practices, lessons learned)
Between Groups
Within Groups
.034
242.869
1
472
.034
.515
.066
.798
Total 242.903 473
Better on-the-job training of employees Between Groups .002 1 .002 .004 .950
Within Groups 252.641 472 .535
Total 252.643 473
Enhanced enterprise innovation and creativity Between Groups 4.660 1 4.660 6.957 .009*
Within Groups 316.169 472 .670
Total 320.829 473
Improved overall enterprise performance Between Groups .304 1 .304 .601 .439
Within Groups 238.599 472 .506
Total 238.903 473
Enhanced client relations - better client interaction Between Groups .357 1 .357 .522 .470
Within Groups 322.110 472 .682
Total 322.466 473
Development of an entrepreneurial culture for
enterprise growth and success
Between Groups
Within Groups
5.705
378.954
1
472
5.705
.803
7.105
.008*
Total 384.658 473
Improved employee retention Between Groups 2.027 1 2.027 2.477 .116
Within Groups 386.287 472 .818
Total 388.314 473
Improved ability to sustain a competitive advantage Between Groups .103 1 .103 .184 .668
Within Groups 264.895 472 .561
Total 264.998 473
Enhanced transfer of knowledge from one employee
to another
Between Groups
Within Groups
.540
253.932
1
472
.540
.538
1.004 .317
Total 254.473 473
Means to identify industry best practices Between Groups 9.190 1 9.190 16.144 .000*
Within Groups 268.675 472 .569
106
Total 277.865 473
Better methods for enterprise-wide problem solving Between Groups 1.116 1 1.116 1.948 .163
Within Groups 270.346 472 .573
Total 271.462 473
Enhance the development of business strategies Between Groups 1.116 1 1.116 1.750 .186
Within Groups 300.945 472 .638
Total 302.061 473
Enhance business development and the creation of
enterprise opportunities
Between Groups
Within Groups
.171
286.911
1
472
.171
.608
.281 .596
Total 287.082 473
Enhanced and streamlined internal administrative
processes
Between Groups
Within Groups
27.418
324.194
1
472
27.418
.687
39.918 .000*
Total 351.612 473
* Significant at p < 0.05
Table 83: ANOVA of KMP Variables by Country
Sum of
Squares df
Mean
Square F Sig.
The organizational benefits of a knowledge-centric Between
Groups organization are clearly understood by everyone in our Within
Groups
organization Total
21.521
545.890
567.411
1
472
473
21.521 18.608
1.157
.000*
Knowledge management is a top priority in our Between Groups
organization Within Groups
10.044
565.696
1
472
10.044 8.381
1.199
.004*
Total 575.741 473
Our organization has a clear and strong commitment Between Groups to
knowledge management initiatives from senior Within Groups
management Total
.053
575.105
575.158
1
472
473
.053
1.218
.043
.835
Our organization has sufficient financial resources to Between Groups
support knowledge management initiatives Within Groups
2.439
518.034
1
472
2.439
1.098
2.222 .137
Total 520.473 473
Our organizational culture encourages knowledge
sharing
Between Groups
Within Groups
.255
616.068
1
472
.255
1.305
.196 .659
Total 616.323 473
People in our organization have the time to share
information
Between Groups
Within Groups
.053
477.046
1
472
.053
1.011
.052 .819
Total 477.099 473
Teamwork is a critical component of our
organization's culture, structure and processes
Between Groups
Within Groups
.053
556.068
1
472
.053
1.178
.045 .833
107
Total 556.120 473
Our organizational strategies, structures, policies,
procedures, processes and reward systems
focus on long-term growth
Between Groups
Within Groups Total
3.901
558.059
561.960
1
472
473
3.901
1.182
3.299 .070
Our organization has evolved from a rigid
hierarchical structure to a process-oriented
structure
Between Groups
Within Groups Total
10.635
502.338
512.973
1
472
473
10.635 9.993
1.064
.002*
Our organization has invested in knowledge
management technologies (i.e. intranet, database,
email and digital libraries)
Between Groups
Within Groups Total
7.344
483.451
490.795
1
472
473
7.344 7.170
1.024
.008*
Our organization has the human resources to
support our information technology systems,
software and network
Between Groups
Within Groups Total
5.487
519.114
524.601
1
472
473
5.487 4.989
1.100
.026*
People in our organization are often rewarded for
continuous learning or knowledge sharing
Between Groups
Within Groups
19.443
556.700
1
472
19.443 16.485
1.179
.000*
Total 576.143 473
* Significant at p < 0.05
Impact of Control Variables
We will now investigate the effects of our control variables on the means of the two country
groups for each of the KM indexes. In particular we will focus on the following variables:
•Job Type
•Company Size
•Industry Type
•Business Focus
We present a General Linear Model procedure to test for the null hypotheses about the
effects of our control variables on the means of various groupings for each KM index. This
allows us to investigate interactions between factors as well as help determine the
significance of each individual factor. For each control variable we perform a Bonferroni
test to evaluate the KM index significance level with multiple comparisons.
We begin by evaluating the effects of the control variables on the KMF Index.
Table 84: Bonferroni Analysis of KMF Index by Country by Job Position Level
(I) Position Level: (J) Position Level: Mean 95% Confidence Interval
Difference (I-J) Sig. Lower Bound Upper Bound
Executive Manager/Director d
Other
im
e
n
s
-.0819
-.1046
-.1007
1.000
1.000
1.000
-.2529
-.3112
-.3147
.0890
.1021
.1134
108
io Support Staff
n3
Technical Staff
-.0301 1.000 -.2089 .1487
Manager/Director Executive d
Other
i
m
e
n
s
i Support Staff
o
n3
Technical Staff
.0819
-.0226
-.0187
.0518
1.000
1.000
1.000
1.000
-.0890
-.1893
-.1945
-.0788
.2529
.1440
.1570
.1824
Other
dimension2
Executive d
Manager/Director
i
m
e
n
s
i Support Staff
o
n
3
.1046
.0226
.0039
1.000
1.000
1.000
-.1021
-.1440
-.2067
.3112
.1893
.2146
Technical Staff .0745 1.000 -.1003 .2492
Support Staff Executive d
Manager/Director
im
e
n
s
io Other
n3
.1007
.0187
-.0039
1.000
1.000
1.000
-.1134
-.1570
-.2146
.3147
.1945
.2067
Technical Staff .0705 1.000 -.1129 .2540
Technical Staff Executive d
Manager/Director
i
m
e
n
s
i Other
o
n
3
.0301
-.0518
-.0745
1.000
1.000
1.000
-.1487
-.1824
-.2492
.2089
.0788
.1003
Support Staff -.0705 1.000 -.2540 .1129
Based on Table 84, where we report the results of the Bonferroni test (post hoc test), we find
no significant differences in model-predicted means for each pair of factor levels. Therefore
there do not appear to be significant differences in the moderating factor of the participant’s
job position level.
Similarly Table 85, Table 86, and Table 87 indicate no significant differences in the moderating
factor of the company industry type, business focus, and size (number of employees),
respectively.
Table 85: Bonferroni Analysis of KMF Index by Country by Industry Type
(I) Industry Type (J) Industry Type Mean 95% Confidence Interval
Difference
(I-J) Sig.
Lower
Bound
Upper
Bound
Business Education -.0561 1.000 -.1871 .0749
Government -.0619 1.000 -.2399 .1160
Other .0302 1.000 -.1057 .1661
Education Business .0561 1.000 -.0749 .1871
Government -.0058 1.000 -.2068 .1951
Other .0863 .997 -.0786 .2512
109
Government
Business Education
.0619
.0058
1.000
1.000
-.1160
-.1951
.2399
.2068
Other .0922 1.000 -.1121 .2964
Other Business -.0302 1.000 -.1661 .1057
Education -.0863 .997 -.2512 .0786
Government -.0922 1.000 -.2964 .1121
For the Bonferroni test of the extended list of business sectors, see Appendix 4 (Table 99 on page 167)
Table 86: Bonferroni Analysis of KMF Index by Country by Business Focus
(I) Business focus: (J) Business focus: Mean
Difference
(I-J) Sig.
95% Confidenc
Lower
Bound
e Interval
Upper
Bound
Products Services -.0585 .743 -.1798 .0629
Products & Services -.0039 1.000 -.1482 .1403
Services Products .0585 .743 -.0629 .1798
Products & Services .0545 .714 -.0563 .1654
Products & Services Products .0039 1.000 -.1403 .1482
Services -.0545 .714 -.1654 .0563
Table 87: Bonferroni Analysis of KMF Index by Country by Company Size (Number of Employees)
(I) Number of Employees: (J) Number of Mean 95% Confidence Interval
Employees: Difference Lower Upper
(I-J) Sig. Bound Bound
<100 >10,000 -.0687 1.000 -.2156 .0782
5,000-
10,000
-.0611 1.000 -.2303 .1082
1,000-5,000 -.0607 1.000 -.2238 .1023
100-999 -.1083 .521 -.2653 .0486
>10,000 <100 .0687 1.000 -.0782 .2156
5,000-
10,000
.0076 1.000 -.1630 .1783
1,000-5,000 .0079 1.000 -.1565 .1724
100-999 -.0396 1.000 -.1980 .1188
5,000-10,000 <100 .0611 1.000 -.1082 .2303
>10,000 -.0076 1.000 -.1783 .1630
1,000-5,000 .0003 1.000 -.1844 .1851
100-999 -.0473 1.000 -.2266 .1321
1,000-5,000 <100 .0607 1.000 -.1023 .2238
>10,000 -.0079 1.000 -.1724 .1565
5,000-
10,000
-.0003 1.000 -.1851 .1844
100-999 -.0476 1.000 -.2211 .1259
110
100-999 <100 .1083 .521 -.0486 .2653
>10,000 .0396 1.000 -.1188 .1980
5,000-
10,000
.0473 1.000 -.1321 .2266
1,000-5,000 .0476 1.000 -.1259 .2211
We report the same type of Bonferroni test on the KME Index on Tables 89, 90, 91 and 92
where again we find no significant differences in the moderating factor of the participants’
job position level, and the company industry type, business focus, and size (number of
employees), respectively.
Table 88: Bonferroni Analysis of KME Index by Country by Job Position Level
(I) Position Level: (J) Position Level: Mean 95% Confidence Interval
Difference Lower Upper
(I-J) Sig. Bound Bound
Executive Manager/Director .0282 1.000 -.1808 .2371
Other -.0694 1.000 -.3219 .1832
Support Staff .1541 .973 -.1075 .4157
Technical Staff .1351 .819 -.0834 .3537
Manager/Director Executive -.0282 1.000 -.2371 .1808
Other -.0976 1.000 -.3013 .1061
Support Staff .1259 .990 -.0889 .3407
Technical Staff .1069 .594 -.0527 .2666
Other Executive .0694 1.000 -.1832 .3219
Manager/Director .0976 1.000 -.1061 .3013
Support Staff .2235 .147 -.0340 .4809
Technical Staff .2045 .072 -.0091 .4181
Support Staff Executive -.1541 .973 -.4157 .1075
111
Technical Staff
Executive
Manager/Director
Other
-.1351
-.1069
-.2045
.819
.594
.072
-.3537
-.2666
-.4181
.0834
.0527
.0091
Support Staff .0190 1.000 -.2052 .2431
Manager/Director -.1259 .990 -.3407 .0889
Other -.2235 .147 -.4809 .0340
Technical Staff -.0190 1.000 -.2431 .2052
Table 89: Bonferroni Analysis of KME Index by Country by Industry Type
(I) Industry Type (J) Industry Type Mean 95% Confidence Interval
Difference
(I-J) Sig.
Lower Bound Upper
Bound
Business Education .0568 1.000 -.1051 .2187
Government -.0351 1.000 -.2551 .1850
Other .0364 1.000 -.1316 .2044
Education Business -.0568 1.000 -.2187 .1051
Government -.0919 1.000 -.3404 .1566
Other -.0204 1.000 -.2243 .1834
Government Business .0351 1.000 -.1850 .2551
Education .0919 1.000 -.1566 .3404
Other .0714 1.000 -.1810 .3239
Other Business -.0364 1.000 -.2044 .1316
Education .0204 1.000 -.1834 .2243
Government -.0714 1.000 -.3239 .1810
For the Bonferroni test of the extended list of business sectors, see Appendix 4 (Table 100 on page 170)
Table 90: Bonferroni Analysis of KME Index by Country by Business Focus
(I) Business focus: (J) Business focus: Mean 95% Confidence Interval
Difference
(I-J) Sig.
Lower Bound Upper
Bound
Products Services -.0232 1.000 -.1736 .1273
Products & Services -.0443 1.000 -.2230 .1345
Services Products .0232 1.000 -.1273 .1736
Products & Services -.0211 1.000 -.1585 .1164
Products & Products Services .0443.0211 1.0001.000 -.1345-.1164
.2230.1585 Services
Table 91: Bonferroni Analysis of KME Index by Country by Company Size (Number of Employees) (I)
Part 1/Q 7: (J) Part 1/Q 7: Number of Mean 95% Confidence Interval
Number of Employees: Employees: Difference
(I-J) Sig.
Lower Bound Upper
Bound
<100 >10,000 .0121 1.000 -.1694 .1935
112
5,000-10,000 .0803 1.000 -.1288 .2895
1,000-5,000 .0713 1.000 -.1302 .2727
100-999 -.0770 1.000 -.2710 .1169
>10,000 <100 -.0121 1.000 -.1935 .1694
5,000-10,000 .0682 1.000 -.1426 .2791
1,000-5,000 .0592 1.000 -.1440 .2624
100-999 -.0891 1.000 -.2848 .1066
5,000-10,000 <100 -.0803 1.000 -.2895 .1288
>10,000 -.0682 1.000 -.2791 .1426
1,000-5,000 -.0091 1.000 -.2373 .2192
100-999 -.1574 .458 -.3790 .0643
1,000-5,000 <100 -.0713 1.000 -.2727 .1302
>10,000 -.0592 1.000 -.2624 .1440
5,000-10,000 .0091 1.000 -.2192 .2373
100-999 -.1483 .517 -.3627 .0661
100-999 <100 .0770 1.000 -.1169 .2710
>10,000 .0891 1.000 -.1066 .2848
5,000-10,000 .1574 .458 -.0643 .3790
1,000-5,000 .1483 .517 -.0661 .3627
On the KMP Index we find no significant differences in the moderating factors of the
participants’ job position level (Table 92), business focus (Table 94) and company size
(Table 95), but we do find that significant differences between subset groups of the company
industry type (Table 93).
Table 92: Bonferroni Analysis of KMP Index by Country by Job Position Level
(I) Position Level: (J) Position Level: Mean 95% Confidence Interval
Difference
(I-J) Sig.
Lower Bound Upper
Bound
Executive Manager/Director .0902 1.000 -.2047 .3851
Other .0110 1.000 -.3455 .3674
Support Staff .0817 1.000 -.2875 .4509
Technical Staff .1448 1.000 -.1637 .4532
113
Technical Staff
Executive
Manager/Director
Other
-.1448
-.0545
-.1338
1.000
1.000
1.000
-.4532
-.2798
-.4352
.1637
.1707
.1676
Support Staff -.0631 1.000 -.3795 .2534
Manager/Director Executive -.0902 1.000 -.3851 .2047
Other -.0792 1.000 -.3667 .2082
Support Staff -.0085 1.000 -.3117 .2946
Technical Staff .0545 1.000 -.1707 .2798
Other Executive -.0110 1.000 -.3674 .3455
Manager/Director .0792 1.000 -.2082 .3667
Support Staff .0707 1.000 -.2926 .4341
Technical Staff .1338 1.000 -.1676 .4352
Support Staff Executive -.0817 1.000 -.4509 .2875
Manager/Director .0085 1.000 -.2946 .3117
Other -.0707 1.000 -.4341 .2926
Technical Staff .0631 1.000 -.2534 .3795
Table 93: Bonferroni Analysis of KMP Index by Country by Industry Type
(I) Industry Type (J) Industry Type Mean
Difference
(I-J)
Sig.
95% Confidence
Interval
Lower
Bound
Upper
Bound
Business Education
Government
.2023
.4272*
.091
.001
-.0177 .128
3
.4223
.7261
Other .0809 1.000 -.1474 .3091
Education Business -.2023 .091 -.4223 .0177
Government .2249 .469 -.1127 .5625
Other -.1214 1.000 -.3984 .1555
Government Business -.4272* .001 -.7261 -.1283
Education Other -.2249
-.3463*
.469 .0
46
-.5625
-.6893
.1127
-.0033
Other Business -.0809 1.000 -.3091 .1474
Education Government .1214
.3463*
1.000 .0
46
-.1555 .003
3
.3984
.6893
*. The mean difference is significant at the .05 level.
For the Bonferroni test of the extended list of business sectors, see Appendix 4 (Table 101 on page 172)
We note that
Table 93 reports significant differences when comparing responses from the government sector
with the business (p = .001 < 0.05), and the non-classified sectors of other (p =
0.046 < 0.05).
114
Table 94: Bonferroni Analysis of KMP Index by Country by Business Focus
(I) Business focus: (J) Business focus: Mean 95% Confidence Interval
Difference
(I-J) Sig.
Lower Bound Upper
Bound
Products Services -.0459 1.000 -.2542 .1624
Products & Services -.0230 1.000 -.2705 .2245
Services Products .0459 1.000 -.1624 .2542
Products & Services .0229 1.000 -.1674 .2131
Products & Services Products .0230 1.000 -.2245 .2705
Services -.0229 1.000 -.2131 .1674
Table 95: Bonferroni Analysis of KMP Index by Country by Company Size (Number of Employees)
(I) Number of Employees: (J) Number of Mean 95% Confidence Interval
Employees: Difference
(I-J) Sig.
Lower Bound Upper
Bound
<100 >10,000 .0733 1.000 -.1750 .3217
5,000-10,000 .2127 .366 -.0735 .4989
1,000-5,000 .2417 .138 -.0340 .5175
100-999 .2537 .073 -.0117 .5191
>10,000 <100 -.0733 1.000 -.3217 .1750
5,000-10,000 .1394 1.000 -.1491 .4279
1,000-5,000 .1684 .883 -.1097 .4465
100-999 .1804 .581 -.0875 .4482
5,000-10,000 <100 -.2127 .366 -.4989 .0735
>10,000 -.1394 1.000 -.4279 .1491
1,000-5,000 .0290 1.000 -.2834 .3414
100-999 .0410 1.000 -.2623 .3443
1,000-5,000 <100 -.2417 .138 -.5175 .0340
>10,000 -.1684 .883 -.4465 .1097
5,000-10,000 -.0290 1.000 -.3414 .2834
100-999 .0120 1.000 -.2815 .3054
100-999 <100 -.2537 .073 -.5191 .0117
>10,000 -.1804 .581 -.4482 .0875
5,000-10,000 -.0410 1.000 -.3443 .2623
1,000-5,000 -.0120 1.000 -.3054 .2815
115
Summary of Chapter 5
In Chapter 5 we provided descriptive statistics for all factors of successful KM (KMF),
KM expectations (KME), and KM practices (KMP). Overall we found that the combined
country group scores were relatively high for KMF and KME variables, but lower for
KMP variables. Moreover, KMP scores had standard deviation values higher than those of
KMF and KME variables, indicating a larger spread over a larger range of values.
Descriptive statistics grouped by country for all KM variables were provided to compare
scores between the Italian and U.S. sample populations. We found that mean score values
were relatively close between the two country groups. Moreover, standard deviation
values for each variable were comparatively close between country groups, indicating that
the spread over the range of values was similar between country groups.
We provided a quantitative measure of the relative importance each country group gave to
KM factors, expectations and practices by computing individual index scores (KMF
Index, KME Index, and KMP Index). Cronbach’s Alpha values validated the use of the
derived indexes for representing the KME, KMF, and KMP constructs. Analysis of
Variance (ANOVA) was used to compare the means between our U.S. and Italian
responses to the KM indexes. Our ANOVA results confirmed our hypotheses that there
would not be a significant difference in beliefs, expectations, and practices of knowledge
management between Italian and American respondents.
Although the data within the aggregate values of our indexes failed to reject our
hypotheses, individual ANOVAs on all variables did find areas with significant
differences. Generalized Linear Model (GLM) univariate tests were performed to look for
differences within our control variables: participant’s job position level, company industry
type, business focus, and size. The differences revealed by the individual ANOVAs and
the GLM will be further explored in Chapter 6.
“We can't have full knowledge all at once. We must start by
believing; then afterwards we may be led on to master the
evidence for ourselves.”
Saint Thomas Aquinas
116
Chapter 6 Conclusions
In this final chapter, we will review our research aim and draw conclusions from our
findings. In particular, we will discuss the similarities in perceptions that were found
between American and Italian participants—which support our hypotheses—and interpret the
individual areas of knowledge management beliefs, expectations and practices where
significant differences were found.
Brief Review of the Purpose of our Study
Extensive literature has been published in support of the importance of knowledge management
for achieving and maintaining competitive advantage across all types of organizations.
However only limited research is available to understand how KM may be influenced by
national culture.
The George Washington University’s Institute for Knowledge and Innovation has undertaken
KM-related research studies, and has completed, or is in the process of completing, findings
from various parts of the world. In particular, in 2004 Wang completed a study, which
compared KM perceptions between Taiwanese and American knowledge workers. In his
study, Wang found that the two dissimilar cultures of Taiwan and USA produced significant
differences in beliefs, expectations, and practices of knowledge management.
Since the dissimilar cultures of the U.S. and Taiwan have shown significant differences in
KM perceptions, we anticipated through our hypotheses that the similar cultures of Italy
and the USA would show primarily similarities. For the reader’s convenience we re-
present our research question and stipulated hypotheses below.
117
The primary question motivating our research was:
“Do Italian and American cultural attributes influence the respective beliefs, expectations
and practices of their citizens regarding Knowledge Management?”
This primary question resulted in the following hypotheses:
H1: There is a positive relationship between the cultural dimensions of
Italian and US respondents and their beliefs about factors
influencing successful knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their expectations about the
benefits of knowledge management.
There is a positive relationship between the cultural dimensions of
Italian and US respondents and their knowledge management
practices.
H2:
H3:
Overview of our Research Methodology
For continuity and comparative purposes we used Wang’s research methodology as a
starting point, including the usage of a previously validated survey instrument for assessing
an individual’s perception of KM beliefs, expectations, and practices (from Bixler 2000
and Calabrese 2000). We have solicited participation in our study to Italian and American
knowledge workers; the subjects of our study were employees and managers expected to
be involved in knowledge management activities. Questionnaires were completed online
at www.km-research.com in English by U.S. nationals and in Italian by the Italian
nationals.
We sent out at various points during our data collection phase a total of 1,935 survey
invitations, which included an individually assigned access key to prevent duplicate
submissions. We received a total of 637 responses, of which 474 were accepted; 237 for
the U.S. and 237 for the Italian sample population. Almost 95% of the responses included
companies whose headquarters were either in the USA or in Italy. Demographical data,
118
including participant’s job level position, and company industry type, size (number of
employees) and focus (products vs. services) were collected for use as control variables.
Overview of our Research Findings
Overall the combined country group scores were relatively high for success factors of KM
(KMF) and KM expectations (KME), but lower for the KM practices (KMP) variables.
Moreover, KMP scores had standard deviation values higher than those of KMF and
KME variables, indicating a larger spread over a larger range of values.
Descriptive statistics grouped by country for all KM variables show that mean score values
were relatively close between the two country groups. Moreover, standard deviation
values for each variable were comparatively close between country groups, indicating that
the spread over the range of values was similar between country groups.
We provided a quantitative measure of the relative importance each country group gave to
KM factors, expectations and practices by computing individual index scores (KMF
Index, KME Index, and KMP Index). Cronbach’s Alpha values validated the use of the
derived indexes for representing the KME, KMF, and KMP constructs. Analysis of
Variance (ANOVA) on such indexes confirmed our hypotheses that there would not be a
significant difference in beliefs, expectations, and practices of knowledge management
between Italian and American respondents (Table 80 on page 111 reports low scores for
the F value and p values above 0.05, indicating no significant differences between
country groups).
Exceptions
Although the data within the aggregate values of our indexes failed to reject our
hypotheses, individual ANOVAs on all variables did find areas with significant differences
between Italian and American respondents (See Table 96 below).
Table 96: KM Variables with p < 0.05 scores for ANOVA by Country
Index Variables p Scores
KMF index Improvements in IT infrastructure .014
Organizational buy-in and support .048
119
Leadership involvement, support, and advocating .020
Climate of openness and thinking "outside the box" .003
KM advocates and champions within the enterprise .000
Gathering and formalizing existing external enterprise
knowledge
.002
Allocating resources to manage enterprise knowledge as to
relevance, accuracy and value to the enterprise
.001
Effective and efficient methodology of distributing knowledge
to employees
.007
KME Index
Enhanced enterprise innovation and creativity .009
Development of an entrepreneurial culture for enterprise
growth and success
.008
Means to identify industry best practices .000
Enhanced and streamlined internal administrative processes .000
KMP Index
The organizational benefits of a knowledge-centric
organization are clearly understood by everyone in our
organization
.000*
Knowledge Management is a top priority in our organization .004*
Our organization has evolved from a rigid hierarchical structure
to a process-oriented structure
.002*
Our organization has invested in knowledge management
technologies
.008
Our organization has the human resources to support our
information technology systems, software and network
.026
People in our organization are often rewarded for continuous
learning or knowledge sharing
.000
* Denotes that variable was affected by the industry type control variable
Chart 54, Chart 55, and Chart 56 provide an illustration of the means for each of the
significantly different variables from Table 96. From these charts we notice relatively
small differences in mean values between Italy and the USA. While the mean values may
appear to be close between the two country groups, such observation cannot be
considered statistically significant; the ANOVA test has determined that the means
between the two groups are significantly different.
120
Chart 54: KM success factors where significant changes where found between Italy and USA
Chart 55: KM expectations where significant changes where found between Italy and USA
Chart 56: KM practices where significant changes where found between Italy and USA
121
In Chapter 5 we investigated the effects of our control variables (job position level,
company size, industry type, and focus) for the two country groups. For each control
variable we performed a Bonferroni test to evaluate for significant differences for each of
the three KM indexes. The test revealed no significant differences on all control
variables for the KMF and KME indexes, but found some differences on the industry type
control variable for the KMP index. Table 97 re-proposes the cross-evaluation of
industry types where such differences (p < 0.05) were calculated.
Table 97: Significant differences of KMP Index by Country by Industry Type (from
Table 93 p. 120)
Industry (I) Industry (J) p Score
Government Business .001
Government Other .046
Additional Bonferroni tests for the cross-evaluation by country and by industry type are proposed
from Table 102 to Table 107.
Impact of Uncertainty Avoidance in KM Perceptions
As stated earlier, Italy and the U.S. are part of two adjacent country clusters; their index
scores for power distance, individualism, masculinity, and uncertainty avoidance are
relatively close. The largest difference between Italian and American dimensions is that
122
of uncertainty avoidance (UA); Italy’s UA was higher. In Chapter 3, we observed that the
difference in uncertainty avoidance between the two countries was greater than one full
standard deviation, and hence could account for country effects on knowledge
management perceptions.
Societies that score high in uncertainty avoidance seek orderliness, formal business
meetings, meticulous records, consistency, structure, procedures and written rules and
regulations to alleviate the unpredictability of events and situations in their daily life (House
2004). The aversion to uncertainty makes high UA societies take additional measures to
reduce risks. While the additional overhead may slow down innovation, new product
implementations tend to be easier because of the additional controls and documented
procedures (House 2004).
In Table 98 we present a list of key differences between low and high uncertainty
avoidance index (UAI) societies that are expected to be either a barrier or an enabler for
knowledge management. This list is drawn from a longer list of key differences discussed
by Hofstede (2001).
Table 98: Key differences between low and high UAI societies (From Hofstede 2001 p. 169-168)
Low UA High UA
In Motivation
Hope for success. Fear of Failure.
Preference for tasks with uncertain Preference for tasks with sure outcomes, no
outcomes, calculated risks, and requiring risks, and following instructions. problem
solving.
In the Work Situation
Weak loyalty to employer; short average Strong loyalty to employer, long average
duration of employment. duration of employment.
Preference for smaller organizations but little
self-employment.
Preference for larger organizations but at
the same time much self-employment.
Skepticism towards technological solutions. Strong appeal of technological solutions.
Innovators feel independent of rules. Innovators feel constrained by rules.
123
Renegade championing. Rational championing.
Top managers involved in strategy. Top managers involved in operations.
Power of superiors depends on position and
relationships.
Power of superiors depends on control of
uncertainties.
Tolerance for ambiguity in structures and
procedures.
Highly formalized conception of
management.
Appeal of transformational leader role. Appeal of hierarchical control role.
Innovations welcomed but not necessarily
taken seriously.
Innovations resisted, but, if accepted,
applied consistently.
Belief in generalists and common sense. Belief in specialists and expertise.
Superiors optimistic about employees’
ambition and leadership capacities.
Superiors pessimistic about employees’
ambition and leadership capacities.
Next, we will review each of the KMF, KME, and KMP variables where areas of significant
differences were found.
Factors of Successful KM (KMF) affected by UA
We begin by looking at the differences that relate to critical factors for developing
successful knowledge management within the enterprise. We will report the mean scores
and standard deviations for each affected variable for each country. While the ANOVA for
these variables did find significant differences in variance between the two distinct sample
populations, descriptive statistics report relatively small differences; the gap in country
means is less than a single standard deviation of the total population sample (see Table 75
on page 105).
Improvements in IT infrastructure to support KM
For this variable Italy’s mean score was slightly higher than that of the U.S. (4.02 vs.
3.92) and greater consensus was found within the Italian sample population than in that of
the U.S. (0.683 vs. 0.965 standard deviations). Here, the American sample population
may be expressing skepticism towards technological solutions, not in discarding
technology per se, rather, in the sense that creativity is favored over rote adherence to
rule-derived results. Conversely, the Italian sample population may initially resist the
adoption of IT, but once accepted it will apply it consistently in support of KM initiatives.
124
Organizational buy-in and support
For this variable the U.S. mean score was slightly higher than that of Italy (4.52 vs. 4.39),
consensus within the two sample population was relatively similar (0.699 vs. 0.690
standard deviations). The ANOVA significance for this variable was at p = 0.048 < 0.05,
which barely accounts for a significant difference. Most barriers to success with
knowledge management are highly related to the culture and structure of the organization.
For both countries, promoting a collaborative culture right from the beginning will more
likely allow for organizational buy-in and support. For Italians a rational championing of
KM initiatives may increase buy-in and support.
Leadership involvement, advocacy and support
For this variable the U.S. mean score was slightly higher than that of Italy (4.59 vs. 4.45),
consensus within the two sample populations was relatively similar (0.635 vs. 0.666 standard
deviations). The higher uncertainty avoidance of Italy, which results in appeal for hierarchical
control, may hinder the free-flow of knowledge. Therefore, companies should make sure that
senior management is receptive to ideas from employees and thus fostering internal relationships
across the hierarchy.
Climate of openness and thinking “outside the box”
For this variable Italy’s mean score was relatively higher than that of the U.S (4.31 vs.
4.09) and greater consensus was found within the Italian sample population than for the
U.S. (0.709 vs. 0.871 standard deviations). Knowledge workers may be too busy to share
knowledge or may intentionally not want to share, under the notion that “knowledge is
power” and for job preservation. In the typically less structured approach of low
uncertainty avoidance societies may benefit from more formalized sharing by, for
example, requiring knowledge workers to find the time to share. KM must be a priority at
the organizational level. The high employee turnover of countries with low uncertainty
avoidance makes knowledge capturing even more critical for preventing employee
knowhow to leave with the employee.
Gathering and formalizing existing external enterprise knowledge
For this variable the U.S. mean score was slightly higher than that of Italy (3.88 vs. 3.66),
consensus within the two sample population was relatively similar (0.744 vs. 0.810
standard deviations). Overall this factor received the second lowest score within the KMF
variables. U.S. and Italian knowledge workers may need to better understand the
125
importance of acquiring external knowledge. Such knowledge can be acquired through
online resources (e.g. online communities of interest and communities of practice),
conferences and training, and collaborating on projects with business partners and so on.
At least in part, the fact that today English is the most used business language, gives U.S.
knowledge workers an advantage when seeking external knowledge. In fact, the lower
score on the part of the Italians may be due in part to not having access to the same
amount of information in the Italian language.
Allocating resources to manage enterprise knowledge as to relevance,
accuracy, and value to the enterprise
For this variable the U.S. mean score was slightly higher than that of Italy (4.27 vs. 4.01),
consensus within the two sample population was higher for the U.S. (0.788 vs. 0.892
standard deviations). The ability to eliminate old, outdated, incorrect, or unnecessary
information and knowledge is perceived as more important to the American knowledge
worker. Supporting technologies must be designed to allow content to be versioned,
annotated, rated, and archived in an easy and intuitive way. The lower Italian score can be
partially explained by the high proportion of small and medium business which tend to
have fewer resources at disposal for such projects.
Effective and efficient methodology of distributing knowledge to employees
For this variable the U.S. mean score was slightly higher than that of Italy (4.37 vs. 4.19),
consensus within the two sample population was higher for the U.S. (0.680 vs. 0.738
standard deviations). Automating information and knowledge for easy access and making
interfaces that interconnect information and knowledge from within commonly used
applications (e.g. document repositories, database, and company web portals) will overall
increase the value and hence the acceptance by high uncertainty avoidance societies.
KM Expectations (KME) affected by UA
We will now look at the differences between Italian and U.S. expected benefits from
knowledge management initiatives. Also here, we will report the mean scores and standard
deviations recorded for each variable by each country. While the ANOVA for these
variables did find significant differences in variance between the two sample populations,
descriptive statistics report relatively small differences; the gap in country means is less
than a single standard deviation of the total population sample (see Table 76 on page 106).
126
Enhanced enterprise innovation and creativity
For this variable Italy’s mean score was slightly higher than that of the U.S (4.08 vs.
3.88) and more consensus was found within the Italian sample population than in that of the
U.S. (0.791 vs. 0.845 standard deviations). The higher score of the Italian sample
population can be explained from the higher appeal that technological solutions have to
high UA societies. Hence, the expected benefit of having KM—which may be perceived as
a technological solution—is greater for Italians. Additionally, high UA societies will tend
to favor the presence of more accessible information because this would tend to minimize
risks for innovative efforts.
Development of an entrepreneurial culture for enterprise growth and success
For this variable Italy’s mean score was slightly higher than that of the U.S (3.78 vs.
3.56) and greater consensus was found within the Italian sample population than in that of
the U.S. (0.855 vs. 0.935 standard deviations). Both countries did not score this highly;
indicating that KM is not perceived as a substitute for genuine entrepreneurial
breakthroughs. However, the higher Italian score points to the cultural preferences that
Italians have for having more information available to minimize potential problems and
risks associated with entrepreneurial activity and enterprise growth initiatives. For
example, the American might approach a business opportunity more readily without
having all the facts; an Italian, might be more cautious, but with additional information,
may go forward.
Means to identify industry best practices
For this variable Italy’s mean score was slightly higher than that of the U.S (4.12 vs.
3.84) and greater consensus was found within the Italian sample population than in that of
the U.S. (0.699 vs. 0.806 standard deviations). Because high UA societies tend to place
more importance on leadership or specialist positions, having access to “expert”
knowledge is equivalent to obtaining best practices. Low UA societies tend to be more
skeptical of formalized approaches and hence may not view KM as a conduit to best
practice as much.
Enhanced and streamlined internal administrative processes
For this variable the U.S. mean score was slightly higher than that of the Italy (3.82 vs.
3.34) and greater consensus was found within the American sample population than in that
of the Italian (0.852 vs. 0.805 standard deviations). High UA societies may already have
127
highly structured administrative processes due to the increased bureaucratic and regulatory
environment; hence, KM’s impact may be perceived as less of an enabler.
KM Practice (KMP) affected by UA
Overall the mean scores for all KMP variables are lower than that of the KME and KMF.
Moreover the standard deviations are higher than in KME and KMF, indicating fewer
consensuses within the individual ratings of knowledge management practices. To
visualize the complete descriptive statistics for the KMP variables the reader may refer
back to Table 78 on page 118. In Chapter 5 we reported significant differences within the
KMP Index scores when performing multiple-comparisons on the participant’s industry
type control variable, in particular when evaluating differences between government and
business sectors. Such finding indicates that the ANOVA differences in KM practices for
each KMP variable may at least in part due to differences by country within the business
sectors.
The organizational benefits of a knowledge-centric organization are clearly
understood by everyone in our organization.
For this variable Italy’s mean score was higher than that of the U.S (2.90 vs. 2.48) and
slightly less consensus was found within the Italian sample population than in that of the
U.S. (1.051 vs. 1.099 standard deviations). As KM is a relatively new phenomenon, this
in itself may explain the low scores. Organizations should implement awareness programs
(e.g. posters, “brown bag” lunches, webinars, round table discussions, etc.) to heighten the
awareness of the benefits of KM.
Knowledge management is a top priority in our organization.
For this variable Italy’s mean score was higher than that of the U.S (2.92 vs. 2.63) and slightly
more consensus was found within the Italian sample population than in that of the
U.S. (1.106 vs. 1.084 standard deviations). As above, low scores are probably due to the
relative newness of KM. This is unfortunate due to the significant gains that any
organization can obtain through the controlled sharing of vital knowledge among its work
force. Organizations that make KM a priority will see the fruits of this effort sooner than
those that put it off—it takes time for the gains to become apparent. Additionally,
organizations are not used to putting a monetary value on their knowledge base; hence it
is more complex to assign budgets and priorities to implement KM initiatives. Companies
128
should develop ways to value their knowledge to better anchor KM investment decisions
and priorities.
Our organization has evolved from a rigid hierarchical structure to a
processoriented structure
For this variable Italy’s mean score was higher than that of the U.S (3.08 vs. 2.78), but less
consensus was found within the Italian sample population than in that of the U.S. (1.078
vs. 0.983 standard deviations). Italy’s higher score may be explained as follows. Since
high UA societies start from a more rigid hierarchical structure, their change from these
rigid hierarchical structures to process-based ones are more apparent and impactful.
Our organization has invested in knowledge management technologies
For this variable the U.S. mean score was higher than that of the Italy (3.73 vs. 3.49) and
greater consensus was found within the American sample population than in that of the
Italian (0.962 vs. 1.060 standard deviations). The higher U.S. score is most likely due to
the fact that KM initiatives have been around longer in the U.S. than in Italy. But
companies in both nations need to more fully understand how to value their knowledge
base to justify KM investment. Additionally, Italians, as high UA individuals, tend to resist
innovation initially, but will embrace it more consistently once it is accepted.
Hence there could be a lag before wide-spread adoption occurs.
Our organization has the human resources to support our information
technology systems, software and network
For this variable Italy’s mean score was higher than that of the U.S (3.67 vs.3.46), but less
consensus was found within the Italian sample population than in that of the U.S. (1.062
vs. 1.035 standard deviations). Both countries seem to be saying that staff exists
presently to support technology and information systems in general. Hence, the
implementation of a KM solution is not hindered by the lack of human resources.
People in our organization are often rewarded for continuous learning or
knowledge sharing
For this variable the U.S. mean score was higher than that of the Italy (3.01 vs. 2.60).
The level of consensus between the American and Italian sample populations was almost
equal (standard deviations were 1.085 for U.S. and 1.087 for Italy). Recognition and
rewards are important enablers of KM, but must be carefully administered. If an
129
organization rewards employees for the number of contributions to the organization’s
knowledge (e.g. number of entries within a KM system), this may cause employees to
record as much as they can, regardless of the usefulness of what is being recorded, just to
get the reward. Knowledge contributions must, when possible, be tied to outcomes (e.g.
new products, patents, or lessons learned). Organizations should reward employees who
contribute the best-quality knowledge entered. Rewarding employees with training may
also be a good way to further foster learning; most high performing employees will gladly
accept the opportunity to receive additional training.
Conclusions
As global markets become increasingly local, and national boundaries become less
meaningful, it is precisely the cultural differences between business actors that will
become increasingly significant. While knowledge management itself is well documented
in the literature, the important characterization of how it is perceived under a culture-
specific lens is less so. Our study contributes to the need of approaching knowledge
management initiatives in light of cultural differences.
Our exploratory study found that Italian and American knowledge workers do not have
significant differences in belief, expectations and practices of knowledge management
indexes. Therefore, for the most part, knowledge management and its organizational aspects
(strategies, policies and procedures) should not vary much between the two countries.
What works in the United States should work in Italy.
The survey scores on beliefs and expectations at the individual level were relatively high
for both countries. Participants from both countries indicated a positive perception, both
on the factors influencing successful knowledge management, and on the expected benefits
of having a knowledge management initiative in place. On the other hand, participants
from both countries scored relatively lower on the knowledge management practices being
followed by their organizations. Therefore, while there is an overall positive perspective
on knowledge management, participants felt that KM best-practices are not currently being
adopted or followed to the extend that they could be.
The lower that optimal adoption of KM best-practices requires that practitioners find ways
to justify investments in KM initiatives. The impact that knowledge management has on
130
an organization’s bottom-line is not easy to quantify. Moreover, tangible and intangible
results of a KM program are not immediately apparent; the fruits of such efforts may be
seen only after some time. Tying knowledge management objectives to tangible and
measurable outcomes may particularly benefit organizations operating in high uncertainty
avoidance societies like Italy who may otherwise resist adoption.
Future Research
Being an exploratory study, our research did not focus on a specific business type or
sector, or on tests from comparable company sizes. Future research may want to further
explore differences within such control variables, particularly relating to knowledge
management practices. We would be willing to partner with future researchers in the field
of study, by facilitating replication of our study in different languages and for different
countries through our online survey application.
A considerable amount of effort was put in developing our online survey application, which
to date continues to receive new survey submissions. Not only did the application facilitate
the completion of the survey, but it included administrative tools for soliciting participation
and most importantly for evaluating data as it was being collected. We are considering
developing end-user tools for dynamically reporting the findings from our survey data;
allowing web users to interact with our data (comparing subsets, slicing, reporting, etc).
We would also like to make the experience of completing the survey more interactive, for
example once completed, the system could give the participant a personalized view of how
his or her perceptions compare to others from the same country and/or from similar control
variables. We would also like to allow organizations to use our tool as an internal KM
assessment tool. Collected data for the organizations’ employees could be reported back to
the organization and continue to enrich our data and help us further our insights into the
field of study.
131
