Post on 13-Mar-2023
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Author: Pelletier Pui Man Ho
Title of Project: Organisational and Subgroup Cultures: Implications for
Knowledge Sharing within a Project-Based Organisation
in the United Kingdom
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Table of Contents
Chapter 1. Introduction 4 1.1. Statement of the Research Question 7
Chapter 2. Literature Review 8 2.1. Challenges of Knowledge Sharing in Project-Based Organisations (PBOs) 10 2.2 Organisational Culture as a Predictor of Knowledge Sharing Behaviours 11 2.3 Subgroup Culture as a Moderator 16
Chapter 3. Research Methodology 19 3.1 Settings 19 3.2 Research Design 20 3.3. Data Collection 21 3.4 Data Analysis 26
Chapter 4. Results of Analysis 29 4.1 Demographic Information 29 4.2 Reliability 30 4.3 Organisational Culture 30 4.4 Subgroup Cultures 31 4.5 Knowledge Sharing 34 4.6 Validity – Testing Hypotheses 36 4.7 Analysing Focus Groups 41
Chapter 5. Discussion 47 5.1 Individual Effects of Organisational Culture on Knowledge Sharing 48 5.2 Combined Effects of Organisational and Subgroup Culture on Knowledge Sharing 50
Chapter 6. Limitations and Opportunities 53 Chapter 7. Conclusion and Recommendations 55 References 59 Appendices 65
Appendix D1: Questions of Online questionnaire (with assigned data codes) 65 Appendix D2: Questions of Focus Groups 67 Appendix D3: Reliability Statistics 68 Appendix D4: Focus Group Responses (Coded) 78
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Executive Summary
Numerous researchers had confirmed the relationship between organisational culture
and its effects on knowledge sharing behaviours (Lindkvist, 2005; Ajmal & Koskinen,
2008; Suppiah & Sandhu, 2011; Wiewiora et al., 2013). As Project-Based
Organisations (PBOs) gain prominence in organisational research, the formation of
interdependent yet autonomous project units and the associated impact of subgroup
cultures on knowledge sharing, have began to emerge as a topic of interest for other
scholars such as Eskerod and Skriver (2007), Mueller (2012), and Carton and
Cummings (2013).
This study reports the findings from a cross-sectional mixed methods research
conducted in a UK-based project-based organization (PBO). Corroboration of findings
from qualitative and quantitative analyses indicated that, within the context studied,
different organisational and subgroup cultural orientations have had different effects on
knowledge sharing behaviours. Although some limitations were identified, overall, this
study indicates that diagnosis of organisational and subgroup cultures, together with an
enriched analysis of project units’ embedded knowledge sharing behaviours, can
inform organisational efforts to enhance knowledge sharing practices and
organisational effectiveness.
Moreover, due to the scarcity of empirical research found in the application of the
Competing Values Framework (CVF) for assessing presence of subgroup cultures, this
research aims to contribute to this under-developed area of research on subgroup
cultures with organisations, as well as the wider body of knowledge management
research.
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Chapter 1. Introduction
During the latter half of the 20th century, there was a major shift in the way businesses
operated. Many organisations began to shift from business models that originally
focused on mass production to offer customised services to meet the demanding needs
of technically advanced and competitive markets. This also necessitated a shift from
traditionally functional organisational structures, which relied on systematic
consistency, to become project-based organisations with a focus on agile work and
flexibility (Wiewiora et al., 2009).
Project-based organisations (PBOs) are inherently flexible, and are regarded as ideally
suited for coping with today’s technological advances, needs for innovation and
responsiveness to market changes (Hobday, 2000). Agile work and the application of
project management methodologies for guiding operational activities have also gained
increasing prominence in organisational approaches for improving overall adaptive
capacities (Ajmal & Koskinen, 2008).
PBOs function in highly volatile and knowledge intensive environments, whereby the
‘project’ forms its primary business function (Wiewiora et al., 2013). This requires
effective knowledge sharing, and integration of different knowledge types and
functional skills, so to achieve project goals such as time, budget and quality of output
(Bredin, 2008). Knowledge sharing on a project level requires socialisation of project
stakeholders as well as accessing relevant project documents through shared
information networks (Pemsel & Wiewiora, 2013).
From an organisational learning perspective1, lessons may be learnt via sharing of
project members’ experiences and best practices (Swan et al., 2010). Such knowledge
sharing can help reduce risks of ‘reinventing the wheel’ as well as repeating past
project mistakes (Davenport & Prusak, 1998). The successful use of organisational
1 Organisational learning is defined as a process of modifying and improving organisational actions as a result of reflection, new knowledge and insight (Edmondson, 2002 cited in Swan et al., 2010).
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knowledge and the cultivation of a knowledge-friendly culture are also believed to be
conducive to an organisation’s competitive advantage (Ajmal & Koskinen, 2008).
However, the context of PBOs is complex. The combination of engaged knowledge
sources, the composition of professionals from varied backgrounds at different stages
of a project, together with the multiplicity of knowledge types, poses a challenging task
for PBOs to effectively manage their project knowledge (Wiewiora et al., 2013). In
addition, project units tend to work autonomously on each project stage within the
project’s life cycle; together with managerial push for time and productivity, cross-
project learning and communication is often rendered a neglected task (Hobday, 2000).
The risk of losing accumulated knowledge throughout projects poses severe
consequences for PBOs in terms of organisational effectiveness and organisational
learning. Upon investigating variables that can impact knowledge sharing behaviours
within PBO’s, various empirical studies were found to indicate significant influence of
organisational culture in shaping patterns and qualities of interactions needed to
leverage knowledge sharing among individuals (De Long & Fahey, 2000; Gray &
Densten, 2005; Wiewiora et al., 2013).
Organisational Culture is defined as “taken-for-granted values, underlying
assumptions, expectations, collective memories, and definitions present in an
organisation” (Cameron & Quinn, 2006, p.16). As such, culture establishes an
organisational context for social interaction and creates cultural norms for defining how
people communicate and share knowledge (Ajmal & Koskinen, 2008). According to
De Long and Fahey (2000), cultural attributes can influence knowledge sharing
horizontally across organisational units, and vertically throughout different levels of the
organisation hierarchy.
In a PBO context, project work typically involves multiple project units that each has
unique ways of working, and may not be in harmony with one another or with the
prevailing culture of the PBO (Ajmal & Koskinen, 2008). Cameron and Quinn (2006)
affirm that units within organisations such as functional departments, hierarchical
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levels or teams each have unique cultures. During such instances, cultural differences
amongst project units can be a source of creativity and learning, or a source of conflict
and miscommunication (Wiewiora et al., 2013).
Eskerod and Skriver (2007) have also suggested that reluctance found in knowledge
sharing activities between project managers may be explained by organisational
subcultures, where work organisation by projects was found to create knowledge silos
and constrain knowledge transfer. Hence, it is important that PBOs are aware of the
distinctive cultural influences for predicting respective knowledge sharing behaviours,
so that they may structure suitable knowledge sharing mechanisms around these
behaviours for enhancing project performance and organisational learning.
The purpose of this study was, therefore, to examine the individual and combined
effects of organisational culture and subgroup cultures on knowledge sharing
behaviours within a selected project-based organisation in the United Kingdom.
Hypotheses for testing empirical relationships between organisational culture and
subgroup cultures on knowledge sharing behaviours were developed subsequent to
reviewing existing bodies of relevant literature on project-based organisations,
organisational culture, subgroup cultures, and knowledge management with a specific
focus on knowledge sharing practices.
Approaches from both quantitative correlational and qualitative case study designs
were combined in this cross-sectional mixed methods research. Both statistical and
non-statistical data obtained was used to analyse how organisational and subgroup
cultural orientations may affect patterns of knowledge sharing behaviours within the
PBO. Limitations relating to selection of population, adaptation of measurement scales,
and research design as well as opportunities identified for future research was also
discussed. This paper concludes with a summary of developed recommendations for
informing senior managements’ efforts to enhance knowledge sharing and
organisational learning effectiveness. The results of this study will be presented to the
PBO’s senior management team at a later date, and are anticipated to positively
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influence the PBO studied where developed recommendations will be considered for
initiating subsequent cultural changes in the coming year.
1.1. Statement of the Research Question
Typically, in mixed methods research studies, researchers should incorporate at least
one qualitative and one quantitative research question. Following advice from
Onwuegbuzie & Leech (2006), both questions should be open-ended and non-
directional when seeking to “discover, explore, or describe a particular
participant(s)… context…experience, process” (p.486).
For the purposes of this study, the mixed methods research question: “What are the
Implications of Organisational Culture and Subgroup Cultures on Knowledge
Sharing Behaviours within a Project-Based Organisation?” incorporated both a
quantitative correlational research design and a qualitative case study design.
The quantitative correlation design of the research question was intended to measure
the organisation’s dominant cultural orientation, existing subgroup cultural orientations
for predicting levels of knowledge sharing between project units within the PBO. The
qualitative case study design of the research question was intended to analyse the
embedded knowledge sharing behaviours of project units, and provided insight into
perceptions of dominant organisational culture and subgroup cultures, and how such
perceptions have influenced their knowledge sharing practices within the PBO.
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Chapter 2. Literature Review
Knowledge is the result of processing data and information through individual
experiences, interpretation and reflection (Davenport et al., 1998). Knowledge is
categorised as either tacit or explicit. According to Nonaka (1994), tacit knowledge
refers to developed cognitive models that define how individuals perceive and define
their world, while “explicit or codified knowledge refers to knowledge that is
transmittable in formal, systematic language” (p.16). Nonaka and Takeuchi (1995) add
that knowledge may be viewed at the individual, group or organisational levels. In
agreement, De Long (1997) specifies that the purpose of knowledge management is to
ensure knowledge generated at the individual and group levels are fully captured and
leveraged at an organisational level to drive business success.
Where “new knowledge always begins with the individual”, tacit knowledge is
individualistic and context specific, which makes it difficult to formalised and
communicate (Nonaka, 1991, p.164). In organisations, tacit knowledge is usually
captured and made explicit via documents, databases and processes (Ajmal &
Koskinen, 2008). For PBOs, incremental learning occurs through tacit accumulation of
project members’ experiences, which can be applied to improve team and
organisational performance quality over time (Swan et al., 2010). The knowledge
workers tacit knowledge may contain specific know-how, workmanship and skills that
contribute to the overall success of projects, and risk of losing such invaluable
knowledge could result in unnecessary errors, wasted efforts and time loss (Wiewiora
et al., 2013).
From a resource-based view, knowledge is perceived as the primary asset of all
organisations (Argote & Ingram, 2000). Davenport and Prusak (1998) explains the
value of knowledge, which increases, with its level of accessibility. The mere existence
of knowledge somewhere, inaccessible within an organisation, alludes to a “search-
transfer problem” where members are unable to find information or access relevant
knowledge sources due to weak network ties within the organisation (Hansen, 1999).
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Davenport and Prusak (1998) proposes two dimensions in studying knowledge transfer,
specifically how knowledge is transferred (i.e. practice) and the richness of the
knowledge transferred (i.e. quality). Nonaka (1991) suggests that ‘Knowledge-Creating
Companies’ are not only successful due to their abilities to generate new knowledge,
but in their effective management of knowledge processes. It is believed that effective
utilisation of valuable knowledge can differentiate organisations from their competitors
for sustaining competitive advantage (Davenport & Prusak, 1998).
According to Nonaka and Takeuchi (1995), new knowledge is created through the
dynamic interaction between tacit and explicit knowledge. In the context of a PBO,
effective knowledge management will ensure that any new knowledge derived from
projects is shared through socialization and externalization of project members, which
are triggered by dialogue and teamwork; and may be facilitated through the use of
technology and shared databases (Nonaka & Takeuchi, 1995). Given the strategic value
of the knowledge workers’ professional experience and technical expertise,
organisations cannot afford to ignore the respective values and behavioural norms of
these individuals, often labelled as ‘culture’, when implementing their knowledge
management strategy (De Long, 1997).
This section focuses on establishing significance of this study by critically reviewing
the relevant literature on organisational culture, subgroup cultures and knowledge
management with a specific focus on both the practices of knowledge sharing as well
as the quality of knowledge shared within the context of project-based organisations. In
particular, this section aims to outline the key challenges of knowledge sharing within
PBOs, as well as propose possible empirical relationships between organisational
culture, subgroup cultures and levels of knowledge sharing.
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2.1. Challenges of Knowledge Sharing in Project-Based Organisations (PBOs)
Lindkvist (2005) refers to PBOs as knowledge ‘collectivities’ where projects require an
effective assimilation of knowledge workers (p.1200). One of the key challenges within
PBOs is that project knowledge tends to reside within the minds of individual
knowledge workers during and after project cycles, rather than being shared on a
common knowledge base. In earlier works of Lindkvist & Söderlund (2002, cited in
Lindkvist, 2005 p.1202), the authors explained that project managers should learn from
trial-and-error processes so to minimize prospective errors. Eskerod and Skriver (2007)
confirm the serious problem of sharing tacit knowledge experienced by PBOs, and their
challenges in making inter-intra project-based learning more explicit or accessible
through knowledge transfer.
PBOs in its purest form are organized solely around ‘projects’ with no formal
functional structure or organisational hierarchies, allowing the organisation to respond
more readily to market demands and change (Hobday, 2000). Unlike functional
managers, project managers within a PBO act as connectors between projects and the
organisation, and are able to assign dedicated interim resources and personnel across
business functions to work on a temporary project (Wiewiora et al., 2009). Each
project operates with a high degree of autonomy during the project cycle and its
members are disbanded when the project finishes to be regrouped to work on a new
project (Pemsel & Wiewiora, 2013). Moreover, during the project cycle, each project
unit is assigned distinct functional tasks to be completed during different project stages,
and decision-making is left to individual expertise and discretion (Swan et al., 2010).
The lack of formal links within project units and across projects, in addition to the
temporary nature of project work, have resulted in weak communication and poor
knowledge sharing amongst project members (Hobday, 2000). Newell et al. (2003)
adds that even when significant project-based learning was generated, PBOs are
ineffective in capturing, retaining or converting such learning to improve their existing
processes. Coupled with pressures on productivity and delivery, there is little
motivation or time for project members to reflect on their project experiences or
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document transferable knowledge for future reference (Pemsel & Wiewiora, 2013).
Other reasons such as geographic dispersion of projects and the lack of knowledge
management strategies have further hampered communication and motivation to share
knowledge within PBOs (Hobday, 2000).
2.2 Organisational Culture as a Predictor of Knowledge Sharing Behaviours
The humanistic nature of knowledge creation and the social dimension for knowledge
transfer has called for an analysis into organisational culture as either an enabler or
barrier of knowledge sharing (Lilleoere & Hansen, 2011). Knowledge outside of its
context turns into information without reference (Nonaka & Konno, 2005). The
complexity and uncertainty of PBOs has provided a unique context for knowledge
management, distinct from other business organisations (Ajmal & Koskinen, 2008).
Lindkvist (2005) refers to this context as a “project culture” (p.1206).
Schein (2010) defines organisational culture as the “product of group experiences” and
is exhibited through artefacts, espoused beliefs and values, and basic underlying
assumptions. The author describes organisational culture as assimilated patterns of
basic assumptions that are developed by its existing members as they learn to cope with
problems at work, and are to be imparted to new members as the correct way to
perceive, think and act within the same context (Schein, 2005 cited in Nonaka &
Takeuchi, 1995). Martin and Siehl (1983) have also defined organisational culture as an
expression of corporate ideology or management philosophy that guides the behaviours
and practices of its employees.
According to De Long and Fahey (2000), there are four ways in which a project culture
can influence knowledge sharing behaviours within a PBO. First, it creates a unique
context, as discussed in the previous section, in which social interaction and knowledge
sharing amongst project members are manifested. Cultural contexts can determine the
acceptability of discussing sensitive topics, perceived approachability of senior
managers or appropriateness of behaviours relating to how knowledge is managed
(Abou-Zeid, 2005). Culture also defines the knowledge structures and processes by
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which knowledge is utilised and distributed across the PBO. “Knowledge structures
can vary from one that is highly diffused and group-based to one that is task-specific
and individual-based” (Lam, 1997 cited in Abou-Zeid, 2005, p.149) Lastly, culture
shapes project members’ assumptions when evaluating the validity and value of
knowledge or its source (De Long & Fahey, 2000).
De Long (1997) advocates the intimate relationship between organisational knowledge
and culture, and furthers that an organisation’s ability to transfer and apply knowledge
would be impossible without ensuring cultural support for such behaviours. We,
therefore, turn our attention to identifying the relevant theories and tools used to
diagnose organisational culture so that we may understand how different cultural
attributes can support or hinder knowledge sharing within organisations.
The Competing Values Framework (CVF) was identified as one of the most widely
used tools in research on organisational culture (Suppiah & Sandhu, 2011). The CVF,
developed by Cameron and Quinn (2006), provides a holistic view of organisational
culture and has been validated in numerous international contexts (Wiewiora et al.,
2013). The CVF characterized the complexity of organisational culture into two
dimensions: flexibility and discretion versus stability and control, and internal versus
external orientations. Holistically, these two dimensions represent four quadrants to
represent the distinctive and competing assumptions, each characterized by unique
cultural attributes. These four cultural types are namely Clan, Adhocracy, Hierarchy
and Market (Cameron & Quinn, 2006). [See Figure 1]
The upper left quadrant identifies values of Clan cultures with an internal focus. Clan
cultures tend to emphasize on a shared vision and commitment through teamwork and
empowerment, representing core values of participation, tradition and loyalty
(Cameron & Quinn, 2006). The lower left quadrant identifies values of Hierarchy
cultures with a focus on internal control and stability. Hierarchy culture tends to
emphasize on formal rules and procedures with centralization and control as its core
values (Wiewiora et al., 2013). Global conglomerates such as Ford Motors and
McDonalds whose business models required stable processes for mass production, as
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well as many governmental departments had adopted hierarchical organisational
cultures (Cameron & Quinn, 2006).
The lower right quadrant identifies values of Market cultures with a strong external
focus rather than an orientation towards internal affairs. Market-type organisations
value competition, productivity and achieving bottom-line targets (Cameron and
Quinn, 2006). Market cultures, therefore, place heavy emphasis on sustaining
competitive advantage and profitability. Jack Welch, former CEO of General Electric,
was a strong advocate of this highly competitive culture (Cameron and Quinn, 2006).
Similarly, the upper right quadrant identifies Adhocracy cultures with a strong external
orientation. Adhocracy cultures are also referred to as open systems, which emphasize
flexibility, adaptability and innovation (Wiewiora et al., 2013). Adhocracy cultures,
therefore, do not have centralized power and encourages individuality and risk taking
(Cameron and Quinn, 2006). [See Figure 2]
According to Davenport et al. (1998), the cultivation of a knowledge-friendly culture is
one of the most important enablers for knowledge sharing. In such cultures, the
organisation holds a positive orientation to knowledge creation and places emphasis on
knowledge sharing and organisational learning (Davenport et al., 1998). This induces
knowledge sharing behaviours where individuals will proactively seek out
knowledgeable colleagues as knowledge sources and trusted advisors (Connelly &
Kelloway, 2003). However, not all cultural attributes will positively influence
knowledge sharing (De Long & Fahey, 2000).
Knowledge sharing can also be seen as a process of knowledge exchange (Lilleoere &
Hansen, 2011). Of the numerous cultural attributes identified in past empirical research,
characteristics relating to ‘trust’ had received the most attention in relation to its
positive influence on knowledge sharing (Wang & Noe, 2010). Wiewiora et al. (2013)
proposes that knowledge sharing is contingent upon personal choice and disposition,
where individuals are reluctant to share knowledge without some degree of personal
gain in return, or if they perceived their working environment to lack trust. In
innovative cultures, where employees are encouraged to ‘think outside the box’,
sharing experiences of trial and error are perceived as opportunities for learning and
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improvement rather than blame (Mueller, 2014). Davenport et al. (1998) emphasizes
that it is important employees do not feel that they are at risk when sharing knowledge
with others.
However, it’s been posited that where knowledge is recognised as power, employees
are generally motivated to hoard rather than share knowledge so to maintain a
competitive advantage in their careers (Suppiah & Sandhu, 2011). Similarly,
organisational cultures that promote competition and individualism were found to
impede knowledge sharing, whereas collaborative cultures that highlight shared
responsibilities and teamwork was found to enhance knowledge sharing amongst its
team members (Wang & Noe, 2010). This was mainly due to employees’ fears of
letting their colleagues down and so would exhibit greater efforts to support each other
by sharing valuable knowledge (De Long, 1997). In addition, cultures that reward
individuals for sharing behaviours were found to create different knowledge sharing
patterns than cultures that do not promote such activities (De Long & Fahey, 2000).
Evans (2012) adds that in hierarchical cultures, formal organisational structures may
run contrary to open access and free flow of communication that is demanded by a
collaboration and knowledge sharing to occur. Other case study results also indicate
that market cultures tend to have a negative impact on inter-project knowledge sharing
(Wiewiora et al., 2013). Hence, in summary and with reference to the cultural attributes
of the four culture types offered by the CVF, it is proposed that organisational
orientation towards clan and adhocracy cultures would facilitate knowledge sharing,
whereas organisational orientation towards market and hierarchy cultures would
impede knowledge sharing within the context of a PBO.
Hypothesis 1 (H1): Dominant organisational orientation towards clan culture has a positive influence on knowledge sharing.
Hypothesis 2 (H2): Dominant organisational orientation towards adhocracy culture has a positive influence on knowledge sharing.
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Hypothesis 3 (H3): Dominant organisational orientation towards market culture
has a negative influence on knowledge sharing.
Hypothesis 4 (H4): Dominant organisational orientation towards hierarchy culture has a negative influence on knowledge sharing.
Figure 1: The Competing Values Framework (Cameron & Quinn, 2006)
Figure 2: Attributes of Clan, Adhocracy, Hierarchy and Market Cultures (Cameron & Quinn, 2006)
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2.3 Subgroup Culture as a Moderator
Organisations with strong organisational cultures provide clear and coherent values so
that all of its members are defined by the same behaviours, and are driven towards the
same goals (Boisnier & Chatman, 2002). Strong organisational cultures and high levels
of agreement, however, impose stability and consistency, which are contrary to the
agile nature of PBOs and have been found to inhibit an organisation’s ability to change,
adapt and innovate (Boisnier & Chatman, 2002). Given the technically advanced and
competitive markets within which businesses operate, it would be difficult for modern
organisations to operate with only one stable culture.
Subgroup cultures are small interdependent groups within an organisation that share a
set of norms, values, and beliefs (Carton & Cummings, 2013). Membership in
subgroups may be formed from workgroups, levels of hierarchies, geographical base,
socio-demographic similarities or friendships (Hansen et al., 2005). Through frequent
interactions, subgroup members will form shared mental models that can impact
behaviours relating to communication, learning and knowledge sharing (Wang & Noe,
2010). Due to the smaller composition of subgroups, it is believed that they are more
malleable and can better respond to the agile needs of a PBO (Boisnier & Chatman,
2002).
Subgroup cultures in PBOs are formed because of the dissimilarities in tasks, expertise
and challenges encountered by different project units (Martin & Siehl, 1983). In such
knowledge rich environments, knowledge-based subgroups are formed from project
members who share the same technical language and approach to problem solving
(Carton & Cummings, 2013). Members of the same knowledge base possess similar
mental models that allow them to process or filter knowledge in similar ways (Hansen
et al., 2005). Chen and Huang (2007) explain that apart from accessibility, the value of
knowledge is also determined by its application. Where each knowledge-based
subgroup represents a unique source of knowledge and learning, knowledge acquired
can be applied to increase decision quality, creative outputs, reduce costs of errors and
ultimately enhance organisational performance (Van Knippenberg & Schippers, 2007).
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However, evidence for both the positive effects as well as the negative effects of
subgroups on organisational effectiveness was found (Van Knippenberg & Schippers,
2007). In order to sustain competitive advantage, PBOs will need to harness the diverse
knowledge sources within the organisation whilst defusing any potential sources of
conflict arising from its subgroups (Carton & Cumming, 2013). One of the negative
effects of heterogeneous subgroups is the lack of shared mental models that may induce
deviations away from the common norms and values defined by the organisation’s
dominant culture (Carton & Cummings, 2013). This raises questions as to whether and
how subgroup cultures, formed from project units, may influence the existing
relationship between dominant organisational culture and knowledge sharing within the
PBO.
Martin and Siehl (1983) expressed that assumptions and values defined by an
organisation’s dominant culture are shared by the majority of its members, but not
necessarily all of its members. Martin and Siehl (1983) developed three typologies for
the possible relationships between subgroup culture and organisational culture:
enhancing, orthogonal or neutral, and countercultural. Subgroups that enhance the
dominant organisational culture are generally more enthusiastic about the
organisation’s espoused values than other members of their organisation. Subgroups
that are orthogonal will embrace the values and beliefs of the dominant organisational
culture, but also hold a distinctive set of values, beliefs and norms that are unique to
their subgroup members. As these subgroups are neutral, they will not threaten the
cohesiveness of the dominant culture (Martin and Siehl, 1983). It is, however, the
countercultural subgroups that most organisations are concerned with, as they tend to
disagree with the core values of the dominant organisational culture and hold values
that directly conflict with core organisational values (Boisnier & Chatman, 2002).
Following the parent-child metaphor, Wolfgang and Ferracuti (1970 cited in Boisnier
& Chatman, 2002) suggested that a subgroup culture, like a child, would not be entirely
different from its ‘parent’ culture. So although, some subgroup cultures may conflict
with the dominant culture of the organisation, others may not. Hence, it is anticipated
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that each corresponding relationship described above may affect project members’
prioritisation and motivation to share knowledge inter and intra their subgroups.
The notable competing values of each quadrant were the main cause for the name:
Competing Values Framework (Cameron & Quinn, 2006). Each continuum on the
vertical and horizontal axes of the CVF highlights opposing core values, namely
flexibility versus stability, internal versus external. These dimensions, therefore,
produce countercultural relationships between subgroup culture and organisational
culture on the diagonal, namely clan versus market, and hierarchy versus adhocracy.
However, adjacent quadrants, which share the same orientations on each axis, although
are negatively correlated, produce neutral relationships between subgroup culture and
organisational culture (Cameron & Quinn, 2006). When both subgroup and
organisational cultures belong to the same quadrant, the relationship between subgroup
and organisational culture is enhancing.
Assuming that the dominant organisational culture is conducive to knowledge-friendly
behaviours, in instances where subgroup cultures are complimentary, it is predicted that
creativity and learning would be enhanced (Wiewiora et al., 2013). However, in
instances where subgroup cultures clash with the organisation’s knowledge-friendly
culture, conflict and miscommunication are expected to flourish (Eskerod & Skriver,
2007). Similarly, it is predicted that knowledge-friendly subgroup cultures can reduce
the negative impact of non-knowledge-friendly organisational cultures on knowledge
sharing. It is therefore, hypothesized that subgroup cultures may either facilitate or
impede the relationship between dominant organisational culture on knowledge
sharing.
Hypothesis 5 (H5): Subgroup culture moderates the relationship between dominant organisational culture on knowledge sharing.
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Chapter 3. Research Methodology
This research aims to examine the individual and combined effects of organisational
culture and subgroup cultures on knowledge sharing behaviours within a project-based
organisation.
3.1 Settings
The selected PBO is a marketing agency offering direct response marketing and digital
engagement solutions to large national corporations as well as small to medium
enterprises in the United Kingdom. Due to variations in project scales, the PBO is
divided into two offices with the London office managing longer-term projects of
larger scales, and the Ipswich office managing shorter-term projects of smaller scales.
Projects’ scales range in accordance to budgets of over £2 million for large projects to
£20,000 for smaller projects, and usually last for a maximum of 1-2 years to a
minimum of 3 weeks. Allocation of projects is usually subject to decisions of the
PBO’s senior management team. Project teams may vary from 20 persons for large
projects to 3 persons for small projects. Within the project teams, functional units are
formed where each unit is responsible for individual stages of project cycles.
Following Hobday’s (2000) categorisation of organisational structures, the selected
PBO may be categorised as a ‘lightweight project management structure’ where project
managers operate within a matrix, subordinate to senior functional managers, and do
not have direct control over allocation of resources for their projects. [See Figure 3]
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Figure 3: Lightweight Project Management Structure of PBO adapted from Hobday (2000)
3.2 Research Design
Empirical inquiries into the combined effects of organisational culture and subgroup
cultures on knowledge sharing behaviours are extremely limited. Although there has
been some research on the effects of organisational culture on knowledge sharing
behaviours, most investigations were undertaken from a qualitative stance (Lindkvist,
2005; Ajmal & Koskinen, 2008; Wiewiora et al., 2013). There has been much debate
relating to both merits and boundaries of using either qualitative and quantitative
research methods. While qualitative methods adopt a constructivist approach and allow
an in-depth inquiry into the experiences and perceptions of participants studied,
quantitative methods adopt a deductive approach and are able to maximise objectivity,
replicability, and generalizability of findings that are useful for predicting causal effects
(Harwell, 2011). Nonetheless, quantitative methods are also critiqued for offering a
single ‘truth’, independent of human perception, and qualitative methods fall short in
developing generalizable inferences and may be subject to researcher’s personal biases
(Johnson & Onwuegbuzie, 2004).
Triangulation allows confirmation and cross-validation of findings from using both
deductive and constructivist approaches to research (Onwuegbuzie & Leech, 2006).
Harwell (2011) proposes that in a mixed-methods design, weaknesses of either
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qualitative or quantitative data can be offset by strengths of the other. In order to
quantify the individual and combined effects of organisational culture and subgroup
cultures on knowledge sharing behaviours, we’d require descriptive and inferential
statistics, which are obtained from quantitative methods (Dewberry, 2004).
Hofstede (1998) reminds that in order to fully comprehend the complex nature of
organisational and subgroup cultures, one would require insight into the minds of
organisational and subgroup members. The case study method was suggested as the
one of the most complementary designs to be aligned with descriptive and exploratory
research designs, as they are both non-directional in nature (Onwuegbuzie & Leech,
2006). A case study design can, therefore, elicit qualitative data regarding research
participants’ perceptual assessments of their organisation’s culture, existing subgroup
cultures and how such perceptions have influenced their knowledge sharing
behaviours.
Hence, this study combined approaches from both quantitative correlational and
qualitative case study designs in order to benefit from the methodological triangulation,
which has the potential to enhance the fidelity of the research design and significance
of research findings (Onwuegbuzie & Leech, 2006).
3.3. Data Collection
The PBO consists of 46 full time employees across both London and Ipswich offices,
in addition to 4 senior managers and their CEO. With the consent of their CEO, all 51
members were invited to participate in this research.
To ensure adequate triangulation, data was collected from multiple sources: 1) online
questionnaire, 2) focus groups and 3) review of the PBO’s corporate website and
documentation. Based on the theoretical framework provided in Chapter 2,
organisational cultural orientation, subgroup cultural orientations and levels of
knowledge sharing were operationalized through the use of an online questionnaire.
Focus group questions were designed to capture data around perceptions of the PBO’s
22
organisational culture, perceptions of subgroup cultures, perceived effectiveness of
knowledge sharing processes and types of knowledge shared. The focus groups were
also employed as opportunities for organisational members to propose
recommendations for senior managers’ regarding improvements that could be made to
enhance knowledge sharing within the PBO.
Before launching the questionnaire online, all items were peer reviewed by
postgraduate colleagues at Birkbeck to ensure questions were phrased in a
comprehensible manner. Comments were gathered to improve the final version of the
questionnaire. Likewise, focus group questions were piloted with the first 3 focus
groups before conducting the remaining sessions.
The online questionnaire comprised of 4 sections and all questions were close-ended.
[See Appendix D1] Response settings for all questions were designed as mandatory to
avoid missing cases. The Organisational Culture Assessment Instrument (OCAI),
which was the developed tool based on the CVF by Cameron & Quinn (2006), was
adopted in the first section of the questionnaire for diagnosing the PBO and subgroup
cultures across six key characteristics: Dominant Characteristics, Organisational
Leadership, Management of Employees, Organisational Glue, Strategic Emphasis and
Criteria of Success. Descriptions for each characteristic are provided in Figure 4.
Figure 4: Descriptions of key characteristics on OCAI (Wiewiora et al., 2013, p.1166)
23
The full and adapted versions of the OCAI have been used in more than 1000
organisations internationally in studies relating to organisational change, knowledge
management and organisational effectiveness (Suppiah & Sandhu, 2011). The
fundamental principle of the CVF is that as organisations evolve in response to
challenges and changes in their operating environment, they tend to develop a
dominant cultural orientation, but are seldom categorized by any single cultural type
(Cameron and Quinn, 2006). It is for this reason, the CVF and OCAI was chosen as a
preferred tool for assessing the dominant cultural orientations of the PBO studied.
Contrarily, only a single study was found to have used the OCAI for diagnosing
subgroup cultures in predicting agreement patterns within a department of defence at a
military university (Paparone, 2003). Results from Paparone’s study indicated that the
use of OCAI in diagnosing subgroup culture, when cross-validated with qualitative
data, was effective for identifying existence of subgroups. A decision was made to
undertake the same approach in this study and an abridged version of the OCAI was
adopted for diagnosing the subgroup cultures within the PBO. The results of this study
are expected to test the generalizability of Paparone’s methodology in using the OCAI
for diagnosing subgroup cultures in other contexts.
The second section of the questionnaire incorporated sociometric questions to test the
asymmetry of knowledge sharing between project units. Tsai (2002) asserts that one
unit sharing its knowledge with another unit does not mean that knowledge sharing is
reciprocal or symmetrical. Respondents were asked to select any or all units within the
PBO from which they had received new knowledge or technical expertise. To validate
whether knowledge sharing was asymmetrical, an opposite question asked respondents
to select any or all units to which they had provided new knowledge or technical
expertise (Hansen, 1999).
The third section of the questionnaire was designed to analyse levels of knowledge
sharing across 12 items [See Figure 5]. Items developed were based on investigations
of knowledge sharing practices across organisational units conducted by Gamble and
24
Blackwell (2001), Tsai (2002), and Mueller (2014). The last section of the
questionnaire collected demographic information from respondents.
Figure 5: Item topics and simplified version of question items with referenced authors
In addition to the online questionnaire, 13 focus groups were held to provide a richer
insight into the embedded knowledge sharing behaviours, the quality of knowledge
shared, perceptions of organisational culture and subgroup cultures. Kitzinger (1995)
adds that focus groups are useful for exploring individual knowledge and experiences
by tapping into different forms of interpersonal communication, which can highlight
subcultural values and norms.
Pre-prepared questions were used to guide discussions, when necessary, probing
questions were asked for clarification. [See Appendix D2] Questions developed were
based around findings from the online questionnaire and on relevant literature
investigating effects of project units’ perceptions of organisational culture and
subgroup culture on knowledge sharing, categorisation of knowledge, and barriers and
enablers of knowledge sharing (Sackmann, 1992; Lilleoere & Hansen, 2011; Evans,
2012; Mueller, 2012; Wiewiora et al., 2013;) [See Figure 6] Subgroup cultures
identified from the online questionnaire were used to guide groupings of focus group
participants. All focus group sessions, with permission from the PBO, were audio
recorded and transcribed.
25
Lastly, review of the PBO’s corporate website and marketing materials provided a
better understanding of their business nature, public value statements and
organisational structure.
Figure 6: Focus group item topics, overview of question items and expected themes with
referenced authors
26
3.4 Data Analysis
A cross-sectional mixed-methods design was employed as the individual and combined
effects of organisational culture and subgroup cultures on knowledge sharing was only
measured once within the selected PBO (Schwab, 2011). Quantitative data2 collected
from the online questionnaire was triangulated with qualitative data obtained from
focus groups and review of the PBO’s corporate website and marketing materials.
Thereafter, findings from triangulation was compared to existing literature taking into
account both conflicting and convergent perspectives when analysing the
generalizability of this research.
To ensure that data collected from the online questionnaire was free from random
errors, the Cronbach’s alpha was used to calculate the reliability of the question items’
measurement scales (Dewberry, 2004). Schwab (2011) advises that an alpha coefficient
of 0.70 is usually taken as the minimum level accepted. However, Suppiah and Sandhu
(2011) suggest that it is possible to obtain a coefficient value of below 0.70 when
measuring diverse psychological constructs such as perceptual assessments of
organisational culture or subgroup cultures.
For the online questionnaire, the OCAI consisted of 24 questions and uses an ipsative
rating scale in which respondents were asked to divide 100 points amongst 4 questions
corresponding to each CVF quadrant for all six key characteristics (Cameron and
Quinn, 2006). The total points collected from the key characteristics were averaged
according to each quadrant for each respondent. The aggregated mean of the quadrant
results from all respondents were graphed on the CVF to visualise the cultural
orientations of the organisation.
For the purposes of this study, subgroup culture was operationally defined as
identifiable patterns observed on the CVF based on responses obtained from
questionnaire items relating to subgroup culture. Following Hofstede’s (1998)
empirical approach for identifying subcultures, a Hierarchical Cluster Analysis using 2 All qualitative data were computed and analyzed using the Statistical Package for Social Sciences (SPSS) version 22.
27
Ward's method was used to identify subgroupings based on similarities of responses
relating to subgroup culture. Demographic data was also included in the cluster
analysis to explore whether any subgroupings were formed based on membership
criterion suggested by Hansen et al. (2005). Squared Euclidean Distance was applied as
the similarity measure to determine the optimum number of subgroups within the PBO
(Dewberry, 2004). Thereafter, a Chi-square test was conducted to examine which
membership variables were statistically significant against the formed clusters. This
method differs from the discriminant analysis approach taken by Paparone (2003),
which presumes prior knowledge of membership for subgroup formation.
Responses from sociometric questions were crosstabulated to identify whether
knowledge-sharing relationships were asymmetrical. Descriptive statistics from
sociometric responses also revealed knowledge sharing patterns within the PBO.
Levels of knowledge sharing were measured using a Likert 5-point scale. Items for
‘modes of communication’ were accorded a maximum of 4 points to a minimum of 0
points depending of frequency of use. Items for positive knowledge sharing behaviours
were accorded a maximum of 4 points to a minimum of 0 points depending on levels of
agreement. The item for measuring ‘time pressure’ was regarded as a reversed question
and, hence, scoring for this item was inverted. The aggregated mean of these items was
used as a single value scale representing the levels of knowledge sharing.
Validity refers to “the truth of a research conclusion or inference” (Schwab, 2011,
p.14). The Pearson product-moment correlation coefficient was used to examine the
strength and direction of the hypothesised association between organisational culture
and levels of knowledge sharing (i.e. H1 – H4). Statistical significance for each
correlation was also checked. The hypothesised moderation of subgroup cultures on
organisational culture and knowledge sharing (i.e. H5) was tested using the standard
multiple regression technique. In standard multiple regression, a dependent variable is
related to two or more independent variables (Schwab, 2011). Cultural orientations of
subgroups, as identified using Ward’s method, were used as a categorical moderator
indicating subgroup culture. Moderation effect was produced by multiplying the
predictor variable (i.e. organisational culture) and the moderator variable (i.e. subgroup
28
culture). Significance was checked for any variance the moderated effect had on the
dependent variable (i.e. levels of knowledge sharing).
Systematic analysis of qualitative data collected from focus groups was conducted in
two stages: during and after focus groups. Pre-prepared questions for guiding group
discussions were adapted into a standardised theme-based template for capturing
responses during focus groups in accordance to expected themes [ref. Figure 6]. Other
responses and emergent themes, which did not fall within the pre-designed template,
were captured as ‘Additional Notes’. Content analysis from field notes and transcripts
of audio recordings were conducted after each focus group session, within the same
day. Any key themes across and within groups were recorded. Consistency was
observed for words used, frequency of comments, degree of agreement, specificity, and
intensity of feelings towards a particular topic (Krueger and Casey, 2002). Special
attention was also accorded for evaluating the impact of group dynamics between focus
group participants (Kitzinger, 1995).
29
Chapter 4. Results of Analysis
4.1 Demographic Information
Response rate for the online questionnaire was 75%. However, 3 cases were deleted
due to response errors, hence, a total of 36 cases were computed for data analysis.
Figure 7 exhibits the demographic characteristics of respondents. Responses from the
Ipswich office were double those of the London office, which was reflective of the
actual size variance between the two locations, with Ipswich employing more than 70%
of the PBOs full-time employees. Overall, males outnumbered females by
approximately 2:1, which again was reflective of the gender disproportions within the
PBO. Almost 90% of respondents were below 39 years of age and almost 45% had
been working for less than a year in the PBO. This reflects the high turnover rates
within the organization, which was subsequently confirmed by senior managers during
the focus groups.
Figure 7: Demography of online questionnaire respondents
30
4.2 Reliability
The reliability of all major factors was measured. Organisational culture items of the
OCAI were grouped according to CVF quadrants but generated alpha coefficients
below minimum level acceptable: Clan Culture (α = 0.4), Adhocracy Culture (α = 0.6),
Market Culture (α = 0.6), Hierarchy Culture (α = 0.6). These findings were similar to
reliability values found by Suppiah and Sandhu (2011), who confirmed that coefficient
values of below 0.70 can be realistically expected for scales measuring diverse
psychological constructs such as perceptual assessments of organisational culture may
obtain coefficient values [i.e. acceptable indicators for Adhocracy culture (α = 0.6) and
Hierarchy culture (α = 0.6)].
On the contrary, an abridged version of the OCAI was used to measure subgroup
cultures and generated a high alpha coefficient of 0.8. Knowledge sharing items
generated an acceptable alpha coefficient of 0.7. [See Figure 8; Appendix D3]
Figure 8: Descriptive Statistics: Inter-item correlations for Organisational Culture, Subgroup Culture
and Knowledge Sharing
4.3 Organisational Culture
The mean scores of Clan, Adhocracy, Market and Hierarchy cultures were graphed on
the CVF [see Figure 9]. The PBO’s organisational cultural orientation was analysed
across the vertical and horizontal axes. Higher weightings of approximately 11% and
46% were respectively attributed to external/ differentiation and stability/control
dimensions. Analysis of quadrant values indicated higher weightings were attributed to
Market and Hierarchy dimensions as compared with Clan and Adhocracy dimensions.
31
Combining values on the vertical and horizontal axes with quadrant values revealed the
dominant Market orientation of the PBO’s organisational culture.
Figure 9: Organisational Culture of PBO
4.4 Subgroup Cultures
Items of subgroup culture and demography were subject to a Hierarchical Cluster
Analysis using Ward’s Method (Hofstede, 1998). The process of combination is
provided on a dendrogram [see Figure 10]. A single solution of 2 clusters was
identified with 27 cases falling under the first cluster and 9 cases under the second
cluster [see Figure 11]. Therefore, questionnaire respondents are separated into 2 main
subgroups within the PBO.
The Chi-square test was conducted to examine which membership variables were
statistically significant against the formed clusters (Dewberry, 2004). Results revealed
that difference between demographic items (i.e. age, function & tenure) were
statistically significant against the formed clusters [see Figure 12]. However, results
32
also revealed that differences between all subgroup culture items and 2 demographic
items (i.e. gender & location) were not statistically significant. Hence, variances of
functions, age and tenure separated the 2 subgroups within the PBO, whilst
convergence of responses on subgroup culture, gender and location were identified as
the basis for subgroup membership and development of shared mental models (Wang
& Noe, 2010). Descriptive statistics also revealed the composition of cluster 1 with a
majority of cases belonging to Clan cultures (78%) that are predominantly male (74%),
and are based in Ipswich (63%). Even though cluster 2 had fewer cases, its composition
was similar to that of cluster 1. [See Figure 13]
Figure 10: Dendrogram of Subgroup Cultures
33
Figure 11: Distribution of cases by clusters
Figure 12: Results of Chi-Square Test: Ward’s Method (Clusters) x membership variables
Figure 13: Descriptive Statistics: Composition of Clusters by Subgroup Culture, Gender and Location
34
4.5 Knowledge Sharing
Crosstabulation of sociometric responses confirmed Tsai’s (2002) assertion relating to
asymmetrical knowledge-sharing relationships across all project units [see Figure 14].
Validated responses for knowledge provided and knowledge received revealed that
majority of knowledge sharing activities were accounted by the senior management
(34%) and design & production teams (29%). [See Figure 15]
Figure 14: Crosstabulation: Knowledge sharing relationships
Figure 15: Crosstabulation: Validated responses for ‘knowledge provided’ and ‘knowledge received’
Levels of knowledge sharing were calculated by aggregating the means of 12
knowledge-sharing items into a single continuous variable. Frequency statistics of
central tendency and dispersion were calculated, and levels of knowledge sharing
ranged from a lowest score of 16 to a highest score of 37 [see Figure 16]. Percentiles
revealed low (below 33.33%), medium (between 33.33 – 66.66%) and high levels
(above 66.66%) of knowledge sharing. A positive skewed distribution revealed low to
medium levels of knowledge sharing across the PBO [see Figure 17].
35
Aggregate scores (n) of n ≤ 22.22 were regarded as low levels of knowledge sharing,
those 22.22 < n ≤ 27.11 were regarded as medium levels of knowledge sharing, and n >
27.11 were regarded as high levels of knowledge sharing. Descriptive statistics further
revealed low to medium levels of knowledge sharing in cluster 1 and medium to high
levels of knowledge sharing in cluster 2 [see figure 18].
Figure 16: Frequency Statistics for Levels of Knowledge Sharing
Figure 17: Histogram of KS Aggregate Scores X Frequency, positive skew
36
Figure 18: Descriptive Statistics: Composition of Clusters by Levels of Knowledge Sharing
4.6 Validity – Testing Hypotheses
Bivariate correlations analyses were conducted for each of the CVF organisational
culture types and levels of knowledge sharing [see Figure 19]. Results revealed a
significant positive correlation of medium-sized effects was found between Clan-type
organisational culture and knowledge sharing (r = 0.3, p = 0.04), and that a significant
negative correlation of medium-sized effects was also found between Hierarchy-type
organisational cultures and knowledge sharing (r = -0.3, p = 0.04). However, results
indicated that both positive and negative non-significant correlations of small-sized
effects were found between Adhocracy and Market-type organisational cultures and
knowledge sharing. These respective relationships yielded r = 0.14, p = 0.20 and r = -
0.1, p = 0.30. Therefore, hypotheses H2 and H3 were rejected.
With reference to OCAI scores in section 4.3, there was a relative insignificant
weighting of Adhocracy-type culture (102.64). Hence, its was expected that its
corresponding hypothesis (H2) was to be rejected due its inability to effectively
measure for effects of adhocracy attributes on knowledge sharing. Conversely, while
OCAI score for Clan-type organisational culture was also relatively low, its presence
was strong amongst clusters found in section 4.4, which may have accounted for its
significant effect on knowledge sharing inter-intra subgroups. This inference is to be
cross-validated with subsequent analysis of focus group findings. Also, OCAI scores
indicated significant dominance of a Market-type culture (235.28) while its
corresponding hypothesis (H3) was rejected.
37
According to Field (2009), rejecting an alternative hypothesis does not mean that the
null hypothesis is true (i.e. where there is no effect in the population). Therefore,
although non-significant results are generally not interpreted, the rejected hypotheses
were kept in the research model so that implications of both accepted and rejected
hypotheses may be discussed within the context of the PBO studied in Chapter 5.
Figure 19: Correlation Matrix: KS Aggregate (Levels of knowledge sharing) and
CVF Organisational Culture Types
Four moderator*predictor variables were created by multiplying four predictor
variables (i.e. CVF organisational cultures) with the moderator variable (i.e. subgroup
culture). A standard multiple regression analysis was carried out using knowledge
sharing as the dependent variable, and CVF organisational cultures and
moderator*predictors as the predictor variables [see Figure 20]. Dewberry (2004)
advises that correlation coefficients greater than 0.7 are usually indicative of high
associations. The coefficients from the analysis reveal that both negative and positive
correlations between levels of knowledge sharing and all predictor variables are weak.
However, correlations between predictor variables: market*subgroup culture (r = -0.35,
p = 0.02) and hierarchy*subgroup culture (r = -0.44, p = 0.004) to knowledge sharing
38
were statistically significant. This may be due to the high organisational cultural
orientations of Market and Hierarchy type organisational orientations on the CVF
against a high orientation of Clan-type subgroup culture producing counter-cultural
effects as suggested by Boisnier and Chatman (2002). However, this interpretation
again would be cross-validated with subsequent analysis of focus group findings.
The multiple correlations coefficient (R) and the square of the multiple correlations
coefficient (R2) were calculated but revealed that only 36% of the variation in levels of
knowledge sharing were explained with the predictor variables [See Figure 21]. Beta
values were calculated to indicate which predictor variables uniquely predicts levels of
knowledge sharing in the PBO. The beta values revealed that the best unique predictor
of levels of knowledge sharing is the combined effect of Clan-type subgroup culture
and Hierarchy-type organisational culture (highest beta value of 2.7), albeit not
statistically significant against other predictor variables [see Figure 22]. With reference
to literature reviewed in section 2.3, these two cultural orientations, when viewed on
the CVF, are expected to be negatively correlated. Nonetheless, with a statistically
non-significant ANOVA (i.e. Analysis of Variance) score, hypothesis H5 must be
rejected due to its inability to test for all three of the expected moderating relationships:
enhancing, orthogonal and counter-cultural [see Figure 23]. Again, the rejected
hypothesis was kept in the research model so that implications may be discussed within
the context of the PBO.
39
Figure 20: Correlation Matrix: KS Aggregate (Levels of knowledge sharing), CVF Organisational
Culture Types, and Product of CVF Organisational Culture Types*Subgroups
40
Figure 21: Model Summary: Multiple Regression Coefficient (R) and the square of the multiple
correlations coefficient (R2)
Figure 22: Coefficients, Beta values, t-test (t) and significance figures (Sig.)
Figure 23: Analysis of Variance (ANOVA)
41
4.7 Analysing Focus Groups
A total of 13 focus groups were held across both London and Ipswich offices, and
demographic information of participants is displayed in figure 24. Due to logistical
reasons, it was challenging to arrange group sessions based purely on the criteria of
subgroup membership as identified in section 4.4. An agreement was reached to
arrange respondents by location and function, and when possible, by gender.
Figure 24: Demography of focus group participants
Each focus group was analysed separately to identify whether participants’ responses
fell under expected themes or emergent themes. With reference to previous literature
(e.g. Sackmann, 1992; Lilleoere & Hansen, 2011; Evans, 2012; Mueller, 2012;
Wiewiora et al., 2013), all data were condensed into expected themes. A thematic table
for each question was built and all responses were coded. The coded responses were
constructed on a bar graph to indicate the frequency of comments and degree of
agreement [see Appendix D4]. As participants ratios were almost 2:1 for both location
and gender, and are expected to produce effects for sampling bias, responses were
coded only by functions. An example of the bar graph is shown in Figure 25. Some
themes were visibly more prevalent and were marked as major themes for each
question across all groups.
From part 1 of the focus group questions (i.e. perceptions of organisational culture),
responses relating to divisions by functions and locations, emphasis on efficiency,
competition and hierarchy were most frequent [see Figure 26].
42
Figure 25: Bar graph example of themes derived from focus group questions 2.1 & 2.2
Figure 26: Respondent remarks on Organisational Culture
43
Part 2 of the focus group questions examined participants’ perceptions of subgroup
culture and revealed emphasis on collaboration, support and openness. Response by
participants indicated that underlying assumption relating to both divisions by location
and functions were perceived as causes for subgroup formation [see Figure 27].
Figure 27: Respondent remarks on Subgroup Culture
Part 3 of the focus group questions attempted to categorise types of knowledge shared
within the PBO. Naturally, each group’s specialisation within the project cycle
influenced the types of knowledge shared with other team members. Generally,
dictionary knowledge relating to project timelines, client information and work
specifications was most widely shared across teams. Other knowledge types included
technical know-how’s, instructions from senior managers, recommendations for clients
and general explanations for technical queries.
Questions related to barriers of knowledge sharing generated frequent responses
regarding the organization’s culture, organisational processes and challenges in
communication. Other comments included: low morale, non user-friendly technology,
and organisational push for productivity and timely output. Explanations regarding
cultural barriers to knowledge sharing reiterated and expanded on remarks in part 1.
Repeated references were made regarding the perceived lack of trust from senior
managers, internal competition amongst the 2 office locations, lack of learning
orientation as well as the lack of receptiveness to change. Remarks relating to process
barriers included the lack of efficiency, lack of value, inconsistent practice and non-
consultative approaches to decision making [see Figure 28]. Contrasting concepts were
44
identified in the last section of the focus group as enablers of knowledge sharing.
Themes identified were largely related to cultural change, improved processes and
facilitated communication. Additional feedback was captured relating to exemplary
leadership, greater job satisfaction, and user-friendly technology. [See Figure 29]
Some overlaps were identified across themes and participant responses, which
illustrated the intricacy between individual perceptions of organisational culture,
subgroup culture and knowledge sharing behaviours. From observation, some group
participants (such as account managers and digital technicians) were generally more
vocal than others, and in most cases lead to a higher degree of agreement amongst
members of the same group. From a researcher’s point of view, such incidents were
beneficial for enhancing input from more inhibited participants, as well as facilitated
conversations around presumably sensitive comments such as dislike of particular
senior managers, unequal pay and for making specific recounts of unpleasant
experiences at work.
47
Chapter 5. Discussion
Due to the complexity of interactions between knowledge workers and across project
units, the phenomenon of knowledge sharing in PBOs is not easily understood (Ajmal
& Koskinen, 2008). The purpose of this study aims to examine the individual and
combined effects of organisational culture and subgroup cultures on knowledge sharing
behaviours within a selected PBO in the United Kingdom. Approaches from both
quantitative correlational and qualitative case study designs were combined in this
cross-sectional mixed methods research to explore and analyze how perceptions of
organisational and subgroup cultural attributes had affected knowledge sharing within
the PBO studied.
Johnson and Onwuegbuzie (2004) proposes that mixed methods research is an
inclusive and pluralistic form of research; which refrains a researcher from
methodological bias by legitimizing the use of both confirmatory and exploratory
approaches to answering a research question. Although, through statistical analysis, not
all hypotheses were accepted, a decision was made to retain both accepted and rejected
hypotheses so that ontological and epistemological implications of their respective and
interrelated effects may be discussed within the unique context of the PBO [see Figure
30]. In addition, by leveraging the benefits of triangulation, this chapter intends to
provide a holistic view of the intricate relationships upon which the research question
was found.
Figure 30: Summary of analysis of hypotheses
48
Overall, cross-validation of findings from qualitative and quantitative analysis
indicated that in the context of the PBO studied, different organisational cultural
orientations can have different effects on project knowledge sharing behaviours. This
finding is consistent with previous research conducted in Hong Kong, Australia and
Germany (Mueller, 2012; Wiewiora et al., 2013). This chapter intends to critically
evaluate the findings of this study with reference to the theoretical framework
developed from the literature review in Chapter 2. Limitations relating to selection of
population, adaptation of measurement scales, and research design as well as
opportunities identified for future research will also be discussed in the concluding
chapter.
5.1 Individual Effects of Organisational Culture on Knowledge Sharing
Cross-validation of statistical and non-statistical data have identified high
organisational orientations of Market-type and Hierarchy-type cultures with a
particularly high emphasis on stability and control within the PBO. Responses from
focus groups revealed that perceived competition between the two office locations and
amongst senior managers, as well as high emphasis on productivity and deadlines, had
in fact contributed to respondents’ reluctance to share any knowledge relating to project
shortcomings or “bad news”. De Long and Fahey (2000) cautions against market-type
cultures in knowledge rich organizations as it emphases power dynamics and
competition that leads to knowledge hoarding behaviours. However, the causal effects
of the PBO’s hierarchical organisational structure, underlined by rigid organisational
processes, were observed and validated3 to have an even more significant negative
impact, as compared to effects of Market-type cultures, on knowledge sharing within
the PBO.
Evans (2012) states that when people are culturally inhibited from interacting across
departments and functions, they avoid sharing data and information outside of their
silos (Evans, 2012). Respondents commented that knowledge sharing across project 3 In research, validity should be interpreted in terms of verisimilitude (i.e. appearance of truth), rather than absolute truth (Schwab, 2011).
49
units was not encouraged by senior managers. They were also reluctant to reach for
support from other units, which were managed by different functional managers, as it
would be perceived as “stepping on someone’s shoes”. This was validated by a
statistically significant negative correlation between Hierarchy-type organisational
cultures and knowledge sharing (H4).
Hobday (2000) explained that in order for PBOs to remain agile and responsive to
market changes, its internal structures must be flexible with systems that encourage
individuals to exercise autonomy and decision-making powers regarding their work.
Cameron and Quinn (2006) also explained that Adhocracy-type culture is generally
adopted by agile organisations and focuses on risk-taking, entrepreneurship and
creativity. However, in the context of the PBO studied, respondents reported that they
were generally assigned work by senior managers, and without any consultative
process in place, they were unable to exercise any decision-making powers or offer
suggestions for improvements at work.
From the OCAI scores, the PBO studied was not of an Adhocracy-type culture; hence
the original research model was unable to measure for effects of adhocracy attributes
on knowledge sharing. Remarks from focus groups reflected perceptions of the PBO’s
lack of interest to change or generate innovative work, often leading to low morale
amongst its members and induced a high turnover rate within the organization. Risk
avoidance was also recognized as a potential barrier of knowledge sharing where
employees were less inclined to speak up during meetings fearing that due to a lack of
clarity on project scope and status, their reputation or performance reviews may be at
risked. Several remarks were made with regards to the need for clearer project briefs or
documentation processes to reduce miscommunication cross units.
Lindkvist (2005) suggested that silo mentalities are manifested due to inherent project
cultures where organization of work by projects inhibits knowledge sharing amongst
project teams. However, the findings of this research supports an alternative view by
Evan’s (2012) that formal hierarchical structures, in fact, had a greater negative impact
on the effectiveness of knowledge sharing between different project units. De Long &
50
Fahey (2000) reminded that knowledge sharing within an organization occurs
horizontally and vertically. Findings of this study confirmed the asymmetrical
knowledge sharing relationships proposed by Tsai (2002), and found that senior
managers had accounted for the majority of knowledge sharing activities within the
PBO. Remarks from focus groups revealed that one of the underlying assumptions of
the PBO’s hierarchical culture was due to strict managerial control and top-down
communication.
Hierarchical culture within the PBO was also found to reinforce status hierarchies
across functions and locations. One respondent’s feedback revealed, “The Ipswich
office is sometimes looked down because they have smaller accounts [as compared to
the London office]”. Another respondent expressed that the account management team
was perceived to be more valued than other teams as they held key functions for
business development and income generation, “it’s totally unfair that they get to ask
for overtime pay and we don’t...besides, they already get reimbursed for food and
travel when they head out for meetings”. Clustering analysis also confirmed the
statistical differences amongst subgroups based on functions. According to De Long
(1997), perceptions that functions are valued differently or unequally reinforce silo
mentalities and undermine cross-functional knowledge sharing. In such circumstance,
individual or teams will form subgroups that seek to defend the same knowledge bases.
5.2 Combined Effects of Organisational and Subgroup Culture on Knowledge Sharing
Within the PBO studied, statistical analysis accepts that Clan-type organisational
culture is conducive to positive knowledge sharing outcomes (H1). However, OCAI
scores on organisational culture indicated a low orientation in this CVF quadrant.
Clan-type orientation, nonetheless, was found to have a high presence within subgroups
as identified by both cluster analysis and focus group data. Cameron and Quinn (2006)
confirmed that when assessing an organization’s culture, it’s important to consider the
aggregated effects of subgroups, which can provide an approximation of the dynamic
51
relationships within the organization. Focus groups claimed that emphasis on
collaboration and support within functional units was conducive to open
communication and positive knowledge sharing behaviours within their own units.
Response from focus groups also confirmed the criticality of trust and employee
involvement, which also noted by Wang and Noe (2010), as key enablers of knowledge
sharing within organisations.
Nevertheless, strong organisational cultures, particularly strong market-type and
hierarchical organisational cultures as identified with the PBO studied, impose internal
competition, stability and resistance to change (Boisnier and Chatman, 2002). As
observed on the CVF graph [ref.: Figure 9], the PBO is highly oriented on
stability/control dimensions on the vertical axis. Where cultural differences can be a
source of creativity and learning, or a source of conflict and miscommunication, in the
case of this PBO, cultural difference between a strong market-type and hierarchical
organisational cultures against a highly Clan-oriented subgroup culture had been
observed to produce countercultural effects (Wiewiora et al., 2013; Boisnier &
Chatman, 2002). So, although, members were effectively sharing knowledge within
their own units, they were ineffective sharing knowledge across units.
From statistical analysis, significant negative correlations between Market-type and
Hierarchy-type organizations against Clan-oriented subgroup culture correspond with
this interpretation. Focus group remarks also indicated that divisions by function and
location were main causes for the lack of cross-functional and cross-location
collaboration, socialisation and organisational learning. References to competition were
also made regarding challenges within the account management team, initiated by
senior managers with the purpose of driving higher goal achievements, but were
resisted by members who felt ‘embarrassed’ and ‘awkward’ to compete against their
team members.
Without dominant presence of Clan-type or Adhocracy-type organisational cultures
within the PBO, our original research model was unable to measure for the full
moderation effects of enhancing, orthogonal and counter-cultural organisational-
52
subgroup relationships on levels of knowledge sharing. Hence, based on deductions
from hypotheses 1 and 4, and in conjunction with our constructivist approach, this
research found that dominance of hierarchy-type organisational culture negatively
impacts knowledge sharing across project units, while aggregation of clan-type
subgroup culture within the PBO positively impacts knowledge sharing within the
boundaries of individual project units. Respective impacts were also justified by the
identified variations in levels of knowledge sharing from our statistical analysis.
53
Chapter 6. Limitations and Opportunities
The current research design neglected to consider challenges in obtaining significant
weightings in all four of CVF’s orientations from a single organization. Opportunities
for future research should consider adopting a between-cases design so varying impacts
of different cultural orientations on levels of knowledge sharing and pattern of
knowledge sharing behaviours may be measured and compared across cases. Also,
while the selected context was not ideal for investigating the hypothesized endogenous
effects of organisational culture and subgroup culture on knowledge sharing, it
produces opportunities for investigating exogenous effects such as employees’
personalities, where levels of confidence and sociability may predict varying levels of
knowledge sharing, even in absence of knowledge-friendly organisational cultures.
Other limitations were identified relating to the low reliability coefficients of the OCAI
instrument. Limitations with this measurement’s scale were also reported during focus
group discussions where several respondents provided feedback regarding difficulties
experienced in completing the OCAI component of the online questionnaire. The
OCAI uses an ipsative rating scale, which requires respondents to divide 100 points
amongst 4 questions corresponding to the CVF quadrants (Cameron and Quinn, 2006).
3 cases were subsequently excluded for data analysis due to miscalculations of points
for some questions. However, an abridge version of the OCAI made used of a nominal
scale for measuring subgroup cultures, and generated a high Cronbach’s alpha of 0.8.
Hence, future researchers may consider adopting a similar scale or redesigning the
point allocation system to facilitate ease of calculations for respondents using the
OCAI.
Other feedbacks regarding the OCAI included comments on wording ambiguities,
which made some items difficult to understand, and possibly led to unintended
variances in the reliability analysis. Schwab (2011) advises that it’s often dangerous to
assume respondents will share the researcher’s frame of reference, such as
understanding intentions of the underlying competing values of the OCAI instrument.
Schein (2010) also cautions against the use of survey instruments, such as OCAI, as
54
such measurement methods tend to reduce the complexities of the cultural
phenomenon.
Although the use of a mixed-methods approach provided insightful findings through
corroboration of different data types, mixed-methods inquiries require more time,
resources, and effort to effectively organize and implement (Johnson & Onwuegbuzie,
2004). The qualitative analysis of this study was limited to perceptions of project unit
members, rather than senior managers, because of their practical responsibilities within
the project cycle where knowledge sharing inter-intra project units would be most
affected. Contrasting responses collected from between senior managers and team
members during focus groups were valuable for providing a holistic view of the PBO’s
cultural manifestations, knowledge sharing behaviours and rationale for its
organisational structure. Nonetheless, a limitation regarding the use of mixed-methods
relates to the huge amount of data collected, which although are interesting and useful
for providing enriched understandings, full discussions all data would far exceed the
scope of this dissertation report.
55
Chapter 7. Conclusion and Recommendations
The complexity of project-based organisations has provided a unique context for
knowledge sharing. This research has examined the individual and combined effects of
organisational culture and subgroup cultures on knowledge sharing behaviours within a
selected project-based organization in the United Kingdom. This research conforms to
the theoretical framework of Cameron and Quinn (2006) whereby organizations are
seldom categorized by any single cultural type. By using the Competing Values
Framework, this study has found that, within the project–based context studied,
different organisational and subgroup cultural orientations have had different effects on
knowledge sharing behaviours. Specifically, through the Organisational Culture
Assessment Instrument, dominance of hierarchy-type organisational culture was found
to impede knowledge sharing across project units, while clan-type subgroup culture
had facilitated knowledge sharing within individual project units.
This research validates the methodological approach of Paparone (2003) whereby the
Organisational Culture Assessment Instrument may be applied for diagnosing cultural
orientation across organisational levels as well as subgroup levels, and within different
contexts. Numerous researchers such as Lindkvist (2005), Mueller (2012) and
Wiewiora et al. (2013) have studied and established the respective effects of
organisational culture and subgroup culture on knowledge sharing behaviours.
However, this research provides a new paradigm and empirical model for studying
knowledge sharing behaviour by integrating the effects of both organisational culture
and subgroup cultures in order to provide a deeper understanding into the dynamic and
multi-level effects of culture within organisations. The discussions in the previous
chapter also points to the criticality of contextualisation to ensure that inferences made
about the population studied are correct and that any exogenous factors arising from
contexts studied should be controlled within the research design.
The benefits of effective knowledge sharing and organisational learning such as
productivity, problem solving capabilities and employee satisfaction were already
discussed in the early sections of this report. Although some limitations have been
56
identified for our research model, overall the results of this study indicates that
diagnosis of organisational and subgroup cultures, together with an enriched analysis of
project units’ embedded knowledge sharing behaviours, can inform organisational
efforts for enabling knowledge sharing and enhancing organisational effectiveness.
The challenge with knowledge sharing within project-based organisations, however,
lies in their ability to harness the wealth of tacit knowledge from their knowledge
workers and making such knowledge explicit and accessible by others within the
organisation. As reviewed in Chapter 2, Davenport and Prusak (1998) proposed two
dimensions for ensuring effective knowledge sharing, specifically the process via
which knowledge is shared and the quality of knowledge shared. Nonaka (1991) also
expressed that successful Knowledge-Creating Companies are not only effective in
their abilities to generate new knowledge, but in their effective management of
knowledge processes to ensure that valuable knowledge captured from individual or
sub-levels are leveraged at the organisational level.
As observed from the findings of this research, culture defines the structures and
processes by which knowledge is utilised and shared across the PBO. Culture also
shapes project members’ perceptions relating to value of knowledge and
approachability of knowledge sources (De Long & Fahey, 2000). Therefore, upon
recognising the critical role of culture for predicting knowledge sharing behaviours, we
may now negotiate culture shifts and for putting in place the relevant structures to
facilitate knowledge sharing and organisational learning.
3 key recommendations are offered for senior managers at the PBO:
First, efforts for cultural shift within the PBO should be dispensed towards cultivating
an organic environment within which each knowledge worker within the organization
are able to served as trusted advisors and be acknowledged as sources for
organisational learning. Socialisation of knowledge workers will require a supportive
culture that emphases trust, open communication, and commitment towards
organisational goals through teamwork and empowerment. Such cultural shifts are
57
necessary for redefining the existing negative perceptual assessments of existing
members towards the organization, so that positive basic underlying assumptions
regarding constructive knowledge sharing practices and orientation for continuous
improvement may be imparted to new members of the PBO (Schein, 1992). Exemplary
leadership should also emphases on the creation of a shared vision to increase
organisational members’ understanding of organisational objectives and ties to one
another so that they may break away from existing mental models of alienation and
divisions by location or function.
Although the captured responses from the PBO’s members provide conceptual
directions for initiating culture change, as De Long (1997) rightly posits, initiatives for
cultural change are likely to be incremental. Hence, cultivation of a knowledge-
friendly culture will require on-going efforts and commitment of senior managers and
all of its members as key change agents.
Second, captured responses from focus groups relating to procedural barriers for
knowledge sharing should be reviewed. Senior managements’ recognition and reaction
to feedback offered by organisational members demonstrates management’s visible
commitment in breaking away from existing ways of working towards developing a
more collaborative and consultative process. From a social exchange perspective,
individuals are generally reluctant to alter their behaviours without some degree of
personal gain in return. De Long (1997) points out that by rewarding the right
knowledge sharing behaviours, organizations can improve the motivation for
employees to exhibit those behaviours. As observed from the findings of this research,
the lack of recognition and reward for project members’ contributions towards project
successes was one of the most cited barriers to effective knowledge sharing. Where
perceptions of unequal rewards reinforced status hierarchies and silo mentalities,
procedural review should ensure that reward systems are transparent and consistent
across all functions and locations.
In addition, integration and collaboration activities should be facilitated by collective
decision-making and accessibility through open systems of communication and
58
effective use of technology. Resource constraints such as money and time are often
perceived to be challenges related to implementation of change initiatives. However, by
undergoing a procedural review, it is expected that some existing inefficiencies may be
eliminated and that through a collaborative process, organisational members are able to
agree on collective objectives and performance targets for the PBO so that returns may
be reinvested in system enhancements and facilitative tools to support their work.
Lastly, organizations can only successfully promote a knowledge-sharing culture by
directly incorporating knowledge management in its business strategy (Evans, 2012).
Following Nonaka’s (1991) notion of Knowledge-Creating Companies, knowledge
management strategies should not only ensure acquisition of internal knowledge but
also external knowledge from sources such as clients and competitors. This reinforces
the recognition that both knowledge itself and the source of knowledge as critical assets
of organizations. Aligning knowledge management with business strategy will require
conscious efforts of all members within the organization to effectively convert and
apply knowledge acquired towards achievement of organisational goals such as
differentiation from competitors and preservation of competitive advantage.
It is often difficult for organisations to question its existing norms and ideologies
during processes of change. Organisational learning, however, requires habitual
practices of ‘un-learning’ and ‘re-learning’ to order to ensure that organizations remain
adaptive within an ever evolving and demanding operating environment. While this
cross-sectional research study was able to present existing knowledge sharing
challenges relating to individual and combined effects of organisational and subgroup
cultures, it is suggested that a follow-up organization-wide research be conducted as a
means to evaluating the practicalities of these recommendations after they have been
implemented within the PBO.
59
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