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Purpose The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems. Design/methodology/approach To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics. Practical implications The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value This is the first typology of data-based decision-making considering advanced analytics.
Journal of Knowledge Management
Information and reformation in KM systems: big data and strategic decision-making
Ali Intezari, Simone Gressel,
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Ali Intezari, Simone Gressel, (2017) "Information and reformation in KM systems: big data and strategic decision-making",
Journal of Knowledge Management, Vol. 21 Issue: 1, pp.71-91, doi: 10.1108/JKM-07-2015-0293
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Information and reformation in KM
systems: big data and strategic
decision-making
Ali Intezari and Simone Gressel
Ali Intezari is based at
UQ Business School,
University of Queensland,
Brisbane, Australia.
Simone Gressel is based
at School of
Management, Massey
University, Auckland,
New Zealand.
Abstract
Purpose The purpose of this paper is to provide a theoretical framework of how knowledge
management (KM) systems can facilitate the incorporation of big data into strategic decisions.
Advanced analytics are becoming increasingly critical in making strategic decisions in any organization
from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite
the growing importance of capturing, sharing and implementing people’s knowledge in organizations,
it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform
the design and implementation of KM systems.
Design/methodology/approach To address this gap, a combined approach has been applied. The
KM and data analysis systems implemented by companies were analyzed, and the analysis was
complemented by a review of the extant literature.
Findings Four types of data-based decisions and a set of ground rules are identified toward enabling
KM systems to handle big data and advanced analytics.
Practical implications The paper proposes a practical framework that takes into account the diverse
combinations of data-based decisions. Suggestions are provided about how KM systems can be
reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic
decision-making.
Originality/value This is the first typology of data-based decision-making considering advanced
analytics.
Keywords Knowledge management systems, Big data, Advanced analytics, Data-based decisions,
Strategic decision-making
Paper type Conceptual paper
Introduction
To Simon (1960), a Nobel laureate and one of the founding fathers of the scientific domain
of decision-making, “decision-making” is synonymous with the entire process of
management. Decision-making is central to what managers do (Hickson et al., 1989;
Michel, 2007;Stewart, 2006), and is integrated into all kinds of management functions
(Harrison, 1999). Making effective strategic decisions is one of the critical abilities that
managers are required to have and develop to lead their organizations in the increasingly
volatile and competitive business world. As Porter (1985) emphasizes, the success or
failure of a firm relies mainly on the managers’ competitive ability to make strategic
decisions.
Strategic decisions address ambiguous and complex issues, engage various departments
and involve a high level of organizational resources (Amason, 1996). Because of the
extensive uncertainty, ambiguity and risk associated with strategic decisions (McKenzie
et al., 2011), gathering, analyzing and considering reliable data and information
are critically important in strategic decision-making (Nicolas, 2004). In a turbulent and
Received 30 July 2015
Revised 21 February 2016
13 March 2016
Accepted 12 May 2016
DOI 10.1108/JKM-07-2015-0293 VOL. 21 NO. 1 2017, pp. 71-91, © Emerald Publishing Limited, ISSN 1367-3270 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 71
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volatile business context, organizations need to link their strategic dimension with their
knowledge assets (Nonaka, 1988,1994). Knowledge originates in the minds of people
(Davenport and Prusak, 2000) and if effectively managed can help organizations to
generate value. Knowledge management (KM) is generally defined as a systematic
process for creating, sharing and implementing knowledge. A KM system is an information
technology (IT) system developed to facilitate and support the creation, dissemination and
implementation of knowledge in organizations (Alavi and Leidner, 2001). KM systems are
considered to be a class of information systems designed and implemented to manage
organizational knowledge. KM initiatives involve social and cultural facets of the
organization and rely on IT as an enabler (Alavi and Leidner, 2001). From the very early
versions of KM systems (such as discussion forums, knowledge repositories,
computer-supported cooperative work, knowledge bases and inference engines) to the
more recently developed KM systems (such as the new KM portal in Microsoft Office 365
and SharePoint Portals), KM systems have been widely used to identify, share and utilize
knowledge, as well as to incorporate knowledge into problem-finding and problem-solving
processes.
While KM systems are becoming integrated parts of business processes by providing
text document analysis in many organizations – for example, Xerox (Cox, 2007) – the
emergence of big data is raising new challenges. Big data is perceived by scholars and
practitioners as an opportunity to generate valuable insights, improve decision-making
and gain competitive advantage (Davenport, 2013;Delen and Demirkan, 2013). With
the support of the right technology and sufficient skills, organizations can benefit from
big data’s most prominent characteristics, i.e. its velocity, volume and variety. The
analysis of streaming data allows organizations to take immediate actions, adapt
business processes and improve customer experiences (Watson and Marjanovic,
2013). The volume of big data can provide more robust and valid results. The most
critical of the big data characteristics in the context of this paper is its variety. The
variety of big data refers to different types and sources of data that are available to
organizations. These characteristics of big data often exceed the capabilities of
traditional analytics tools, leading to the need for advanced analytics. “Advanced
analytics is a general term which simply means applying various advanced analytic
techniques to data to answer questions or solve problems” (Bose, 2009, p. 156).
Advanced analytics is also referred to as predictive and prescriptive analytics and
describes a group of tools that are combined to extract information, supporting
managers in predicting and optimizing outcomes (Barton and Court, 2012;Gartner,
2014).
One of the main challenges that organizations encounter when working with big data is
the management of these various data sources and the integration of structured and
unstructured data that an organization has access to. Structured data are perceived as
data with fixed coded meanings and formats, mostly numeric, and normally stored in
database fields. Unstructured data, in contrast, have no fixed format and mostly derive
from human interactions (Kopenhagen et al., 2011). Structured data can be directly
processed by computing equipment, while unstructured data are mostly non-numeric
and can rarely be computed without any prior transformation. Examples of structured
data are purchase order data, product IDs and quantities, customer IDs and click
streams. Examples of unstructured data are customer reviews, calls, chats, sounds,
transcripts, social networking, blogs, forums, emails, images, colors and shapes. These
types of data cannot be easily put in columns and rows and therefore have no place in
a relational database.
Unstructured data pose a challenge to organizations that have traditionally dealt with
structured data stored in their relational databases, such as transactional data,
enterprise resource planning (ERP) or customer relationship management (CRM) data.
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Many companies have recently begun to tackle the challenge of finding ways to store
and analyze unstructured data, with the aim to gain more insights from these additional
sources. Google’s PageRank algorithm [used to rank websites in its search engine
results based on the importance of website pages (Google.com, 2015)], the Monk
Project [an open-source digital environment designed to help scholars discover and
also analyze patterns in the papers and texts that they study (Monk Library, 2014)], SAS
Text Analytics, Topsy (a real-time search engine for searching and analyzing the Social
Web [http://about.topsy.com/support/search/], the British Newspaper Archive (a
project to digitize around 40 million newspaper pages from the British Library’s
collection and to enable readers to search news articles, family notices, letters to the
editor, obituaries and advertisements [www.britishnewspaperarchive.co.uk/help/about]
and Factiva (a text research tool that provides access to the latest industry and
business news and information sources [http://new.dowjones.com/products/factiva/]
are examples of the tools that help with text manipulation, analysis and visualization.
Image, video and audio analysis tools are still under-developed compared to
text-based analysis tools. Google Goggles can be mentioned as a photo analysis tool,
which is an image recognition app. In December 2011, the New York Metropolitan
Museum of Art started collaborating with Google to provide information about the
artworks in the museum (www.metmuseum.org). Every minute, millions of pixels are
generated, disseminated and stored through digital and video recording devices,
surveillance cameras and video-sharing websites such as YouTube.
To extract information from big data that is of value to an organization, new techniques and
advanced tools have to be developed and applied, such as advanced data mining or new
artificial intelligence tools (O’Leary, 2013). This can be challenging, especially when an
organization wants to get the most out of its current expensive KM systems by expecting
its KM systems to handle big data as well. Potentially valuable intangible assets can be
found in a diverse range of resources inside and outside the organization, many of which
may or may not fit within the traditional KM systems and frameworks (Erickson and
Rothberg, 2014). To utilize these resources and fulfil the consequently advanced
requirements, the existing systems need to be upgraded to “advanced KM systems”, as
they will be referred to throughout this paper. Advanced KM systems refer to a particular
type of KM system that can help an organization to integrate big data into its knowledge
and knowledge repository and generate more value from the organization’s existing KM
systems. Advanced KM systems, however, are more than simple mechanisms to just link
knowledge repositories to data warehouses and data marts. This paper attempts to
address the question: how would big data and the need for advanced analytics in strategic
decisions inform and, if necessary, reform (design and implementation) KM systems? We
suggest a conceptual framework about how KM systems can incorporate big data into
strategic decisions.
The structure of this paper is organized as follows. The working assumptions underlying the
core argument of this paper will be explained. Next, big data is discussed by identifying its
main characteristics, followed by a discussion of two main types of decision-making. This
is followed by an argument of how the combination of different types of big data and
decision-making leads to four main forms of data-driven decision-making. The paper
concludes by offering some suggestions on what aspects advanced KM systems must
have to be able to support the integration of big data and knowledge into strategic
decision-making.
The working assumptions
This paper draws on three main assumptions:
1. Data do not mean knowledge (Ackoff, 1989;Nonaka and Takeuchi, 1995).
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Data are “a representation of an object” (Miller et al., 2001, p. 365). Knowledge,
however, is defined by Davenport and Prusak (2000) as a “fluid mix of framed
experience, values, contextual information, and expert insight that provides a
framework for evaluating and incorporating new experiences and information. It
originates and is applied in the minds of knowers. In organizations, it often becomes
embedded not only in documents or repositories but also in organizational routines,
processes practices, and norms” (p. 5). Knowledge resides in a human’s mind
(Davenport and Prusak, 2000) and engages other qualities such as experience,
reflection, judgement and other practices that provide a deeper understanding
(Erickson and Rothberg, 2014). Knowledge includes “new insights based on past work
challenges and expectations for new opportunities and contexts” (Wiig, 2011,
p. 239). From an organizational perspective, while data are found in records,
knowledge derives from minds at work and develops over time through experience
(Davenport and Prusak, 1998). Knowledge is an essential capital (Davenport and
Prusak, 1998) and “the most strategic resource” (Roth, 2003, p. 32) that enables
managers to adapt to the rapidly changing business world by making effective
decisions (McKenzie et al., 2011).
2. As with knowledge, return on big data is associated with making decisions cheaper,
faster and better than before (Davenport, 2014).
The increasing volume, variety and velocity of big data, along with dropping costs of
data and databases, can enable organizations and companies to make better
strategic, tactical and operational decisions (Erickson and Rothberg, 2014).
3. While big data and advanced analytics have the potential to add value by providing
transparency through immediate performance feedback and more objective
decision-making (algorithms rather than humans) (Manyika et al., 2011), effective
formulation and implementation of strategic decisions, it is not just the result of having
access to big data and cheap databases.
Because of uncertainty and ambiguity surrounding strategic decision-makers, the success
of strategic decisions relies on the individual and organizational capacity to learn and to
continuously reconfigure the organization’s knowledge base (McKenzie et al., 2011).
Human and social capital – pertaining to expertise and knowledge gained through
on-the-job experience, training and education, as well as innate and learned abilities – have
direct impact on the effectiveness of strategic decisions (Ahearne et al., 2014;
Sundaramurthy et al., 2014).
Looking into (big) data
Although some organizations see big data as a new challenge and complex
phenomenon that is unwieldy and difficult to manage, other organizations and
researchers see it as an opportunity for competitive advantage and new insights
(Davenport, 2013;McAfee and Brynjolfsson, 2012). However, because of its affiliation
with established technologies such as data warehouses and database management
systems (Chen et al., 2012), big data should not be perceived as a complete novelty
(Agarwal and Dhar, 2014). Data, in admittedly simpler and more structured form, have
been used since the 1950s to support decisions and business processes in more
traditional functions of business intelligence (BI) and analytics (Petter et al., 2012).
Seen in the context of earlier information systems and types of data, big data is just a
further step in the evolution of data and their applications.
Davenport (2013) describes the evolution of big data with a focus on analytics. Analytics
1.0 is the first era of analytics, the era of BI. In this first stage of analytics, the use of data
for business applications was discovered, and data on customers and production were
primarily utilized to optimize and support decision-making. Analytics 2.0 is the era of big
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data, limited to companies that are internet-based or in the social networking business. The
final era is Analytics 3.0, the era of data-enriched offerings. This era marks the transition
from big data used mainly in one specific industry to big data being used by virtually all
industries and companies ranging from start-ups to multinational conglomerates
(Davenport, 2013).
Chen et al. (2012) propose a similar framework incorporating BI and Analytics (BI&A)
perspectives. In BI&A 1.0, the nature of the collected data is mostly structured, and the
data are stored in relational database management systems (DBMS). In BI&A 2.0, the
collected data are Web-based and unstructured. The use of data shifts from mere business
reporting functions to the analysis of customer online behavior, optimization of Web
presences and product recommendations. In the final stage, BI&A 3.0, data are mobile and
sensor-based. This enables operations and transactions that are targeted at individuals
and are adapted to a specific context or location (Chen et al., 2012).
These frameworks of the gradual evolution of data and analytics’ potential imply that big
data brings certain new challenges with it but also demonstrates that it is built on familiar
technologies and principles.
Three V’s
Originally defined by Doug Laney from the IT research and advisory firm Gartner (Laney,
2001), big data is now commonly specified by the three V’s, which serve as a distinction
between big data and traditional data sets: volume, velocity and variety (Chen et al., 2012;
Jagadish et al., 2014;Kudyba, 2014;McAfee and Brynjolfsson, 2012;O’Leary, 2013;
Watson and Marjanovic, 2013). Recent literature also suggests the addition of veracity,as
seen in Sathi (2012) and Jagadish et al. (2014).
The volume of big data exceeds the size of regular data sets by far and creates challenges
for traditional DBMS and data warehouses in terms of data storage and analysis (Kaisler
et al., 2013;Katal et al., 2013;Provost and Fawcett, 2013;Watson and Marjanovic, 2013).
This increase in data volume is attributed to the continuous growth of data that are
produced every second over the internet, sensors, customer transactions and so forth
(McAfee and Brynjolfsson, 2012;Watson and Marjanovic, 2013). Because of various
developments in the area of data storing capabilities, companies can access more storage
space for lower costs. The growing market of cloud computing (Gantz and Reinsel, 2012),
for example, offers organizations of all sizes tailored solutions and capacities for their data
storage (Chen et al., 2012;Delen and Demirkan, 2013). The option of analytics-as-a-service
allows users to not only have the ability to access their information from remote devices but
also use the necessary analytics tools for data processing on demand at any given time
(Delen and Demirkan, 2013;Hazen et al., 2014). This service assists especially in
encompassing the other two components of big data, namely, velocity and variety.
The velocity of data is characterized by the speed of data creation and analysis. Regarding
velocity, a data set can only be classified as big data if the data are processed in real-time
or near real-time (Hazen et al., 2014;McAfee and Brynjolfsson, 2012). Data are not
analyzed in hindsight, but in “continuous flows and processes” (Davenport et al., 2013,
p. 23), providing more flexibility and faster reactions. Instead of the more traditional
analysis of historic data gathered from past events, there is a shift toward the analysis of
streaming data, providing information about live events (Davenport, 2014). This especially
enables decisions that affect data that are simultaneously gathered and analyzed (Chen
et al., 2012;O’Leary, 2013).
The variety of big data refers to the different sources and types of data that are stored
(Davenport, 2013;Hazen et al., 2014;McAfee and Brynjolfsson, 2012). Data are not limited
to structured, numerical data anymore; data are gathered in unstructured forms from social
networks, texts, audio or video files, sensor data, GPS signals and so on (O’Leary, 2013),
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from both inside and outside the firm (Erickson and Rothberg, 2014). This variety of data
offers a new spectrum of possibilities but challenges that are beyond the capabilities of
traditional DBMS, referring to the analysis of unstructured data and their integration with
structured data (McAfee and Brynjolfsson, 2012).
An extension of these criteria can be found in the veracity factor that was suggested in
recent literature (Jagadish et al., 2014;Sathi, 2012). Veracity assesses how credible a
data source is and how well the data suit the organization’s audience. To benefit
decision-making and analytics in general, the data sources have to be credible to
ensure data correctness and accuracy (Sathi, 2012). The first step to manage the big
data characteristic of veracity is “creating an inventory of available data sources and
the metadata that describes the quality of those sources in terms of completeness,
validity, consistency, timeliness, and accuracy” (Miller and Mork, 2013, p. 57).
Structured and unstructured data
As defined by the variety characteristic in the previous section, big data refers to both
structured (e.g. click streams) and unstructured data (e.g. customers’ verbal feedback)
(Kudyba, 2014). Big data technologies enable companies to gain insights from diverse
data sources that outperform the traditional internal and structured data that organizations
relied on in the past. Traditionally, organizations relied on data that were stored in their
relational databases and easily queried. Sources of structured data were therefore often
internal information systems, such as CRM or ERP systems. Because of their structured
nature, different types of data, such as graph data or transactional data, could be
integrated and used to gain insights. The rise of unstructured data sources, such as sensor
data, Web data, blogs, emails, social media data, etc. poses new challenges for the
integration of different data types (Kudyba, 2014;Lodha et al., 2014).
Unstructured data, such as social media data, can provide an in-depth insight into human
behavior, as can be seen in the example of Twitter data. Twitter has been the focus of
various research papers on opinion mining, event detection and political discourse and
provide valuable insights for researchers in the areas of marketing, education, etc.
(Goonetilleke et al., 2014). The reliability and quality of social media data, however, can
vary greatly. In the example of Twitter, not all contributions are user-generated; some are
posted by automated programs, therefore compromising the insight into human behavior
(Edwards et al., 2014). User-generated data can therefore make a valuable contribution but
should be integrated with more reliable sources to provide valid results.
A prominent example of overreliance on big data, and specifically unstructured data,
for predictive analytics is Google Flu Trends (GFT): “Quantity of data does not mean
that one can ignore foundational issues of measurement and construct validity and
reliability and dependencies among data” (Lazer et al., 2014, p. 1,203). By using
search terms and social media to predict flu trends, GFT managed on several
occasions to surpass predictions from the Centres for Disease Control and Prevention.
However, in the long run – by overlooking information that would have been attainable
by using traditional statistical methods – Google Flu Trends produced large errors in its
predictions (Lazer et al., 2014).
Structured and unstructured decision-making
Organizational decisions can be categorized from different angles (Scherpereel, 2006).
One widely accepted typology is structured and unstructured decisions, which categorizes
decisions based on the complexity of the decision problems (i.e. simple/structured
problems vs complex/ill-structured problems) (Turban et al., 2005). Depending on the level
of the complexity of the problem, the processes through which an organization makes
decisions may be structured or unstructured (Langley et al., 1995).
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Another approach to classifying decision types is according to their contexts, which is
demonstrated by the Cynefin framework (Snowden and Boone, 2007). The framework
identifies five contexts, namely, simple, complicated, complex, chaotic and disorder, if
no other context is applicable. These different contexts are essentially defined by the
(lack of) a cause and effect relationship. While simple and complicated contexts show
a cause and effect relationship, complex and chaotic contexts are unprecedented and
unpredictable. Simple and complicated contexts can therefore be compared to
structured decisions that require managers to categorize the issue that they are facing
and, in certain circumstances, require the use of analysis to find the right answer.
Complex and chaotic contexts can be compared to unstructured decisions that require
probing or acting, and only allow an assessment of the correctness of the answer in
hindsight.
Structured decisions can be described by classic mathematical models (e.g. statistical
methods and linear programing), whereas there is no standard and global method for
obtaining an optimal solution to address unstructured decision problems (Zhang et al.,
2015). Structured decision-making as an orderly or sequential process is well illustrated by
Drucker (1967). Drucker argues that an executive’s effective decision is made through “a
systematic process with clearly defined elements and in a distinct sequence of steps”
(p. 98). The systematic process typically involves six main steps:
1. the classification of the problem;
2. the definition of the problem;
3. the specifications which the answer to the problem must satisfy;
4. the decision as to what is right, rather than what is acceptable, to meet the boundary
conditions;
5. the action planning built into the decision; and
6. the feedback which tests the validity and effectiveness of the decision against the
actual course of events (Drucker, 1967).
Unstructured decisions refer to the “decision processes that have not been encountered in
quite the same form and for which no predetermined and explicit set of ordered responses
exists in the organization” (Mintzberg et al., 1976). As unstructured decision problems are
vague, uncertain and fuzzy, for which no pre-defined process and optimal solution exists,
human intuition, experience and judgement are often the basis for the decision-making
(Zhang et al., 2015). Advocates of unstructured decision-making would strongly argue that
managers do not necessarily make decisions by following clearly structured and
pre-defined phases (Isenberg, 1984,1986). Instead, they make their decisions on the basis
of a combination of data, experience and feeling. The steps of identifying and articulating
the problem and the decision context, identifying, comparing and evaluating alternatives
may overlap. Some steps may even be skipped, and a different order can be followed in
the process of decision-making. Depending on the decision circumstances, organizational
strategies, time frame, the level of the impact of the decision consequences on the
organization or stakeholder and so forth, the importance of each phase in the whole
process of decision-making may vary. Mintzberg and Westley (2010) stress that
decision-making is not necessarily always a “thinking first” process, a linear process which
begins with “defanging the problem” and then evaluating and choosing from alternatives.
Unstructured decision-making is “a dynamic cycle set in a complex and chaotic
environment, and influenced by the interactions between complex human beings”
(McKenna and Martin-Smith, 2005, p. 832).
There is no clear-cut border between the two processes, and it is unlikely that an
organization would use only one type of decision-making. The diversity of problems and
also the involvement of a wide range of stakeholders in strategic decisions require
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organizations to use a combination of these two types of decision-making processes,
depending on the nature of the problem (McKenzie et al., 2009). While some decisions can
or need to be made through pre-defined processes and procedures, for other decisions, it
might be difficult to roll out a set of specific procedures. Even though the structured or
unstructured nature of the management decisions can be determined by a number of
factors such as the size and structure of the organization, operational decisions and
decisions about predictable circumstances often can be made through structured
processes. Strategic decisions or decisions regarding unforeseen and unique decision
circumstances may require unstructured processes.
The decision-data quadrants
Currently, petabytes of information are freely available, and meaningful inferences from
this information can improve business decisions. Nevertheless, a report by The
Economist Intelligence Unit (2012) shows that even though organizations admit that the
need for incorporating big data into decisions is critical, many of the organizations are
struggling with the enormous volumes and poor quality of data. One reason for this
could be a lack of understanding of to what extent strategic decision-making and big
data are related. As Figure 1 illustrates, depending on whether an organization bases
its strategic decisions on structured or unstructured data, and also depending on
whether those decisions are made through structured or unstructured processes, four
major types of decision-making can be identified: structured decisions based on
structured data (SD-SD), structured decisions based on unstructured data (SD-UD),
unstructured decisions based on structured data (UD-SD) and unstructured decisions
based on unstructured data (UD-UD).
Making decisions on whether to continue or discontinue a product based on the actual
movements in the stock price over a six-month period of time is an example of making
decisions based on structured data. In addition to structured data, unstructured data
are important in informing strategic decisions. The same decision may be based on
unstructured data such as customer comments and feedback on social media across
a diverse range of networks such as negative/positive comments on Twitter, audio
recordings or videos on YouTube or likes/dislikes on photos and videos on Facebook
Figure 1 The decision-data quadrants
UD-UD
Unstructured Decisions based on
Unstructured Data
(Mainly rely on human knowledge,
experience, interpretaon and
expert insight. May require
techniques such as text-mining and
content discovery)
SD-UD
Structured Decisions based
on Unstructured Data
(May require techniques
such as text-mining and
content discovery)
Unstructured Data
Unstructured
DM
SD-SD
Structured Decisions based on
Structured Data
(Can be formulated by using
advanced analycs for
automated and programmed
decision-making)
Structured Data
UD-SD
Unstructured Decisions based on
Structured Data
(Mainly rely on human knowledge,
experience, interpretaon and insig ht.
May require advanced data mining
and query techniques for ad-hoc data
analysis)
Structured
DM
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and suchlike. In either of the cases, the central decision-makers in the organization may
or may not have pre-defined procedures to follow when making the decisions.
Structured decisions based on structured data
An organization may have structured processes and pre-defined procedures to make
strategic decisions based on structured data. Following instructions and pre-defined
processes is critical to make effective decisions. Structured decisions made based on
structured data (SD-SD) can be formulated using mathematical modeling, which
represents mathematical relationships between variables (e.g. explanatory models for
forecasting or algebraic models for optimization). Furthermore, advanced analytics can
be applied to make automated decisions based on gathered and organized data.
Operational decisions provide good examples for this. Other examples include decision
management systems (Taylor, 2012), and data- and model-driven decision support
systems (Gachet, 2004). SD-SD can be used to gather and present data and information
about inventories, compare sales figures between different periods of time and present
projected revenue figures. Companies may use software packages such as financial
modeling software (e.g. Extensity, and Trueblue Systems), forecasting software (e.g.
OpenForecast) and spreadsheets (e.g. MS Excel and OpenOffice.org). Microsoft Azure
Machine Learning can be mentioned as another example which can be used to develop a
predictive model of, for example, an individual’s credit risk, or yield failure on a
manufacturing process (https://azure.microsoft.com/).
Structured decisions based on unstructured data
“Weak signals” from social media and other sources contain powerful insights and
should be part of the data-creation and decision-making process (Harrysson et al.,
2014). The decisions that are made based on unstructured data but through
pre-defined procedures are in this category. A company may set procedures around,
for example, how and how often video footage of assembly lines must be analyzed and
reported to corresponding managers. The company may also want to use pre-defined
rules about analyzing customers’ calls (audio) and textual feedback to fine-tune its
customer support. BNY Mellon ties unstructured data on customer interactions to
enterprise-wide data systems to obtain a clearer picture of its customers’ banking
needs and create better collaboration with customers (BNY Mellon, 2013). A typical
example of SD-UD is the Delphi decision-making technique, which is a systematic
technique to solicit the collective view of a group of experts related to a subject matter
(Custer et al., 1999). Feedback databases are examples of SD-UD. The organization’s
members can enter feedback into the database. Then the information, which is often in
the form of text, will be examined through an integrated approach to extract patterns
and understand the shared information.
Although applying models to utilize inherent patterns and structures in unstructured data
might be challenging, techniques such as mathematical modeling can be used in SD-UD.
Compared to SD-SD, SD-UD may require additional steps to turn unstructured data into
structured data. Microsoft Azure Machine Learning (https://azure.microsoft.com/) offers a
text analytics feature – Text Analytics API – that can support this type of decision-making.
Text Analytics API is a Web service that uses advanced natural language processing
techniques to analyze unstructured texts for tasks such as sentiment analysis and key
phrase extraction.
Unstructured decisions based on structured data
Decision-making as an unstructured process suggests that the steps of decision-making
do not necessarily follow a set order of tasks (Galotti, 2002). Unstructured decisions rely
more on human judgement, experience, prior knowledge and interpretation of the decision
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context and alternatives (Zhang et al., 2015). UD-SD is when the individual decision maker
or the organization has access to structured data, but there are little or no clear and
pre-defined decision-making procedures to follow to integrate the data into the decision.
Facebook’s CEO’s decision on its $19bn acquisition of WhatsApp is an example of this type
of decision-making. The acquisition was hashed out in Mark Zuckerberg’s house over a
conversation (Olson, 2014). The structured data available at the time that the decision was
made could comprise the number of existing and active subscribers (over 450 million
active users in December 2013), and the increasing number of users joining the network
every day (adding 1 million new users a day) (Donald, 2014).
The structured and organized textual output of an expert system, as well as BI tools with
reliable and fast reporting systems, are important and can be very useful in making
UD-SD. BI&A 1.0 tools, where data are mostly structured, and online analytical
processing (OLAP) and database query languages, which are used to explore
important data (Chen et al., 2012), are examples of systems and techniques that can
support UD-SD. Other well-established business reporting mechanisms such as
scorecards in business performance management used to visualize various
performance metrics can also be implemented in UD-SD. The main IT vendors such as
IBM, SAP and Oracle have included most of these techniques in their BI platforms
(Sallam et al., 2011).
Unstructured decisions based on unstructured data
Incorporating unstructured data, such as social media data, into strategic decisions can be
very challenging when there is little or no clear decision procedure or structure. Compared
to the other decision-data quadrants, UD-UD is the least structured data-based decision.
An example of UD-UD is when a company makes an unstructured decision based on
document management systems. Document management systems offer a search function
that enables the user to search documents such as procedures and policies, reports,
training materials, etc. for key words or phrases and retrieve required information. The
company may also want to use external data sources such as Twitter and Facebook to
inform and back up their decision.
As unstructured decisions are often made to address unstructured and complex problems,
context analysis is critically important (Bhidé, 2010;Macfadyen and Dawson, 2012;Shah
et al., 2012). Social interactions play a critical role in UD-SD and UD-UD types of
decision-making; the uncertainty surrounding strategic decisions and the unstructured
nature of the data may require more negotiation and discussion between senior managers.
To acquire a more accurate assessment of the decision situation and alternatives,
managers involved in strategic decisions gather most of their information through their
social ties (Jansen et al., 2011). Although data-driven decision support techniques can be
useful in all four decision-data quadrants, UD-UD relies mainly on human interpretation and
insight, rather than mathematical analysis.
To support all four data-driven decision types, KM systems need to accommodate both
data analytics and human insight. We suggest five key features that characterize advanced
KM systems. The extant literature reflects some of these features. However, little attention
has been paid to the interconnectedness of all these features. We argue that these features
should not be considered in isolation but incorporated into KM systems as integrated
features aligned with organizational strategic decisions. We also articulate how the features
are associated with the four types of data-driven decisions.
Setting the ground rules for advanced knowledge management systems
We characterize advanced KM systems as social, cross-lingual, integrative, dynamic and
agile, as well as simple and understandable.
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Social
As the McKinsey data experts and principals Pyle and San Jose (2015) note, although the
roles of big data and advanced analytics in enhancing business decisions are undeniable,
only human managers (not machines) can decide on critical issues, for example, which
critical business problems a company should try to solve. Knowledge is built on know-how
and develops based on the person’s own experiences, perceptions, preferences,
perspectives, values and beliefs. Accordingly, sharing knowledge requires other
mechanisms such as social networks rather than standard databases and procedures
(Wang et al., 2015). Bebensee et al. (2011) report that social media fundamentally change
the way employees handle knowledge processes such as knowledge creation, sharing and
implementation. Bebensee et al. (2011) argue that Web 2.0 has three layers that can
support KM:
1. Web 2.0 is founded on social principles, such as unbounded collaboration and peer
production.
2. Web 2.0 offers a series of applications including blogs, social bookmarking, media
sharing, data mashups and editing platforms, which are easy to use and intuitive to
understand.
3. Web 2.0 is based on infrastructures such as open platforms that make the use of social
media significantly inexpensive.
Many social media platforms attract users with diverse interests and offer easy search
facilities that enable the users to find the most relevant expertise (Von Krogh, 2012).
The fact that connecting “people to people” must be part of all KM systems is not a new
thing (Anand et al., 2008, p. 22). In advanced KM systems, however, the level of social
interaction is determined by the type of the data-driven decisions. While data analytics
underlie SD-SD and SD-UD, social interactions play a critical role in UD-SD and UD-UD
by evaluating and integrating big data and human insight. As strategic decisions are
usually made by a group of people and through less structured processes, the
interaction between managers across organizational departments and levels is vital in
making effective strategies (Mintzberg, 1996). The data and knowledge that are
exchanged through the social connections can enhance the formulation and
implementation of strategies (Ahearne et al., 2014). Accordingly, the incorporation of
big data into strategic decisions requires a reliable facilitated collaboration between
those who are responsible for formulating the organization’s strategies (i.e. managers
and strategy analysts) and those who deal with data analytics (i.e. data analysts). This
cooperation is vital, as it ensures the alignment between big data analysis and the
organization’s strategic direction.
Advanced KM systems not only encourage and facilitate social interactions within the
organization but also support interactions between the organization and outside
stakeholders. The social interaction across organizational levels and with stakeholders
outside the organization is particularly important, because the strong ties among senior
management staff may lead them to cognitively block out ideas coming from outside the
group (e.g. middle managers) (Mintzberg, 1996).
“Microblogging” is an example of the features that advanced KM systems can offer to
facilitate knowledge sharing through social interactions. Microblogging allows users to
share a list of their experiences and interests with others and engage in discussions
(Cleveland and Ellis, 2015). KM systems should facilitate discussion and feed the
discussions with reliable and up-to-date data. By providing data analytics features, on one
hand, and social interaction facilities, on the other hand, advanced KM systems can
connect analytical concretes to interpretations and negotiations that take place in social
interactions and conversations.
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Given the globalization and increasing multi-cultural and multi-national work environments
in the current businesses, the “social” feature of KM systems is augmented by another
feature that supports interaction among people with different languages: “cross-lingual”.
Cross-lingual: cross-lingual knowledge retrieval
Over 150 languages are used on the internet (Kornai, 2013). Although this figure is a small
portion (2 per cent) of the approximately 7,000 languages spoken currently, it indicates that
information systems and, more particularly, KM systems need to be able to handle
cross-lingual interactions by offering an effective cross-lingual knowledge retrieval (CLKR)
feature. CLKR can be defined as computer tools that enable the user to search for required
knowledge and expertise across a number of sources, which are originally distributed
across different languages. The ability to facilitate communication among users speaking
different languages is an inherent and distinctive characteristic of advanced KM systems.
Cross-lingual information retrieval (CLIR) – also referred to as multi-lingual information
retrieval, which is mainly concerned with data and information, will be complementary to
CLKR. The CLKR-CLIR combined feature allows for advanced KM systems to be able to
explore, store and retrieve data, information and knowledge. Tools such as English-Dutch
CLIR (Vulic´ et al., 2015), the Dark Web Forum Portal, which gather content generated by
users in different languages on social media (Dang et al., 2011), Mulinex, Keizai, UCLIR,
MIRACLE and MultiLexExplorer (Ahmed and Nurnberger, 2012;Talvensaari et al., 2007)
are examples of CLIR. These tools, however, are designed to handle data and information,
rather than knowledge. Furthermore, most of these tools, as Baur et al. (2015) report, lack
or have limited analytics capabilities such as data collection, analysis, aggregation and
visual output.
An advanced KM system goes beyond a simple data content look-up, translation and
multi-lingual text analysis (i.e. detecting the language a document is written in and
translating it into a desired language). Advanced KM systems are also semantic and can
support verbal conversations by providing voice interpretation. Companies such as Apple,
Samsung and Microsoft have already incorporated voice recognition technology into their
products, including mobile phones, laptops, tablets and gaming consoles. This feature,
however, is significantly missing in the conventional KM systems. The semantic aspect of
CLKR may considerably enhance the effectiveness and efficiency of KM systems by
increasing the speed of storing and retrieving knowledge, which would otherwise take
enormous amounts of time and money. An example of a similar feature is the natural
language processing tool that UnitedHealthcare uses to better understand their customers’
level of satisfaction. The tool converts the records of customer voice calls into text and then
searches for indications of the customer’s (dis)satisfaction.
The CLKR-CLIR feature enhances the integrity of advanced KM systems by providing a
dynamic yet strategically aligned environment, where exploring and capturing the required
knowledge and expertise, as well as locating those individuals or departments lacking
knowledge and expertise, are effectively supported and facilitated. Drawing on an
integrated feature of CLKR-CLIR, advanced KM systems can support all the decision types
of SD-SD, UD-SD, SD-UD and UD-UD. CLKR enhances the integration of advanced KM
systems.
Integrative
CLKR allows an advanced KM system to enable its users to freely and in a controllable
manner open up their knowledge, expertise, insight, experiences, expectations,
perceptions, personal and professional perspectives, values and beliefs either generally or
regarding a specific matter. The users can assess the knowledge not only in regard to the
subject matter or the decision context but also in association with the participant’s
personality and professional background and expertise. This way, users are more capable
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of and have more control over sharing and absorbing knowledge across a network of users
within (e.g. managers, colleagues and subordinates) and outside the organization (e.g.
customers, vendors and stockholders). The multi-dimensional assessment of knowledge
leads to what we call “knowledge fit”. Users can make a more informed decision on whether
the expert being approached and the knowledge and expertise sought are suitable for the
knowledge seeker, the subject matter and, more importantly, the strategic direction of the
department and organization.
The integrative feature of an advanced KM system does not necessarily mean that all the
scattered data and data generated by different sources and in different forms must be
necessarily stored in one single database, and then processed from there. However,
the KM system should enable the users to handle the diversity by drawing a meaningful
linkage between various sources and forms of data and knowledge. In this sense, a KM
system characterized as “integrative” assists with the following factors:
Capturing and aggregating the data and knowledge that are fragmented and scattered
across the organization: Fragmented data could be the result of data being generated
at different times, by different agents/employees, on different subject matters and in
dissimilar contexts. There is often no clear connection or linkage among the
fragmented data.
Drawing meaningful connections between structured and unstructured data: The
diversity of data sources (e.g. sources that provide structured data, such as
transactional data or CRM and ERP data, as well as sources that provide unstructured
data, such as sensors, customer feedback via Twitter, emails, or reviews on websites)
may provide an opportunity to consider multiple perspectives.
Furthermore, the output of the KM systems should not be limited to textual outputs: it should
also support non-textual expertise, for example, visual reasoning (Carbonell et al., 1987), or
acoustic analysis (Oxman, 1991).
Dynamic and agile
The social, cross-lingual and integrative aspects lead advanced KM systems to be
dynamic and agile. To make relevant and viable decisions, dynamism and agility are vital
in the decision-making process (Shimizu, and Hitt, 2004). As Davenport (2014) stresses,
the primary objective of exploring big data is to make decisions in real-time. Advanced KM
systems must be able to handle the velocity of the available data by allowing for collecting,
aggregating and sharing the data and the knowledge generated during the interaction of
the participants. This is particularly important as the speed of strategic decision-making is
directly related to performance (Baum and Wally, 2003;Eisenhardt, 1989). Organizations
will not be able to effectively respond to the turbulent business environment, and rapidly
changing market demands, unless they are able to enhance the speed of their decisions
in strategic and tactical areas (Dewhurst and Willmott, 2014). The importance of this feature
is evident in management practices. Intel Corporation has shortened their product life cycle
from six months to 10 weeks to reach the market faster (Buchholz et al., 2012). Similarly,
DaimlerChrysler (2001) Corporation has used a new product development system to
reduce vehicle development times and improve quality. The fast pace of data generation
provides an opportunity to incorporate real-time data and information into decision-making.
As Davenport (2014) stresses, big data-enabled systems can help with rapid data capture,
aggregation, processing and analytics.
For example, a KM system that does not support virtual and live collaborations (online
meetings and real-time notifications) is less likely to benefit from the velocity of big data.
Integrating data analytics into KM systems enables it to support fast decision-making by
decreasing the time lag between analyzing real-time data and the final decision. This can
be done by elaborating on the “integrative” feature of advanced KM systems. Incorporating
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a diverse range of data sources, advanced KM systems can accelerate the process of
defining and evaluating alternatives.
The dynamic-agile feature is concerned with collecting, integrating and reporting on data
and knowledge in a reasonably fast manner. The velocity of the data can boost the speed
of decision-making. However, faster analysis of data does not necessarily lead to an
effective decision if the required expertise, experience, knowledge and insight are not
made available to interpret and integrate the data analysis into the decision. While SD-SD
and SD-UD may exhibit more agility, UD-SD and UD-UD are more dynamic. Drawing on
analytics and pre-developed algorithms allows SD-SD and SD-UD to be more agile.
However, as most strategic decisions are made through less structured processes (i.e.
UD-SD and UD-UD), agility is more or less traded off with dynamism in UD-SD and UD-UD
decisions. Accordingly, KM systems need to be both dynamic and agile. This is mainly
because the emergence of big data has led many companies to consider big data, in
addition to knowledge, expertise, experiences, preferences and insight, as an important
input into their strategy formulation.
For the KM systems to be able to benefit from agility and dynamism, the systems must be
simple and understandable for people throughout the organization.
Simple and understandable
The effective execution of big data and the integration of knowledge into decision-making
rely on more than just the technical aspects of the KM systems. Advanced KM systems
must be designed in such a way that the decision maker and all other participants involved
in the KM system are able to understand the tool and, more importantly, have confidence
in it. While developing algorithms to support SD-SD may require a high level of analytical
and technical skills, and UD-UD and UD-SD rely more on expert knowledge and insight.
The need for the engagement of a diverse range of users with different knowledge and
skills in advanced KM systems toward supporting all four types of decisions may lead the
KM system to be complicated. Unnecessary complication of the system reduces the
likelihood and level of user participation. In an interview about putting big data and
advanced analytics to work, McKinsey director Court (2012), emphasizes that to use big
data to improve decisions, the decision support tools used by the decision makers must be
simple and understandable, otherwise people will not use them: “for a company, if you have
100,000 employees and you’ve got only 14 that actually know this stuff and how to use it,
you’re not going to get sustainable change” (para. 11). An advanced KM system engages
experience, embodied and tacit knowledge, integrates various interpretations and
enhances the applicability of data to a decision-making situation. Accordingly, people with
different knowledge and work experiences should be able to work with advanced KM
systems.
The easy-to-use feature accompanied by the other main aspects such as social,
cross-lingual, integrative and dynamic and agile allow advanced KM systems to support all
four types of data-driven decisions by accommodating diverse sources of data and
knowledge. However, designing and developing advanced KM systems is not simple and
may be significantly challenging. Bringing together a diverse range of users such as data
analysts, managers, business experts and strategy analysts may lead to a KM system that
provides moderately satisfying features. The KM system may not offer all the features of an
advanced KM system to the extent that has been discussed in this paper, instead offering
moderate and satisfactory features. For example, while data analysts may put more
emphasis on the analytical features of the system, the business experts and other
knowledge users may consider communication and simplicity as more fundamental. In an
organization with little language diversity, the management may prefer not to invest hugely
in the cross-lingual feature of the system. Similarly, a multi-national organization may prefer
to emphasize on the simplicity of the advanced KM system if the majority of their employees
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are less technically inclined and language diversity is high within the organization and
across the countries where organization is operating in. We emphasize that the design of
the KM systems may vary depending on factors such as organizational objectives and
strategies, users’ preferences and organizational culture. We suggest that while the ground
rules are regarded as necessary components of advanced KM systems, the extent to which
an organization emphasizes on any of the features should reflect the organizational
objectives and culture, as well as users’ preferences.
Discussion and conclusion
The emergence of big data and the diversity of the data and knowledge sources and forms
within and outside organizations have increased the complexity of strategic
decision-making processes. Strategic decisions are often surrounded by ambiguity,
uncertainty and risk and rely to no small degree on the knowledge, expertise, experience,
expectations, perceptions, preferences, values and beliefs of the individuals and teams in
an organization. Moreover, the high volume, velocity and variety of big data play critical
roles in informing and enhancing the quality of strategic decisions which should not be
underestimated.
We argued how the emergence of big data requires inevitable adjustments in KM systems
to enable organizations and managers to integrate big data into their knowledge and expert
insight toward making more effective strategic decisions. We suggest a conceptual
framework to address the question: how would big data and the need for advanced
analytics in strategic decisions inform and, if necessary, reform (design and
implementation) KM systems?
We argue that in addition to encouraging and facilitating knowledge processes such as
knowledge creation, storing, retrieving, disseminating and application, KM systems
should support strategic decisions by integrating big data into them. Organizations
need to make sure that their KM systems are (re-)designed in such a way that they
support the seamless integration of knowledge and big data. We refer to these systems
as advanced KM systems and characterize them as social, cross-lingual, integrative,
dynamic and agile; and simple and understandable. Advanced KM systems go beyond
a simple text mining tool, or a document analysis mechanism, or a mere knowledge
sharing system. Advanced KM systems allow for the integration of human knowledge
and insight with big data and facilitate the incorporation of big data and knowledge into
strategic decisions.
We have identified four main types of decision-making that depend on whether a decision
and the underlying data are structured or unstructured: SD-SD, SD-UD, UD-SD and
UD-UD. We argue that advanced KM systems support four types of data-driven decisions.
This paper contributes to the KM and decision-making literature by introducing and
characterizing advanced KM systems and suggesting a typology of data-driven
decision-making. The typology of data-driven decisions is new and, to our best knowledge,
did not exist in the literature. Prior work has not provided explanations of how KM systems
can integrate big data into knowledge toward making more effective strategic decisions. In
particular, the introduction of the data-driven decision typology in association with big data
and knowledge offers a significant contribution to the extant literature.
This paper also provides insight for practitioners. The argument should provoke critical
thinking that practitioners including systems designers, organization management and
users need to do on the effectiveness evaluation of their KM systems. Our discussion of the
ground rules sheds light on how to align KM systems with organization strategies and how
to manage financial investment in the design and implementation of KM systems and the
required training.
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Our typology of data-driven decisions and our characterization of advanced KM systems
shed light on how KM systems should be reformed to integrate big data into knowledge to
support strategic decisions. However, there are some challenges in the conceptual
framework which can inform future studies that are interested in the same topic. For
example, the typology of data-driven decisions poses some challenges: the
inter-relationship of the decision-data quadrants is central to them. Further investigation
may extend our understanding of how the importance of each quadrant may vary
depending on the industry. What combination of the four quadrants may be the best
combination depending on the levels of the complexity of the decisions: simple decisions,
complicated decisions, or complex decisions (McKenzie et al., 2011)? In the same vein,
how is the transition from one quadrant to another explained? If a manager is in a UD-UD
situation and intends to make a more structured decision (i.e. moving to SD-UD), what
process does the decision maker need to go through to make that transition happen? What
features and guidelines are required?
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Corresponding author
Ali Intezari can be contacted at: A.Intezari@uq.edu.au
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... Artificial Intelligence (AI) (Lloyd & Cranmore, 2003), (Kruse et al., 2013), (Gergin & Koch, 2013), (Sturm et al., 2021), (Johnson et al., 2015) Big Data (BD) (Tian, 2017), (Uden & He, 2017), (Intezari & Gressel, 2017), (Thomas & Chopra, 2020) Communities of Practice (CoP) (Dinter et al., 2016), (Erat et al., 2006), (Randhawa et al., 2017), (Bell et al., 2012), (Huang & Zhang, 2016) Digital Artifacts (DAs) (Thomas & Chopra, 2020), (Fang et al., 2022), (Krogh & Haefliger, 2010) Enterprise Social Media (ESM) (Huang & Zhang, 2016), (Sultan, 2013), (Krogh, 2012), (Leonardi, 2014), (Schlagwein & Hu, 2017), Gamification (Friedrich et al., 2020), (Thneibat, 2021), (Žemaitis, 2014) Open Innovation (OI) (Dinter et al., 2016), (Randhawa et al., 2017), (Žemaitis, 2014), (Naqshbandi & Jasimuddin, 2018), (Santoro et al., 2018), (Lopes et al., 2017), (Haapalainen & Kantola, 2015), (Draghici et al., 2015), (Xu et al., 2018) Virtual Reality (VR) (Mueller et al., 2011), (García-Álvarez, 2015, (OLeary, 2013) In the following, we briefly introduce the identified innovative concepts within KM. We start with brief definitions and the relevance for KM, review the dimensions of the literature and provide further research opportunities as well as application areas. ...
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The study aimed to know the impact of the strategic mind of leaders in making strategic decisions at the University of Tikrit, and to achieve this goal, the researchers used the analytical Approach to analyze the data collected by the questionnaire, which is the main tool of the study. The study population was represented by all the deans and heads of departments at the University of Tikrit, who numbered (130) individuals. A sample of (97) individuals was drawn from them, and the questionnaire was distributed to them using a non-random sampling method (intentional). (93) valid questionnaires were retrieved for analysis and based on structural equation modeling by the partial squares method (SEM: PLS3). The study concluded that the strategic mind of the leaders at the University of Tikrit contributes constructively to the decision-making, and accordingly the study proposes the development of the systemic thinking of the leaders so that they can reformulate all the decisions that are taken and are thus reflected on the effectiveness of the strategic decisions in the studied university.
Chapter
This chapter outlines the adoption and implementation of knowledge management within the New Zealand Reserve Bank. In 1999, the Bank recognised that it had a very high exposure to loss of knowledge on departure of key staff. This was mainly due to two factors: recruitment of staff from a limited global pool of specifically skilled labour, and an average length of service of more than nine years during which time staff members accumulated an extensive knowledge of the Bank and its operations. In response to this and other challenges, the Bank embarked on an ongoing knowledge management program. The Bank invested significant resources into the program and from an initial corporate vision developed a knowledge management framework that led to the identification of potential areas of improvement within the organisation. The resulting knowledge strategy encompassed several key initiatives, the most significant of which was the goal of changing the organisational culture. Other initiatives included the consolidation of the Bank’s contact management into a single system, a review of the existing document management system, and information mapping. To date, while some initiatives have been achieved, others remain to be done. The challenge for the Bank now is to move from structured to unstructured processes for knowledge management and maintain the knowledge management focus while balancing available resources. The Bank must also consider how best to progress initiatives without necessarily attaching a specific knowledge management label, and identify ways to move ongoing development of knowledge management strategies to the next level.
Conference Paper
Market research has long relied on reactive means of data gathering, such as questionnaires or focus groups. With the wide-spread use of social media, millions of comments about customer opinions and feedback regarding products and brands are available. However, before using this ‘wisdom of the crowd’ as a source for marketing research, several challenges have to be tackled: the sheer volume of posts, their unstructured format, and the dozens of different languages used on the internet. All of them make automated usage of this data challenging. In this paper, we draw on dashboard design principles and follow a design science research approach to develop a framework for search, integration, and analysis of cross-language user-generated content. With ‘MarketMiner’, we implement the framework in the automotive industry by analyzing Chinese auto forums. The results are promising in that MarketMiner can dramatically improve utilization of foreign-language social media content for market intelligence purposes.