Chap 11 for final - Management Information System | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố HCM

11-1 What is the role of knowledge management systems in business?
● Define knowledge management and explain its value to businesses.
Knowledge management is the set of processes developed in an organization to create, gather,
store, maintain, transfer, apply, and disseminate the firm's knowledge. As the textbook points
out, knowledge management promotes organizational learning, enables the organization to
learn from its environment and incorporate this new knowledge into its business processes.
Knowledge management helps firms do things more effectively and efficiently, which can help
managers make better decisions and generate better outcomes. Besides that, it cannot be
easily duplicated by other organizations. This “in–house” knowledge is a very valuable asset
and is a major source of profit and competitive advantage.
● Describe the important dimensions of knowledge. (DB)
KNOWLEDGE IS A FIRM ASSET: Knowledge is an intangible asset. The transformation of
data into useful information and knowledge requires organizational resources. Knowledge is
not subject to the law of diminishing returns as are physical assets but instead experiences
network effects because its value increases as more people share it.
KNOWLEDGE HAS DIFFERENT FORMS: Knowledge can be either tacit or explicit
(codified). Knowledge involves know-how, craft, and skill. Knowledge involves knowing how
to follow procedures. Knowledge involves knowing why, not simply when, things happen (causality).
KNOWLEDGE HAS A LOCATION: Knowledge is a cognitive event involving mental
models and maps of individuals. There is both a social and an individual basis of
knowledge. Knowledge is “sticky” (hard to move), situated (enmeshed in a firm’s culture),
and contextual (works only in certain situations).
KNOWLEDGE IS SITUATIONAL: Knowledge is conditional; knowing when to apply a
procedure is just as important as knowing the procedure (conditional). Knowledge is related
to context; it is essential to know how to use a certain tool and under what circumstances.
● Distinguish between data, knowledge, and wisdom and between tacit knowledge and
explicit knowledge. (DB)
Chapter 1 defines data as flows of events or transactions captured by an organization’s
systems that are useful for transacting but little else. To turn data into useful information, a
firm must expend resources to organize data into categories of understanding, such as
monthly, daily, regional, or store-based reports of total sales. To transform information into
knowledge, a firm must expend additional resources to discover patterns, rules, and
contexts where the knowledge works. Finally, wisdom is thought to be the collective and
individual experience of applying knowledge to the solution of problems. Wisdom involves
where, when, and how to apply knowledge.
Knowledge residing in the minds of employees that has not been documented is called
tacit knowledge, whereas knowledge that has been documented is called explicit knowledge.
● Describe the stages in the knowledge management value chain. (DB)
The first stage is knowledge acquisition. Organizations acquire knowledge in a number of
ways, depending on the type of knowledge they seek. The first knowledge management
systems sought to build corporate repositories of documents, reports, presentations, and
best practices. These efforts have been extended to include unstructured documents. In
other cases, organizations acquire knowledge by developing online expert networks so that
employees can “find the expert” in the company who is personally knowledgeable. In still
other cases, firms must acquire new knowledge by discovering patterns in corporate data
via machine learning or by using knowledge workstations where engineers can discover
new knowledge. A coherent and organized knowledge system also requires business
analytics using data from the firm’s transaction processing systems that track sales,
payments, inventory, customers, and other vital areas as well as data from external sources
such as news feeds, industry reports, legal opinions, scientific research, and government statistics.
The second stage is knowledge storage. Knowledge storage generally involves the creation
of a database. Document management systems that digitize, index, and tag documents
according to a coherent framework are large databases adept at storing collections of
documents. Expert systems also help corporations preserve the knowledge that is acquired
by incorporating that knowledge into organizational processes and culture.
Thirdly, disseminating knowledge should be conducted. Portals, email, instant messaging,
wikis, social business tools, and search engine technology have added to an existing array
of collaboration tools for sharing calendars, documents, data, and graphics.
Finally, knowledge application, new knowledge must be built into a firm’s business
processes and key application systems, including enterprise applications for managing
crucial internal business processes and relationships with customers and suppliers.
Management supports this process by creating—based on new knowledge—new business
practices, new products and services, and new markets for the firm.
11-2 What are artificial intelligence (AI) and machine learning? How do businesses use AI?
● Define artificial intelligence (AI) and the major AI techniques. (DB)
“Intelligent” techniques are often described as artificial intelligence (AI). There are many
definitions of artificial intelligence. In the most ambitious vision, AI involves the attempt to
build computer systems that think and act like humans.
Artificial intelligence is a family of programming techniques and technologies, each of which
has advantages in select applications. Table 11.2 describes the major types of AI: expert
systems, machine learning, neural networks, deep learning, genetic algorithms, natural
language processing, computer vision systems, robotics, and intelligent agents.
● Define an expert system, describe how it works, and explain its value to business. (DB)
Expert systems capture the knowledge of individual experts in an organization through in-
depth interviews, and represent that knowledge as sets of rules. These rules are then
converted into computer code in the form of IF-THEN rules. Such programs are often used
to develop apps that walk users through a process of decision making.
Expert systems model human knowledge as a set of rules that collectively are called the
knowledge base. Expert systems can have from a handful to many thousands of rules,
depending on the complexity of the decision-making problem. The strategy used to search
through the collection of rules and formulate conclusions is called the inference engine. The
inference engine works by searching through the rules and firing those rules that are
triggered by facts the user gathers and enters.
Expert systems provide benefits such as improved decisions, reduced errors, reduced
costs, reduced training time, and better quality and service. They have been used in
applications for making decisions about granting credit and for diagnosing equipment
problems, as well as in medical diagnostics, legal research, civil engineering, building
maintenance, drawing up building plans, and educational technology (personalized learning and responsive testing).
● Define machine learning, explain how it works, and give some examples of the kinds
of problems it can solve. (DB)
More than 75 percent of AI development today involves some kind of machine learning (ML)
accomplished by neural networks, genetic algorithms, and deep learning networks, with the
main focus on finding patterns in data, and classifying data inputs into known (and
unknown) outputs. Machine learning is based on an entirely different AI paradigm than
expert systems. In machine learning there are no experts, and there is no effort to write
computer code for rules reflecting an expert’s understanding. Instead, ML begins with very
large data sets with tens to hundreds of millions of data points and automatically finds
patterns and relationships by analyzing a large set of examples and making a statistical inference.
● Define neural networks and deep learning neural networks, describing how they work
and how they benefit organizations. NEURAL NETWORK:
A neural network is composed of interconnected units called neurons. Each neuron can take
data from other neurons, and transfer data to other neurons in the system. Neural networks find
patterns and relationships in very large amounts of data that would be too complicated and
difficult for a human being to analyze by using machine learning algorithms and computational
models that are loosely based on how the biological human brain is thought to operate. In other
words, neural networks are pattern detection programs. Neural networks learn patterns from
large quantities of data by sifting through the data, and ultimately finding pathways through the
network of thousands of neurons. DEEP LEARNING:
"Deep learning" neural networks are more complex, with many layers of transformation of the
input data to produce a target output. Used almost exclusively for pattern detection on unlabeled
data where the system is not told what to look for specifically but to simply discover patterns in
the data. The system is expected to be self-taught.
-> Neural network applications in medicine, science, and business address problems in pattern
classification, prediction, and control and optimization.
● Define and describe genetic algorithms, and intelligent agents. Explain how each
works and the kinds of problems for which each is suited. GENETIC ALGORITHMS:
Genetic algorithms are problem-solving methods that promote the evolution of solutions to
specific problems using the model of living organisms adapting to their environment. -
Searches a population of randomly generated strings of binary digits to identify the right
string representing the best possible solution for the problem. -
As the solutions alter and combine, the worst ones are discarded and the better ones go
on to produce better solutions.
Genetic algorithms are used to solve problems that are very dynamic and complex, involving
hundreds or thousands of variables or formulas. INTELLIGENT AGENT:
Software program that uses a built-in and/or learned knowledge base to carry out specific,
repetitive, and predictable tasks for an individual user, business process, or software application
that work in the background without direct human intervention.
The agent uses a limited built-in or learned knowledge base to accomplish tasks or make
decisions on the user's behalf, such as deleting junk email, scheduling appointments, or finding
the cheapest airfare to California.
● Define and describe computer vision systems, natural language processing systems,
and robotics and give examples of their applications in organizations.
COMPUTER VISION SYSTEMS:
Systems that try to emulate the human visual system to view and extract information from real
world images. Such systems incorporate image processing, pattern recognition, and image understanding.
Eg: An example is Facebook's facial recognition tool called DeepFace, which is nearly as
accurate as the human brain in recognizing a face.
NATURAL LANGUAGE PROCESSING SYSTEMS:
AI technique for enabling a computer to understand and analyze natural language as opposed
to language formatted to be understood by computers.
Eg: You can see natural language processing at work in leading search engines such as
Google, spam filtering systems, and text mining sentiment analysis ROBOTICS:
Design, construction, operation, and use of moveable machines that can substitute for human
movements as well as computer systems for their control, sensory feedback, and information processing.
Eg: They are often used in dangerous environments (such as bomb detection and deactivation),
manufacturing processes, military operations (drones), and medical procedures (surgical robots).
11-3 What types of systems are used for enterprise wide knowledge management, and
how do they provide value for businesses?
● Define and describe the various types of enterprise-wide knowledge management
systems and explain how they provide value for businesses.
According to experts, at least 80 percent of an organization’s business content is semi
structured or unstructured—information in folders, messages, memos, proposals, emails,
graphics, electronic slide presentations, and even videos created in different formats and stored
in many locations. Enterprise content management (ECM) systems help organizations manage
both types of information. They have capabilities for knowledge capture, storage, retrieval,
distribution, and preservation to help firms improve their business processes and decisions.
Such systems include corporate repositories of documents, reports, presentations, and best
practices, as well as capabilities for collecting and organizing semistructured knowledge such as email.
● Describe the role of the following in facilitating knowledge management: taxonomies,
MOOCs, and learning management systems.
TAXONOMY: Taxonomy, also known as a method of classifying things according to a
predetermined system. Once the categories for classifying knowledge have been created, each
knowledge object needs to be "tagged," or classified so that it can be easily retrieved.
MASSIVE OPEN ONLINE COURSE (MOOC): A MOOC is an online course made available via
the web to very large numbers of participants. Companies view MOOCs as a new way to design
and deliver online learning where learners can collaborate with each other, watch short videos,
and participate in threaded discussion groups.
LEARNING MANAGEMENT SYSTEM: Companies need ways to keep track of and manage
employee learning and to integrate it more fully into their knowledge management and other
corporate systems. A learning management system (LMS) provides tools for the management,
delivery, tracking, and assessment of various types of employee learning and training.
11-4 What are the major types of knowledge work systems, and how do they provide value for firms?
● Define knowledge work systems and describe the generic requirements of knowledge work systems. (Hưng)
Knowledge workers perform three key roles that are critical to the organization and to the
managers who work within the organization: -
Keeping the organization current in knowledge as it develops in the external world—in
technology, science, social thought, and the arts -
Serving as internal consultants regarding the areas of their knowledge, the changes
taking place, and opportunities -
Acting as change agents, evaluating, initiating, and promoting change projects
Most knowledge workers rely on traditional office systems but often require highly specialized
knowledge work systems with powerful graphics, analytical tools, and communications and
document management capabilities. These systems require great computing power, access to
external databases, easy-to-use interfaces, and optimization for the specific tasks to be performed
● Describe how the following systems support knowledge work: CAD, virtual reality, and augmented reality.
● CAD systems automate the creation and revision of designs using computers and
sophisticated graphics software. Benefits include the production of more sophisticated
and functional designs, reducing the time required to produce designs, reducing
expensive engineering changes, preparing fewer prototypes, and facilitating the tooling and manufacturing process.
● Virtual reality systems have visualization, rendering, and simulation capabilities. This
type of system uses interactive graphics software to create computer-generated
simulations that are so close to reality that users believe they are participating in a real
world. The users actually feel immersed in the computer-generated world. Virtual reality
provides educational, scientific, and business benefits.
● Augmented reality is related to virtual reality and enhances visualization by providing a
live direct or indirect view of a physical real-world environment whose elements are
augmented by virtual computer-generated imagery. The user remains grounded in the
real physical world, and the virtual images are merged with the real view to create an augmented display.