knowledge management case study insurance

Knowledge Management Case Studies: How Leading Companies Use Knowledge Management to Drive Success

Knowledge Management Case Studies: In today’s fast-paced business environment, knowledge is power. Companies that can effectively manage and leverage their collective knowledge often outperform their competitors. Knowledge Management (KM) involves the process of capturing, distributing, and effectively using knowledge within an organization. Leading companies across various industries have successfully implemented KM systems , significantly improving productivity, innovation, and competitive advantage. This article explores how some of these companies have utilized knowledge management to drive success.

What is Knowledge Management?

Knowledge Management is a systematic approach to capturing, organizing, sharing, and analyzing an organization’s knowledge in terms of resources, documents, and people skills. It involves various practices and technologies that help efficiently handle knowledge.

Benefits of Knowledge Management

• Improved Decision Making: Access to comprehensive and up-to-date information aids in making informed decisions.
• Enhanced Efficiency: Streamlined processes and better resource allocation result in higher productivity.
• Innovation: Sharing knowledge fosters creativity and innovation.
• Customer Satisfaction: Better service delivery and product quality lead to increased customer satisfaction.
• Competitive Advantage: Companies that effectively manage their knowledge assets outperform their competitors.

Case Studies of Leading Companies Using Knowledge Management

Overview: IBM, a global leader in technology and consulting, has long recognized the importance of knowledge management. With a vast network of employees and operations in over 170 countries, managing knowledge effectively is crucial for IBM.

KM Strategy: IBM’s KM strategy revolves around creating a culture of knowledge sharing and continuous learning. The company has implemented various tools and practices to facilitate knowledge management, including:

• Knowledge Repositories: IBM has developed extensive knowledge repositories where employees can access technical documents, best practices, and case studies.
• Communities of Practice: These are groups of employees who share a common interest in a particular field. They regularly share insights, solve problems, and develop new ideas.
• Training and Development: IBM invests heavily in employee training programs to ensure that its workforce is always at the cutting edge of technology and industry practices.

Results: IBM’s commitment to knowledge management has led to significant improvements in innovation, efficiency, and customer satisfaction. For instance, the company’s ability to rapidly adapt to new technologies and market changes has been a key factor in maintaining its competitive edge.

Overview: Google, a tech giant known for its innovation and cutting-edge technology, has a unique approach to knowledge management. The company’s success is largely attributed to its ability to manage and leverage its vast knowledge assets.

KM Strategy: Google’s KM strategy focuses on fostering a culture of collaboration and continuous learning. Some of the key elements of Google’s KM approach include:

• Open Communication: Google encourages open communication and information sharing among employees. Tools like Google Drive and Google Docs facilitate real-time collaboration.
• Learning and Development: Google offers extensive learning and development programs to its employees, including access to online courses, workshops, and seminars.
• Data-Driven Decision Making: Google uses advanced data analytics to gain insights and make informed decisions.

Results: Google’s effective knowledge management practices have led to numerous innovations, including the development of products like Google Search, Google Maps, and Google Assistant. The company’s ability to harness and leverage knowledge has been a key driver of its success.

Overview: Siemens , a global powerhouse in electronics and electrical engineering, has a comprehensive knowledge management system that has played a crucial role in its success.

KM Strategy: Siemens’ KM strategy involves creating a knowledge-sharing culture and implementing advanced KM technologies. Key components of Siemens’ KM approach include:

• Knowledge Sharing Platforms: Siemens has developed several platforms for knowledge sharing, including intranets, wikis, and forums.
• Communities of Practice: Siemens supports various communities of practice where employees can share knowledge and collaborate on projects.
• Continuous Improvement: Siemens fosters a culture of continuous improvement, encouraging employees to share insights and learn from each other.

Results: Siemens’ commitment to knowledge management has resulted in significant improvements in efficiency and innovation. The company’s ability to rapidly develop and deploy new technologies has been a key factor in maintaining its competitive edge.

Overview: Toyota, one of the world’s leading automotive manufacturers, has a robust knowledge management system that has been instrumental in its success.

KM Strategy: Toyota’s KM strategy focuses on continuous improvement and knowledge sharing. Key elements of Toyota’s KM approach include:

• Kaizen: Toyota’s philosophy of continuous improvement, known as Kaizen, encourages employees to constantly seek ways to improve processes and share their insights.
• Knowledge Repositories: Toyota has developed extensive knowledge repositories where employees can access technical documents, best practices, and case studies.
• Training and Development: Toyota invests heavily in employee training programs to ensure that its workforce is always at the cutting edge of technology and industry practices.

Results: Toyota’s effective knowledge management practices have led to significant improvements in efficiency, quality, and innovation. The company’s ability to harness and leverage knowledge has been a key driver of its success.

5. Microsoft

Overview: Microsoft, a global leader in software and technology, has a comprehensive knowledge management system that has been instrumental in its success.

KM Strategy: Microsoft’s KM strategy involves creating a culture of knowledge sharing and continuous learning. Key components of Microsoft’s KM approach include:

• Knowledge Sharing Platforms: Microsoft has developed several platforms for knowledge sharing, including intranets, wikis, and forums.
• Communities of Practice: Microsoft supports various communities of practice where employees can share knowledge and collaborate on projects.
• Continuous Improvement: Microsoft fosters a culture of continuous improvement, encouraging employees to share insights and learn from each other.

Results: Microsoft’s commitment to knowledge management has resulted in significant improvements in innovation, efficiency, and customer satisfaction. The company’s ability to rapidly adapt to new technologies and market changes has been a key factor in maintaining its competitive edge.

Best Practices in Knowledge Management

• Encourage a Knowledge-Sharing Culture: Foster an environment where employees feel comfortable sharing their knowledge and insights.
• Invest in KM Technologies: Implement advanced KM technologies to facilitate knowledge capture, storage, and sharing.
• Provide Continuous Learning Opportunities: Offer regular training and development programs to keep employees up-to-date with the latest industry trends and practices.
• Measure and Improve: Regularly assess the effectiveness of your KM practices and make necessary improvements.

Knowledge management is a critical factor in the success of leading companies across various industries. By effectively managing and leveraging their collective knowledge, these companies have achieved significant improvements in productivity, innovation, and competitive advantage. Implementing a robust KM system and fostering a culture of knowledge sharing can help organizations of all sizes drive success and stay ahead of the competition.

• Davenport, T. H., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press.
• Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
• Wiig, K. M. (1993). Knowledge Management Foundations: Thinking About Thinking – How People and Organizations Create, Represent, and Use Knowledge. Schema Press.

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Insurance Knowledge Management 101: Empowering You with Industry Expertise

Insurance Knowledge Management: Unlocking the Power of Knowledge

Imagine a world where insurance professionals have instant access to the knowledge they need to make informed decisions, resolve complex cases, and provide exceptional customer service. This is the transformative power of insurance knowledge management.

Challenges and Opportunities

Insurance professionals often struggle to navigate a vast sea of information, with policies, regulations, and industry best practices constantly evolving. Siloed systems and fragmented knowledge hinder productivity and limit the ability to share expertise across the organization. By embracing insurance knowledge management, insurers can overcome these challenges and unlock a world of opportunities.

The Transformative Target

Insurance knowledge management aims to centralize, organize, and make accessible all relevant knowledge to empower insurance professionals. This includes policies, claims data, industry best practices, and expert insights. By capturing and sharing this knowledge effectively, insurers can:

• Improve operational efficiency
• Enhance decision-making
• Reduce risk and compliance issues
• Provide superior customer experiences

Key Benefits and Considerations

Implementing a robust insurance knowledge management system requires careful planning and execution. Key considerations include:

• Employing technology to capture and organize knowledge
• Establishing a governance framework to ensure accuracy and consistency
• Fostering a culture of knowledge sharing and collaboration

By embracing insurance knowledge management, insurers can unlock the full potential of their workforce, enhance customer satisfaction, and gain a competitive edge in the ever-evolving insurance landscape.

Insurance Knowledge Management: A Comprehensive Guide

Introduction

In the competitive insurance industry, effective knowledge management is crucial for organizations to stay ahead of the curve and deliver exceptional customer experiences. Insurance knowledge management involves the systematic capture, organization, retrieval, and dissemination of knowledge within the enterprise. By leveraging knowledge management strategies, insurers can improve decision-making, enhance employee productivity, and drive innovation.

Benefits of Insurance Knowledge Management

• Improved Decision-Making: Access to relevant and up-to-date knowledge enables underwriters, claims adjusters, and other decision-makers to make informed and accurate judgments.
• Enhanced Employee Productivity: Employees can quickly and easily access the information they need to perform their jobs effectively, reducing time spent on research and increasing efficiency.
• Reduced Knowledge Loss: Knowledge management systems preserve valuable information, preventing its loss due to employee turnover or organizational changes.
• Improved Customer Service: Knowledge management empowers insurance agents and customer service representatives with the information needed to answer customer inquiries promptly and accurately.
• Increased Innovation: Sharing and collaboration through knowledge management fosters innovation by allowing employees to learn from each other’s experiences and insights.

Key Components of Insurance Knowledge Management

• Content Management: Organizing and categorizing insurance-related documents, policies, and industry best practices.
• Search and Retrieval: Implementing search tools and mechanisms to enable quick and efficient access to knowledge.
• Collaboration and Sharing: Establishing platforms for employees to share knowledge, ask questions, and collaborate on projects.
• Expertise Management: Identifying and connecting subject matter experts with individuals seeking specific knowledge.
• Knowledge Capture: Formalizing processes for capturing knowledge from various sources, including employee interviews, industry research, and case studies.

Best Practices for Insurance Knowledge Management

• Align with Business Objectives: Ensure that the knowledge management system supports the strategic goals and objectives of the organization.
• Use Technology Effectively: Leverage knowledge management tools and platforms to automate processes and enhance accessibility.
• Foster a Culture of Knowledge Sharing: Encourage employees to contribute their knowledge and expertise to the system.
• Provide Continuous Training: Train employees on how to use the knowledge management system effectively.
• Measure and Evaluate: Regularly track and analyze the effectiveness of the knowledge management system and make adjustments as needed.

Challenges of Insurance Knowledge Management

• Information Overload: The sheer volume of insurance-related information can make it challenging to organize and retrieve relevant knowledge.
• Lack of Standardization: Insurance policies and industry practices vary widely, making it difficult to establish a standardized knowledge management system.
• Employee Resistance: Some employees may be reluctant to share knowledge if they believe it will undermine their value to the organization.
• Data Security: Protecting sensitive insurance-related data and complying with regulatory requirements is paramount.
• Rapidly Changing Industry: Keeping up with industry regulations and technology advancements can challenge knowledge management systems.

Emerging Trends in Insurance Knowledge Management

• Artificial Intelligence (AI): AI-powered tools can automate knowledge capture and improve search and retrieval capabilities.
• Machine Learning (ML): ML algorithms can analyze knowledge patterns and identify trends to support decision-making.
• Chatbots: Chatbots can provide instant access to knowledge and assist employees and customers with inquiries.
• Knowledge Graphs: Knowledge graphs represent knowledge in a structured and interconnected way, enhancing search precision.
• Blockchain Technology: Blockchain can enhance data security and ensure the integrity of knowledge within the system.

Effective insurance knowledge management is essential for insurers to navigate the complex and competitive landscape. By adopting best practices, embracing emerging technologies, and addressing challenges, organizations can harness the power of knowledge to drive innovation, improve decision-making, and deliver superior customer experiences.

What are the key benefits of insurance knowledge management? Improved decision-making, enhanced employee productivity, reduced knowledge loss, improved customer service, and increased innovation.

What are the challenges of implementing insurance knowledge management? Information overload, lack of standardization, employee resistance, data security, and a rapidly changing industry.

What are the emerging trends in insurance knowledge management? Artificial Intelligence (AI), Machine Learning (ML), Chatbots, Knowledge Graphs, and Blockchain Technology.

How can insurance companies overcome employee resistance to knowledge sharing? By fostering a culture of collaboration, providing incentives for knowledge contributions, and addressing concerns about job security.

What metrics can insurance companies use to evaluate the effectiveness of their knowledge management system? Knowledge retrieval time, employee satisfaction, customer satisfaction, and impact on business outcomes.

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• DOI: 10.1080/14778238.2021.1970490
• Corpus ID: 239629473

Improve enterprise knowledge management with internet of things: a case study from auto insurance industry

• Lixin Liu , Wenzhuo Li , +1 author J. Zhang
• Published in Knowledge Management Research… 31 August 2021
• Computer Science, Business

7 Citations

Sustainability analysis of enterprise performance management driven by big data and internet of things, business management in the information age: use of systems, data processing and scalability for organizational efficiency, investigating the emerging and future trends of knowledge management in small and medium enterprises: a science mapping approach, enterprise systems, emerging technologies, and the data-driven knowledge organisation, developing a model of insurance securitisation in iranian environmental conditions, liability coverage in emerging technologies: challenges and solutions, exploring industry-level fairness of auto insurance premiums by statistical modeling of automobile rate and classification data, 82 references, how the internet of things can help knowledge management: a case study from the automotive domain, the internet of things: building a knowledge management system for open innovation and knowledge management capacity, leveraging internet of things and big data analytics initiatives in european and american firms: is data quality a way to extract business value, the internet of things, dynamic data and information processing capabilities, and operational agility, internet of things for enterprise systems of modern manufacturing, the internet of things (iot): applications, investments, and challenges for enterprises, a qualitative evaluation of iot-driven ehealth: knowledge management, business models and opportunities, deployment and evolution, technology used in knowledge management by global professional event services, mapping knowledge risks: towards a better understanding of knowledge management, does big data mean big knowledge km perspectives on big data and analytics, related papers.

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How an insurance knowledge management system can improve your cx strategy.

A knowledge management system is a key way to improve your customer experience strategy from the inside out. From your internal staff needs to the way in which policyholders access information, knowledge management makes processes smoother and creates efficiencies across the board for insurers. This article will take a look at how an effective knowledge management system can strengthen customer experiences, reduce costs, and build a more efficient insurance team.

What is a Knowledge Management System?

As more and more processes within the insurance industry go digital, it can become daunting to keep important data and sensitive customer information organised. A digital knowledge management system is a solution that helps business operations and teams improve their processes, collaboration, and overall understanding. 

For insurance agencies, knowledge management offers a central place for policyholders, agents, and other staff to access information they need at a moment’s notice. Whether an agent is helping a customer file a claim or change policies, they will be able to access all required user and account information from one central place in order to provide quick and effective service.

With vast amounts of data being digitally generated every day, it’s important for insurers to preserve and access that information in order to better serve their customer base.

Why is Knowledge Management Important for the Insurance Industry?

Insurance agencies typically have large amounts of information sitting in disparate systems. Whether they need customer data or account details, a knowledge management system brings everything together for easy access.

Like many of today’s industries, the new digital insurance customer is also looking for anytime-anywhere insurance buying and servicing. Previously, insurance agencies were a product-centric industry, putting the focus of most interactions on selling more to their customers. While products and offerings are still important, a more customer-centric approach is the ideal way for insurers to add value and retain happy, loyal users as we move into a more digital landscape.

Few Customer Interactions Mean Lower Engagement Opportunities

One potential reason why customer experience (CX) was put on the back burner is because insurance agencies typically don’t have many interactions with customers to begin with. According to Bain & Company, the insurance industry is such a low-touch business that agencies sometimes interact with customers less than once per year . With so few opportunities to engage with policyholders, insurers need to make each interaction, human or digital, as frictionless and positive as possible.

A Competitive Insurance Market Gives Buyers More Options

In the past, insurance companies were viewed as a commodity. Customers chose who they did business with based on what was available in a slimmer marketplace. But in the past decade, the market has become much more competitive, creating more informed and sophisticated buyers who have more options than ever to choose from.

So how does implementing a knowledge management system to support your CX strategy help you stand out from the competition? By creating a more buyer-centric experience , agents can provide more efficient service, while customers can easily access the information that they need to make a claim. With users looking for more simplified buying experiences, having access to exactly what they need makes a typically stressful time much easier.

Knowledge Management Features

Whether you’re just beginning the digitisation journey or looking for ways to further optimise your CX strategy, there are a few must-have knowledge management features to simplify customer experiences. Current policyholders need to be able to access information about filing claims, policy types, policy terms, and other important personal data. Potential customers should be able to quickly get relevant product information and real-time policy quotes.

Knowledge Management Features for Policyholders

Here are a few examples of knowledge management features that help enhance digital experiences for policyholders:

• FAQ forums - A well-written frequently asked questions (FAQ) section offers easy-to-digest answers to commonly asked questions. Customers can get information they need quickly, without the need for assistance from an employee. Helpful FAQ forums should include simple questions that have short and sweet answers. For example, “How do I update my account address?” or “Where can I find policy term information?”.
• Articles and guides - How-to articles and tutorials are tools that can help policyholders learn more about services and guide them, step by step, through simple account tasks. This could include guides for claim applications, policy comparison articles, or educational insurance videos.
• A knowledge base - This self-service feature provides a repository of information that helps customers make informed policy-related decisions. An insurance knowledge base can include a glossary of legal terms or policy comparisons. Because customers can access this information on their own, you’ll decrease your cost-to-serve and free up time for staff.

Knowledge Management for Insurance Staff

A combination of seamless digital interaction and efficient human assistance is the key to insurance companies staying competitive and relevant today. But in order for insurance agents to provide exceptional one-on-one service, they need to be equipped with accurate information effortlessly. In addition to assisting customers, it’s important for agents to be up-to-date with relevant training, company information, and changes to products or processes. Here are a few knowledge management features that help insurance staff uphold better CX strategies:

• Customer information database - When customers need access to important documents or need time-sensitive assistance, agents should be able to pull data from a centralised knowledge pool. Knowledge management tools can help organise documents like evidence of property forms, certificates of insurance, or loss run reports.
• Onboarding training - With a rise in remote and hybrid work environments, effective onboarding is imperative to create and uphold standard processes. Documentation related to employee training can be retrieved when needed and updated as procedures change. Digital knowledge management systems help to keep company information centralised and easy to access.
• Webinars and education programs - Digital training, lectures, and educational gatherings are great ways to share knowledge across the organisation. Webinars can be recorded, saved, and used again for future employee training.

The Benefits of Knowledge Management

Knowledge management helps to ensure that policyholders have a seamless and consistent experience with an insurance agency. Whether they are accessing policy information on a website or making account updates on a mobile app, knowledge systems make it simple to find what they need. By creating more informed customers, insurers don’t have to rely so heavily on employing more agents to assist with endless customer service needs that can be resolved through self-service options. In addition to a lower cost-to-serve, knowledge management helps to simplify the customer experience, which in turn creates satisfied policyholders.

Liferay Digital Insurance Solutions

Knowledge management systems ultimately help companies create a more collaborative and knowledgeable workforce. The insurance industry is an ever-changing workspace where both customer experience and collaborative knowledge help build loyalty and long-term success.

Position your company for digital success. Find out more about how Liferay’s solutions can be leveraged to create exceptional digital experiences for your agents, insurance customers, and employees.

Related Content

Why Insurance Knowledge Management Is the Need of Hour?

• By Saadiya Munir
• Last updated: September 9, 2022
• 5 minute read

With an end-to-end insurance knowledge management system, insurance companies can reduce the piles of paperwork and make more sales.

Stay up to date with VIDIZMO's latest news:

The challenge of knowledge capture & transfer for an organization before an employee’s departure is not new. It’s always on the agenda of the knowledge managers & supervisors.

But the loss of knowledge & not being able to manage it properly is an issue that’s getting a lot of attention these days. Knowledge administrators, Human Resource offices and senior management are all worried about this concern.

Capturing knowledge inside an organization is one of the primary motivations behind knowledge management professionals.

Organizations use knowledge management platforms to ensure that the knowledge doesn’t leave with them when individuals leave. In this blog, we’ll be discussing one case, Insurance Knowledge Management.

Let’s see why knowledge management systems are essential for insurance. What are some of the challenges faced by insurance companies while managing knowledge? How can VIDIZMO’s video content management platform help insurance companies securely manage their knowledge?

What Is Knowledge Management & Why It Is Important?

Knowledge management is described as a set of activities aimed at capturing, managing, sharing, and retrieving knowledge.

The term Knowledge Management was first used in the early 1980s by Peter Senge who wrote about how companies could use information technology to improve their performance. He coined the phrase “ knowledge management ” to describe this concept.

In his book entitled The Fifth Discipline , Senge stated that “ organizations are complex adaptive systems that have the ability to learn and therefore change over time .”

In the late 1990s, the term Knowledge Management began to gain popularity among business leaders. At that point, the focus shifted from simply improving organizational performance to developing strategies to help employees work together effectively.

Today, many businesses are using Knowledge Management to increase productivity, reduce costs, and enhance customer service.

Public and private organizations are battling the loss of knowledge because of employee turnover. Additionally, recruiting expenses, loss of productivity, unavailability of centralized content management, and training & on-boarding processes to replace the employees can reach tremendous value.

Managing Knowledge in Insurance Companies

Knowledge management for insurance has become an essential tool that helps

set cost strategies,

decide risks,

increase employee productivity,

improve corporate communication,

develop client support,

and control costs.

Insurance knowledge management is critical for both the front-line insurance organizations and their reinsurance accomplices. Knowledge management carries your insurance business higher than ever.

Keep your customers fulfilled and develop income by unifying every one of your information in one spot. With an end-to-end insurance knowledge management framework , companies can reduce the piles of paperwork and make more sales by offering the best plans for portioned customers.

Why Apply Knowledge Management to the Insurance Industry

For insurance organizations, morals must be a fundamental component in creating relations and keeping up with them, in the long run, with clients, related insurance organizations, or the general population.

The insurance business has answered novel and challenging situations throughout the long term, offering innovative products fit for the interest. It is a continuous trend, and dealers and agents are giving their all to fit the client’s needs in the market.

In the insurance business, trade secrets, classified data and significant thoughts are essential for the workforce knowledge. Recruiting, selecting, training, and overseeing insurance agents are genuine challenges for insurance organizations.

For larger organizations, insurance knowledge management also plays a part in their corporate communication & training. Another delicate ethical issue is the situation of insurance agents leaving their bosses to move to a contender insurance organization and are expected to bring as many clients as possible from the old manager.

In a new Hay Group study of 5000 leaders, 46% showed they hope to stay for only two to five years. The expense of this misfortune can be gigantic.

In most cases, the takeoff of only one sales supervisor could cost many euros because of the deficiency of essential deals and client knowledge. Besides, recruiting expenses lost productivity, and employee training to replacement can arrive at values above half of an employee’s salary.

Challenges Facing by the Insurance Industry

A few challenges the insurance industry faces can be resolved by using a centralized video content management system.

Failure to delineate a client-driven business approach.

Testing to coordinate vast measures of information decisively.

Can’t give the perfect data at the ideal chance to customers coming about reduced consumer loyalty.

Reduced speed of knowledge transfer process bringing about decreased workforce efficiencies and expanded training costs.

Quickly changing regulatory or billing policies are difficult to adjust to with a static knowledge management framework.

The knowledge management system is challenging to make, share, and oversee, assuming that the knowledge and data are incredibly intricate.

Maybe more than in some other industries, the insurance providers are continually looking for ways of decreasing paperwork.

Video knowledge management for insurance organizations lessens the vast assortments of paper applications and cases while further developing efficiencies.

The insurance organization should use innovation as an essential strategy to prepare its representatives and customers with meaningful data at the right second. This produces lower costs, expanded efficiency, fulfilled patients, and better worker execution.

The advantages of knowledge management range the whole insurance value chain, from further developing agents’ efficiency with collaboration tools to lessening an opportunity to showcase new items.

Simultaneously, you increase consumer loyalty with self-service choices, laid out FAQs, and agents furnished with precise information to give the best solutions.

How Does VIDIZMO Solve the Challenges in the Knowledge Management Insurance Industry?

One of the main challenges faced by insurance companies, as discussed above, is knowledge transfer of retiring employees & onboarding of new employees .

Lucky for modern-day organizations, there is a generally versatile, savvy, and engaging medium to capture, record and preserve basic knowledge and skill from retiring agents.

Your organization can utilize video to rapidly and innovatively capture & manage knowledge, use it for progression training, and save it for the years to come. Despite its wide-ranging advantages for versatility, cost-saving, time-effective knowledge transfer, and engagement, video isn’t brutal to manage compared to documents.

Video requires specific skills to transfer, store, manage and distribute to a massive workforce in a unified, versatile, and cost-effective solution. Before considering video as your knowledge sharing system, you need to keep a few things in mind:

Video files are of different formats

It’ll take storage space on your servers,

You’ll need too much Intranet bandwidth to stream

It must be optimized for all devices

How will you calculate video ROI or monitor user engagement?

How to make the whole process of video-sharing & distribution secure?

Looking into all these factors, it’s best to opt for an online video streaming platform like VIDIZMO .

VIDIZMO EnterpriseTube is a complete video knowledge management platform that can be used to capture, upload, host, share, stream & organize your knowledge in the form of videos. It can be deployed on your company’s cloud, on-premises, or hybrid.

You can use the VIDIZMO enterprise video platform to:

Make the recordings visible on any gadget, including cell phones and tablets.

Support all recordings wi th consequently produced records and shut inscriptions.

Relegate different client jobs and authorizations to various clients, limiting who can make, moderate, alter, distribute, view or offer substance.

Alter recordings by managing the start, center, end or clasp longer recordings into more limited, microlearning recordings. 

Incorporate quizzes in your video to make them captivating and intuitive.

Make learning courses with scores, identifications, declarations, grades, positions, and lists of competitors, and establish a learning environment.

Convey updates for representatives to see any unwatched recordings from their relegated recordings. 

Direct live video instructional courses open to a few employees across the organization.

Case Study: VIDIZMO as Knowledge Management Platform

One of the most renowned insurance companies is using VIDIZMO for its internal knowledge base. The organization is generally perceived for its service greatness, supportability practices, trust, and uprightness.

They have been using this enterprise knowledge management system for years to manage, store & distribute their knowledge. 

Searching for a solution to safely transfer live and on-demand video, VIDIZMO empowered the organization to transfer recordings across their internal organization to empower communication and give training to their workers.

EnterpriseTube addressed their challenges by empowering them to safely transfer, manage, and impart recordings on an integrated platform to devices, such as quizzes for knowledge checks and access controls to guarantee protection and security.

If you're looking for insurance knowledge management for your company, let us help you.

Contact our team today to give you a free demo of VIDIZMO. We'd love to hear what you need & how can we help!

Posted by Saadiya Munir

Content Strategist & B2B Marketer. Jack of all & master of editing. Computer Science graduate who hates to code. Introverted. Mostly hungry or sleepy. You can ping her at [email protected]

• knowledge management

Topics: knowledge management

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The Challenge

The Objectives

To continue delivering the best possible service to health practitioners and medical students, Avant knew that it needed a new approach to knowledge management. The organization wanted to empower contact centre staff with the tools to deliver accurate, consistent answers quickly and confidently – so it began looking for a solution that could:

• Centralise information into a single, up-to-date source of truth
• Accelerate onboarding and cut time to competency for new hires
• Eliminate ‘ask your neighbour’ culture – instead, staff should be able to

rely on the system livepro met all of Avant’s needs, and more. Not only did livepro enable the medical defence organization to consolidate its information into a single knowledge base, the ‘Compass’ feature also makes it easy for operators to find precisely what they need. This simple yet dynamic question and answer tool cuts through complexity and guides staff to outcomes based on each individual customer’s situation.

The Outcome

Avant initially piloted livepro with a small team, and the hype travelled fast – it wasn’t long before all call centre staff were asking to use the new solution! Now, operators trust the system. They can rapidly find answers without having to consult their peers, and they can be certain that those answers are correct. With livepro in place, Avant is seeing major performance improvements:

• AHT is down 20%
• Customer satisfaction is up 5%
• Employee satisfaction is up 13%
• First contact resolution (FCR) has increased from 50% to 67%
• Unhappy customer retention has increased from 40% to 70%
• Transfers have dropped by 33%
• Wrap-up time is down from 17 minutes to 7 minutes

What’s more, livepro has completely transformed the new employee induction process. The solution is so easy to use that staff can deliver outstanding service with minimal training:

• Onboarding now takes just 2 weeks, down from 3 months
• Speed to competency has been cut from 12 months to less than 1 month

Avant’s contact centre teams are now better positioned than ever to serve its doctor and medical student members. Every employee, whether they are experienced or brand new, has the tools to handle enquires quickly and accurately.

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Almost immediately, new starters using livepro performed significantly better than existing staff using our old system.

Andrea Pepper-Edwards

, Change Specialist at Avant Mutual

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Why Knowledge Management Is Critical for the Insurance Industry

Posted Oct 10, 2022 by Insurance-Canada.ca

By Mitja Alexander Linss, Senior Director – Marketing, ProNavigator —

Finding, managing, and sharing information quickly has value for any organization, but especially in the insurance industry. Insurers offer a vast number of products and services, each with its own underlying complex legal agreement – the insurance policy. Buyers and sellers alike need to have confidence in the information available to them.

As IBM points out in a recent article on knowledge management (KM) , “When knowledge is not easily accessible within an organization, it can be incredibly costly.” In insurance, that’s true on several fronts: wasted time, employee morale, claims payouts, omissions suits, and lost business.

Here are six knowledge management challenges unique to insurance:

Information Overload

The insurance industry is notorious for siloed information and policy documents that change constantly while previous versions remain in force. Employees must be able to navigate the latest policy documents and keep track of previous iterations and revisions.

The sheer volume of insurance documentation makes for a head-spinning experience for frontline staff who often have to sift through thousands of documents – including some 150-page manuals – in real-time.

Not surprisingly, this can spell information overload and search fatigue in both new hires and tenured employees.

Knowledge Gap

One of the biggest challenges facing insurers in 2022 is the knowledge gap that occurs when experienced employees leave the industry before new hires can be brought up to speed.

Part of the problem is finding new hires. As far back as 2016, NPR reported that millennials “simply don’t consider insurance as a potential career – or think it’s boring,” after a survey found that only 4 percent were considering a career in the industry.

As a result, Deloitte’s 2022 Insurance Industry Outlook reports that as many as “43% of insurance talent respondents feel it’s getting harder to find skilled candidates” – despite plans to increase staff numbers as the economy recovers.

This challenge extends to frontline workers and customer support teams. As PwC notes , the problem “isn’t just about losing developers to Silicon Valley…. The industry [as a whole] is often seen as both limited and limiting by geography, incentives, career paths and more.”

The Bureau of Labor Statistics reported high turnover in the labor market overall, with as many as 75.6 million hires and 47.8 million quits in 2021. In the insurance sector, an increasing number of employees are “ exploring their options ” and may not reveal their true reasons for leaving during exit interviews.

Not only does it take time to hire and train replacements, but the onboarding process may not take into account the underlying reasons for employee dissatisfaction.

In many cases, it’s the lack of access to insurance information at their fingertips – and a constantly-shifting landscape of policy documents and procedures – that makes it hard for customer support staff to do their jobs successfully.

Remote Work

Compounding the issue is the shift to remote work, and the fact that many employees don’t have any desire to come back to the office at all. This isn’t entirely unwelcome news for employers: Deloitte reports that only 3% of insurance companies foresee a return to full-time in-person employment.

Still, remote and hybrid work environments present some challenges, such as a lack of day-to-day access to colleagues and managers. Instead of turning to a coworker to ask a question, new hires have to rely on asynchronous communication methods like email and chat rooms – or attempt to look up the answer themselves.

This can lead to employee frustration and increased wait times for customers if the answer can’t be found easily and quickly.

Legacy Systems

The pace of change in the industry threatens to strain, not just the workforce, but legacy technology for information discovery, access, and storage which has proven inadequate to meet customer needs.

Today’s consumers and business are used to a Google-like search experience with instant results. But current enterprise search and discovery tools simply aren’t up to the challenge, and general knowledge management platforms aren’t designed with the insurance industry in mind. Insurers need to look for other ways to modernize their workflows.

Potential areas of innovation include artificial intelligence and the blockchain. Accenture reports that 80% of insurance industry executives have plans to use “distributed ledger technology” in at least one department, while McKinsey makes the case for building innovative products using an accelerator model.

Customer Satisfaction

Insurance customers have come to expect the same level of support from insurance companies that they’ve received in other industries that have more readily adapted to the on-demand economy.

Processing times for home insurance claims increased from an average of three days in 2020 to 18 days in 2021 – largely because digital claims systems weren’t ready for the demand, resulting in a backlog for human representatives to review.

Frontline insurance workers need to have instant access to critical information and be able to explain complex policies to customers in plain language. Although the first order of business of a knowledge management system is to make life easier for employees, at the end of the day, it increases customer satisfaction too.

In Part 2 of our Knowledge Management blog series, we’ll explore how insurance companies can use modern tools to address their knowledge management challenges.

• Learn more about ProNavigator in the InsurTech Spotlight .

About the Author

Mitja Alexander Linss is the Sr. Director of Marketing at ProNavigator. He frequently writes about knowledge management, information discovery, artificial intelligence, and InsurTech.

About ProNavigator

ProNavigator provides a knowledge-sharing platform, Ask Sage, used by some of the largest insurance organizations in North America to save time, provide superior service, and seize revenue opportunities. The platform leverages the latest advancements in artificial intelligence and natural language understanding to instantly, automatically, and accurately retrieve answers to employees’ questions. For more information, please visit pronavigator.ai .

SOURCE: ProNavigator

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The Impact of Knowledge Management on The Competitiveness of Insurance Firms in Kenya

The purpose of this chapter is to investigate and present the impact of knowledge management strategies on insurance firms in Kenya. The objectives of the study, that informs this chapter, were to examine the current knowledge management strategies used in insurance firms; analyse how the knowledge management strategies used have contributed to the competitiveness of the firms; determine the challenges insurance firms face while using knowledge management as a strategic tool for achieving competitive advantage; and to suggest possible solutions to the identified challenges. The study is a case study of UAP Insurance Company. Data were collected using interviews from 105 respondents selected through information-oriented purposive sampling. The data were analysed through descriptive statistics. The findings indicate that knowledge management strategies are being used as tools for gaining competitive advantage in the insurance industry in Kenya. The authors also reveal weaknesses in th...

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• Silvola R Tolonen A Harkonen J Haapasalo H Mannisto T (2019) Defining one product data for a product International Journal of Business Information Systems 10.1504/ijbis.2019.099308 30 :4 (489-520) Online publication date: 16-Apr-2019 https://dl.acm.org/doi/10.1504/ijbis.2019.099308
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An investigation into the factors affecting knowledge management adoption and practice in the life insurance business

• Published: 25 February 2011
• Volume 9 , pages 58–72, ( 2011 )

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• Li-Su Huang 1 ,
• Mohammed Quaddus 2 ,
• Anna L Rowe 2 &
• Cheng-Po Lai 3  

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Knowledge management (KM) is crucial for organizations to enhance competitive advantage. While the issues of KM have been widely discussed by numerous researchers, there is a paucity of studies pertaining to KM adoption and practice for the life insurance industry. Therefore, this paper aims to investigate the main factors affecting the life insurance business in adopting and applying KM. An exploratory field study utilizing an inductive methodology involving a multiple-case study approach was undertaken by conducting interviews with 10 key knowledge workers from life insurance enterprises in various stages of KM development and use. We utilized content analysis techniques to identify the factors with their associated variables and further developed a research model. This study offers a comprehensive model for future KM research and provides managerial implications for organizations, particularly life insurance enterprises, to better realize the worth of KM and the possible impediments involved in the processes of adopting and implementing KM.

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Introduction

The trend of globalization does not only provide opportunities for firms to bring products and services to wider market, but also increases the intensity of competition. To survive in such an extremely competitive environment, organizations should utilize their knowledge resources effectively for creating competitive advantages and developing a greater ability to act and adapt ( Handzic et al., 2008 ). Treating knowledge as a significant organizational resource, studies in the area of knowledge management (KM) have grown dramatically over the last decade ( Hislop et al., 2000 ; Feng et al., 2004 ). Particularly, KM has become the focal point for debates on mechanisms to facilitate firms acquiring greater competitive edge in the emerging global information economy ( Clarke & Turner, 2004 ). Thus, according to Carlsson (2001 , p. 195), KM in this study is defined as ‘the process of identifying, managing and leveraging individual and collective knowledge to support the firm becoming more competitive’. The definition highlights the primary components of KM in the increasingly competitive world. First, both individual and collective knowledge should be identified. Second, KM involves the process of collecting and integrating the knowledge. Third, KM is primarily employed to increase competitiveness.

Life insurance can be seen as an arrangement through which the risk of specific individuals can be shared by the general majority of people ( Hsiao, 2003 ). In contrast to other industries, the products sold by the life insurance business are comparatively ‘invisible’ and ‘untouchable’ ( Hsiao, 2003 ). ‘People’ play an important role in conveying the knowledge and services to the customers in the life insurance industry. Besides, most of the life insurance contracts were long term and therefore the life insurance enterprises should provide lasting, sometimes lifelong, services for the customers. KM would be imperative for life insurance enterprises to enhance performance and gain a competitive edge ( Wang, 2005 ). KM has been enriched with methods, ideas and technologies by contributions from diverse sources as management science, social science and information science areas ( Gherardi, 2000 ; Spender, 2003 ; Hung, 2004 ). However, there is a dearth of empirical studies of KM adoption and practice conducted in the life insurance industry. Meanwhile, little research is available on people’s perceptions that may affect the practice of KM through their attitudes. Therefore, some questions emerge as: What must be done to adopt or initiate KM in the life insurance business? What factors are important in KM adoption and applications in the life insurance enterprises? These are the main research questions in this study. This study addresses these research questions via undertaking a qualitative field study among the life insurance enterprises in Taiwan. A conceptual framework was suggested based on extensive literature reviews on Innovation Diffusion (ID), Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA). Utilizing semi-structured interview techniques, 10 in-depth interviews were conducted to collect the data to develop a comprehensive model. The primary objectives of this research are therefore as follows:

To identify the factors and variables for or against KM adoption and practice through the employees’ perceptions in the life insurance business in Taiwan.

To examine how the factors and variables affect KM adoption and practice in this context.

To investigate how KM is perceived to affect the performance of the organization in this context.

This article is organized into five sections beginning with this introduction. The next section presents the research background with relevant KM literature and the main theories applied in forming the conceptual framework. This is followed by research method section, which describes the processes of data collection and data analysis approach. Emanating from the data, the subsequent section presents the results of this study revealing the main factors and variables affecting the adoption and practice of KM in the life insurance enterprises. A comprehensive model of KM adoption and practice is thus emerged from the literature review and the field study. Finally, conclusions and future research directions are presented.

Research background

The life insurance business has been growing exponentially and playing a significant role in the financial industry in Taiwan. According to the important indexes of insurance industry in Taiwan ( Taiwan Insurance Institute, 2009 ), the total asset of Taiwan life insurance industry in 2007 was NT$ 8.721 billion or 21.86% of the total assets of financial institutions nation wide. The population of household registered in Taiwan was 22.958 million up to 2007, and the ratio of life insurance policies to population was 1.96%. The premium income of Taiwan life insurance industry in 2007 was US$ 49,813 million and ranked top 10 globally. Due to the enormity of premium income from the general public, and the associated social responsibility, the life insurance enterprises aim at providing better professional knowledge and services to achieve superseding competitive advantages.

The life insurance industry is an example of a knowledge-based industry with its main products being insurance contracts, which are commitments supported by professional knowledge and services. Nonetheless, the life insurance business has been facing the problems involved in dealing with more and more documents, customers’ demands for rapid and quality services, as well as selling various and complicated policies ( Wang, 2005 ). The CEO of Cathay Life Insurance Company, ranking top in the life insurance industry in Taiwan, sensed that it had been losing competitive advantages since the organization was large with more than 20,000 employees and information could not be transmitted smoothly. Therefore, Cathay Life Insurance Company recognized the significance of KM and inaugurated its so-called ‘quiet revolution’, that is, KM project ( Microsoft, Taiwan, 2005 ). Given the fact that KM has been widely applied in organizations ( Bonner, 2000 ; Alavi & Leidner, 2001 ), the topic of KM has not been well explored by researchers empirically in the life insurance sector. Therefore, this research attempts to fill this gap by examining the adoption and practice of KM among life insurance enterprises based on the literature as elaborated below.

Knowledge and KM

Concepts and practices evolved through the 1990s realized that knowledge was perhaps the critical resource, compared to land, machines or capital ( Drucker, 1993 ; Earl, 2001 ). The nature of knowledge has been described as ‘justified true belief’ ( Nonaka & Takeuchi, 1995 ). Nonetheless, definitions of knowledge range from ‘complex, accumulated expertise that resides in individuals and is partly of largely inexpressible’ to ‘much more structured and explicit content’ ( Davenport & Prusak, 1998 ; Becerra-Fernandez & Sabherwal, 2001 ). According to Davenport and Prusak (1998) , knowledge is a fluid of framed experience, values, contextual information and expert insight that provides a framework for evaluating and incorporating new experiences and information. Moreover, Bollinger and Smith (2001) describe knowledge as ‘the understanding, awareness, or familiarity acquired through study, investigation, observation, or experience over the course of time’; that is, knowledge is an individual's interpretation of information based on personal experiences, skills and competencies. For example, in the case of life insurance business, ‘knowledge’ comprises the familiarity and professional capability in underwriting, claim, customer services and so on.

KM has also been defined in numerous ways depending on the purpose of research. Alavi and Leidner (1999) define KM as ‘a systemic and organizationally specified process for acquiring, organizing and communicating both tacit and explicit knowledge for employees so that other employees may make use of it to be more effective and productive at work’. Duffy (1999) describes KM as ‘a process capitalizing on organizational intellect and experience to drive innovations’. The American Productivity & Quality Center (2007) advocates that KM is the strategies and processes of identifying, capturing and leveraging knowledge to help the firm compete. Earl (2001) suggests that KM can be viewed from seven dimensions with their focuses as follows: (i) system: technology; (ii) cartographic: maps; (iii) engineering: processes; (iv) commercial: income; (v) organizational: networks; (vi) spatial: space; and (vii) strategic: mindset; namely, it aims at knowledge capability and knowledge is seen as a key resource and KM as a way to gain competitive advantage. This research mainly takes the strategic standpoints and adopts the definition, as stated earlier in the introduction, proposed by Carlsson (2001) .

Managing knowledge well can develop new opportunities, creating value for customers, obtaining competitive advantages or improving performance ( Lloria, 2008 ). The activities of KM include knowledge capture, documentation, retrieval and reuse, creation, transfer and sharing of its knowledge assets integrated in its operational and business processes ( Dayan & Evans, 2006 ). The processes of KM adoption and practice would involve the systematic organization, planning, scheduling, monitoring, and deployment of people, processes, technology and environment, with appropriate targets and feedback mechanisms, to facilitate the retention, sharing, identification, acquisition, utilization of knowledge and new ideas, in order to achieve strategic aims, for example, improved competitiveness or improved performance, subject to financial, legal, resource, political, technical, cultural and societal constrains ( Lehaney et al., 2004 ).

The empirical studies on KM in Taiwan are summarized in Table 1 . Most of the existing research in Taiwan centres on the subjects of KM strategies and their effects on performance. Few studies were found to investigate the external factors that could affect KM applications via employees’ perceptions. However, the review identifies the significance of KM strategies and mechanisms with their influences on performance, and reveals a list of external factors affecting the adoption and practice of KM in a context of the organizations in Taiwan.

ID and related theoretical bases

Rogers (1995) depicts an innovation as an idea, practice or object that is perceived as new by an individual or another unit of adoption. While KM has been used and operated in the business world for decades, its applications (e.g., recognition of knowledge, development of information system and support of organization), have only been initiated recently in the life insurance enterprises ( Yang, 2004 ). Accordingly, KM is viewed as an innovation to Taiwan life insurance enterprises and their employees in this study. The role of KM among the life insurance enterprises can thus be examined by the following innovation characteristics: (i) relative advantage: how KM is perceived as better than the idea it supersedes; (ii) compatibility: how KM is seen consistent with the values, experiences and need of potential adopters; (iii) complexity: the difficulty of understanding and using KM; (iv) trialability: the degree to which KM may be experimented with on a limited basis; and (v) observability: the ability to have the results of KM visible to others. Further, diffusion of an innovation is proposed by Rogers (1995) to be the process by which an innovation is communicated through certain channels over time among the members of a social system ( Rogers, 1995 ). In this study, the social system, that is, a set of interrelated units that are engaged in joint problem-solving to accomplish a common goal ( Rogers, 1995 ), refers to a life insurance enterprise along with its employees. Hence, KM adoption and practice refers to the process by which KM is communicated via certain channels over time among the employees of the life insurance enterprises. ID has attracted much interest researchers in several areas such as new information technology (IT), electronic data interchange and internet ( Baptista, 1999 ; Carter et al., 2001 ; Wolcott et al., 2001 ). However, there is little literature found on the adoption and use of KM in the life insurance domain. Thus, this study lays emphasis on the adoption and diffusion of innovation to develop a KM model for the life insurance business.

The TRA, drawn from social psychology, has been suggested as a primary theoretical foundation and gone through rigorous testing in diverse disciplines predicting human behaviours ( Ajzen & Fishbein 1980 ; Swanson, 1982 ; Sheppard et al. 1988 ; Venkatesh et al., 2003 ). It is suggested that a person's behaviour is a function of the person's intention determined by the attitude toward the act and the beliefs about the expectations of others, that is, social normative beliefs ( Ajzen & Fishbein, 1980 ). The person's attitude toward the behaviour is affected by the beliefs that the behaviour will lead to certain outcomes and by his or her evaluation of the outcomes. The subjective norms are influenced by the beliefs that specific referents think that the person should or should not perform the behaviour and by the motivations to comply with the specific referents ( Ajzen & Fishbein, 1980 ). Extended from the TRA, TAM ( Davis, 1986 ) proposes that a person’s intention to use technology is determined by perceived usefulness (PU) and perceived ease of use (PEOU). PU refers to the degree to which a person believes that using a particular system would enhance his or her job performance, while PEOU refers to the degree to which a person believes that using a particular system would be free of effort ( Davis, 1989 ). Davis et al. (1992) expanded TAM to suggest that an individual's intention to use computers is influenced by extrinsic motivations, perceiving an activity to be instrumental in achieving valued outcomes, as well as intrinsic motivations, referring to the performance of an activity for no apparent reinforcement other than the process of performing the activity per se . TAM has been extensively used and accepted as a robust model to investigate IT acceptance and usage ( Taylor & Todd, 1995 ; Venkatesh et al., 2003 ).

Although little work has been published utilizing the TRA and TAM on the research of KM, the TRA helps explaining why an employee would accept/apply KM. The suggestions of TAM can be applied in examining what benefits KM would bring to the employees in increasing their job performance and whether KM projects with relevant IT usage are easy or complicated for the employees. Yang (2004) also reported that the life insurance enterprises in Taiwan put most of their efforts on IT in having KM into place. Therefore, it is considered plausible that the TRA and TAM may enlighten our understanding of this phenomenon in this research.

Conceptual framework

There is abundant literature on the adoption and diffusion of innovations ( Norton and Bass, 1987 ; Nakicenovic & Grubler, 1991 ; Rogers, 1995 ; Quaddus & Intrapairot, 2001 ; Xu, 2003 ). Previous innovation studies have identified a number of factors which affect the adoption and diffusion of innovations ( Belassi & Fadlalla, 1998 ; Agarwal & Prasad, 2000 ; Liu, 2004 ). Most of the innovation research also uses the TRA ( Ajzen & Fishbein, 1980 ) and Davis (1986) TAM. The TRA model ( Ajzen & Fishbein, 1980 ) posits that external variables, for example, individual demographic variables, would affect intentions and behaviours indirectly through beliefs, outcome evaluations and normative beliefs. TAM studies, such as Davis (1986) , Davis et al. (1989) , Igbaria et al., (1995) and Szajna (1996) , point out that individuals, system design features and organizational characteristics could be the external variables that have influences on the technology acceptance and usage through perceived of usefulness, PEOU and attitudes. Both the TRA and TAM models provide a base for this research with the external variables as the causes of perceptions.

Furthermore, organizations have launched KM initiatives, to create strategic competitiveness ( Holsapple & Wu, 2008 ) by promoting productivity ( Wiig & Jooste, 2003 ), adding to agility ( Dove, 2003 ), fostering customer loyalty ( Housel & Bell, 2001 ) and increasing innovation ( Amidon & Mahdjoubi 2003 ). Alavi and Leidner (1999) reported that the perceived organizational benefits of KM scheme could be shown in two primary dimensions: process improvement (e.g., enhancing communication, reducing problem-solving time, better serving the clients and providing accountability) and organizational outcomes (e.g., cost reduction, increased sales, personnel reduction and higher profitability). Consequently, this study postulates that, some external factors influence the employees’ perceptions, namely ‘PU’, ‘complexity’ and ‘subjective norm’, which in turn affect their attitudes toward KM adoption, and the practice of KM would be influenced by such attitudes and have impact on the perceived performance for the organization.

Research method

While the conceptual framework developed for embracing KM is derived from generalizable TRA and TAM theoretical models, one must be mindful of the uniqueness of the life insurance business in Taiwan. First, KM in the life insurance sector has not been well investigated. Second, understanding the social construction and meaning of KM adoption in Taiwan may differ from those in the West, thereby requiring preliminary ‘emic’ analysis. An ‘emic’ research describes the unique values of a particular society (i.e., Taiwan), whereas an ‘etic’ analysis applies to generalised theoretical model across several societies ( Brislin, 1976 ).

Hence, to fine-tune the proposed conceptual framework, which had been based on the literature, this research utilized an inductive methodology involving a multiple-case study approach ( Yin, 2003 ). The choice of an inductive (qualitative) approach was governed by the lack of adequate existing KM research on the life insurance sector – particularly, in Taiwan. The strength of multiple cases lies in their capability to seek ‘emic’ knowledge from participants and permit replication logic, ensuring that the insights gained are not idiosyncratic to a single case but instead are consistently replicated (literally or theoretically) across multiple cases. In fact, its ‘emphasis on developing constructs, measures and testable theoretical propositions makes inductive case research consistent with the emphasis on testable theory within mainstream deductive research’ ( Eisenhardt and Graebner, 2007 , p. 25).

In developing a comprehensive research model for future survey, we conducted an exploratory field study to explore why and how the life insurance business would adopt and apply KM via their employees’ believes, attitudes and activities. This qualitative field study of multiple-case approach focused on capturing respondents’ interpretations of multiple realities rather than measuring an assumed single reality of adopting KM. The ‘realities’ presented by the interview participants must be interpreted and understood ( Rowe, 2006 ).

According to Glock (1987) , a major source of data in survey research was the qualitative interview conducted in the planning phases of the research. Such interviews, with a small but roughly representative sample of the population, provided an indispensable way to learn about the nature of variation and how to operationalize it ( Glock, 1987 ). The details of our field study research processes are presented as follows.

Sample selection

The sample of this study relied on available subjects, who were close at hand or easily accessible ( Zikmund, 2000 ). The main criteria for selecting the subjects were that they must be knowledge workers involved in some knowledge tasks in their organizations. There were 21 local life insurance enterprises and eight foreign life insurance enterprises operating in Taiwan ( Taiwan Insurance Institute, 2009 ). Among the 29 life insurance enterprises in Taiwan, 10 interviewees, including managers and staff from six life insurance companies with various backgrounds in different stages of KM practice, were invited via telephone to participate in the field study. In four participating companies, that is, enterprise B, enterprise C, enterprise D and enterprise F, two interviews were conducted for each company, respectively. All the participants took part in this research on a voluntary basis and were also diverse in terms of position, tenure and gender.

Data collection

The data were collected by using the semi-structured interview approach. An interview protocol was designed based on the conceptual framework as described above. The semi-structured interview protocol (see Appendix ) aimed at exploring the factors and variables affecting the adoption and practice of KM in the life insurance enterprises. We developed the interview schedule following the guidelines proposed by Berg (2004) . The guiding semi-structured questions focused on the following areas of information which was required in this research: (i) general perception and understanding of KM; (ii) the adoption and application processes of KM; (iii) the motivations to adopt and apply KM; (iv) the major factors influencing the initiation of KM in the organization and the links between those factors; (v) obstacles to having KM put into place in the organization; (vi) incentives that would encourage employees to apply KM; (vii) resources and facilitating factors of KM implementation; and (viii) the benefits of KM for both employees and the organization.

A pre-test was conducted using the guiding semi-structured questions to interview the first respondent. With minor adjustments made based on the feedbacks from the pre-test, the interview questions proved to be working well in achieving the research objectives of this study. Finally, 10 interviews in total were conducted in the field study. The tacit knowledge derived from initial interviews was of such in-depth quality that it facilitated the refinement of the interview protocol and sharpened research directions. The interviews were audio taped whenever possible and field notes were immediately documented within 3 days in Taiwan, so as not to lose the vital nuance and cues observed. The taped interviews were transcribed and rigorously reviewed for errors by the principal researcher. Tapes were carefully listened to following Strauss and Corbin (1990) , and corrections were made.

Data analysis

The focus of this study also required that ‘content analysis’ ( Patton, 1990 ; Berg, 2004; Silverman, 2000 ) of interview transcripts be used from the firm participants’ perspective, leading to ‘emic’ or ‘insiders’ approach to the development of categories. As Patton (1990 , p. 381) says, ‘Content analysis is the process of identifying, coding, and categorizing the primary patterns in the data. This means analyzing the content of interviews and observations’.

Interviews and field notes are often not amenable to analyses until the information they convey has been condensed. An objective coding scheme was applied to interpret the interview transcripts and field notes. As the nature of this study is more exploratory than confirmatory, content analysis is cost-effective and useful in analysing interview data ( Berg, 2004 ).

The procedures of content analysis were divided into two stages. The first stage dealt with single interview transcripts by the following steps ( Berg, 2004 , p. 285):

Review the interview transcripts thoroughly and find the key themes and patterns.

Produce labels for these key themes and phrases.

Revise the labels to be the systematic categories, which match the literature.

Sort the interview transcripts into the systematic categories.

Find the links among the factors and variables for the individual interview.

Provide the tables of systematic categories with the factors and variables from each interview.

The second stage of content analysis dealt with cross interview transcripts, and aimed at the integration of all the individual factors, variables and links from all interviews, in order to develop a comprehensive model of KM adoption and practice. The stepwise procedures in the second stage are as follows ( Berg, 2004 , p. 286):

Revisit the individual interview transcripts with the systematic categories of factors and variables, as well as their links obtained from the first stage.

Examine the differences and similarities of the variables in each factor.

Combine the similar variables and generate a common name, while retaining the unique variables.

Integrate the links among the factors among the six enterprises.

Establish the tables of integrated factors, variables and their links.

Propose the comprehensive model of KM adoption and practice.

Background information

Table 2 presents the background information of the enterprises involved in this research. There were one foreign life insurance enterprise, two local life insurance enterprises, and three local life insurance enterprises with foreign capital, of which some were new entrants while others were existing enterprises, which had history for decades. The number of employees in the enterprises ranged from 300 to over 3000. The interviewees’ positions varied from department manager to general staff and their tenures ranged from 4 to 22 years. The participating enterprises were involved in different stages of KM adoption and practice.

Factors and variables of KM adoption and practice

Twelve factors and 93 variables were identified from this field study through extensive content analysis procedures. The factors and variables have been labeled, where possible, in line with the literature (e.g., Ajzen & Fishbein, 1980 ; Davis, 1986 , 1989 ; Rogers, 1995 ; Belassi & Fadlalla, 1998 ; Alavi & Leidner, 1999 , 2001 ; Holsapple & Joshi, 2000 ; Gold et al. 2001 ; Venkatesh et al., 2003 ). For instance, the variable, rules and regulations, is identified according to Holsapple and Joshi (2000) indicating that environmental influences, for example, governmental and political climate, played a pivotal role in the success of KM in organization. As the interviewee from enterprise A said, ‘The external environments, especially the rules and regulations, would influence our acceptance of KM’. Nevertheless, unlike past research, the variables gathered in each factor and their meanings are more specific to KM, particularly its adoption and practice in the life insurance business. For example, the results from the field study bring out ‘KM promotion’ as distinct from KM practice. The interviewees stressed that, before implementing KM, there should be some KM promotion schemes, such as KM plan or project, guidelines and training, that appear to signify an external factor in influencing the employees’ opinions regarding KM and deciding whether or not they would adopt and apply KM in undertaking their daily tasks. Emerging from the data were some variables pertaining to ‘environments and industrial factors’ (e.g., too much paper usage in the life insurance industry).

Table 3 shows the list of variables identified in each factor, as well as the anonymous companies which mentioned the variables. Out of the 93 identified variables, the eight variables confirmed by all enterprises were: hardware infrastructure , software infrastructure , KM manager , top management support , vision, value and objective , time saving , gathering knowledge, as well as attitude toward KM adoption . Twenty-one variables were stated by more than four enterprises. Most of the enterprises emphasized that the employees’ attitudes toward KM would affect their perceptions concerning KM. Having the KM team, taking the appropriate strategy and policy, as well as creating a culture of trust and commitment were also important to the adoption of KM.

Links among the factors

Table 4 presents the causal links among the factors of KM adoption and practice. The information regarding the perceived links emerged during the interviews and was extracted from the interview scripts through content analyses. For example, the link from environments and industrial factors to PU was identified in enterprise A based on the following statement: ‘in the trend that KM has been applied in many organizations, adopting KM would help improve our performance at work and let us feel more competent’.

Comprehensive model

Figure 1 presents the comprehensive model of KM adoption and practice, which has been developed based on the conceptual framework as described earlier with the factors and variables identified in the field study (as presented in Table 3 ). Accordingly, this research proposes that the adoption and practice of KM can be observed as per the model that involves the following:

Comprehensive model of KM adoption and practice.

Discussions and implications

Theoretical implications.

This study confirmed the significance of the relationship posited by the TRA model, in which a positive perception of the benefits translated into a positive attitude, which in turn affected their behaviours in conducting KM activities. The results also supported that perceive usefulness, complexity and subjective norm, which were the perceptive factors adapted from the TRA, TAM and ID, had significant influences on KM practice via employees’ attitudes toward KM adoption. To extend the existing theories, this study identified environments and industrial factors, individual characteristics, IT support, KM promotion, organizational characteristics and cultural factors, as the external factors that affected the perceptive factors in the context of KM adoption and practice. Further, we suggest that KM practice would improve the organizational performance in operational efficiency, customer service, ability to adapt to changes, etc. The comprehensive model of KM adoption and practice as presented in Figure 1 is particularly unique, since it is developed based on both the literature and the data collected from 10 interviewees in the life insurance business. This comprehensive model can be taken as a research model for proposition development and further empirical investigation. Further research is required to develop appropriate research hypotheses to carry on with the above research. The researchers plan to examine this model further using structural equation modelling ( Barclay et al., 1995 ) to test a number of hypotheses. Parts of this model can also be extracted and examined in detail. For example, how the external factors affect complexity can be studied to identify the potential initiatives and obstacles for people to adopt and apply KM in an organization. This model also indicates the processes involved in KM, as well as the perceived performance that the KM practice is expected to bring for the organization. Future studies can further explore the exact KM activities in the life insurance industry and the influence of KM practice on organizational performance.

Managerial implications

In terms of managerial implications, the comprehensive model shows a practical model of KM adoption and practice in the life insurance enterprises. The managers would realize that, whether KM brings benefits for the employees to enhance their job performance, if KM is difficult to operate, and how other people think of KM, might influence their attitudes to adopt KM and thus affect the implementation of KM. Further, the factors and variables are gathered from the real world. As a result, the model is more specific for the life insurance business in adopting and employing KM. Organizations, especially the life insurance enterprises in Taiwan, may find this model quite useful in providing the elements for successfully adopting and using KM, and realize the barriers embedded in KM processes.

Conclusions

This study presents an inductive approach utilizing multiple-case method to seek the main factors, variables and links of KM adoption and practice among the life insurance enterprises in Taiwan. A conceptual framework was proposed first by literature reviews. On the basis of the conceptual framework, a more extensive research model was then developed using the data collected from the field study, which was undertaken by inviting 10 interviews from six different life insurance enterprises. The interviewees were varied in terms of position, tenure and gender. The enterprises were in different phases of KM adoption and practice. The interviews were transcribed by the researchers, and the contents were analysed utilizing content analysis approach. The data resonated well with the literature and the analyses resulted in 12 factors and 93 unique variables. The casual models for six individual enterprises were developed first and then combined to propose the comprehensive model of KM adoption and practice.

The findings generally supported the conceptual framework with some revisions, revealing that several external factors would affect employees’ perceptions concerning KM and their attitudes, which would in turn influence KM practice and have impact on the perceived performance for the organization. All enterprises highlighted the importance of sufficient hardware and software infrastructure , KM manager , support from top management , vision, value and objective of an organization, collecting knowledge, as well as attitude toward KM , in determining the employees’ perceptions of KM. Organizations, particularly those in the life insurance business, which attempt to adopt or embark on KM, can look into the variables carefully for managing knowledge effectively.

This study contributes to the KM literature in the sense that it developed a comprehensive model drawing from rich qualitative evidence that enhanced generalized conceptual framework facilitating future ‘testable theoretical propositions’ ( Eisenhardt and Graebner, 2007 , p. 25). Most of the existing studies on innovation adoption and diffusion have been quantitative in nature such as confirmatory study by testing hypotheses. In addition, there is paucity in studies that investigate the factors and variables of KM adoption and practice in the context of life insurance enterprises. Therefore, this exploratory study adopting qualitative methodology involving a multiple-case study approach proposed a research model combining the literature based on deductive mainstream research with inductive field study. From the practical perspectives, this study provides a better understanding of the determinant factors and variables of KM among the life insurance enterprises. The model can help organizations, particularly life insurance enterprises, to better design their KM schemes and generate fruitful outcomes for both employees and organizations.

In conclusion, this exploratory multiple-case study is a fresh comprehensive model ‘that bridges well from rich qualitative evidence to mainstream deductive research’ ( Eisenhardt and Graebner, 2007 , p. 30). This comprehensive model can be utilized for future studies in examining the adoption and applications of KM in the life insurance industry. The researchers attempt to further examine this comprehensive model by taking quantitative research method through empirical surveys. A structural equation modelling technique will be used to test the research model and the relevant proposed hypotheses. This study can also be extended for KM research in other financial service business, for example, banks and various geographic contexts.

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Huang, LS., Quaddus, M., Rowe, A. et al. An investigation into the factors affecting knowledge management adoption and practice in the life insurance business. Knowl Manage Res Pract 9 , 58–72 (2011). https://doi.org/10.1057/kmrp.2011.2

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Received : 17 June 2009

Revised : 07 July 2010

Accepted : 09 November 2010

Published : 25 February 2011

Issue Date : 01 March 2011

DOI : https://doi.org/10.1057/kmrp.2011.2

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There are various definitions of Business Intelligence (BI) in existing literature. BI has become an important IT tool or mechanism that can help organizations to manage, develop, and communicate intangible assets such as information and knowledge. It is often considered essential for organizations operating in the current knowledge-based economy (Alnoukari, 2009). BI is discussed by Gansor, Totok, & Stock (2010) as an analytical process that transfers internal and external data into appropriate knowledge to support decision-making. The term BI has also been defined as the collection, saving, analysis, and provision of data to support the decision-making processes of a company (Seufert & Oehler, 2009).

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