Whilst the principal objectives of the central bank remain unchanged, the new knowledge management strategies refocus the Bank’s policies and practices in managing knowledge as a key corporate asset, and in leveraging and exploiting knowledge to better achieve these objectives.

Governor of Bank Negara Malaysia, official launch of
‘Towards a Knowledge-Based Organisation’
program, October 2000¹

Knowledge management (KM) plays a critical role in all industries. Last summer vacation, I completed an internship at a commercial bank, therefore, I began to look back on this internship experience and think about the application of KM in the banking industry.

Why does the banking industry need knowledge management?

Complex structure of banks

The banking industry is different from other industries in that it has a complex organizational structure, which is often divided into head offices, regional branches, and city branches (even specifically to a certain street). The banking industry has the characteristics of wide knowledge distribution, high personnel participation, multiple levels, and a wide geographical and professional span. Therefore, the construction of a bank KM system must not only satisfy the horizontal KM within a single branch but also pay attention to hierarchical vertical integration in the entire banking organization.

Information overload

Knowledge is divided into explicit and tacit knowledge. For the banking industry, explicit knowledge is prone to information overload. There are a large number of static explicit knowledge collection and communication needs such as customer information, work plans, statistical tables, etc. However, most contemporary banking knowledge repositories are outdated. Internal staff still heavily rely on scattered documents and information from various isolated sources. The absence of a centralized web or cloud-based data repository means that the accuracy and currency of the information they access and share with customers cannot be guaranteed.

Services quality

As a service industry, the banking industry has a top priority in improving service quality and maintaining good customer relationships. This link mainly involves the enterprise’s tacit KM. Compared with explicit knowledge, enterprises are more likely to neglect the management of dynamic tacit knowledge, such as work experience and employees’ unique work skills.

Knowledge management best practices in banking

World Bank

World Bank has proposed the idea of a knowledge bank². To collect and share knowledge, the World Bank has defined 80 areas of expertise and established global, informal communities of practice, each controlled through a help desk with the help of full-time knowledge managers and operational staff. An electronic bulletin board has been set up to collect best practices and lessons learned from relevant projects. In order to create an internal culture of knowledge sharing, the World Bank has established mechanisms such as knowledge-sharing awards in the talent evaluation system to enhance the initiative of all employees in improving knowledge sharing.

Wachovia Bank

Wachovia Bank has formed a unique KM and knowledge sharing model, divided into three stages³. The first stage allows call center employees to access department knowledge bases and seek back-end knowledge support, thereby reducing customer problem processing time. The second stage is the establishment of various departments within the industry. A knowledge resource team is responsible for combing, confirming, and retrieving the information entering the knowledge base to ensure the accuracy of the knowledge base. The third stage is to build a KM platform covering the entire bank and promote the realization of paper documents electronification and structured electronic documents covering the entire bank, achieving a double cycle of personnel and knowledge.

Central Bank of Malaysia

The Knowledge Management Progress of the Central Bank of Malaysia⁴ focuses more on IT tools in managing knowledge.

The strategy of embedding KM practices into work processes began in 2000. A key milestone was the successful completion of the Bank’s corporate taxonomy project. The taxonomy is the Bank’s information classification framework that has been deployed as a foundation to develop a knowledge repository management system referred to as the Bank’s Knowledge Hub. Supported by search engines and information security policies, the Knowledge Hub serves to enhance knowledge visibility and accessibility, thus facilitating further the process of knowledge acquisition, reuse, sharing, and creation.

Banking Knowledge Management Model

Based on their review of KM in the banking sector, Ali and Ahmad propose the Banking Knowledge Management Model (BKMM)⁵ as a new approach based on the concept of KM postulated by Wiig and Prusak⁶.

A constantly changing environment may compel organizations to initiate KM practice. The working environment demands that organizations respond rapidly, capitalizing on lessons learned. However, this approach has many limitations as the decisions made based on past experience may not be the most appropriate one. Consequently, there is a need for a sophisticated level of “know-how”, “know-what”, “know-who”, “know-where” and “know-why”.

People and technology are two factors that affect the efficient implementation of KM. It is challenging to get employees to embrace a KM-oriented culture. According to Duffy⁷, sharing knowledge especially proprietary or individual knowledge could result in power redistribution and cultural resistance. Along with mergers, acquisitions, and alliances, banks are expanding and their business types are becoming more and more diversified. The knowledge that banks possess and the knowledge they need to handle their business is also more fragmented. Information technology is only effective if used properly in data management.

Ali and Ahmad theorized that people and technology are the elements that contribute to knowledge progress. Knowledge progress can be divided into three components namely knowledge creation, knowledge retention, and knowledge sharing. Knowledge creation is the progress in which knowledge is captured and defined. Through this codification process, tacit knowledge is transformed into explicit knowledge. The main purpose of knowledge retention is to allow knowledge reuse. At the same time, it is equally important to protect knowledge and how to plan security measures to ensure the integrity of knowledge. Erroneous knowledge is just as damaging as inaccessible knowledge if not more. Finally, there is knowledge sharing. Explicit knowledge can be shared more easily, with little risk of errors being made in the process. Tacit knowledge is difficult to express and is a challenging part of knowledge sharing. Regardless, knowledge sharing⁸ should be as direct and as minimally intermediary as possible.

Facing future challenges

In the current environment, KM in the banking sector isn’t a nice to have – it’s a necessity. The BKMM model provides a framework for future research in KM integration in the banking sector. However, with accelerating development, commercial banks are facing increasing risks, including credit risk, liquidity risk, financial innovation and derivative business risk, and internal management risk. How to apply KM to control risks is a new dimension.

Around 2015, the banking industry began to come into contact with the concept of knowledge graphs playing great value in risk control. For example, for the prediction of potential risky customers, by establishing a knowledge graph of customers, companies, and industries, and connecting data between industries and companies, banks can discover potential risky customers in a timely manner and reduce credit risks.

Facing unknown developments in the future, banks should increase the development and implementation of KM systems and optimize the powerful functions of information capturing, storing, and retrieving in a centralized hub.

Article source: Adapted from Knowledge Management in Banking Industry, prepared as part of the requirements for completion of course KM6304 Knowledge Management Strategies and Policies in the Nanyang Technological University Singapore Master of Science in Knowledge Management (KM).

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

References:

In this presentation to the KMGN Research Community meeting of 21 June 2023, I discuss my PhD research, which had the objective of exploring the utility of existing sociological theories for the practice of designing and managing contemporary project teams.

1. Overview of Industry 4.0

1.1 Definition of Industry 4.0

Through the use of advanced technologies like digital technology, Internet of Things, big data analysis, artificial intelligence, and machine learning, Industry 4.0 refers to the intelligentization and automation of the manufacturing process known as the fourth industrial revolution. German Chancellor Angela Merkel once said¹ that with Industry 4.0, our production techniques will be reinvented, just as the Internet altered how we live. To echo the words of sci-fi master Arthur C. Clark², the “magic” of Industry 4.0 is indeed sufficient to make the seemingly impossible possible.

1.2 Characteristics of Industry 4.0

1.2.1 Connectivity and the Internet of Things (IoT). Industry 4.0 is built on the Internet of Things. To share and collect data in real time, various devices, machines, and sensors are able to attach and converse with one another. Greater transparency and coordination are produced when interconnectivity exists in the production process. Manufacturing, like the “Internet,” has an “Internet of Things.” Similar to sharing content online, these devices and factories can collaborate and exchange data.

1.2.2 Big data analysis. Industry 4.0’s very core is big data analysis. Advanced analytics and vast amounts of data collection allow companies to make better-informed decisions by offering insights into production processes, products, and market trends. In the United States, Amazon optimizes its supply chain and logistics using big data analysis³, delivering orders faster.

1.2.3 Automation and intelligence. Industry 4.0 is characterized by automated production with intelligence. Complex tasks are performed by automated production lines and intelligent robots with less human oversight, leading to boosted productivity and accuracy. Intelligence is like the “brain” of the manufacturing industry. It uses intelligent technology to give production lines the ability to learn and adapt. Just like the human brain, it can continuously evolve and improve performance.

1.2.4 Personalized manufacturing. Customers need-based, Industry 4.0 enables personalized manufacturing. The production process can be adjusted according to the requirements of customers to produce products that meet individual needs. During the US implementation of Industry 4.0 technology, Nike sports shoes adopted the innovation to tailor shoes according to individual preferences⁴ in terms of style and color options.

1.2.5 Flexibility and quick response. Adapting to market alterations is now simpler for businesses thanks to Industry 4.0. Thanks to adaptable production procedures, companies can speed up the development of new goods or modify their manufacturing strategies. As former Google CEO Eric Schmidt said: Industry 4.0’s digital turn will manipulate every production line element into a digital tool, creating a smart factory. Industry 4.0 functions similarly to a well-coordinated symphony orchestra. Together, different technology and equipment instruments produce wonderful music. These technologies work together in the Industry 4.0 setting to establish a very intelligent production ecology for businesses. Simultaneous to producing an efficient rhythm, this orchestra provided a competitive edge to the company.

2. The concept and importance of knowledge management

2.1 Definition of knowledge management

Utilized to attain corporate goals and boost efficiency, KM comprises a deliberate approach to amass, organize, and apply intelligence assets. All of these comprise an organization’s knowledge assets: employee experience, expertise, documents, data, and information.

2.2 Importance of knowledge management

KM is like a corporate treasure map, which guides us to find valuable knowledge treasures in the ocean of information. These treasures may be hidden in the minds of employees, or buried in corporate files and databases. KM is to discover, organize and share these treasures so that they can maximize their value.

In enterprise management, the importance of KM is apparent in various aspects:

2.2.1 Improving the quality of decision-making. KM enables decisions to be grounded in robust information and seasoned insights. By analyzing market trends, customer requirements and competition more precisely, companies can make more informed strategic choices⁵ through efficient KM.

With both Industry 4.0 and KM working in tandem, IBM makes full use of the opportunities. An example of their cognitive computing system “Watson” is vivid. Watson’s intelligence is not solely based on question answering; it has a vast KM system⁶. The knowledge base of Watson is constructed by IBM through the convergence of global professional knowledge, literature, and case studies. Not only does Watson receive support from this knowledge base in regards to answering diverse questions, but it also supplies relevant knowledge resources for sectors including medical care, finance, and education. The integration of Industry 4.0 technology enables Watson to obtain and update its knowledge base more quickly. By pairing Industry 4.0 with KM, IBM developed a sophisticated cognitive computing system to assist enterprises in resolving challenging issues.

2.2.2 Promoting innovation. KM helps promote innovation. The intersection of knowledge sharing and collaboration breeds new ideas, solutions, and products/services within an organization.

2.2.3 Improve production efficiency. Productivity increases when effective KM is employed. Greater ease in accessing knowledge enables employees to avoid wasting time and resources through redundant efforts. With digital transformation, GE has experienced remarkable success. Improving product performance and efficiency have resulted from the introduction of Industry 4.0 technology in aviation, energy, medical, and other fields. A unified knowledge base is the result of GE’s integration of employees’ professional knowledge and best practices, which takes place at the same time as a global KM strategy. In many fields, innovation and competitive advantages have been attained via GE’s dual-engine strategy⁷.

2.2.4 Improve training and performance management. Better training and performance management are facilitated through KM within organizations. Targeting training and development programs based on employee skills and knowledge levels is crucial for enhancing workforce performance. KM is like a Swiss army knife that has multiple functions that help businesses survive and thrive in a competitive market. Just like a Swiss Army knife can be used to cut, open bottles, and tighten screws, KM can also play a variety of roles in production, market analysis, and innovation, providing companies with a versatile competitive advantage.

3. The relevance of Industry 4.0 to knowledge management

3.1 Data collection and analysis

The sheer volume of data generated by Industry 4.0 technology includes production process data, equipment status data, market data, and more. Without effective management, this valuable knowledge goes unrealized. KM enables companies⁸ to gather, store, and analyze data so they may glean insights regarding manufacturing processes, product performance, and market trends.

Industry 4.0 and KM are like tacit dancing partners, playing together the symphony of corporate success. For example, Huawei combines Industry 4.0 technology with KM to establish a globally shared technology database⁹. This database records the company’s technology patents, R&D results and best practices, providing valuable knowledge resources for global teams. This platform not only facilitates technological innovation but also accelerates product development cycles. Industry 4.0 and KM work together to accelerate market expansion.

3.2 Decision support

With Industry 4.0 systems, management gains access to timely data and analysis to aid decision-making. Decision quality is enhanced when KM ensures that choices are based on prior expertise and understanding. Viewing solutions from previous situations can aid management in solving current problems, thanks to KM systems.

Siemens excels in implementing digital factory practices, positioning them as Industry 4.0 leaders¹⁰. Automated production lines and big data analysis technology were implemented to enhance production efficiency. KM holds the secret, though. Through a global KM platform, Siemens allows critical staff to share best practices collectively. Both the factory’s productivity and problem-solving efficiency are boosted by knowledge sharing. For example, China’s factories yield engineers problems to solve. On the KM platform, he was able to quickly contact his German coworkers, share the problem details, and get a quick solution. KM and Industry 4.0 work together via synergy.

3.3 Training and skills development

Acquiring new skills and knowledge, employees must adapt to operate Industry 4.0 technologies and equipment. To support Industry 4.0 implementation, KM can help organizations manage and provide training resources. Training plan creation, outcome documentation, and best practice sharing fall under this category.

3.4 Continuous improvement

Continuous improvement and optimization of production processes is stressed with Industry 4.0. Sharing experiences is an aspect of KM. Employees can quickly learn from past projects through KM, which also provides problem solutions and improvement suggestions at their fingertips.

4. Common goals of Industry 4.0 and knowledge management

4.1 Improve competitiveness

An objective of Industry 4.0 is boosting business competitiveness. Digitalization, automation, and intelligent transformation allow enterprises to produce goods more efficiently and respond swiftly to market demands. KM supports this goal by ensuring that companies can make full use of internal and external knowledge assets to better respond to competitive pressures.

Industry 4.0 and knowledge management, Apple represents exceptionally well. Industry 4.0 tech enhances production procedures, optimizes products, and raises output effectiveness. Rapid product assembly and a reduction of human errors are made possible through the deployment of robots and automated systems on the production line as an example. Improved production efficiency is achieved as a result of Apple’s simultaneous use¹¹ of the KM system to track employee training and skill advancement. Apple is like a high-speed rocket, Industry 4.0 provides the power, and KM is the navigation system to ensure the smooth flight.

4.2 Risk reduction

Data security, privacy protection, and other risks associated with Industry 4.0 can be managed through KM.

4.3 Realize sustainable development

Industry 4.0 and KM complement sustainable development goals. Minimizing resource waste and environmental impact requires Industry 4.0 to adopt more intelligent manufacturing methods. What occurs simultaneously is that KM leads to better sustainable development by managing and sharing best practices.

5. Conclusions and prospects

Industry 4.0 and KM are like dancing partners. They work together to let enterprises dance on the market stage. Industry 4.0 provides stage lighting and music, allowing companies to produce more efficiently and intelligently. KM provides dancers with dance steps and techniques to ensure they can perform their best on stage. It is this tacit dance that allows companies to become superstars in the fiercely competitive market.

Industry 4.0 and KM are indispensable elements in today’s business management. They complement each other and jointly promote the success of enterprises. Industry 4.0 technology provides a large amount of data and information, and KM ensures that this information can be effectively collected, organized and applied. Together, they help enterprises improve competitiveness, reduce risks, and achieve sustainable development, while also promoting innovation and continuous improvement.

In the future, with the continuous development of technology, the relationship between Industry 4.0 and KM will become even closer. Emerging technologies such as 5G, blockchain and augmented reality will further change the way manufacturing and knowledge are managed. Therefore, enterprises need to continuously adapt to these changes and continuously optimize the integration of Industry 4.0 and KM to remain competitive and achieve continued success.

Article source: Adapted from Knowledge management in the era of Industry 4.0, prepared as part of the requirements for completion of course KM6304 Knowledge Management Strategies and Policies in the Nanyang Technological University Singapore Master of Science in Knowledge Management (KM).

Header image source: Teahub.

References:

In May 2009, the Australian Government announced up to $77.4 million of funding for the Hawkesbury-Nepean River Recovery Program with the aim of improving the health of an iconic river system in New South Wales (NSW), Australia. The program comprised seven projects carried out by six different NSW Government agencies.

I commenced in the role of overall Program Manager in June 2009. The program concluded two and a half years later, having exceeded its intended outcomes. It was completed on time and under budget, despite significant challenges, and subsequently won two major awards.

Pivotal to the success of the Hawkesbury-Nepean River Recovery Program was the overall program management approach that I used, as well as the project management approaches used by the six project managers.

In this article, I discuss this approach, with the aim of stimulating the further development and application of agile methods to program management.

Agile methods

An agile method relies upon incremental and iterative completion of goals with a self-managing team. It is often presented in opposition to a “waterfall” process (Figure 1) that sequentially gathers requirements, completes a design, and then builds a final product.

Hirotaka Takeuchi and Ikujiro Nonaka proposed the core agile concept of iterative, continuous delivery in 1986¹. They are acknowledged² as the inspiration for Scrum (Figure 2), a popular methodology for delivering IT projects today.

Co-created by Ken Schwaber, Jeff Sutherland, John Scumniotales and Jeff McKenna, the term “Scrum” is often used interchangeably with “agile”. However, properly speaking, “Scrum” is a specific methodology whereas “agile” can be any technique that focuses on iterative delivery and empowerment. Agile primarily focuses on efficiently segmenting the business processing cycle of the problem-solving pattern into “chunks” that can be executed in parallel.

While initially focused on IT projects, agile methods have now been extended to wider business and management applications. For example, the late Mike Beedle, an Agile Manifesto signatory and described as a business agility visionary, developed Enterprise Scrum which “offers a way to agilize and entire company from top to bottom (hierarchy), or from “side to side” (collaboration), or even in subsumption (dependent knowledge levels).”

Program management vs. project management

Before exploring the application of agile methods to program management, an understanding of the differences between program and project management is necessary.

As discussed in the paper Program and Project Management: Understanding the Differences³, the terms “program management” and “project management” are often used interchangeably, but the two are actually distinctively different disciplines. The three most important differences are:

1. Program management is strategic in nature, while project management is tactical in nature … program management focuses on achievement of the intended strategic business results through the coordination of multiple projects.

2. Program management is entirely cross-functional, while project management focuses on a single function, or limited cross-functional alignment at best.

3. Program management integrates the individual elements of the projects in order to achieve a common objective.

Additionally:

Coordinated management of multiple projects means that the activities for each project are synchronized through the framework of a common lifecycle executed at the program level. If an organization is using a phase-gate lifecycle model for example, all projects within the program pass through the phases and gates simultaneously. Program management ensures the effective coordination and synchronization of the multiple projects through the lifecycle.

This article focuses on the application of agile methods to program management rather than project management, in this case an agile approach to the overall program management of the Hawkesbury-Nepean River Recovery Program rather than any of its seven projects.

Applying agile methods to program management

With the degradation of the Hawkesbury-Nepean River having been the focus of considerable media attention, both the Australian and NSW Governments were keen to ensure that the Hawkesbury-Nepean River Recovery Program was delivered successfully in accordance with its original objectives.

Consistent with this, the Australian Government required three-monthly progress reporting, rather than reports on the normal six-monthly cycle. The reports were required to be reviewed by a Program Steering Committee before being submitted to the Australian Government. I recommended that the Program Steering Committee primarily comprise the project managers of the seven projects, and determined that the three-monthly reports the project managers submitted to the Program Steering Committee at the review meeting would include more than just the basic reporting of progress.

I also included the following additional requirements in the three-monthly project reports:

• An explanation of any delays that occurred in the reporting period and the actions to be taken to address the delays

• Risk assessment review, involving the review of the project schedule of the comprehensive risk management report prepared at the beginning of the program, as discussed in part 7 of this series

• A detailed explanation of the work to be undertaken in the next reporting period

• Any potential difficulties, issues or risks anticipated in the next reporting period and the actions that would be taken to mitigate these potential difficulties.

These requirements have parallels with the three questions asked in the daily meetings that are a fundamental part of the Scrum framework mentioned above.

The additional requirements had the effect of turning each three-monthly reporting period into an incremental and iterative agile stage. Breaking down the overall program timeline into the smaller iterative cycles meant that the project teams were focused on reaching immediate and much more readily achievable goals, rather than feeling overwhelmed and highly stressed by everything that must be achieved in the overall program.

To emphasise the three-monthly cycles, they were also tracked through Basecamp where information from across the program was also shared.

Critically, through the three-monthly iterative approach, unexpected “Black Swan” events that could otherwise have derailed the Hawkesbury-Nepean River Recovery Program were identified and addressed at the earliest possible opportunity.

In his book The Black Swan: The Impact of the Highly Improbable⁴ Nassim Nicholas Taleb proposes what has become known as “Black Swan Theory”.

He uses the unexpected discovery of black swans to highlight the limitations of knowledge:

Before the discovery of Australia, people in the old world were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence. The sighting of the first black swan might have been an interesting surprise for a few ornithologists (and others extremely concerned with the coloring of birds), but that is not where the significance of the story lies. It illustrates a severe limitation to our learning from observations or experience and the fragility of our knowledge. One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans.

He describes such “Black Swan” events as having three attributes:

First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme impact. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

He states that rather than trying to predict such events, we should instead build resilience against the impacts of negative “Black Swans” and be poised to take advantage of positive ones.

There’s no shortage of examples of programs that have failed or run over time or budget due to the occurrence of unexpected events. However, because of our tendency to concoct explanations after the fact, we draw the wrong conclusions about what went wrong, for example we believe that our risk management was inadequate. But while risk management can often be done better, Black Swan Theory tells us that it is impossible to anticipate outlier events. Rather, we need frameworks that enable us to quickly respond to such events when they do occur.

An example of a “Black Swan” event experienced during the Hawkesbury-Nepean River Recovery Program is delays in the water sharing plan for the Hawkesbury-Nepean catchment, which created serious problems in regard to securing water savings from the program. The three-monthly incremental and iterative cycle created the drive to quickly identify this issue and resolve it through an interim legislative amendment.

As Program Manager, I was located within the former NSW Office of the Hawkesbury-Nepean, a separate entity to the agencies responsible for the projects. I was not the line manager of the project managers, rather having the role of a central coordinator. The recommendations of the Final Report⁵ of the Hawkesbury-Nepean River Recovery Program include that:

Future programs should appoint a central coordinating body with overall responsibility for the program. The Office of the Hawkesbury-Nepean played a critical role as broker and coordinator for the Hawkesbury-Nepean River Recovery Program.

The Program Steering Committee always met face-to-face for its three-monthly meetings despite being widely dispersed, and for much of the program the meetings were followed by one or two days of field visits to inspect sites from all seven projects. This further enhanced both the three-monthly cycle emphasis and knowledge sharing across the projects of the program.

Cycles shorter than three months at a program management level would negatively impact on the effective management of the projects, and cycles longer than three months would make issue identification and resolution too slow.

See also: Does discomfort help to explain the effectiveness of agile and other incremental / cyclic methods?

Editor’s note: This article was first published on 3 March 2016 as “Case study: An agile approach to program management.” It has been updated and added to the series of articles on knowledge management (KM) in project-based and temporary organisations.

Header image source: Alvaro Reyes on Unsplash.

References:

When C P Snow, the British novelist and physical chemist, wrote in 1959 that “the intellectual life of the whole of Western society is increasingly being split into two polar groups”, he was talking of the differences between scientists and literary intellectuals. But he could as easily have been talking about science and the visual arts.

To many, science embodies the rational and analytical end of human experience, while art comes from the empathic and expressive. Science can prove truths to us, while art can only make us feel them.

These differences are compounded as science becomes responsible for the official narrative of our lives, through medicine and genetics, while contemporary art retains a mystical ‘outsider’ status, both in its intellectual obscurity and the inflated prices of the international art market. Nevertheless, where science meets art and the two work together, the result can be extraordinarily productive, as horizons are broadened and gaps in our understanding of both are filled.

In the 20th century, science has revolutionised art’s means of production, from the introduction of fast-drying polymer-based acrylic paints in the 1960s, to the ubiquity of computer-based image generation today. Science has also offered us a key to some of the traditional mysteries of artistic practice. For example, Dr John Tchalenko’s ‘painter’s eye’ project attempted to demystify the way in which a painter transfers the image of a model to paper by tracking eye and hand movements to discover the length of an artist’s visual memory: the time during which he or she can maintain the image in the mind as it is transferred to paper or canvas.

Science also provides aesthetic inspiration. In 1951, at the Festival of Britain, the Festival Pattern Group combined post-war optimism about both science and design. Textile, wallpaper, ceramic and other material designs were produced based on recent developments in X-ray crystallography, a technique that reveals the complex internal structure of chemical and biological substances. The designs pervaded the Festival, on London’s South Bank, including the wallpaper of the Regatta restaurant, but in the absence of mass production the styles never became widely popular.

In return for such advances, artists have often lent their services to promote the understanding of science. As anatomy became increasingly important to medicine in the 18th century, but cadavers to examine were in short supply, wax model making came into its own as a means of instructing both medics and the general public in the workings of the human machine.

Joseph Towne, the official model-maker at Guy’s Hospital in London, made over 1,000 anatomical models, some of which were on display in Exquisite Bodies, in his 50 years at the hospital. Even today sculptors like Eleanor Crook produce educational models that show in three dimensions what photography can’t.

We can now see what blood vessels, vitamins and cancer cells look like. Sometimes this requires direct collaboration between scientists and artists. Dave McCarthy and Annie Cavanagh produced an image of a fly on sugar crystals for which McCarthy operated an electron microscope to produce a black-and-white image, after which the colour was added by Cavanagh.

Though such images can be both beautiful and instructive, adding artificial colour to scientific images can be controversial. Luke Jerram’s series of blown-glass sculptures of viruses such as HIV and H1N1 present the microbes as transparent, devoid of any of the colour.

Jerram (who is himself colour-blind) feels that the artificial and garish colouring of images communicates unnecessary fear. His elegant and complex structures confer not only simplicity, but also some kind of beauty to widely reviled pathogens.

Science and art both rely on observation and synthesis: taking what is seen and creating something new from it. Our society could hardly exist without either, but when they come together our culture is enriched, sometimes in unexpected ways.

Article source: The relationship between science and art, Wellcome Collection, CC BY 4.0. Republished by permission.

Header image: From Luke Jerram’s series of blown-glass sculptures of viruses. Source: Wellcome Collection, © Luke Jerram, CC BY 4.0.

Growth mindset long overhyped

As reported in a previous RealKM Magazine “In the know” article, evidence to support the idea of a “growth mindset” has been found seriously wanting.

In the time since, two systematic review and meta-analysis studies have further reviewed the growth mindset academic literature. One study¹ looked at the academic achievement of students, and found that there is little evidence that adopting a growth mindset rather than a fixed mindset leads to higher achievement. The other study² looked at growth mindset interventions, and concluded that, in certain circumstances, there can be positive effects.

Three commentary articles responding to the two systematic review and meta-analysis studies were then published. These commentaries were followed by a reply³ from the authors of the systematic review and meta-analysis that had looked at the academic achievement of students. They report that two of the commentaries support their original findings, and state that the third, which challenged their findings, is flawed. In conclusion, they restate their original observation, which is that:

Apparent effects of growth mindset interventions on academic achievement may be attributable to inadequate study design, reporting flaws, and bias.

Referencing the systematic review and meta-analysis that had looked at the academic achievement of students, Paul Fairlie, PhD writes in a LinkedIn post that:

I remember reviewing the small number of studies that existed 15 years ago to develop an assessment tool for a consulting firm and found the evidence wanting – certainly not enough to support a best-selling book at the time on growth mindsets. I conducted our own study on a national sample and found no relationship between growth mindsets and adult performance in organizations. Given the limited real estate on the firm’s new assessment tool, I recommended that they drop growth mindsets. A bit risky at the time. Since then, a lot of vendors have gotten rich on growth mindset tools.

Fairlie’s reflection shows how, despite only ever having a questionable evidence base, the notion of growth mindset has been overhyped throughout its existence, including by people who have put personal profit ahead of professional rigor.

As previously stated, this is a serious concern, because approaches based on growth mindset are being used in knowledge management (KM). Anyone who is using growth mindset in their KM work should immediately pause doing so, and then carry out a thorough review and reorientation using the two systematic review and meta-analysis studies and reply article referenced above.

With thanks to Bart Verheijen for letting us know about Paul Fairlie, PhD’s post and the two systematic review and meta-analysis studies and associated commentary,

Father introduces Mother of Modern Management

The contribution to knowledge management (KM) of “Father of Modern Management” Peter Drucker is widely recognised. However, as the RealKM Magazine article What about the role of the “Mother of Modern Management” in KM? alerts, the equally important contribution of “Mother of Modern Management” Mary Parker Follett has been largely ignored. As that article discusses, Mary Parker Follett’s work is important not only in regard to the evolution of KM, but also potentially to its future.

A Twitter comment in response to that article reveals that Peter Drucker actually wrote the introduction to Pauline Graham’s book Mary Parker Follett: Prophet of Management⁴. The following excerpt from the introduction shows that Drucker himself had been unaware of Mary Parker Follett and her important contribution. This begs a question: what other important contributions to KM have been or are being ignored?

With thanks to Bruce McTague for letting us know about Peter Drucker’s introduction in Pauline Graham’s book Mary Parker Follett: Prophet of Management.

References:

Implementing arts-based interventions (ABIs) in organisations goes against everything we have been taught. We have been taught to be analytical, logical, rational, and efficient in our work. This means we don’t have time for games and “fooling around”, even if that would make us more effective and ultimately more efficient. We must be serious and so the implementation of ABIs which are playful and fun, and act as catalysts for team building, communication, collaboration, innovation, sustainability, culture change, and all the other things that are the documented benefits of ABIs, is a paradox and unreconcilable with the seriousness of the workplace.

At least that’s what most of us believe because we look at things as an either/or rather than a both/and situation.

But what if that’s not true?

What if it’s not an either/or situation? What if it’s a both/and situation?

We can be serious and be playful and still be efficient and effective, possibly more-so, especially in an increasingly volatile, uncertain, complex, ambiguous world?

I have a case study¹ documenting an organisation that struggled over a problem for years coming together in studio for a week and solving the problem. I’ve had people who struggled had been struggling with a problem for months solve it by doing a 5-minute scribble drawing.

ABIs facilitate diffuse thinking and they open up the possibility for conversations that wouldn’t happen otherwise. Breaking down barriers, insecurities, and uncertainties and replacing them with curiosity and open-mindedness.

We need new ways of working, solving problems, innovating²: the old ways aren’t working anymore. Doing more of the same isn’t going to help.

Introducing arts-based interventions is a way to do that to improve collaboration, teamwork, problem solving, sustainability, and improve the way we work.

Just like we’ve relearned what is good for us to eat: 30 years ago, it was all about low fat and so our food was re-engineered to remove the fat, replacing it in many cases with sugar and chemicals. Now we know that sugar is even worse than fat. And fat, in fact, isn’t bad at all in moderation and so we’re learning to rethink about what we eat and how we approach food. Going back to natural items, limiting sugar and moderating fats. If we can relearn how we’re eating, we can relearn how we’re working.

What do you think?

Organisations are increasingly adopting agile thinking outside of the IT department. Stephanie Barnes uses creativity to help people become more comfortable working in this new work environment. Allowing them to flatten hierarchies, increase collaboration, and transparency, as well as improve employee engagement, not to mention solve problems in creative and innovative ways.

Art is used as a catalyst for doing things differently. Making decisions, learning to give up control, challenging assumptions, adopting the behaviours which allow people and organisations to be more agile: openness, curiosity, confidence, reflection, trust, and respect.

Integrating art, artistic attitudes, and a creative practice into our knowledge work/processes creates: Radical Knowledge Management. It enables the adoption of agile/flexible behaviours and culture change which in turn allows the digital transformation of our organisations, so that they are successful in our knowledge age future.

Get in touch with Stephanie, she can help your organisation (and you) realise your knowledge age potential.

Further information:

• Entelechy: The realisation of potential
• Stephanie Barnes Art (portfolio)
• Stephanie Barnes LinkedIn
• Stephanie Barnes Linktree
• Introduction to radical knowledge management: Making knowledge management sustainable (Business Information Review journal, 2022)
• Radical Knowledge Management: Using Lessons Learned From Artists to Create Sustainable Workplaces (Frontiers in Artificial Intelligence journal, 2021)

Article source: First published on LinkedIn, republished by permission.

Header image source: 愚木混株 cdd20 on Unsplash.

References:

Research conducted by the University of Cambridge reveals a concerning trend hindering the UK’s ability to address pressing issues such as public health and climate change. The study¹ suggests that policymakers often disregard existing evidence when formulating policies, impeding progress in these critical areas.

According to the research findings, this “evidence-neglect” arises from the incentive structures that reward politicians for setting ambitious policy objectives while simultaneously discouraging the implementation of necessary measures to achieve them. Furthermore, conflicting political ideologies and interests further contribute to this challenge, impeding the adoption of effective policies.

Overcoming the flaw

The author proposes two key measures. Firstly, involving citizens more actively in the policy-making process would ensure that their interests take precedence. Secondly, enhancing the accountability of politicians through the implementation of legally binding frameworks for all stages of policy-making would promote responsible decision-making.

Notably, successive UK governments have pledged to tackle major societal issues by setting ambitious targets. These objectives encompass significant milestones such as reducing childhood obesity by 50% before 2030, eradicating smoking by 2030, narrowing the gap in healthy life expectancy by 2030, and achieving net zero carbon emissions by 2050.

“None of these ambitions is on course,” the author explains. “Of course, scientific evidence is just one of many sources of information for policymakers to consider, but neglecting evidence is a sure-fire route to unsuccessful policymaking.”

Poor progress

Forecasts paint a worrisome picture, suggesting that the goal of reducing childhood obesity by 50% by 2030 may fall short, with the likelihood of a doubling rather than a halving of cases during that period. Similarly, the eradication of smoking, initially targeted for 2030, appears to face a delayed realization, now projected to extend beyond 2050.

Furthermore, while the gap in healthy life expectancy between regions is predicted to narrow by 2030, subsequent estimations indicate a potential increase of five years by 2035. These trends suggest substantial challenges in meeting these ambitious objectives.

Realizing each of these aspirations necessitates sustained alterations in various sets of behaviors encompassing individuals across different socio-economic groups. These encompass dietary choices, consumption patterns, tobacco use, and transportation habits.

Lack of evidence

“There are many possible reasons why these policy ambitions are so far off-track, but chief among them is the neglect of evidence, particularly around achieving sustained changes in behavior across populations,” the author continues.

“Put simply, these failures are baked-in, given the policies designed to achieve these ambitions are based on interventions that cannot achieve the change required.”

Politicians are often incentivized to pursue ambitious goals, especially if they’re part of election pledges or are designed to achieve positive publicity. Against this, they can also discourage the kind of policies designed to achieve those goals.

“Fear of electoral damage plays a role here,” the author explains. “Take taxes on tobacco, alcohol, junk food and carbon emissions: these are among the most effective interventions for improving health and the climate, but they are unpopular with the public and so politicians are unwilling to adopt them.”

Resistance to progress

The implementation of such policies may encounter not only public resistance but also opposition rooted in political interests and ideologies. One such ideology is neoliberalism, which advocates for limited government involvement in the economy and public policy, placing greater emphasis on individual responsibility for achieving well-being, wealth, and happiness. Within this framework, government interventions are often portrayed as intrusive “Nanny Statism.”

Moreover, certain industries have a vested interest in promoting personal responsibility, as it dissuades politicians from adopting effective policies that could potentially impede their interests. Industries that stand to be affected by measures aimed at reducing the consumption of fossil fuels, tobacco, alcohol, meat, and unhealthy food often cast doubt on the efficacy of such policies. They employ various lobbying tactics to sway governmental decision-makers, presenting arguments that support the maintenance of the status quo and align with their business objectives.

“There are no quick or single fixes to overcoming these problems, but there are two changes which could help: engaging citizens more in priority setting and policy design, and increasing the accountability of politicians through introducing legally-binding systems for reporting progress on policy ambitions,” the author adds.

Next steps

Policymakers have a range of options at their disposal when it comes to engaging citizens. These options include surveys, focus groups, town hall meetings, citizen assemblies, and collaboration with civil society organizations. By adopting such an approach, policymakers can potentially mitigate the political repercussions of unpopular policies.

This is achieved by exposing citizens to evidence that demonstrates the effectiveness of these policies. Numerous studies have consistently shown that such exposure tends to increase support for these policies. In fact, policies crafted with citizen engagement tend to garner more public backing, as they are perceived to be fairer and more likely to achieve their intended goals.

To address the issue of neglecting evidence, which is crucial for policy success, introducing legally binding systems for reporting policies and monitoring progress towards policy ambitions could prove highly impactful. By implementing such systems, policymakers can ensure that if progress veers off track, appropriate measures are taken to rectify the situation.

An illustrative example of this can be found in the recent Leveling Up strategy paper by the UK government. This paper outlined plans to establish a statutory obligation for the government to provide annual reports on progress towards accomplishing the Leveling Up missions. In conjunction with these plans, the government also released a set of metrics to gauge progress and evaluate the efficacy of the strategy.

“Although these requirements are by no means perfect, the legislation as drafted will at least allow parliament significantly more scrutiny of progress towards a government ambition than is often the case,” the author concludes. “Laudable policy ambitions to improve a nation’s health and protect life on the planet will remain unfulfilled ambitions unless and until evidence is given a more central role in the policy-making process.”

Article source: Ignoring Evidence Affects UK’s Ability To Respond To Major Challenges.

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

Reference:

Equity in knowledge management (KM) for health programs means the health workforce has the information, opportunities, skills, and resources to define and participate in the knowledge cycle—access, creation, sharing, and use—to improve health programs. Embedded power imbalances in global health and KM create unfair differences in the knowledge cycle among groups of the health workforce.

Green knowledge management aims to integrate green or environmental aspects into all dimensions of knowledge management (KM). The need for this has increased greatly because of growing global environmental challenges. As such, green knowledge management can potentially help KM better support the UN Sustainable Development Goals (SDGs). But despite its potential, the research literature addressing green knowledge management is currently very limited, and not well developed.

A recent paper¹ attempts to add some rigor to explorations of green knowledge management by developing and validating a proposed scale for its measurement in organizations. The scale development steps followed established guidelines². The first step involved a literature review and interviews with managers. The collected information was then used to draft a scale which was proofread and refined by experts from industry and academia. After pilot testing, the scale was finalized, a comprehensive survey was initiated, and the collected data were subjected to validation through different statistical tests.

The finalized scale lists a range of factors against five dimensions, as shown below. The paper authors advise that organizations can use it as a checklist to ensure nothing is overlooked when creating their green measurement models.

Shortcomings of the scale

I recommend that the scale is used only to initiate the exploration of green knowledge management in organizations because some shortcomings mean it should not be used as-is.

One of these shortcomings relates to a bias towards explicit knowledge systems, as opposed to tacit knowledge processes. For example, the ‘knowledge storage’ dimension of the scale would be best changed to ‘knowledge retention’ and expanded to include tacit knowledge loss prevention measures such as mentoring.

Another shortcoming relates to the apparent lack of specialist environmental input into the scale. Although the paper extensively references environmental management and sustainability literature, all of the authors come from university departments related to business, economics, and organizational management, rather than environmental science and management departments. Sadly, despite advocating for the effective engagement of the best available knowledge in organizational decision-making, the KM field can itself be knowledge-siloed. As I’ve previously discussed in RealKM Magazine, there’s much that the KM field could learn from the knowledge of environmental scientists and managers. For example, specialist environmental knowledge in regard to the measurement of outcomes could assist the further development of the ‘knowledge application’ dimension of the scale, and in regard to stakeholder knowledge engagement could assist the further development of the ‘knowledge acquisition’ dimension of the scale.

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

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