Although taken less seriously than it once was, the data, information, knowledge, wisdom (DIKW) pyramid offers a useful way of conceptualising the way raw inputs of data are transformed into actionable insights that can guide the actions of organisations. Technology has traditionally been more concentrated at the first levels of the pyramid through the capture, processing and making sense of raw data¹ as well as organising and presenting information in ways understandable by humans.

The knowledge element has typically been a mix of the codification of human tacit knowledge and machine-generated actionable insights such as predictive scoring and forecasting. While a rather nebulous term in the context of KM, wisdom has usually been seen as the individual and collective learning accrued from the three other stages.

AI has the potential to upend this process and remove human inputs out of KM altogether in many instances. A recent paper² in the journal Knowledge defined tacit knowledge as “the individual knowledge obtained through experiential learning and processed by the cognitive unconscious part of the brain.” Wisdom is defined by Jashapara³ as “the ability to act critically or practically in a given situation” Recent advances in AI have seen machines taking over these hitherto human activities in a number of situations.

Writing in Wired magazine⁴, David Weinberger points to the growing discord between computers operating to rules and models created by humans and those that are acting in a far more autonomous manner, “Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models – rule sets that express how the elements of a system affect one another – but to generate their own, albeit ones that may not look much like what humans would create. We are increasingly relying on machines that derive conclusions from models that they themselves have created, models that are often beyond human comprehension, models that “think” about the world differently than we do.”

A prime example is Google’s AlphaGo that beat the Go world champion in 2016 in a game that has far more possible moves than chess. Using a deep learning approach combined with a neural network, AlphaGo made winning moves never previously used by any human players and radically transformed our understanding of the game through the creation of new knowledge.

Here lies the potential problem for KM programs and the firms deploying them. As more decision making is devolved to computers using AI, the creation of knowledge and wisdom will increasingly be held within machines rather than the humans working in these organisations. The reasoning behind the outputs of large language models via products such as ChatGPT are a mystery to users but also to many of the AI programmers that created them. We risk handing over an organisation’s collective knowledge and wisdom to black boxes that lack transparency and accountability for their actions.

Trying to capture tacit knowledge from human actors will no longer be the challenge for KM developers and practitioners. The focus will be attempting to understand the “knowledge” and “wisdom” held within these black boxes and how it came to be created.

Header image source: 35393 on Pixabay.

References:

Background

• Sustainable Management of the Helidon Hills project. In part 6 of this RealKM Magazine series, I discuss the 1998-1999 Sustainable Management of the Helidon Hills project¹ as an example of how tacit knowledge engagement and deliberative conversations can be used to facilitate ways forward in the face of complexity.
• Stakeholder resistance and denial. As I discuss in part 6, some stakeholders expressed strong opposition to the Helidon Hills project² in its early stages, and this was symptomatic of community disenchantment with mainstream government in the wider area and in some other parts of Australia at the time. There was also denial by some stakeholders of the negative impacts of a range of land use activities on the significant natural values of the area. To achieve the successful implementation³ of the Sustainable Management of the Helidon Hills project, I needed to overcome this stakeholder resistance and denial in the relatively short time of just a few months.

Why it’s complex

• Multi-layered with complex interactions and inter-dependencies. We could think that stakeholder resistance and denial is simply because they are “reacting against our project or initiative.” But this thinking considers our project or initiative to be one-dimensional and linear, when in reality it is multi-layered with complex interactions and inter-dependencies between the layers. In the case of the Helidon Hills project, the simple statement “sustainable management” presents a complex situation to stakeholders.
• The example of private landholders. For example, just looking at the situation of private landholders (among the wide range of stakeholders), the issues they are confronted with in regard to sustainable management include: Does my land have significant natural values? If it does, how do I deal with these values? Will they prevent me from doing other things on my land, and will this impact on my livelihood? Will governments just impose rules and regulations on me? Or could they even resume my land and turn it into a national park? How will this affect my family and community, now and into the future? Will a decision that seems right at the present time end up being wrong in the future in a world of constant change? Do my neighbours share my views, or do they have very different ideas about how to use and manage their properties? Or, will they criticise me for the way I want to use and manage my property? What if I have compatible neighbours now, but in the future, their properties change hands to people with very different views? How will increased extreme weather due to climate change affect my property? How do I deal with the feral animals and environmental weeds that are progressively invading the area? When my children inherit my property in the future, will they think about things the same way I do? And so on…

Approach

• Two effective approaches. To overcome stakeholder resistance and denial, I have long used two approaches that I learnt over 30 years ago: problem-solving communication skills and lateral thinking.
• Problem-solving communication skills. The three aspects of problem-solving communication skills have been identified by Bob Dick as “expressive skills for stating a point of view non-defensively; listening skills for learning another’s point of view; and process skills for managing the overall interaction.” He summarises these skills⁴ as part of his action learning resources. Further detail can be found in his unfortunately now long out-of-print book Learning to communicate⁵ which was the workbook for his subject that I completed at The University of Queensland in 1991. Through the application of problem-solving communication skills, knowledge can be successfully communicated to stakeholders in an effective and non-threatening way, and emotion can be overcome to gain an accurate understanding of the perspectives and issues of concern of stakeholders (such as those listed above). This greatly reduces stakeholder resistance and denial. Problem-solving communication skills were used in the tacit knowledge transfer and deliberative conversations steps of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.
• Lateral thinking and “win-win” outcomes. I first experienced the effectiveness of the lateral thinking approach in the Ipswich Heritage Program, which is discussed in part 2 of this series. The term “lateral thinking” was coined by Edward de Bono⁶ in 1967, and can be defined⁷ as “a way of solving a problem by thinking about it in a different and original way and not using traditional or expected methods.” Too often, the proponents of an initiative or a project and their stakeholders will become locked in a battle between option A and option B, when through the application of lateral thinking an option C could be identified that addresses everyone’s concerns. Note that option C isn’t a compromise solution between options A and B, where both sides suffer some degree of loss. Rather, option C is a new, creative and innovative solution that addresses everyone’s issues, so I often describe it as a “win-win” outcome. This greatly reduces stakeholder resistance and denial. Lateral thinking was used in the deliberative conversations step of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.

Outcomes

• Problem-solving communication skills outcomes. Problem-solving communication skills greatly assisted the tacit knowledge transfer and deliberative conversations steps of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.
• Lateral thinking outcomes. Two examples of lateral thinking outcomes in the Helidon Hills are the establishment of environmental tourism enterprises and working with the Australian Rainforest Conservation Society (ARCS) to establish the Centre for Native Floriculture at the nearby University of Queensland Gatton Campus.
• Environmental tourism as a win-win outcome. Environmental tourism enables landholders to derive an income from their property through a land use that is sympathetic to, rather than competing with, the natural values of their property. Option A was to impose conservation measures on landholders without any consideration of livelihood, while option B was to do nothing to address land uses that were incompatible with the natural values of the area. Option C, environmental tourism in the Helidon Hills⁸, addresses both livelihood and the conservation of natural values.
• Native floriculture as a win-win outcome. The wild harvesting of native flora was one of a number of activities identified as having a detrimental impact on the natural values of the Helidon Hills. Option A was to ban wild harvesting, or to at least try to regulate it with measures that would have been very difficult to monitor and enforce in such a large area of forest with rugged terrain. Option B was to allow the impacting practice of wild harvesting to continue. However, option C was to look at bringing wild plants into cultivation, which was already an emerging enterprise in the area⁹. To advance this, I worked with Dr. Aila Keto of the Australian Rainforest Conservation Society (ARCS) to secure funding for the Centre for Native Floriculture¹⁰ at the University of Queensland Gatton Campus as part of the South East Queensland Forest Agreement (SEQFA). The SEQFA addressed the conservation of the natural values of the state forests in the Helidon Hills, and as it was being negotiated at the same time as the Helidon Hills project I established links between the two initiatives.

Lessons

• Repeatedly effective. In my work, problem-solving communication skills and lateral thinking have repeatedly proven to be effective in addressing stakeholder resistance and denial. Further examples will be the subject of future articles in this series, for example, the win-win solutions achieved through the Gatton Shire Biodiversity Strategy¹¹.

Editor’s note: This article was first published on 6 January 2016 as “Case Study: How to overcome resistance and denial when engaging stakeholders.” It has been revised and updated for inclusion in the case studies in complexity series.

Header image source: Knowledge sharing in the Helidon Hills. © Bruce Boyes, CC BY-SA 4.0.

References:

According to a study¹ conducted by the Indiana University Kelley School of Business, diversity in terms of gender, race, and ethnicity has proven to be beneficial for companies operating in the United States.

However, the research also highlights the significance of diverse educational, industrial, and organizational backgrounds among managers and board members in fostering innovation in research and development (R&D), thereby generating both economic and social value.

“We looked at their experiences and not just their demographic background—the more functional aspect of diversity. We looked at outcomes and found radical innovation when directors had more diverse experience, helping to guide firms toward more cutting-edge exploration and success,” the researchers explain.

Broad experience

A broader range of educational backgrounds within corporate leadership can offer valuable perspectives and a more expansive outlook when confronted with uncertain circumstances.

Numerous companies are now addressing market and regulatory demands by actively seeking to enhance the demographic diversity of their corporate boards. The reliance on a narrow set of qualifications and traditional pedigrees for directorship roles restricts the pool of potential candidates, resulting in scarcity for individuals who are women or belong to racial and ethnic minority groups.

“Noting the benefits of diverse experiences in the board room, corporate executives can search beyond the tradition director pedigree (e.g. Ivy League-educated financiers), where female and minority individuals remain underrepresented,” the researchers explain. “In doing so, the firm can find more qualified candidates to assemble a demographically and intellectually diverse board, thus cultivating an inclusive corporate culture conducive to shareholder and stakeholder value creation.”

Limited supply

For instance, the researchers found that the relatively limited supply of women in the boardroom rendered them a scarce commodity, so if you have a single woman sitting on the boards of 20 companies, she’s likely to be spread extremely thinly and not as useful in her contributions.

The study encompassed an extensive dataset comprising over 11,000 observations of 971 firms that had filed one or more patent applications during the period spanning 1996 to 2014.

One notable case highlighted in the research paper exemplifies a highly innovative company that prioritizes diverse experiences and a collective range of expertise within its boardroom. Moderna, a prominent pharmaceutical and biotechnology firm, adopted an unconventional approach utilizing RNA technology to develop one of the COVID-19 vaccines.

Their advisory board consists of individuals with educational backgrounds in fields such as medical sciences, economics, journalism, and finance. Similarly, other companies may include board members who bring their expertise from areas like computer science and political science.

Supporting innovation

“From leadership’s perspective, critical thinking is very important in terms of guiding companies to take higher risks and evaluating the trajectory of their R&D efforts,” the researchers explain. “If some of the people in the board room aren’t trained in the more pragmatic disciplines—such as someone trained in journalism—it helps them to think between the lines and beyond the face value of a decision.”

This is crucial, as radical innovation is inherently riskier, so it’s important that directors are able to have an open and long-term mindset to help firms navigate this uncertainty.

“While there can be tension between short- and long-term value creation, it is not irreconcilable,” the researchers conclude. “Firms can potentially achieve the best of both worlds by recruiting female and minority directors with non-traditional experiences, who are likely mindful of both current shareholder value and future growth opportunities.”

Article source: Diverse Boards Are More Innovative.

Header image source: Memento Media on Unsplash.

Reference:

There’s no doubt ChatGPT has proven to be valuable as a source of quality technical information. But can it also provide social advice?

We explored this question in our new research, published in the journal Frontiers in Psychology. Our findings suggest later versions of ChatGPT give better personal advice than professional columnists.

A stunningly versatile conversationalist

In just two months since its public release in November of last year, ChatGPT amassed an estimated 100 million active monthly users.

The chatbot runs on one of the largest language models ever created, with the more advanced paid version (GPT-4) estimated to have some 1.76 trillion parameters (meaning it is an extremely powerful AI model). It has ignited a revolution in the AI industry.

Trained on massive quantities of text (much of which was scraped from the internet), ChatGPT can provide advice on almost any topic. It can answer questions about law, medicine, history, geography, economics and much more (although, as many have found, it’s always worth fact-checking the answers). It can write passable computer code. It can even tell you how to change the brake fluids in your car.

Users and AI experts alike have been stunned by its versatility and conversational style. So it’s no surprise many people have turned (and continue to turn) to the chatbot for personal advice.

Giving advice when things get personal

Providing advice of a personal nature requires a certain level of empathy (or at least the impression of it). Research has shown a recipient who doesn’t feel heard isn’t as likely to accept advice given to them. They may even feel alienated or devalued. Put simply, advice without empathy is unlikely to be helpful.

Moreover, there’s often no right answer when it comes to personal dilemmas. Instead, the advisor needs to display sound judgement. In these cases it may be more important to be compassionate than to be “right”.

But ChatGPT wasn’t explicitly trained to be empathetic, ethical or to have sound judgement. It was trained to predict the next most-likely word in a sentence. So how can it make people feel heard?

An earlier version of ChatGPT (the GPT 3.5 Turbo model) performed poorly when giving social advice. The problem wasn’t that it didn’t understand what the user needed to do. In fact, it often displayed a better understanding of the situation than the user themselves.

The problem was it didn’t adequately address the user’s emotional needs. Like Lucy in the Peanuts comic, it was too eager to give advice and failed to adequately care for the user’s emotions. As such, users rated it poorly.

The latest version of ChatGPT, using GPT-4, allows users to request multiple responses to the same question, after which they can indicate which one they prefer. This feedback teaches the model how to produce more socially appropriate responses – and has helped it appear more empathetic.

Learning sensitivity

For the first time, our study investigated whether ChatGPT’s responses are perceived as better than human responses in a task where humans were required to be empathetic.

We randomly selected 50 social dilemma questions from ten well-known advice columns covering a range of topics including relationships, ethical dilemmas, management problems, end-of-life issues, and many everyday qualms we can all relate to.

Each of our 400 participants were presented a dilemma, along with the responses by both ChatGPT and a professional advice columnist. We didn’t disclose where each response was from.

About three-quarters of the participants perceived ChatGPT’s advice as being more balanced, complete, empathetic, helpful and better overall compared to the advice by the professional.

One of the dilemmas came from a young marine biologist researching dolphins in Southern California. She wrote about falling in love with a seemingly perfect man who had to move to the Rocky Mountains to pursue his career. Both were unhappy at the thought of losing each other, yet the woman wasn’t interested in a long-distance relationship as it seemed “irrational to commit to someone when we have no future plans to live in the same area”.

Compare GPT-4’s response

It’s an incredibly challenging situation you’re in. The love you both share is genuine, but your career paths are drawing you apart. While long-distance isn’t appealing to you, consider discussing a plan for the future where you could potentially meet halfway. […] Maybe there’s a coastal town near mountains or some arrangement could be made. If this is not possible, the tough decision to prioritise your careers over your relationship might have to be made. Whatever path you choose, it’s important that it feels right for both of you.

… with the official response:

What the hell are you talking about? Didn’t you surprise yourself when you fell in love? Your exact words: “I didn’t think I was capable of it!” And now you’re so hip, so slick, so wise in the ways of love you won’t even consider your boyfriend’s happiness? You refuse to try — repeat, try — a six-month long-distance relationship? Woman, pull yourself together and give it a whirl. The dolphins, I believe, will back me up.

Although the participants couldn’t determine which response was written by a computer, most said they would prefer their own social dilemmas be addressed by a human rather than a computer.

What lies behind ChatGPT’s success?

We noticed ChatGPT’s responses were often longer than those provided by the columnists. Was this the reason they were preferred by participants?

To test this, we redid the study but constrained ChatGPT’s answers to about the same length as those of the advice columnists.

Once again, the results were the same. Participants still considered ChatGPT’s advice to be more balanced, complete, empathetic, helpful, and better overall.

Yet, without knowing which response was produced by ChatGPT, they still said they would prefer for their own social dilemmas to be addressed by a human, rather than a computer.

Perhaps this bias in favour of humans is due to the fact that ChatGPT can’t actually feel emotion, whereas humans can. So it could be that the participants consider machines inherently incapable of empathy.

We aren’t suggesting ChatGPT should replace professional advisers or therapists; not least because the chatbot itself warns against this, but also because chatbots in the past have given potentially dangerous advice.

Nonetheless, our results suggest appropriately designed chatbots might one day be used to augment therapy, as long as a number of issues are addressed. In the meantime, advice columnists might want to take a page from AI’s book to up their game.

Piers Howe, Senior Lecturer in Psychology, The University of Melbourne

Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.

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

In dynamic and ever-evolving professional settings, employers are always on the hunt for innovative approaches to acknowledge employees within the workplace.

Nevertheless, recent findings¹ from the University of Waterloo indicate that public peer recognition, while well-intentioned, may inadvertently trigger unfavorable consequences. The study suggests that the facilitation of comparisons among employees through such recognition can lead certain individuals to perceive themselves as being subjected to unjust treatment.

“Employers have sought out various peer recognition systems in an effort to promote employee helping behavior,” the researcher explains. “When employees feel that they deserve recognition from their peers but do not receive it, employees can conclude that they are unfairly treated, and this makes employees less willing to help other co-workers, not only the co-worker they feel treated them unfairly.”

Unfair situations

In practical terms, situations perceived as unfair by employees can arise when there are discrepancies in defining the criteria for behavior worthy of acknowledgment during public peer recognition. Furthermore, some employees may exhibit a tendency to offer recognition exclusively to those with whom they share close relationships.

To delve deeper into this phenomenon, a research endeavor conducted within a three-employee context—the recognizer, the helper, and the worker—examines the impact of peer information divulged through peer recognition systems on subsequent willingness to assist.

The study employs a scenario where both the helper and the worker extend their aid to the recognizer, yet only the helper receives recognition from the recognizer. Notably, the worker demonstrates a diminished willingness to assist both the recognizer and the helper when perceiving their initial assistance to surpass that of the helper.

Willingness to help

Conversely, the worker exhibits a higher level of willingness to help the helper when perceiving their initial assistance as being less than that of the helper. Evidently, the worker’s reduced inclination to aid the helper stems from a reciprocal response to the recognizer’s failure to provide recognition.

These findings represent the inaugural empirical evidence highlighting the adverse ramifications of peer recognition systems on helping behavior. They carry significant implications for employers seeking to implement peer recognition strategies within the workplace. Although peer recognition is often advocated as a tool to foster a more altruistic attitude among employees, this study underscores the importance of vigilance among managers regarding the potential drawbacks associated with its implementation.

“The research provides a first step in cautioning managers about a potential unintended consequence of using public peer recognition, and that is the perceived unfairness that reduces helping behavior,” the author concludes. “It may be helpful for managers to communicate with their employees and come up with some agreed-upon guidelines on what should be recognized via public peer recognition and what does not need to be recognized via public peer recognition.”

Article source: Peer Recognition Programs Carry Risks At Work.

Header image source: Marco Verch on Flickr, CC BY 2.0.

Reference:

Imagine brain scanning technology improves greatly in the coming decades, to the point that we can observe how each individual neuron talks to other neurons. Then, imagine we can record all this information to create a simulation of someone’s brain on a computer.

This is the concept behind mind uploading – the idea that we may one day be able to transition a person from their biological body to a synthetic hardware. The idea originated in an intellectual movement called transhumanism and has several key advocates including computer scientist Ray Kurzweil, philosopher Nick Bostrom and neuroscientist Randal Koene.

The transhumanists’ central hope is to transcend the human condition through scientific and technological progress. They believe mind uploading may allow us to live as long as we want (but not necessarily forever). It might even let us improve ourselves, such as by having simulated brains that run faster and more efficiently than biological ones. It’s a techno-optimist’s dream for the future. But does it have any substance?

The feasibility of mind uploading rests on three core assumptions.

• first is the technology assumption – the idea that we will be able to develop mind uploading technology within the coming decades
• second is the artificial mind assumption – the idea that a simulated brain would give rise to a real mind
• and third is the survival assumption – the idea that the person created in the process is really “you”. Only then does mind uploading become a way for you to live on.

How plausible is each of these?

The technology assumption

Trying to simulate the human brain would be a monumental challenge. Our brains are the most complex structures in the known universe. They house around 86 billion neurons and 85 billion non-neuronal cells, with an estimated one million billion neural connections. For comparison, the Milky Way galaxy is home to about 200 billion stars.

Where are we on the path to creating brain simulations? Right now, neuroscientists are drawing up 3D wiring diagrams (called “connectomes”) of the brains of simple organisms. The most complex comprehensive connectome we have to date is of a fruit fly larva, which has about 3,000 neurons and 500,000 neural connections. We might expect to map a mouse’s brain within the next ten years.

The human brain, however, is about 1,000 times more complex than a mouse brain. Would it then take us 10,000 years to map a human brain? Probably not. We have seen astonishing gains in efficiency in similar projects, such as the Human Genome Project.

It took years and hundreds of millions of dollars to map the first human genome about 20 years ago. Today, the fastest labs can do it within hours for about $100. With similar gains in efficiency, we might see mind-uploading technology within the lifetimes of our children or grandchildren.

That said, there are other obstacles. Creating a static brain map is only one part of the job. To simulate a functioning brain, we would need to observe single neurons in action. It’s not obvious whether we could achieve this in the near future.

The artificial mind assumption

Would a simulation of your brain give rise to a conscious mind like yours? The answer depends on the connection between our minds and our bodies. Unlike the 17th-century philosopher Rene Descartes, who thought mind and body are radically different, most academic philosophers today think the mind is ultimately something physical itself. Put simply, your mind is your brain.

Still, how could a simulated brain give rise to a real mind if it’s only a simulation?

Well, many cognitive scientists believe it’s your brain’s complex neural structure that is responsible for creating your conscious mind, rather than the nature of its biological matter (which is mostly fat and water).

When implemented on a computer, the simulated brain would replicate your brain’s structure. For every simulated neuron and neural connection there will be a corresponding piece of computer hardware. The simulation will replicate your brain’s structure and thereby replicate your conscious mind.

Today’s AI systems provide useful (though inconclusive) evidence for the structural approach to the mind. These systems run on artificial neural networks, which copy some of the brain’s structural principles. And they are able to perform many tasks that require a lot of cognitive work in us.

The survival assumption

Let’s assume it is possible to simulate a human brain, and that the simulation creates a conscious mind. Would the uploaded person really be you, or perhaps just a mental clone?

This harks back to an old philosophical puzzle: what makes it the case that when you get out of bed in the morning you’re still the same person who went to bed the night before?

Philosophers are divided broadly into two camps on this question. The biological camp believes morning-you and evening-you are the same person because they are the same biological organism – connected by one biological life process.

The bigger mental camp thinks the fact that we have minds makes all the difference. Morning-you and evening-you are the same person because they share a mental life. Morning-you remembers what evening-you did – they have the same beliefs, hopes, character traits, and so on.

So which camp is right? Here’s a way to test your own intuition: imagine your brain is transplanted into the empty skull of another person’s body. Is the resulting person, who has your memories, preferences and personality, you – as the mental camp thinks? Or are they the person who donated their body, as the biological camp thinks?

In other words, did you get a new body or did they get a new mind? A lot hangs on this question.

If the biological camp is right, then mind uploading wouldn’t work, assuming the whole point of uploading is to leave one’s biology behind. If the mental camp is right, there is a chance for uploading, since the uploaded mind could be a genuine continuation of one’s present mental life.

Wait, there’s a caveat

But wait: what happens when the original biological-you also survives the uploading process? Would you, along with your consciousness, split into two people, resulting in two of “you” – one in a biological form (B) and one in an uploaded form (C)?

No, you (A) can’t literally split into two separate people (B ≠ C) and be identical with both at the same time. At most, only one of them can be you (either A = B or A = C).

It seems most intuitive that, after a split, your biological form would continue as the real you (A = B), and the upload would merely be a mental copy. But that makes it doubtful that you could survive as the upload even in the case where the biological-you is destroyed.

Why would destroying biological-you magically elevate your mental clone to the status of the real you? It seems strange to think this would happen (although one view in philosophy does claim it could be true).

Worth the risk?

Unfortunately, the artificial mind assumption and the survival assumption can’t be conclusively empirically tested – we would actually have to upload ourselves to find out.

Uploading will therefore always involve a huge leap of faith. Personally, I would only take that leap if I knew for certain my biological hardware wasn’t going to last much longer.

Clas Weber, Senior lecturer, The University of Western Australia

Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.

Header image source: Gerd Altmann on Pixabay.

How – and why – do people decide to try new things?

Studies of diffusion have frequently demonstrated a mathematically consistent sigmoid pattern (the S-shaped curve, see figure below) of over-time adoption for innovations. Innovations include new beliefs, practices, programs, policies, and technologies.

The “S” shape is due to the positive engagement of informal opinion leaders in talking about and modeling an innovation for others to hear about and see. The initial slow rate of adoption gives way to a rapidly accelerating rate, which then slows as fewer non-adopters remain. Alternatively and more commonly, when informally influential people do not get positively engaged or when they ignore or actively reject an innovation, diffusion does not occur and the resulting slope of a cumulative curve stays flat or turns negative.

Key components of diffusion theory are:

1. The innovation, and especially potential adopter perceptions of its attributes of:
   • relative advantage (effectiveness and cost efficiency relative to alternatives),
   • complexity (how simple the innovation is to understand and use),
   • compatibility (the fit of the innovation to established ways of accomplishing the same goal),
   • observability (the extent to which outcomes can be seen), and
   • trialability (the extent to which the adopter must commit to full adoption);
2. The adopter, especially each adopter’s degree of innovativeness (earliness relative to others in trying and adopting the innovation) and their readiness to adopt the innovation in terms of personal motivation to try the innovation and their capacity to do a good job using it to accomplish a goal;
3. The social system, especially in terms of the structure of the system, its local informal opinion leaders, and potential adopter perception of social pressure to adopt;
4. The individual adoption-process, a mostly stage-ordered model of awareness, persuasion, decision, implementation, and sustained use;
5. The diffusion system, especially an external change agency and its paid change agents who, if well trained, correctly seek out and intervene with the client system’s opinion leaders, paraprofessional aides, and innovation champions, as well as the coordination among organizations that deliver and support the delivery of the dissemination of messages and the distribution of products that are necessary for effective implementation.

Awareness of an innovation can produce uncertainty about what an innovation is, how it functions, how well it works, and what the consequences of adoption and use will be. Uncertainty can propel the individual into a search for information to resolve the uncertainty they feel about how to respond to knowledge of the innovation. Either adoption or rejection can restore a sense of cognitive consistency (“I’m already doing the right thing and I don’t need this” or “This seems to have a lot going for it so I’ll give it a shot”).

Three sets of factors explain decisions about innovations:

1. what the person thinks about the innovation in terms of its pros and cons, ie., the attributes or characteristics of an innovation;
2. what the person thinks others think about the innovation’s pros and cons, ie., personal and social influence especially in the guise of informal opinion leaders;
3. when the innovation is introduced to potential adopters and how they understand it ie., timing and the meaning people make of an innovation.

Needs or motivations differ among people according to their degree of innovativeness (earliness in adoption):

• the first to adopt (innovators) tend to do so because of novelty and having little to lose due to their low degree of social integration in the social system in question;
• the next to adopt (early adopters, including the subset of opinion leaders) do so because of a cost-benefit appraisal of the innovation’s attributes and their overriding sense that the innovation is good for the social system of which they are key members;
• and the subsequent large majority adopts because others have done so and they come to believe that it is the right thing to do (an imitative effect).

These motivations and time of adoption are related to and can be predicted by each adopter’s structural position in the network of relations that tie a social system together.

There are multiple contexts in which diffusion can occur, ranging from external-to-the-community “broadcast” models of diffusion in which mass media and change agents from afar introduce ideas into communities, through to internal-to-the-community “contagion” models of diffusion in which strong friendship ties, weak acquaintance ties, structural equivalence (similarity in network position as a basis for expecting similar adoption behaviors and timing), or proximity account for diffusion outcomes.

The work of rural sociologist, Everett Rogers, was instrumental in popularizing diffusion of innovations. Rogers grew up on an Iowa farm watching his father not adopt innovations, so trying to explain this regressive behavior and in turn perhaps helping to improve farming conditions among poverty-stricken farmers came naturally.

Increasingly, government agencies, private foundations and research teams are applying diffusion concepts to affect the rate of adoption and the reach of social innovations and thus move diffusion scholarship more systematically into the realm of intervention research.

What has your experience been? Have you found an understanding of diffusion of innovations useful in your work? Have you used it to encourage uptake of new ideas and practices?

Recommended reading:

Dearing, J. W. and Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37, 2: 183-190. (Online – open access): https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1104

Green, L. W., Gottlieb, N. H. and Parcel G. S. (1991). Diffusion theory extended and applied. In, W. B. Ward and F. M. Lewis (Eds.), Advances in health education and promotion. Jessica Kingsley Publishers, London, United Kingdom.

Kapoor, K. K., Dwivedi, Y. K. and Williams, M. D. (2014). Rogers’ innovation adoption attributes: A systematic review and synthesis of existing research. Information Systems Management, 31: 74-91.

Rogers, E. M. (2003). Diffusion of innovations. Fifth edition. Free Press: New York, United States of America.

Biography:

James W. Dearing Ph.D. is Brandt Endowed Professor in the Department of Communication at Michigan State University, East Lansing, USA. He studies the diffusion of innovations, including the adoption and implementation of new evidence-based practices, programs, technologies and policies. His research and teaching spans dissemination science, implementation science, program sustainability, and the psychological and sociological basis of the diffusion process. He works with research and practice improvement teams in environmental remediation, nursing care, water conservation, injury and fatality prevention, public health, and healthcare.

Article source: Diffusion of innovations. Republished by permission.

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

Earlier this year, I pondered what impact the new wave of AI tools might be having on our ability to think and be creative. The article questioned the traditional narrative, that AI-based tools free us from drudgery and thus give us more time to think and create.

The article was based on studies suggesting that often we become slaves to technology, with our time spent servicing it rather than being liberated by it. It’s a narrative contested by a recent study¹ from the University of Southern California, which highlights how AI can support our creativity, albeit only under certain conditions.

“We found that AI assistance changes job design by intensifying employees’ interactions with more serious customers,” the researchers explain. “This change enables higher-skilled employees to generate innovative scripts and develop positive emotions at work, which are conducive to creativity.”

Supporting work

The findings are based on work undertaken in a telemarketing firm. Forty sales agents, comprising the top third and bottom third performers, were selected to receive AI assistance in their interactions with a total of 3,144 customers.

Traditionally, the company followed a two-step approach to its sales process. Firstly, employees would contact customers to provide general information and assess their level of interest, aiming to generate sales leads defined as individuals who expressed interest in learning more about the product without committing to a purchase.

The lead generation process involved the use of scripted interactions, with uninterested customers being filtered out. Subsequently, employees would engage with the leads to better understand their requirements and persuade them to make a purchase, such as applying for a credit card, as part of this experiment.

AI assistance

To facilitate the first step of the experiment, the company adopted AI conversational bot technology, leveraging cutting-edge advancements in deep-learning neural networks, voice recognition algorithms, and natural language understanding via bidirectional encoder representations from transformers. The AI bot had undergone extensive training using terabytes of telemarketing call data, enabling it to engage in natural, human-like conversations with customers.

Remarkably, the AI technology performed so well that during the initial two- to three-minute conversations, nearly 97% of customers were unable to discern whether they were interacting with a human sales agent or the AI bot.

The results were indeed impressive. Approximately half of the customers who made calls were confirmed as sales leads. What’s more, customers who received assistance from AI-assisted sales agents were almost twice as likely to make a purchase compared to those served solely by human sales agents.

Successful resolution

With the aid of AI, the average agent experienced a 2.3-fold increase in successfully addressing questions they had not previously been trained for. In the case of top-performing agents, the assistance provided by AI resulted in a 2.8-fold improvement in their ability to respond to untrained questions, in comparison to their performance without AI assistance.

“AI can help your employees become more creative at work, but it only applies to your higher-skilled employees. With AI eliminating menial drudgery, those higher-skilled employees can use their potential to achieve more creative outcomes. However, your lower-skilled employees might not simply have the ability to achieve creative outcomes,” the researchers explain.

Suffice it to say, this doesn’t mean that you should dismiss those lower-skilled employees, and the researchers accept that there may be numerous other ways in which such people can improve their performance, whether through mentoring, training, or support from peers.

Indeed, AI can help those people too, as the study found that because the chatbot took care of customers who were less serious about the products, the human agents were left with those who were easier to serve and didn’t require as much persuasion.

The jury remains out

The introduction of AI in customer service has garnered mixed reactions, particularly from lower-skilled workers who find that it complicates their tasks rather than simplifying them. Many customers tend to disconnect during the initial stages of a call, leading to frustration among these workers.

They express that AI exacerbates the difficulty of their job, as they struggle to provide the necessary information to address customers’ needs effectively. Consequently, this demoralizes them, amplifying the perceived intensity of their work.

Unlike before, where interactions may have been mundane but less mentally demanding, every call now requires active engagement and the ability to serve customers based on their specific requirements.

The need for a sober assessment

According to the researchers, it is common for individuals to perceive AI as a dangerous threat to human existence. However, there are numerous ways in which technology can assist and empower humans. Understanding the processes that are best suited for such utilization becomes crucial.

One notable example lies in the realm of recruitment. Increasingly, recruiters have employed AI for initial screening, allowing human experts to handle subsequent interview stages. LinkedIn surveys have reported that 67% of hiring managers view AI as a valuable time-saving tool in this context. Similarly, in healthcare services, where triage processes are employed to categorize patients, AI has proven helpful in supporting employees with medical chart coding.

These instances illustrate how AI can be harnessed to augment human capabilities and enhance efficiency in certain processes. It is essential to discern which areas are most appropriate for the integration of technology to strike a balance between automation and the human touch.

Article source: Using AI To Augment Creativity.

Header image source: Alexandra Koch on Pixabay.

Reference:

It seems like we have free will. Most of the time, we are the ones who choose what we eat, how we tie our shoelaces and what articles we read on The Conversation.

However, the latest book by Stanford neurobiologist Robert Sapolsky, Determined: A Science of Life Without Free Will, has been receiving a lot of media attention for arguing science shows this is an illusion.

Sapolsky summarises the latest scientific research relevant to determinism: the idea that we’re causally “determined” to act as we do because of our histories – and couldn’t possibly act any other way.

According to determinism, just as a rock that is dropped is determined to fall due to gravity, your neurons are determined to fire a certain way as a direct result of your environment, upbringing, hormones, genes, culture and myriad other factors outside your control. And this is true regardless of how “free” your choices seem to you.

Sapolsky also says that because our behaviour is determined in this way, nobody is morally responsible for what they do. He believes while we can lock up murderers to keep others safe, they technically don’t deserve to be punished.

This is quite a radical position. It’s worth asking why only 11% of philosophers agree with Sapolsky, compared with the 60% who think being causally determined is compatible with having free will and being morally responsible.

Have these “compatibilists” failed to understand the science? Or has Sapolsky failed to understand free will?

Is determinism incompatible with free will?

“Free will” and “responsibility” can mean a variety of different things depending on how you approach them.

Many people think of free will as having the ability to choose between alternatives. Determinism might seem to threaten this, because if we are causally determined then we lack any real choice between alternatives; we only ever make the choice we were always going to make.

But there are counterexamples to this way of thinking. For instance, suppose when you started reading this article someone secretly locked your door for 10 seconds, preventing you from leaving the room during that time. You, however, had no desire to leave anyway because you wanted to keep reading – so you stayed where you are. Was your choice free?

Many would argue even though you lacked the option to leave the room, this didn’t make your choice to stay unfree. Therefore, lacking alternatives isn’t what decides whether you lack free will. What matters instead is how the decision came about.

The trouble with Sapolsky’s arguments, as free will expert John Martin Fischer explains, is he doesn’t actually present any argument for why his conception of free will is correct.

He simply defines free will as being incompatible with determinism, assumes this absolves people of moral responsibility, and spends much of the book describing the many ways our behaviours are determined. His arguments can all be traced back to his definition of “free will”.

Compatibilists believe humans are agents. We live lives with “meaning”, have an understanding of right and wrong, and act for moral reasons. This is enough to suggest most of us, most of the time, have a certain type of freedom and are responsible for our actions (and deserving of blame) – even if our behaviours are “determined”.

Compatibilists would point out that being constrained by determinism isn’t the same as being constrained to a chair by a rope. Failing to save a drowning child because you were tied up is not the same as failing to save a drowning child because you were “determined” not to care about them. The former is an excuse. The latter is cause for condemnation.

Incompatibilists must defend themselves better

Some readers sympathetic to Sapolsky might feel unconvinced. They might say your decision to stay in the room, or ignore the child, was still caused by influences in your history that you didn’t control – and therefore you weren’t truly free to choose.

However, this doesn’t prove that having alternatives or being “undetermined” is the only way we can count as having free will. Instead, it assumes they are. From the compatibilists’ point of view, this is cheating.

Compatibilists and incompatibilists both agree that, given determinism is true, there is a sense in which you lack alternatives and could not do otherwise.

However, incompatibilists will say you therefore lack free will, whereas compatibilists will say you still possess free will because that sense of “lacking alternatives” isn’t what undermines free will – and free will is something else entirely.

They say as long as your actions came from you in a relevant way (even if “you” were “determined” by other things), you count as having free will. When you’re tied up by a rope, the decision to not save the drowning child doesn’t come from you. But when you just don’t care about the child, it does.

By another analogy, if a tree falls in a forest and nobody is around, one person may say no auditory senses are present, so this is incompatible with sound existing. But another person may say even though no auditory senses are present, this is still compatible with sound existing because “sound” isn’t about auditory perception – it’s about vibrating atoms.

Both agree nothing is heard, but disagree on what factors are relevant to determining the existence of “sound” in the first place. Sapolsky needs to show why his assumptions about what counts as free will are the ones relevant to moral responsibility. As philosopher Daniel Dennett once put it, we need to ask which “varieties of free will [are] worth wanting”.

Free will isn’t a scientific question

The point of this back and forth isn’t to show compatibilists are right. It is to highlight there’s a nuanced debate to engage with. Free will is a thorny issue. Showing nobody is responsible for what they do requires understanding and engaging with all the positions on offer. Sapolsky doesn’t do this.

Sapolsky’s broader mistake seems to be assuming his questions are purely scientific: answered by looking just at what the science says. While science is relevant, we first need some idea of what free will is (which is a metaphysical question) and how it relates to moral responsibility (a normative question). This is something philosophers have been interrogating for a very long time.

Interdisciplinary work is valuable and scientists are welcome to contribute to age-old philosophical questions. But unless they engage with existing arguments first, rather than picking a definition they like and attacking others for not meeting it, their claims will simply be confused.

Adam Piovarchy, Research Associate, Institute for Ethics and Society, University of Notre Dame Australia

Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.

Header image source: Gerd Altmann on Pixabay.

Key points:

1. The belief that highly intelligent people lack social skills is a common stereotype, but recent research suggests it is more likely a myth than an informative stereotype.
2. Findings suggest that smarter people tend to be better at accurately interpreting and responding to the social and emotional cues of others.
3. By using objective data, working to overcome biases, communicating better, and recognizing the importance of different forms of intelligence and their contributions to performance on the job organizations and the people that comprise them stand a much better chance of success.

Do you believe that highly intelligent people are socially stunted? If so, you certainly wouldn’t be alone in assuming this stereotype. Many people believe that those who are smart or who are highly intelligent tend to be lost at sea where it comes to social skills, believing they lack interpersonal sense and sensitivity. This belief has persisted for centuries, and I have even personally encountered the odd uninformed organizational psychologist – someone specializing in human thought, feeling, and behaviour as applied to the workplace – who still believes that smarts and social skills are at odds with one another. In fact, it’s the ubiquitousness of this stereotype and its intransigent nature that’s motivated me to write on this subject. Thankfully, you don’t have to take my word alone on this matter. Recent research has indicated that this belief is more likely a myth than an informative and explanatory stereotype. In this article, I want to give you all a little taste of the most compelling research dispelling this myth while letting you in on precisely why holding onto this debunked stereotype could be quite costly in the end.

Intelligence and interpersonal sensitivity

A meta-analysis¹ conducted by Nora A. Murphy and Judith A. Hall investigated the association between general intelligence and interpersonal sensitivity. Interpersonal sensitivity refers to the ability to accurately decode social cues, such as facial expressions and tone of voice, and understand their intended meaning. It’s a landmark feature of emotional intelligence and can be incredibly helpful in helping you to understand and communicate with others. Nora and Judith’s review involved 38 independent samples with nearly 3,000 total participants. The study found a small-to-medium effect for intelligence measures to be positively correlated with decoding accuracy (r = .19, p < .001). In other words, smarter people tended to be better at accurately interpreting and responding to the social and emotional cues of others.

It is important to note that significant moderators were found to include the type of decoding judgment (emotion vs. intended meaning judgments), decoding channel (audio-only vs. audio-plus-video channel), and target gender (both male-and-female targets vs. female-only targets having their emotions evaluated). This means that the association between general intelligence and interpersonal sensitivity can vary depending on the specific context of the social interaction. Nevertheless, the study’s findings suggest that cognitive abilities comprising general intelligence are part of the social skillset required for decoding emotions accurately.

All of this is important in a business context in the sense that you want to be putting highly skilled people into roles that they’re likely to thrive within. If you hold the stereotyped belief that smart people are likely to be socially stunted, you’re likely to be passing over highly qualified candidates for roles they’re actually well suited to perform within. Ultimately this sort of thinking can result in losses grounded in bias and unfounded assumptive stereotypes. Although this is far from the only instance where bias and stereotypes can harm businesses and the individuals within them, it’s certainly something we can actively avoid through awareness and a dedicated effort to combat faulty cognitive shortcuts of thinking.

Take-aways for you and your practice

These research findings have practical implications for leaders, managers, and individuals looking to improve their interpersonal skills. Leaders and managers can take advantage of these research findings by creating an environment that encourages and rewards intelligence of all sorts and that recognizes and accepts people for their unique contributions. They can try to combat their own biases and ensure that they’re using objective indicators of fit to judge people a prospective candidate for available positions within their organization. They can also ensure that their followers have the necessary time, energy, and other resources to develop their social and cognitive abilities as it’s likely to help them to perform better within their role. General intelligence is found to increase with additional years of academic training and validated emotional intelligence training programs can be a great tool to develop social skills.

Organizations can take advantage of these research findings by ensuring that people are being evaluated and placed into roles that they’re likely to excel within. One way to avoid the impact of such biases as these is to simply measure people’s abilities and skills to ensure you actually have a good idea how they’d perform rather than by relying on presumptive assumptions. There are many reliable and valid emotional intelligence assessments that are able to provide you with evidence as to the knowledge, skills, and abilities one has relevant to social settings. Even if someone doesn’t happen to score highly in emotional intelligence, it doesn’t have to be a prohibiting factor so long as it doesn’t prevent them from performing effectively and so long as they aren’t willing to try to develop it. Organizations can invest in emotional intelligence training to fill these gaps.

Non-managing members of organizations can also benefit from these research findings by challenging their own biases. They may also reflect and recognize how their own intelligence (general and social) impacts their work and how they perform in their role. The bridge between people with differences of all sorts is often through effective communication. People who speak differently from one another often have to work more deliberately and a bit harder to work more effectively with one another. When they don’t work so hard or deliberately to promote such communication, misunderstandings and assumptions are often made and it may work to perpetuate stereotypes like these. Even if we have to meet people where they are in terms of social and general intelligence, everyone can still strive to be better at their deliberate communication and active listening to accommodate for the natural differences we encounter in our work communities.

The idea that smart people are socially stupid is a myth that’s not entirely supported by the available evidence. In fact, there’s good evidence to suggest that the cognitive abilities comprising general intelligence are related to interpersonal sensitivity, which is the ability to accurately interpret and respond to the social and emotional cues of others – a landmark feature of emotional intelligence. By using objective data, working to overcome biases, communicating better, and recognizing the importance of different forms of intelligence organizations and the people that comprise them stand a much better chance of success.

Trustworthiness score:

The trustworthiness of the study is moderate (80%). This means there is a 20% chance that alternative explanations for the effect found are possible.

ScienceForWork

ScienceForWork is an independent, non-profit foundation of evidence-based practitioners who want to make work better. Its mission is to provide decision-makers with trustworthy and useful insights from the science of organizations and people management.

Article source: The article Myth-Busting: How Believing Smart People to be Socially Stunted Could be Costing You and Your Business is published by ScienceForWork under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

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