This article is for leaders that want to understand how to motivate employees.

Information technology (IT) has increased the possibilities for remote collaboration significantly. IT entails the ability to work together in setups where time and space is rendered insignificant. This possibility for breaking down geographical barriers has been sieged by smaller and larger corporations worldwide. However, the lacking interactions among team members poses some challenges. One of these is the ability to lead and manage teams where physical interactions are scarce or non-existing (Geister et al., 2006).

An important property, when it comes to leading and managing digital teams is motivation: The ability to understand how a space that motivates your employees is created, and how you facilitate this environment, will generate positive results for businesses (Geister et al., 2006). Motivation is a generically fluid term of which there are many different perceptions as it largely is a subjective feeling, associated with several psychological experiences and expressions. To put this into perspective, this contribution therefore connects the term, with the theme of digital collaboration specifically motivation in digital workplaces.

Among some of the advantages of motivation is an increased productivity and reduction of costs (Gilley, et el., 2009). Talking about motivation in case of groups or teams, we can say that “Motivational processes are crucial not only for individuals but also for the performance of teams” (Geister et al., 2006: 460). The overall performance is enhanced, as collective motivation rises.
As the overall determinant of motivation is external factors, the leader’s responsibility for motivated employees is rather significant. Bearing this in mind, one might argue that the motivation you generate among your employees is the accumulation of the positive influences you are able to make as a leader. For this, we see a variety of influences which you can impose, such as generating goal orientation, create a feedback culture, and attention to differences in people, as described below.

What is motivation?

Neurologically, the feeling can be explained as a relief of dopamine, a reward-activated hormone, which plays a central role in reward-motivated behavior (Stellar and Stellar, 1985). In this lies an attention to the human brain, and its reactions to the external environment. What causes the relief of dopamine is, of course, as subjective as any other feeling or understanding related to individual. Generally speaking, feeling motivated is associated with excitement, the urge to contribute, and interest in what one engages in. Naturally, what interests people is different, but a way to categorize the justifications people have for performing a task, motivation is divided into intrinsic or extrinsic motivation. Intrinsic motivation is when you perform an activity, because you have interest in this. Extrinsic motivation on the other hand, is when you perform an activity to achieve a separate outcome (Deci and Ryan, 2000).

Pitfalls

In his book “Drive: The Surprising Truth About What Motivates Us”, Daniel Pink writes about three elements, which altogether motivate us. Rather than only paying attention to extrinsic motivation, such as monetary rewards, he points out the possibility to develop as an individual as key to motivate employees. (Pink, 2009). The three elements are: autonomy, which is the ability to be self-directed and direct out own lives. Businesses should address this by offering self-determination to its employees. The second is mastery: This addresses our urge to improve our skillset. Third is purpose, which means to recognise that people wish to contribute purposefully, which means that profit alone does not create great results. The core idea in Pink’s book is that stimulating extrinsic motivation alone does not work. Not to say that money will profoundly hinder motivation, moreover that mo ney alone is not a sufficient motivator (Pink, 2009).

A recent study from the Norwegian school of business (2017) shows that extrinsically motivated employees risk to burnout quickly, experience stress and anxiety as pressure increases, as well as conflicts in personal life. From this study, they found that intrinsically motivated employees performed better overall, and experienced job-satisfaction as well as more commitment (Kuvaas, et al., 2017).

How do you keep your employees motivated?

There are two key approaches leaders should be aware of, when motivating employees:

• Create goal-orientation. For this, leaders must address the differences among employees. The individuality should play a role, as to how challenges are perceived individually. Some might be intimidated by the complexity of a task, where others find motivation in mastering new skills. Ways to approach this is first: Know when to set goals. There is a line between being too general where goals are annual or bi-annual event, to micromanagement, where employees are constantly measured on a daily basis. In order to set the adequate goals, leaders must address whether the goals are attainable, and if the employees have the knowledge and tools to reach these goals (Lazenby, 2008).

• Provide feedback. Additionally, leaders should give feedback on these goals, as a follow-up on progress. This involves also a certain degree of recognition among some employees, where feedback should be given as a way to inform whether a task has been executed correctly. Some employees might have the need to get a direction for what to do, in order to obtain a goal, rather than criticism on their work. In some cases, this might work counterproductive demotivate in some cases (Lazenby, 2008).

Summing up, motivation is an important component in businesses, which effective leaders search to impose among employees, as motivated employees will contribute to corporate success, where goal-setting and feedback can be used as means for this.

In I4L we do research to improve leaders’ skills, to prepare them for the digital future.

Bibliography

01. Ryan, Richard & Edward L. Deci (2000). “Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions”. Contemporary Educational Psychology. 25 (1): 54–67.

02. Gilley, A., Gilley, J., & McMillan, H. (2009). ”Organizational change: Motivation, communication, and leadership effectiveness.” Performance Improvement Quarterly, 21(4), 75–94.

03. Geister, S., Konradt, U., & Hertel, G. (2006). ”Effects of process feedback on motivation, satisfaction, and performance in virtual teams.” Small Group Research, 37: 459-489.

04. Kuvaas, B., Buch, R., Weibei, A., Dysvik, A. & Nerstad, C.G.L. (2017). ”Do intrinsic and extrinsic motivation relate differently to employee outcomes?” Journal of Economic Psychology, 61, 244-258.

05. Lazenby, S. (2008), “How to motivate employees: What research is telling us”, Public Management, no. September, pp. 22-25.

06. Pink, D.H. (2009), ”Drive: The surprising truth about what motivates us”. Riverhead Books.

07. Stellar, J.R. & Stellar, E. (1985). “The neurobiology of motivation and reward”, Springer-Verlag, 6-22

This article explains the impact of stereotyping and expectations we have of our employees, as well as how engaging personas can be used as a framework to increase productivity in virtual teams.

Why do we stereotype? And how does stereotyping affect trust and performance in (virtual) teams?

A stereotype is a “fixed, over generalized belief about a particular group or class of people”. (Cardwell, 1996). Stereotypes help us simplify our social world and it enables us to respond rapidly to situations, but a disadvantage according to McLeod (2015) is that it makes us not see the differences between individuals.

Our brains cannot deal with missing information – we will add what we are missing, from what we already know. And if what we already know is negative about a certain group of people, and we attach that to a recently met colleague, the trust will suffer as “you can never give a second first impression” is valid for virtual environments as well. (Jarvenpaa and Leidner, 1998). Moreover, if what we already know about a group of people is not true about that particular person, productivity might suffer, as we will not be able to recognize the potential, and consequently motivate the team member to reach it.

Macrae and Bodenhausen (2001) discuss that at our encounter with a stranger, we tend to see the person as a stereotype, and not as a person that has a “unique constellation of characteristics”, and we tend to add the person to an already known category. Nielsen (2004) argues that the more material is presented to us, the less we need to draw on our previous experiences and encounters.

The effects of the stereotyping in (virtual) teams is two-folded. On the one hand, our expectations derived from our stereotyping end up becoming “self-fulfilling prophecies” explained via the well-known Pygmalion effect (Livingston, 2003). On the other hand, it can have an impact on our team members’ trust amongst each other. However, both factors have an impact on the team’s productivity.

Pygmalion effect and stereotyping

The Pygmalion effect has been widely researched by behavioral scientists, especially in school settings, and it is explained by Livingston, who coined the term Pygmalion, as: “The lucky child who strikes a teacher as bright also picks up on that expectation and will rise to fulfill it. This finding has been confirmed so many times, and in such varied settings, that it’s no longer even debated.” (2003)

The Pygmalion effect is valid in organizational settings as well: an experiment from 1961 on teams in organizational setting, explained in detail in this article in Harvard Business Review, concluded that the productivity of the team that was expected to perform well improved dramatically, while the productivity of the team who was considered as not having a chance to meet the quota, decreased dramatically. (Livingston, 2003)

The beliefs we have of others (either true or expected due to our stereotyping), will influence our communication with them. Livingston (2003) describes how managers are more effective in communicating their low expectations than they are at communicating their high expectations, even though we might believe differently. Passive behavior, indifference, and low frequency of communication could all be perceived as signs of the low expectations we have as leaders from our employees.

The risk we are running is having low expectations of certain groups of people due to stereotyping, and them lowering their performance to meet our expectations.

Trust and stereotyping

As discussed in a previous blog post, an important step in building trust is calculated trust, and a step in building calculated trust is the social introduction. Trust in virtual teams (and any types of teams) is important as from it derives the team’s motivation, which impacts team’s performance. (Zacarro and Bader, 2002)

Jarvenpaa and Leidner (1998) have discovered that a commonality amongst teams that start with low trust levels, is the lack of social introduction. We could conclude from here, that a poor or absent introduction of the team members could lead to stereotyping and low levels of trust.

Virtual teams and stereotyping

Stereotyping is true for teams that work together, but my hypothesis is that it is even more poignant in virtual teams. As layers of virtuality are added, we have less information about the persons we are interacting with: for example, we are not able to see their facial expressions or body language, which is crucial in interpreting sarcasm, humor, or irony. Research shows that: “In the absence of individuating cues about others, we build stereotypical impressions based on limited information.” (Lea and Spears 1992).

This made me wonder which “unique constellation of characteristics” do digital leaders need to encourage virtual team members to disclose about themselves in order to decrease the level of stereotyping and hence, increase the level of trust in virtual teams? Parallel, which information do leaders need to know about their employees in order to increase their expectations of their employees?

Engaging Persona

In I4L we have the honor to have Lene Nielsen as one of our team members – Lene has researched personas for over 15 years. When I told Lene what is my preoccupation in regards to stereotypes, she introduced me to the concepts of “rounded character” from filmography, and the concept of engaging personas.

In her Ph.D. (Nielsen, 2004), Lene has developed a model for engaging personas, based on various rounded character descriptions. The engaging personas model, inspired from rounded characters in filmography, was developed with the purpose of helping companies and designers understand their target audience better. The mechanism behind is to avoid “schematas”, which is the tendency to fill in the missing information with pre-learned models of the world – in other words, stereotyping.

I propose engaging personas as a model for digital leaders to introduce themselves and team members to each other.

According to Nielsen (2004), the characteristics of engaging personas, are:

1. Body: “bodily expression and a posture, a gender and an age” (p. 155).
2. Psyche: “the present state of mind, persistent self-perception, character traits, temper, abilities and attitudes” (p.155);
3. Background: “present knowledge, job and family relations, and persistent beliefs, education, and internalized values and norms” (p.156);
4. Emotions: “emotions, intentions, and attitudes including ambitions and frustrations, wishes and dreams” (p. 156);
5. Cacophony: “character traits in opposition as well as peculiarities” (p.156).

The “engaging persona” characteristics can be used by leaders as a framework for the type of questions they could ask employees to present themselves, but also as a guidance for leaders on how to introduce themselves to the team and act as a role model, which is a critical behaviour for effective team leadership, according to Wade, Mention, and Jolly (1996).

The above framework can be used in the first (virtual) team meeting, or for introducing a new team member – be it a virtual conference call, audio or video, or a message that is written on the enterprise social media or online community wall.

The “Body” characteristic can be compensated for in a virtual setting with a profile picture and a requirement for every new team member to have a profile picture – paralleled, if possible, with a video or audio conference, where team members can be introduced to multiple physical dimensions of each other: seeing, hearing, reading facial expressions, vocal inflections, verbal cues, gestures and body language. These communication dynamics are one of the challenges that virtual teams face, according to Kayworth and Leidner (2015), and in many instances, our profile pictures are the only physical dimension we can convey about ourselves in virtual settings.

“Psyche” can be conveyed through discussing someone’s relation to the technology used in a project for example, or their beliefs and use of technology. For example, I could say about myself that I do use social media, although I have privacy concerns and have a deep belief that social media can be a waste of time and cause dependencies. This shows contradiction and the lack of settlement in my view of social media, as well as openness and skepticism simultaneously towards it.

The “Background” dimension is important for leaders to emphasize on when introducing new team members to each other, pointing out special abilities, courses, experiences or success that team members have achieved previously, and therefore setting the stage for having high expectations of them and of each other. As seen previously, our expectations of each other and of our employees have a direct influence on the team’s productivity. As Livingston (2003) formulates it: “How can you get the best out of our employees? Expect the best.”

Kayworth and Leidner (2015) point out that one’s background might be distorted by the high levels of anonymity that virtual settings allow for, as we can change our username or not fill in information about our job title, location or even choose to set ourselves as “invisible” to the team if we wish to. It is important for digital leaders to set the stage of how employees should fill in the information required in their online profiles used for collaboration, as well as establish standards for collaboration.

“Emotions” can be conveyed by expressing what we feel in relation to the task, project, colleagues or by how we talk about the same. If I say: “I am really excited to start working on the project” – I will convey a high energy and enthusiasm. Similarly, if I say: “Let’s hope we manage to pull the deadline”, I might unwillingly convey skepticism towards my team’s capability to meet deadlines, as well as apathy.

“Cacophony” is described in a guideline for writers by Rukov (2003) as 1+1+1, where 1+1 refer to two oppositional character traits, while the last 1 is a peculiarity. For example, I could say about myself that I consider the cake that we bring to our workplace as a replacement of the social cigarette of the past decade, but I will still accept an invitation to eat cake with my colleagues – this would represent the two oppositional character traits. A peculiarity is that I own 25 plants and counting.

In summary, stereotyping can lead to low levels of trust in virtual teams, as well as set the stage for expectations lower than our employees could raise up to, which will impact a team’s productivity levels. A way to combat stereotyping is making sure to introduce ourselves and our team member’s to each other in a way that doesn’t leave space for stereotyping. A way to do so, is borrowing the framework of building rounded characters from filmography, which can help us build engaging personas around ourselves and virtual team members and use collaborative software to its full potential to create our online profile. 

Bibliography

1. Cardwell, M. (1996). Dictionary of Psychology. Chicago IL: Fitzroy Dearborn.
2. McLeod, S. A. (2015). Stereotypes. Retrieved from simplypsychology.org/katz-braly.html
3. Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and Trust in Global Virtual Teams. Organization Science,10(6), 791-815. doi:10.1287/orsc.10.6.791
4. Lea, M., & Spears, R. (1992). Paralanguage and social perception in computer-mediated communication. Journal of Organizational Computing, 2, 321-342.
5. Timothy R. Kayworth, Dorothy E. Leidner (2002) Leadership Effectiveness in Global Virtual Teams, Journal of Management Information Systems, 18:3, 7-40, DOI: 10.1080/07421222.2002.11045697
6. Livingston, S. (January, 2003). Pygmalion in Management. Harvard Business Review. Retrieved July 4, 2017, from https://hbr.org/2003/01/pygmalion-in-management
7. Macrae, C. N. and Bodenhausen, G. V. (2001), Social cognition: Categorical person perception. British Journal of Psychology, 92: 239–255. doi:10.1348/000712601162059
8. Nielsen, L. (2004). Engaging Personas and Narrative Scenarios(Doctoral dissertation, Copenhagen Business School).
9. Rukov, M. (2003). “Persona workshop”. L. Nielsen. Copenhagen. Sigchi.dk
10. Wade, D.; Mention, C.; and Jolly, J. Teams: Who Needs Them and Why? Houston, TX: Gulf Publishing, 1996
11. Zaccaro, S. J., & Bader, P. (2003). E-Leadership and the Challenges of Leading E-Teams:. Organizational Dynamics, 31(4), 377-387. doi:10.1016/s0090-2616(02)00129-8

This article is for digital leaders that want to understand how to build trust in virtual teams.

Research shows that trust is a very important factor in virtual teams’ motivation.  It can be defined as “the beliefs and expectations that members have of each other, that each member will live up to agreed-upon commitments, that each member is acting with good intentions on behalf of the group, and that each will work hard on behalf of the group.” (Zacarro and Bader, 2002)

Early research on trust and virtual teams suggested that trust is difficult to obtain in virtual teams due to that “trust needs touch” and face to face interactions. (Handy, 1995)

Berne (2015), the father of Transactional Analysis – discusses that the fundamental unit of social action is a “stroke” – which denotes an act of recognizing another’s presence.

If Anna says “Hello” and Claus replies with a “Hi!” they have exchanged two strokes. If Anna asks a question – hence one stroke – and Claus does not answer she will be puzzled. How many times will you say “Hello!” to someone who doesn’t respond?  And how likely is it, in such a case, that you will not trust a person who did not repay a stroke when they later want to build a relation to you?  You may rather want an explanation for their earlier behavior. Or, in Transactional Analysis vocabulary: you want your strokes back and possibly a few extra strokes to regain your trust.

It is important to note that exchanging strokes does not imply only verbal exchanges, it can also mean meeting expectations, keeping your word, smiling or waving back to someone, answering an email, liking someone’s update, giving feedback to employees coming forward with initiatives and ideas, compensate extra hours – anything through which you are “acknowledging someone’s presence”.

Relations, according to Berne, are built upon exchanges of strokes. In the context of psychological contracts “an individual’s belief about the terms and conditions of a reciprocal exchange agreement between the person and another party” (Robinson 1996). When we enter a work collaboration, we expect a set of conditions under which we will exchange our “strokes”, and our trust depends on whether our expectations are met or not.

Zacarro and Bader (2002) propose a three-stage framework for understanding and building trust in virtual teams:

1. Calculated trust: when although we have no proof, we decide to trust the parties we collaborate with;
2. Knowledge trust: emerges when team members have interacted for a while and know what to expect from one another;
3. Identification trust: “emerges when team members come to agree on how the team as a whole should respond to challenges in their external environment through continued dialogue with each other.” The team is an entity they can identify with, and each can act as a representative of it when appropriate.

Digital leaders play a vital role in helping their (virtual) team’s trust move from calculated trust to the highest level of trust, which is identification based.

Now, I would like to step back from all the theory and invite you to dance. In order for us to dance, we would need a dancing floor, shoes, music, a dancing style, but what’s most important, we need a leader in our dance, otherwise, we would end up stepping on each other’s toes. I might be a good dancer, or I might be a bad one – at this point, you don’t know and, since you have accepted my invitation, your only chance is hoping for the best.

Similarly, when a new team is formed, the leader needs to define the roles that each team member will play, describe the tasks, introduce the team members to each other and to the technology they will be using, and assert the expectations. This newly formed space, which is explicitly introduced, is the basis for building calculated trust.

Next, I will explain the steps of the dance and the rules. I will guide and motivate you: if you make a correct step, I will encourage you to make more of those steps, and if you don’t, I will make sure to re-adapt my instructions to your understanding. At this stage, I will know which steps you are good at, and for which steps you need more practice, and I will also know how you react to success and failure – and so will the ones watching. This is knowledge-based trust, and as a digital leader, it is important to provide prompt feedback, establish standard operating procedures, make sure that the tasks are clear to each individual, and react fast (and in calm and positive manner) to when things don’t go in the desired direction.

When you are ready to take over and you know the steps well and you are confident, you can lead the dance and we can sign up for a dancing competition. At this stage, digital leaders have established a high trust team, where members can take the lead and responsibility for various tasks. It is important to maintain a spirit of common purpose and encourage team members to extend their exchanges to more socially-oriented and personalized exchanges.

Jarvenpaa and Leidner (1998) support the dance metaphor, as it can be seen in the figure below, emerged from their analysis on which behaviours facilitate and maintain trust in virtual teams, and which behaviours represent barriers, based on 12 virtual teams:

According to them, timely response is also important, as well as predictable communication, calm response to crises and positive leadership. Unpredictable communication, concerns about technical uncertainties, negative leadership and lack of enthusiasm have all been proved to represent barriers in building trust.

I’ve chosen the metaphor of a dance because it depicts best how the exchange of strokes should happen between a leader and team members: consistently and timely. In a dance, you can’t ignore a wrong step – it will affect the dance, while one good step after another will lead to a smooth dance. Similarly, if you react negatively to a wrong step, you will affect the mood of the dancer and chances are, they won’t want to dance with us again. It’s a good way to think of how to interact with our employees: timely, consistent, and positive, like in a dance.

Thank you for the dance!

In I4L, we don’t dance, but we disseminate research for practitioners and because we want to make research as memorable and entertaining as possible, sometimes we story tell. Make sure to subscribe to our newsletter and follow us on Twitter @i4l_dk.

Bibliography

1. Berne, E. (2015). Transactional analysis in psychotherapy: a systematic individual and social psychiatry. Mansfield Centre, CT: Martino Publishing.
2. Handy, C. (1995). Trust and the virtual organization. Harvard Business Review, 73(3), 40–50.
3. Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and Trust in Global Virtual Teams. Organization Science,10(6), 791-815. doi:10.1287/orsc.10.6.791
4. Robinson, S. L. (1996). Trust and Breach of the Psychological Contract. Administrative Science Quarterly, 41(4), 574. doi:10.2307/2393868
5. Zaccaro, S. J., & Bader, P. (2003). E-Leadership and the Challenges of Leading E-Teams:. Organizational Dynamics, 31(4), 377-387. doi:10.1016/s0090-2616(02)00129-8

Some leaders use fairy tales to frame their communication if they want to engage and entertain their employees.  This article is a folkloric gathering of fairy tales, enriched by Big Data.

“Once upon a time, in a far far land, a poor peasant was in the tooth doctor hut – the blame was on the tobacco in snake oil which gave him a rotten stump. Helped by a mouth speculum, the tooth doctor pulled the stump to prevent decay from spreading to the other teeth. The scream of agony could be heard all the way to the neighbor village, where yet another peasant found himself in a similar situation. With every soul added in heaven, the tooth doctors were learning how to be better doctors, until retirement, when new inexperienced doctors will take their place and gain their knowledge from trials on more poor souls.

Fortunately, today, Big Data changes the way we share and utilize knowledge. Hospitals and medical research centers share their data with all the “villages” and doctors, in order to learn from their experience and nobody needs to die in the name of science.

Data changes our fairy tales – Think of how Hansel and Gretel could’ve checked Trip Advisor and the bad recommendations would’ve kept them away from the witches’ candy hut.”

These are the stories that Søren Pind – the Minister of Higher Education and Science in Denmark opened his presentation with, at the “Join the Data-Driven R(e)volution – Unlocking the Business Potential of Big Data” conference. Needless to say, the audience was engaged and amused.

His body language, voice, breathing, and pace contributed to the quality of his presentation and storytelling, but an advantage that digital leaders have is that they can focus only on the words they write.

The advantages of using storytelling

Stories are part of one’s upbringing, regardless of their interest in technology – or particularly Big Data in this case – thus, by using stories, leaders can create a bridge in communication and rapport with everyone. In this context, research has shown that a more authentic communication (openly sharing feelings and opinions) might make leaders feel more vulnerable, but that it is the vulnerability that makes employees feel more connected (Richter and Wagner 2014).

Using stories also makes it easier for people to remember a difficult concept, if that concept is drawn as a parallel to something already known.

Examples

Here are some more ready-made stories that you can adapt or be inspired by, based on industry and message, to assemble an engaging text. In a previous blog post, I have provided you with tools on how to assemble multiple types of text – in this blog post, we focus on Story Line and Anecdotes.

The stories below have an educative purpose as well, and that is to provide an overview of industries and businesses that were revolutionized by Big Data, all wrapped up in fairy tales.

E-commerce and market intelligence

Do you remember how The Snow White opened the door to the evil Step Mother and bought the poisonous apple that was recommended to her? Today, a product recommender system could’ve also predicted that she likes apples, based on her previous purchases, and the reviews would’ve kept her away from the poisonous recommendation of the evil Step Mother. (Chen et al., 2012, Adomavicius and Tuzhilin, 2005).

Package Delivery

The Little Red Riding Hood could’ve used UPS’ advanced package delivery system to deliver food to grandma. UPS is only one of the package delivery companies revolutionized by the use of Big Data. They are very well known for their use of statistics to anticipate the movement of the package, identify sources of problems (such as the Bad Wolf) and optimize delivery times. (Davenport, 2006).

Security and Public Safety

The 40 thieves from “Alibaba and the 40 Thieves” would’ve been discovered earlier with Big Data. Today, agencies appointed to secure public safety and security are able to gather and combine data from multiple sources, such as criminal records, cyber security threats, and multi-lingual intelligence. Crime and terrorism can be fought by applying models such as criminal network analysis, criminal association rule mining and clustering or cyber-attack analysis. (Chen et al., 2012)

Science and Technology

Once upon a time, Jack was surprised by the magic bean that grew into a Beanstalk all the way to the clouds – but with precision farming, we could’ve measured the right amount of magic spices and we would now all have magic beans that could grow to the clouds.

There are many scientific areas that benefit from the usage of Big Data and tracking of sensors and instruments, such as astrophysics, oceanography, genomics, environment research. To support the transparency and sharing of research, organizations such as the National Science Foundation (NSF) mandate that the investigation projects need to provide data management plans. As an example, in biology, NSF founded iPlant, which enables biologists to track plant biology, learn, share and take decisions. (Chen et al., 2012). Would we all live on our Beanstalks today?

Image recognition and Deep Learning

I would imagine that the vane and mean stepsisters from Cinderella, would’ve posted a lot of selfies on Facebook, and there is also a high probability that Cinderella would’ve been captured in the pictures, sweeping the chimney in the background. With face recognition and deep learning, the Prince would’ve been able to find her faster. Today, the advancement in deep learning allow computers to recognize patterns and identify the objects or faces in unlabelled images. (Singh, 2017)

People matching algorithms

Do you ever wonder whom would’ve Belle chosen, if she was presented with more than two potential romantic partners: Gaston or The Beast? Big Data algorithms make it possible for us to find “The One” online through functions and algorithms that learn our preferences and prompt us with potential matches. Our romance is backed up by statistical discrimination and adverse selection.

The Frog Prince could’ve really needed a dating algorithm as well, then he wouldn’t have ended up with a princess that throws him against a wall – and maybe he could’ve met Belle instead.

Human Capital

Flora, Fauna, and Merryweather were appointed to take care of the Sleeping Beauty and make sure that Aurora never pricks her finger in the spinning wheel – but unfortunately, the fairies got distracted and Aurora did prick her finger. With the use of Big Data, her parents could’ve chosen the best employees for this particular task (from the many working for them in the kingdom) at the right compensation level, through expert use of statistics and modeling. (Davenport, 2012)

Automotive

Although the rest of the fairy tales can be upgraded by the use of Big Data, I must say that Aladdin and the magic carpet is a bit ahead of us because it escaped the long debate on whether self-driving cars need a wheel or not, and it can freely navigate in the environment. Self-driving cars (and the magic carpet) depend on Big Data – “it’s really all about processing Big data, and the road is just another data set to be mined”.  (Vanderbilt, 2012) 

Construction building

The three little pigs would’ve had a better chance at predicting the risk of the Bad Wolf blowing their little house, with construction process risk analysis. Construction industry highly benefits from the use of Big Data, from analyzing team structure, budget, and schedule, to processing larger sets of data, such as drone footage. (McKinsey, 2017)

In I4L we focus on disseminating academic research through practical ideas and tools for digital leaders.

*I would like to thank my very good friend, Msc. Cand. In Digital Innovation and Management, Sonja Zell, and my good colleague, Assistant Professor of Information Systems, Raffaele Ciriello, for contributing with fairy tales ideas.

Bibliography

1. Adomavicius, G., and Tuzhilin, A. 2005. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering (17:6), pp. 734-749.
2. Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: from big data to a big impact. MIS Quarterly, 36(4), 1165–1188.
3. Davenport, T. (2006). Competing on analytics. Harvard Business Review
4. Reinventing Construction: A route to higher productivity(Rep.). (2017). McKinsey & Company.
5. Richter, A., & Wagner, D. (2014). Leadership 2.0: Engaging and Supporting Leaders in the Transition Towards a Networked Organisation. Hawaii International Conference on System Sciences,7
6. Singh, A. (2017). Deep Learning will radically change the ways we interact with technology. Harvard Business Review. https://hbr.org/2017/01/deep-learning-will-radically-change-the-ways-we-interact-with-technology
7. Vanderbilt, T., (2012). Let the robot drive: the autonomous car of the future is here. Wired. https://www.wired.com/2012/01/ff_autonomouscars/

This article explains what disruptive innovation is and how it can spread as a ”virus”, affecting disrupted companies in unrepairable ways.

As a person of simple words, I used to smile sceptically when hearing the term “disruptive”, proudly used by start-ups and well-established companies equally. My train thought was that technology has been evolving since the pre-historic humans, and nobody called the stone tools disruptive technology. Although, as I found later, they could.

Let’s start our journey with the person that coined the term disruptive innovation: Professor Clayton Christensen from Harvard Industry.

He defines it as: “a process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors” (Disruptive innovation, u.d.). Christensen, in an interview, explains that it should not be understood as a “breakthrough innovation that makes technology a lot better”, but more as a product/technology that is made affordable and accessible, through its transformation, to a larger public (Christensen, 2012).

Professor Kai Riemer from the University of Sydney Business School explains (key note) that innovation normally occurs in incremental steps – doing a better version of what you are doing now – that is how we [often] organize innovation inside business. Disruptive innovation is to be understood as a technology that displaces the previous technologies, breaks the path and changes our collective life. For a better understanding, Riemer provides us with a case study from the musical industry. The type of innovation that happen in the musical industry was incremental – first we had the vinyl record, then we had tapes, then CDs. For record companies, it was difficult to predict new formats (like MP3) and their exchange on online platforms (like Napster). Riemer explains, that the challenge with disruptive innovation, is that it comes from another industry and changes the way we think about a specific product, service or technology. MP3s and Napster did change the way we thought and behaved about music. Suddenly, instead of gathering CDs, people could stream, share and listen to their favourite songs anytime and anywhere.

Nobody knew that we needed music in our pants pockets, but now it is integrated in our way of being and thinking about music. If someone would’ve painted this image to record companies some years ago, they wouldn’t have believed you – and it would’ve been very hard to predict.

Riemer has also developed a model for better understanding disruptive innovation, and it gives it an epistemic name: the VIRUS model, explaining that: “it (the name) captured the way in which the disruptive product or service is able to emerge slowly, steadily and unrecognized – when symptoms are first noticed by the wider market, it is often too late, and full-blown disease strikes.” (Riemer, 2015)

The opinions about disruptive innovation vary from it being declared “one of the most influential modern business ideas” (Aiming High, 2011) by The Economist, to that it can be averted (Gans, 2016) or to that “a cautious approach can be disastrous” (Cohen, 2016).

A key question in I4L is how companies can prepare to recognize and even facilitate disruptive innovations. This question will be tackled from various perspectives in our next blog posts.

Bibliography

Aiming High. (2011, June 30). The Economist.

Christensen, C. (2012, March 30). Disruptive Innovation Explained. Harvard Business Review. Retrieved May 22, 2017, from Youtube: https://www.youtube.com/watch?v=qDrMAzCHFUU

Cohen, D. (2016, December 2). Why you shouldn’t wait. MIT Sloan Management Review.

Disruptive innovation. (n.d.). Retrieved from Clayton Christensen: http://www.claytonchristensen.com/key-concepts/

Gans, J. S. (2016, February 22). Keep Calm and Manage Disruption. MIT Sloan Management Review.

Riemer, K. (2015, March 23). Why it’s so hard to react to disruption – the VIRUS model. Retrieved from bbr (backed by research): https://byresearch.wordpress.com/2015/03/23/virus-model/

The I4L project has officially started on May 1st 2017. We are now in the preparation phase, which includes developing the first workshop and setting up the project structure. In the meantime you can read a press release about the project here.