Different Types of Leadership Styles

Leadership Theories:

  • Chapman and Sisodia (2015) define leadership as the value they bring to people. The author’s primary guiding value is that “We measure success by the way we touch the lives of people.” This type of leadership practice stems from treating their followers the similarly to how someone would like their kids to be treated in the work environment. This type of leadership relies on coaching the leader’s followers to build on the follower’s greatness. Then recognition is done that shake employees to the core by involving the employee’s family, so that the employee’s family could be proud of their spouse or parent. The goal of this type of leadership is to have the employee seen, valued, and heard such that they want to be their best and do their best not just for the company but for their coworkers as well.
  • Cashman (2010) defines leadership from an inside-out approach of personal mastery. This type of leadership style is focused on self-awareness of the leader’s conscious beliefs and shadow beliefs to grow and deepen the leader’s authenticity. Cashman pushes the leader to identify, reflect and recognize their core talents, values and purpose. With the purpose of any leadership is understanding “How am I going to make a difference?” and “How am I going to enhance other people’s lives?” Working from the leader’s core purpose releases more of that untapped leader’s energy to do more meaning work that frees the leader and opens leaders up to different possibilities, more so than just working towards a leader’s goals.
  • Open Leadership: Has five rules, which allow for respect and empowerment of the customers and employees, to consistently build trust, nurtures curiosity and humility, holding openness accountable, and allows for forgiving failures (Li, 2010).  These leaders must let go of the old mentality of micromanaging, because once they do let go of micromanagement, these leaders are now open to grow into new opportunities. This thought process is shares commonalities with knowledge sharing, if people were to share the knowledge that they accumulated, these people would be able to let go of your current tasks, such that these people can focus on new and better opportunities. Li stated that open Leadership allows for leaders to build, deepen, and nurture relationships with the customers and employees.  Open leadership is a theory of leadership that is customer and employee centered.
  • Values based leadership requires four principles: self-reflection, balance, humble, and self-confidence (Kraemer, 2015). Through self-reflection, leaders identify their core beliefs and values that matters to the leader. Leaders that view situations from multiple perspectives to gain a deeper understanding of the situation is considered balanced. Humility in leaders refers to not forgetting who the leader is and where the leaders come from to gain appreciation for each person. Finally, self-confidence is the leader accepting themselves as they are, warts and all.

Parts of these leadership theories that resonates

Each of these leadership theories above have a few concepts in common. Most of the leadership theories agree with each other because each leadership theory has a focus on growing the leader’s followers (Cashman, 2010; Chapman & Sisodia, 2015; Li, 2010; Kraemer, 2015). Cashman and Kraemer focuses on self-reflection, so that the leader can understand personal values, strengths, and weaknesses. For Cashman, self-reflection focuses on purpose, which is where there is an unbound level of energy. Whereas Kraemer, self-reflection focuses on defining the leader’s values and constant assessment and realigning the leader’s roles towards the leader’s value.

Resources:

  • Cashman, K. (2010) Leadership from the inside out: Becoming a leader for life. (2nd ed.). San Francisco, Berrett-Koehler Publishing, Inc.
  • Chapman, B. & Sisodia, R. (2015) Everybody matters: The extraordinary power of caring for your people like family. New York, Penguin.
  • Li, C. (2010). Open Leadership: How Social Technology Can Transform the Way You Lead, (1st ed.). Vitalbook file.
  • Kraemer, H. M. J. (2015). Becoming the best. (1st ed.). New Jersey, Wiley.
Advertisements

Higgs Boson: Case Study on an infamous prediction that came true

Definitions:

  • Forecasting (business context): relies on empirical relationships that were created from observations, theory, and consistent patterns, which can have assumptions and limitations that are either known or unknown to give the future state of a certain event (Seeman, 2002). For instance forecasting, income from a simple income statement could help provide key data for how a company is operating, but the assumptions and limitations on using this method can wipe out a business (Garrett, 2013).
  • Predictions (business context): are a more general term in which, is a statement of a future state of a certain event, that can be based on empirical relationships, strategic foresight, or even scenario planning (Seeman, 2002; Ogilvy, 2015).
  • Scenarios: alternate futures that change with time as supportive and challenging forces unfold, usually containing enough data like the likelihood of success or failure, the story of the landscape, innovative opportunities, challenges to be faced, signals, etc. (Ogilvy, 2015; Wade, 2012).

Case Study: An infamous prediction that came true

The Higgs Boson helps tell the origin of mass in the universe (World Science Festival, 2013). Mass is the resistance of an object to be pushed and pulled by other objects or forces in the universe, and mass is made up of the constitute particles of that object (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013).  The question is where does the mass of these particles that give an object its mass comes from?  The universe if filled with an invisible Higgs Field, in which these particles are swimming in and experiencing a form of resistance (when the particle speeds up or slows down), this resistance in the Higgs Field is the mass of the particles (Greene, 2013; World Science Festival, 2013).  Certain particles have mass (electrons), and others don’t (photons), this is because the certain particles interact with the invisible Higgs Field (PBS Space-Time, 2015). Scientist use the large Hadron Collider to speed up particles in such a way that when they collided in the correct way (1:1,000,000,000 chance), the particles’ collisions would be able to clump a bit of the Higgs Field to create a Higgs particle that lasted for a 10-22 second (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013). Therefore, finding the Higgs particle is a direct link to proving that the existence of the Higgs field (PBS Space-Time, 2015).

The importance of proving this prediction correct (World Science Festival, 2013):

  • Understanding where mass comes from
  • The Higgs particle is a new form of particle that doesn’t spin
  • Shows that mathematics lead the way to discovering something about our reality

This was a prediction in the waiting to be confirmed through observation for over 50 years, which got its roots in the form of scientific and mathematical roots of quantum physics and by Higgs in 1964 (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013).

Supporting Forces for the prediction:

  • Technological: the development of technology to study mathematics over the course of 50 years helped facilitate the discovery of this prediction (Greene, 2013; World Science Festival, 2013). The actual technology use is called the ATLAS detector attached to the Large Hadron Collider (Greene, 2013).
  • Financial: Through international collaboration from thousands of scientists and over a dozen of countries, they were able to amass the financial capital to build this $10 Billion Large Hadron Collider.

References:

Business Intelligence: Decision Support Systems

Many years ago a measure of Business Intelligence (BI) systems was on how big the data warehouse was (McNurlin, Sprague,& Bui, 2008).   This measure made no sense, as it’s not all about the quantity of the data but the quality of the data.  A lot of bad data in the warehouse means that it will provide a lot of bad data-driven decisions. Both BI and Decision Support Systems (DSS) help provide data to support data-driven decisions.  However, McNurlin et al. (2008) state that a DSS is one of five principles of BI, along with data mining, executive information systems, expert systems, and agent-based modeling.

  • A BI strategies can include, but is not limited to data extraction, data processing, data mining, data analysis, reporting, dashboards, performance management, actionable decisions, etc. (Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Padhy, Mishra, & Panigrahi, 2012; and McNurlin et al., 2008). This definition along with the fact the DSS is 1/5 principles to BI suggest that DSS was created before BI and that BI is a more new and holistic view of data-driven decision making.
  • A DSS helps execute the project, expand the strategy, improve processes, and improves quality controls in a quickly and timely fashion. Data warehouses’ main role is to support the DSS (Carter, Farmer, & Siegel, 2014).  The three components of a DSS are Data Component (comprising of databases, or data warehouse), Model Component (comprising of a Model base) and a dialog component (Software System, which a user can interact with the DSS) (McNurlin et al., 2008).

McNurlin et al (2008) state a case study, where Ore-Ida Foods, Inc. had a marketing DSS to support its data-driven decisions by looking at the: data retrieved (internal data and external market data), market analysis (was 70% of the use of their DSS, where data was combined, and relationships were discovered), and modeling (which is frequently updated).  The modeling offered great insight for the marketing management.  McNurlin et al. (2008), emphasizes that DSS tend to be defined, but heavily rely on internal data with little or some external data and that vibrational testing on the model/data is rarely done.

The incorporation of internal and external data into the data warehouse helps both BI strategies and DSS.  However, the one thing that BI strategies provide that DSS doesn’t is “What is the right data that should be collected and presented?” DSS are more of the how component, whereas BI systems generate the why, what, and how, because of their constant feedback loop back into the business and the decision makers.  This was seen in a hospital case study and was one of the main key reasons why it succeeded (Topaloglou & Barone, 2015).  As illustrated in the hospital case study, all the data types were consolidated to a unifying definition and type and had a defined roles and responsibilities assigned to it.  Each data entered into the data warehouse had a particular reason, and that was defined through interviews will all different levels of the hospital, which ranged from the business level to the process level, etc.

BI strategies can affect supply chain management in the manufacturing setting.  The 787-8, 787-9, and 787-10 Boeing Dreamliners have outsourced ~30% of its parts and components or more, this approach to outsourcing this much of a product mix is new since the current Boeing 747 is only ~5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial it is to capture data on fault tolerances on each of those outsourced parts.  Other things that BI data could be used is to make decisions on which supplier to keep or not keep.  Companies as huge as Boeing can have multiple suppliers for the same part, if in their inventory analysis they find an unusually larger than average variance in the performance of an item: (1) they can either negotiate a lower price to overcompensate a larger than average variance, or (2) they could all together give the company a notice that if they don’t lower that variance for that part they will terminate their contract.  Same things can apply with the auto manufacturing plants or steel mills, etc.

Resources:

 

Zeno’s Paradox

Some infinities are bigger than others.

A paradox to motion:

Zeno described a paradox of motion, which helps describes the one type of many infinities. Zeno’s paradox is described below (Stanford Encyclopedia of Philosophy, 2010):

“Imagine Achilles chasing a tortoise, and suppose that Achilles is running at 1 m/s, that the tortoise is crawling at 0.1 m/s and that the tortoise starts out 0.9 m ahead of Achilles. On the face of it Achilles should catch the tortoise after 1s, at a distance of 1m from where he starts (and so 0.1m from where the Tortoise starts). We could break Achilles’ motion up … as follows: before Achilles can catch the tortoise he must reach the point where the tortoise started. But in the time he takes to do this the tortoise crawls a little further forward. So next Achilles must reach this new point. But in the time it takes Achilles to achieve this the tortoise crawls forward a tiny bit further. And so on to infinity: every time that Achilles reaches the place where the tortoise was, the tortoise has had enough time to get a little bit further, and so Achilles has another run to make, and so Achilles has in infinite number of finite catch-ups to do before he can catch the tortoise, and so, Zeno concludes, he never catches the tortoise.”

This paradox was used to illustrate that not all infinities are the same, and one infinity can indeed be bigger than another.  An interpretation of this paradox was written poetically in a eulogy for the book of The Fault in Our Stars (Green, 2012):

“There is an infinite between 0 and 1. There’s .1 and .12 and .112 and an infinite collection of others. Of course there is a bigger infinite set of numbers between 0 and 2, or between 0 and a million. Some infinities are bigger than other infinities. … There are days, many days of them, when I resent the size of my unbounded set. I want more numbers than I’m likely to get, and God, I want more numbers for Augustus Waters than he got. But, Gus, my love, I cannot tell you how thankful I am for our little infinity. I wouldn’t trade it for the world. You have me a forever within the numbered days, and I’m grateful.” (pg. 259-260)

So to my readers out there, I want to thank you in advance for the little infinity(ies) I will get to share with each of you through this blog, and for that I am grateful.

Resources:

  • Green, J. (2012). The fault in our stars.  New York, New York: Penguin Group (USA) Inc.
  • Stanford Encyclopedia of Philosophy (2010). Zeno’s Paradoxes. Retrieved from http://plato.stanford.edu/entries/paradox-zeno/#AchTor