Decision Making Techniques

Decision Making Techniques

Delphi Technique

The Delphi technique was initiated as a means to forecast technological trends and has evolved since the 1950s. Delphi methods include qualitative and quantitative components to gather data from expert opinions. Goodarzi et al. (2018) recommend a sample size ranging from 10-20 in a panel of experts, three rounds, and one open-ended question in the first round. Because panel experts often do not agree Hasson et al. (2000) recommends a statistical aggregation of responses scaled to mean, median, and mode for selected index per round. In IT Service Management (ITSM) activities the Delphi technique can be useful. Santiago’s (2017) study showed in project management, information security, and configuration management the Delphi observability into the decision-making process with subject-matter experts impacted decision-making cost control and complexity for small and medium-sized businesses.

Consensus Technique

The consensus decision-making process involves checking the individual responses for consistency then aligning the solution to sufficiently support the consensus. Finally, the consensus decision-making process ranks the options from best to worst alternatives. A consensus model can help when large differences among judgments or decision makers have developed. Lu’s (2018) research discourages the implementation consensus technique when a group is unanimous. The researcher also showed the use of consensus technique with a triangular fuzzy weight computation formula worked best in star hotels wishing to associate with green tourism. Consensus technique is typically viewed as fair and likely to satisfy decision makers with open communication, agreeable, conscientious, non-neurotic communication (Sager, 2006).

Compare and contrast

The Delphi technique uses subject matter experts in qualitative and quantitative methods. The Delphi questioning is typically several rounds; in comparison, the consensus uses open communication to discuss issues and priorities. The Delphi is a more formal method of decision making where the consensus is more liberal, and with the use of open communication, decision makers may be enlightened to other angles or perspectives from other panel members that were not previously considered, therefore potentially changing their recommendation. For this reason, I would lean toward open communication and the consensus technique. Evidence-based management (EBM) promotes evidence-based decision making in times of uncertainty will increase the odds of success (Rousseau, 2018). Leaders can often be rapid and biased (Baron, 2008; Kahneman, 2011) and lead to negative outcomes. However, when EBM seeks to discover the root cause of problems with rational and intuitive solutions decision-making success will increase (Ayad, 2013).

Decision making also includes the non-quantitative insight that is needed to create a competitive advantage and involves the unconscious and intuitive side of experienced leadership (Enriquez de la O, 2015). The experience of an expert panel in the Delphi technique will give decision makers that type of intuitive experience. When compared to the consensus, the pervasiveness, miscommunication, misunderstanding of the root cause, or knowledgeable insight to the problem or solution from any particular member may be a weak link. Szymaniec-Mlicka (2014) research showed that in private organizations the use of SME’s (subject matter experts) was more prevalent than in public organizations.

In conclusion, I would have to recommend the Delphi technique over the consensus technique for decision making because decision making is both an art and a science (Etzioni, 1981). Root cause analysis is also a major factor in decision-making, therefore, giving the edge to Delphi with the combined experiences of the panel to impact the decision making for more positive and sustainable outcomes. 

References

Ayad, A., Rahim, E. (2013). Toward a theory for management success: the role of evidence-based management in the retail industry. Int. J. of Project Organization and Management, Vol.5, No.3, pp.199

Baron, J. (2008). Thinking and Deciding. Cambridge University Press, NY.

Cavaliere, D., Morente-Molinera, J. A., Loia, V., Senatore, S., & Herrera-Viedma, E. (2019). Collective scenario understanding in a multi-vehicle system by consensus decision making. IEEE Transactions on Fuzzy Systems. doi: 10.1109/TFUZZ.2019.2928787

Enriquez de la O, J. F. (2015). Individual decision-making by top executives as a valuable resource for strategic management–A resource-based view and dynamic capability approach. Vezetéstudomány-Budapest Management Review, 46(11), 2-14.

Etzioni, A. (1989): Humble Decision-making. Harvard Business Review, July-Aug: p. 122–126.

Goodarzi, Z., Abbasi, E., & Farhadian, H. (2018). Achieving consensus deal with methodological issues in the Delphi technique. International Journal of Agricultural Management and Development, 8(2), 219-230. Retrieved from http://ijamad.iaurasht.ac.ir/article_540498_d4bd6133361312bb4c273242368de1ee.pdf

Hasson, F., Keeney, S. & McKenna, H. (2000). Research Guidelines for the Delphi Survey Technique. Journal of advanced Nursing, 32(4): 1008-1015. doi:10.1046/j.1365-2648.2000.t01-1-01567.x

Kahneman, D. (2011). Thinking fast and slow. New York: Farrar, Straus, and Giroux.

Lu, P., Yang, X., & Zhou-Jing, W. (2018). Fuzzy group consensus decision making and its use in selecting energy-saving and low-carbon technology schemes in star hotels. International Journal of Environmental Research and Public Health, 15(9) doi:http://dx.doi.org.proxy.cecybrary.com/10.3390/ijerph15092057

Rousseau, D. M. (2018). Making evidence-based organizational decisions in an uncertain world. Organizational Dynamics, 47(3), 135-146. doi:10.1016/j.orgdyn.2018.05.001

Santiago, J. R. (2017). Observability and the decision-making process in information technology service management: A delphi study (Order No. 10615673). Available from ProQuest Dissertations & Theses Global. (1958951575). Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1958951575?accountid=26967

Sager, K. L., & Gastil, J. (2006). The origins and consequences of consensus decision making: A test of the social consensus model. The Southern Communication Journal, 71(1), 1-24. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/226917248?accountid=144789

Szymaniec-Mlicka, K. (2014). Resource-based view in strategic management of public organizations - a review of the literature. Management (1429-9321), 18(2), 19–30. https://doi-org.proxy.cecybrary.com/10.2478/manment-2014-0039

Technology and Trends

Technology and Trends

Technology

Mobile learning is an important short-term development in technology for higher learning in the Horizon Report. Mobile learning (mlearning) can take place on a phone or tablet for the sake of this discussion. In an era of BYOD (bring your own device) people like to learn on their own device. Ferriman (2014), reports the mobile market is $341 billion, with 80% access to the internet and the mobile learning market is estimated at $9.1 billion. Furthermore, 70% of mobile learning showed students were more motivated when mobile devices are leveraged. Budgets for mobile apps start at $75,000 for a small app and can get over $1MM for larger, more integrated apps, with multiple platforms.

Force 1

Mobile development can be costly, time-consuming, complicated and can create bottlenecks. The cost for institutions to internally or externally develop and design mobile applications that align with the physical and online instructional courses may be very slow and costly. Furthermore, with the need for Android and iPhone users, the choice of platform for the mobile app could mean duplicate or parallel programming. Cao’s (2018) research shows that the stock market generally responded positively to mobile app additions from 2009-2016. This might help for-profit educational firms to smell the coffee; however, non-profits need to take notice to be competitive in the future.

Force 2

As with most technology products, there is a growing concern for security. Technology is only as secure as its weakest link. Whether an organization is doing internal or external development, security begins with testing the applications. Patel's (2017) research showed the functional testing requirement is crucial with erroneous other application interruption while sending sensitive information. From malware to jailbreaking and rooting issues, the BYOD offers numerous opportunities for security threats. Research from Patten (2013) suggests that higher education needs increased security recommendations and recommends mapping an IT Model Curriculum with the Accreditation Board for Engineering and Technology (ABET) approach for integrating mobile-device security.

Summary

Mobile learning is a must, in my opinion, for higher learning, especially when you consider the growth and level of engagement of gamification. The need for a useful mobile application grows, and higher learning integrates with mobile phones and tablets. The leadership must embrace the need and support it financially with an implementation process. Additionally, the need for security is at the forefront of mobile learning, with both user and institution at risk for potential breaches. Leadership must ensure the secure development of mobile apps and the integration of mobile devices for successful outcomes and sustained competitive advantage.

(New Media Consortium, 2019)

Trends

Redesigning learning spaces and blended learning designs have been a short trend in the Educause Horizon Report for the last five to seven years, respectively. Some will argue trends begin at five years and are only fads until they reach that mark. The focus on design learning and use of technologies continue to embrace some challenges and technological developments. Redesigning learning space has to do with the bandwidth of classrooms; is tied into the technology of mobile learning; and includes displays, writing surfaces, and flexibility. The New Media Consortium (2019) report states the potential to create more engaging solutions that synchronize team-based learning with personal learning in a programmed extended reality (XR) are current developments. The extension into XR and mobile devices will lend itself to increased blended-learning designs. For example, collaborative and lab type of classrooms could lead to active learning classrooms (ALC) to increase flexibility and incorporate pedagogical and low-tech approaches.

Force 1

Shareholder buy-in can be a major stopping force for the redesigning and blended learning platforms. CTU’s provost Dr. Connie Johnson stated “The transition from face-to-face to a blended model can be daunting….moving to a blended model, for effective design, including training and education faculty, which takes time and resources.” (NMC, 2019). Without the financial and organizational support of stakeholders and commitment to training and technology, redesigning and blending learning platforms could potentially fail or decrease the engagement of students and faculty.

Force 2

In the process of blending learning designs, learning often occurs out of sequence and can offer difficulties for students. While the use of lectures, books, and other traditional types of resources, the traditional flow of knowledge differs for many students. Learning trends are more social and informal, often less structured, and can be overlapping or even parallel activities with different media and devices, including social communities (Milne, 2006). The freedom from traditional learning models and flexibility will be welcome for some and confusing for others; the need for successful integration of students with technology and physical design can offer difficulties.

Summary

Redesigning learning spaces and blended learning designs are needed, and leadership must understand and support the need for new learning models. Better learning and differentiating the organization are two of the top reasons to incorporate these models into a diverse global student population. Making sure the student populations-young to old and culture to culture-understand the redesign and are able to assimilate to the learning designs will be key in a diverse student base. Combining the redefined learning spaces to include blended learning, then putting it on a mobile device will enable higher learning to reach new diverse student populations better and increase engagement and performance from existing populations.

References

Cao, L., Liu, X., & Cao, W. (2018). The effects of search-related and purchase-related mobile app additions on retailers’ shareholder wealth: The roles of firm size, product category, and customer segment. Journal of Retailing, 94(4), 343-351. doi:http://dx.doi.org.proxy.cecybrary.com/10.1016/j.jretai.2018.08.003

Ferriman, J. (2014). 7 Random mobile learning stats. LearnDash Retrieved from https://www.learndash.com/7-random-mobile-learning-stats/

Patel, D., & Patel, A. (2017). Mobile applications testing challenges and related solutions. International Journal of Advanced Research in Computer Science, 8(3) Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1901457642?accountid=144789

Patten, K. P., & Harris, M. A. (2013). The need to address mobile device security in the higher education IT curriculum. Journal of Information Systems Education, 24(1), 41-52. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1438693253?accountid=144789

Milne, A. J. (2006). Designing blended learning space to the student experience. Retrieved fromhttps://www.educause.edu/research-and-publications/books/learning-spaces/chapter-11-designing-blended-learning-space-student-experience

New Media Consortium. (2019). Educause Horizon Report: 2019 Higher Education Edition. Retrieved from https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf?la=en&hash=C8E8D444AF372E705FA1BF9D4FF0DD4CC6F0FDD1

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This Blog

The theme and my goal is to merge the use of technological innovation with a futuring mindset in this blog by incorporating strategies that help improve, differentiate, and create a sustainable competitive advantage in a disruptive and fast-changing global business environment. My hope is to illustrate the use of AI (artificial intelligence) (machine learning) and neural networks to assist and outperform traditional paradigms of the slow bureaucracy of the hierarchical decision-making process. And replace it with a decentralized autonomous auto-machine learning framework based in physical and behavioral sciences. Wang's (2012) research has shown the use of technology in organizational learning improved outcomes. Business intelligence systems (BIS) to increase real-time decision-making for improved outcomes, efficiencies, innovation, creativity, and heterarchical autonomous systems thinking. 

Challenges

Leaders who intermingle EI (Emotional Intelligence) strategies are proponents for strategic success (Goleman, Boyatzis, & McKee, 2013). Now imagine further intermingling emotional AI to help leaders read and interpret feelings, emotions, moods, and intentions! My challenge will be to navigate the new ground of technology and innovation with the new ground of EI strategies. Both emotional AI and EI strategies are in their infancy, and trying to find reliable and valid instruments based on rigorous science will be challenging for me to remain unbiased. There in lies the irony of this topic. The root cause of project management failure is human error or misjudgment in light of cognitive biases; (Cunha, 2015) however, also the mark of a true scholar is able to step back and critically think and observe. One other challenge will be the ability to weigh the reality of limitations of future technology and innovation in practical matters due to costs, applications, security, ethics, and oversight. For example, does the technology improve relationships or create a wall? Increased computing abilities will also create a challenge because a business leader now wants technology that can be implemented in the next 3-5 years with reliability, not some hope of fruition ten years down the road.

References

Cunha, J. A., Viglioni, T., Thomaz, J., & Moura, H. (2014, November). Project Management in Light of Cognitive Biases: A Public Sector IT Organization Case. In European Conference on Management, Leadership & Governance (p. 50). Academic Conferences International Limited.

Goleman, D., Boyatzis, R. E., & McKee, A. (2013). Primal leadership: Unleashing the

power of emotional intelligence. Harvard, MA: Harvard Business Press.

Wang, G., & Seibert, S. E. (2015). The impact of leader emotion display frequency on follower performance: Leader surface acting and mean emotion display as boundary conditions. Leadership Quarterly, 26(4), 577-593. doi:10.1016/j.leaqua.2015.05.007

Blog Introduction Merging Futuring and Innovation with an Emotional Leadership Paradigm

Tell me about yourself

                I am a third-year DBA (Doctor Business Administration) student at Colorado Technical University (CTU), finishing my last elective course in Computer Science CS875 Futuring and Innovation. My dissertation topic is the benefits of merging emotions and leadership and the strategies of implementing a system-thinking approach (Senge, 2006) to create a sustainable and competitive advantage for organizational outcomes and behaviors. I have created an emotional leadership paradigm (ELP) (see Figure 1) to help illustrate how leaders can use emotional intelligence (EI) when understanding their own & others’ emotions to create organizational outcomes. 

Figure 1. Emotional leadership paradigm (ELP) Copyright 2019

Leaders who use EI in recognizing emotional labor (EL) or emotional contagions (EC) better equip organizations in decision-making and the implementation of OCM with reduce resistance. Walter (2011) research found leaders who used EI leadership behaviors positively influenced followers.  Leaders need the ability to use EI and self-awareness in OCM to respond, understand behaviors, and increase performance (Bradberry, 2009, 2015; Goleman, 1995).

Research Question

How can an Emotional Leadership Paradigm (ELP) control the transference of emotional contagions for successful OCM (organizational change management)?

This Class CS875

              This class is futuring and innovation, which is challenging me to research the fourth industrial revolution of technology and innovation. Nathan’s (2015) responsible innovation process model is a stakeholder-oriented circular model of innovation that will be looked at among others. This blog will be using a reflective approach to ongoing research examining the latest technologies and resources available now and within the next 10-20 years and their application to business administration. My goal in this class and blog is to reflect on the usefulness, ethical, economic, and practical application of technologies and innovation to create organizational competitive advantage and a sustainable corporate social responsibility (CSR). 

References

Bradberry, T., & Greaves, J. (2009). Emotional intelligence 2.0 San Diego, CA: Talent Smart.

Bradberry, T., & Antonakis, J. (2015). Is emotional intelligence a good measure of leadership ability? HR Magazine, 60(9), 22-23. Retrieved from https://shrm.org/hr-today/news/hr-magazine/pages/1115-emotional-intelligence.aspx

Goleman, D. P. (1995). Emotional intelligence: Why it can matter more than IQ for character, health and lifelong achievement. New York, NY: Bantam books.

 Nathan, Ganesh. (2015). Innovation process and ethics in technology: An approach to ethical (responsible) innovation governance. Journal on Chain and Network Science. 15. 119-134. 10.3920/JCNS2014.x018.

Senge, PM (2006) The fifth discipline: The art and practice of the learning organization (2nd ed.). New York, NY: Doubleday.

Walter, F., Cole, M. S., & Humphrey, R. H. (2011). Emotional intelligence: Sine qua non of leadership or folderol? Academy of Management Perspectives, 25(1), 45-59. doi:10.5465/amp.25.1.45