Proposal: Socio-Technical Plan for Innovative Proactive Model

Dr. Aly, O.
Computer Science

Socio-Technical Plan for Innovative Proactive Model

The number of the physical objects which are being connected to the Internet is growing at very speed rate realizing the concept of the Internet of Things (Al-Fuqaha, Guizani, Mohammadi, Aledhari, & Ayyash, 2015). The computational intelligence and the machine learning techniques have gained popularity in different domains. The internet of things and internet of people are terms which can indicate the increasing interaction between humans and machines. Internet of Things (IoT) is regarded to be “one of the most promising fuels of Big Data expansion” (De Mauro, Greco, & Grimaldi, 2015). Internet of things is the core component of Web 4.0. The Web has gone from the first generation of Web 1.0 which was about static web pages, broadcasting information for read-only. Web 1.0 was innovated by Berners-Lee (Aghaei, Nematbakhsh, & Farsani, 2012; Choudhury, 2014; Kambil, 2008; Patel, 2013), and is known as the “Web of Information Connections” (Aghaei et al., 2012). Web 2.0 which came out in 2004 is read-write and is known as the “Web of People Connections (Aghaei et al., 2012) to connect people. Web 3.0 which came out in 2006 is known as “Semantic Web” or the “Web of Knowledge Connections” to share knowledge, followed by Web 4.0 is known as the “Web of Intelligence Connections” where Artificial Intelligence (AI) is expected to play a role.

The current technology as indicated in TED’s video of (Hougland, 2014) can assist people to save lives in case of unexpected health issues such as the heart attack or stroke, by wearing a band in hand. There are also other tools for elder people to save them when they fall, and they need help while living alone by themselves with no assistance. These tools are reactive tools based on a reactive model which can assist after the fact. The major question for this project is:

“Can the “Web of Intelligence Connections” be intelligent enough to be proactive and provide us with useful information on a daily basis?”

To answer this very critical question, the researcher is proposing a Proactive Model and the required Socio-Technical plan, besides the methods, models, scenario planning for the future and the analytical plan for the innovative model.

Introduction

Internet of Things (IoT) is a novel paradigm which is rapidly gaining ground in modern wireless telecommunications domain (Atzori, Iera, & Morabito, 2010). The underlying concept of the IoT is the pervasive presence of a variety of things or objects around us such as Radio-Frequency Identification (RFID) tags, sensors, actuators, mobile phones, and so forth. The RFID and sensor network technologies will meet the challenge where information and communication systems are invisibly embedded in the environment around us (Gubbi, Buyya, Marusic, & Palaniswami, 2013).

For technology to get embedded as it gets disappeared from the consciousness of the users, the IoT demands things, such as a shared understanding of the situation of the users and their appliances (Gubbi et al., 2013). Other demands of the IoT for the technology to disappear from the conscious of the users include the software frameworks and pervasive communication networks to process and convey the contextual information to where it is relevant, and the analytics tools of IoT aiming for autonomous and smart behavior (Gubbi et al., 2013). Figure 1 illustrates the Semantics of the IoT showing the end users and application areas based on data, adapted from (Gubbi et al., 2013).

Figure 1: Internet of Things Semantics showing the end users and application areas based on data. Adapted from (Gubbi et al., 2013).

Giving the potentials of the IoT and Semantic Webs, the researcher is proposing an innovative model called “Proactive Model” which will change how we live our lives. This new model will introduce advanced tools through which people will communicate daily about what to eat, what to exercise, what to drink, and basically what to do to live a healthy life and to avoid any unexpected catastrophic event such as stroke or heart attack. The existing tools will still be available. However, the innovative approach of the Proactive Model will be pervasive and embedded into our daily lives, and the use of the reactive models will be minimum. The Proactive Model is based on the Internet of Things technologies and Web 4.0 and semantic web.

Forces such as technology, ease of use, user acceptance, culture and so forth may affect the implementation of the Proactive Model. The IoT is still facing technical issues such as the bottleneck when processing large-scale of data, DNS and TCP which need to be modified to better serve the IoT services (Atzori et al., 2010; Gubbi et al., 2013), and the Proactive Model which is based on the IoT technology. Security and privacy may be an obstacle to the implementation of the Proactive Model as the data about our body, our activities and ourselves will be transmitted continuously on a second-by-second basis somewhere in the Cloud. The supporting forces, as well as the challenging forces, are discussed in detail later in this project.

Scope

The current model is a reactive model which waits until the catastrophic event such as heart attack, a stroke happens. As indicated in TED’s video of (Hougland, 2014), you can place a band in your hand which can assist elder people or people who are highly likely exposed to some health issues such as heart attack which can prevent them from living their lives. There are additional similar tools which can help elder people who live alone to call for help when they fall, or any unexpected thing happens to them. All these tools are reactive tools which will still exist after the Proactive Model but will have minimal use because the Proactive Model will take over.

The features of the Proactive Model which distinguish it from the current reactive model include the monitor of the health activities such as exercises, the monitor of the healthy diets and the health factors levels such as potassium, and cholesterol, and the monitor of the daily energy and performance. These three major monitor features are the key factors for the success of the Proactive Model. The Proactive Model is expected to be intelligent and smart to guide individuals. It is not limited to the elder people, but it will be available to all people at all age levels. Thus, the result is promising for the young generation and the elder generation, which will lead to the more cognitive ability to their activities, diets and daily energy and performance.

The future of the Proactive Model is very promising as the plan is to extend it to act as a personal assistant providing guidance not only at the activities, diets or energy level but also at the financial level. The Proactive Model is expected to provide financial recommendations such as closest and less expensive gym, and gas stations and so forth. Thus, the benefits of the Proactive Model will embrace every aspect of our lives.

While the Proactive Model provides very promising benefits to all people at all age levels, it has the limitation that it does not measure psychological emotions or feelings, nor depressions or emotional disorders. Moreover, the cost of the Proactive Model may not be affordable especially for the initial production release, which will make it only available for those who can afford it. However, the plan is to make it more affordable for all users.

Purpose

The purpose of this project is to propose an innovative Proactive Model and its Socio-Technical plan in the age of Big Data and Internet of Things (IoT). The innovative Proactive Model is based on the IoT technology, which is based on Web 4.0 the Semantic Web. The Socio-Technical plan is a critical component of the proposed model. The key elements of both technical and social systems relevant to IoT technology which is the underlying technology of the Proactive Model are identified through the analysis of the current forces in both systems. The identification of these elements will allow investigating how the technical and social systems can be integrated together to create an environment which supports effective Proactive Model while suppressing the dysfunctional aspects of this new work environment. The Socio-Technical plan includes not only the social and technology system, but also other systems such as the medical system, policy makers and governance system, and users, community, and culture system.

The potential impact of the Socio-Technical plan in the context of the IoT technology and the Proactive Model will involve not only the positive outcome and the impact which is reflected in the “Joint Optimization,” but also in the “Affectability” of the system, which will lead to better innovation, better performance, and a better dialogical approach involving careful cognitive awareness of values, emotions, and interests of all social groups.

Proactive Model Forces

The Socio-Technical plan will consider forces and factors which can affect the innovation such as complexity, compatibility, acceptance, ease of use, culture, trust, security, privacy and so forth. Figure 2 illustrates the integration of the technical system with the social system and the technical and social forces for the proposed innovative “Proactive Model.” These forces are not isolated or autonomous, nor they are static. They are dynamic, and the changes must be considered at both level the technical level as well as the social level. Thus, the arrows from technical to social and from social to technical illustrate the dynamic nature of the Socio-Technical system of the Proactive Model.

Figure 2. The Dynamic Innovative Proactive Model Technical and Social Forces.

Supporting Forces

As illustrated in Figure 2, the technical forces include the communication, energy, interoperability, security, device management, data analytics, and recycling management. The social forces include the ease of use, user acceptance, privacy and ethics, education and training, governance, management support, business dynamics, partner collaboration, and culture and religion. Some of these forces are supporting forces while other are challenging forces. The supported forces include the Web 4.0 technology and semantic web which provides a new innovative paradigm which can support this new innovative Proactive Model Socio-Technical system. The success of the existing reactive model and tools which did not exist a few years ago is an indication about the possibility of advanced and better tools and models which are more intelligent using the Web Intelligence technology. The need for better health and better cognitive awareness system is a supporting factor. When using the current reactive model, the ambulance can be called as indicated in TED’s video of (Hougland, 2014), to save the life of the person. Such a service is costly and can add stress to the patients. However, with the Proactive Model, this cost is reduced to the minimum. Thus, the reduction in cost is a supporting factor in this innovative Proactive Model. Moreover, the existing support of integrating Social and Technical systems together is another supporting factor as the Proactive Model will not start from scratch when integrating these two systems together, but rather expanding on the current integrated systems to enhance the optimization as well as the affectibility.

Challenging Forces

While there are supporting forces for the Proactive Model, there are more challenging forces than the supporting forces. The concept might be new and not convincing that there will be a device to be used on a daily basis other than the smartphones or tablets. The device that is based on the Proactive Model is to guide the person on what food to eat, what exercises to do, what food to avoid, what time to sleep, and so forth. The device will act intelligently as a bodyguard for the body be based on the measure of the vitamins in the body and the nutrition elements that are needed for the body. If there is a smart device now to measure the glucose level of the diabetic people which is based on the current technology, there must be other types of devices that are smarter based on the Web of Intelligence Connections and Semantic Web of Web 4.0. The technical complexity of developing such a device which will act as a doctor who diagnoses the body on a daily basis. Sensors might be required; blood reading might be required as the case with the diabetic people, or saliva test, a patch to measure the blood pressure. These requirements must be communicated to the medical experts. Thus, the communication between technical experts and medical expert is another challenging, in addition to the challenges of communicating this new technology and model to the users. The security is another challenge which will require securing all these data which will be collected daily on the body needs, and organs functions. This data is expected to generate a large amount of data which can be categorized as Big Data. The analytical aspect of such streaming data is another challenge which may require new algorithm. The recycling data is another challenge. This data which is collected on a daily basis might be needed for a year for the analytical purpose. However, after the end of the life cycle of the data, it must be recycled fully and completely to protect the privacy of the users. The privacy and the ethics are challenges for this Proactive Model Socio-Technical system as it is very critical to ensure the protection of the sensitive data especially if this data deals with the body and the health of the person. The device of the Proactive Model may face additional challenges in the culture and religion domain, despite the anticipated benefits of such a technology. The business dynamics is another challenge for this new model. The partner collaboration is another challenge which needs to be addressed to make sure all parties such as medical experts, technical experts, executives and so forth are collaborating and working together to achieve such a promising model.

Methods

As indicated in the challenging forces, collaboration and communication among the involved parties are required for the success of such a model. A method must be used to guarantee not only the successful communication and collaboration among the involved parties but also the success of such a new model. Thus, there is a requirement and need for a structure of a group which focuses on providing a solution to a particular problem in this new paradigm and new technology such as the Think Tank (Caliva & Scheier, 1992). The “Think Tank” is the proposed method to be used in the process of the development and implementation of this new model. Think Tank has two models; the “one roof” model and the “without a roof” models (Whittenhauer, n.d.). The “without roof” model is described to be more effective than the “one roof” model because it does not require the funding which is required for the “one roof” on travel costs and so forth. Thus, the Proactive Model will be using the “without a roof” Think Tank model. The Think Tank for the Proactive Model will be named as Proactive Think Tank. The main objective of the “Proactive Think Tank” is to drive not only the innovation of the Proactive Model but also the adoption of the new devices at every age from teenagers, to adults to seniors. The Proactive Think Tank will ensure the integration of the technical system and social system to enhance the optimization and the affectability of the Socio-Technical plan of the Proactive Model.

Besides the Think Tank approach, the Delphi method will be used to provide the group communication process and make it effective enough to allow a group of individuals from different domains functioning as a whole to be able to deal with the complexity of this innovation (Saizarbitoria Iñaki, Arana Landín, & Casadesús Fa, 2006), which involves experts from technology and computer science domain, policy makers, users, community, and medical domain. The panel of the experts will not only be involved in the current design but also in the future of the Proactive Model and the Socio-Technical plan for that model. The key factors to ensure the success of the Proactive Model and the Socio-Technical plan are the selection of the members of the panel which should be based on their knowledge, capabilities, and independence. When using the Delphi method, the danger of dominant influence of any of the panel members is minimized because the identification of the members is hidden when expressing opinions.

Models

The traditional Socio-Technical approach is to design the technical component and then fit people to it (Appelbaum, 1997; L. Chen & Nath, 2008). This traditional approach leads to performance issues at high social costs (Appelbaum, 1997). Thus, the integration of social and technical elements is very critical. As indicated in (Geels, 2004), the focus should not just be on innovation, but also on the use and the functionality. In (Geels, 2004), the sectoral systems of innovation are expanded to be socio-technical systems. While the emphasis of the existing innovation systems is on the production side where innovations emerge, the expanded Socio-Technical systems involve production, diffusion, and use of technology (Geels, 2004). See Figure 3 for the basic elements and resources of Socio-Technical Systems, adapted from (Geels, 2004).

Figure 3. The Basic Elements and Resources of Socio-Technical System. Adapted from (Geels, 2004).

The Socio-Technical systems are not autonomous systems. However, they are the outcome of the human activities, which are embedded in social groups sharing certain characteristics such as certain roles, responsibilities, norms, perceptions and so forth (Geels, 2004). On the production side, the social groups can include education entities such as schools and universities, public/private laboratories, technical institutes, suppliers, banks, engineers and so forth. On the functional and user side, the social group includes public authorities, consumers, media, and so forth. Thus, the Socio-Technical systems can form a structuring context for human actions on both sides of the production and functional and user sides (Geels, 2004).

The Socio-Technical theory, which was introduced at the Tavistock Institute in London in mid of 20^thcentury, indicates that any organization or the organizational work system has two independent sub-systems; the social and the technical sub-systems (L. Chen & Nath, 2008). The social sub-system is concerned with the attributes of people and users such as attitude, skills, values and so forth, and the relationship between people, reward systems, and the authority structures, while the technical sub-system is concerned with the processes, tasks, and technology required to transform inputs to outputs (L. Chen & Nath, 2008). The underlying concept of the Socio-Technical theory is that the technical system and social system must be integrated to determine the best overall solutions for the organization (L. Chen & Nath, 2008). In contrast with the traditional and conventional approach, using the Socio-Technical theory, the re-design of the work system of the organization must consider the impact of each sub-system on the other and the requirement for each sub-system simultaneously (L. Chen & Nath, 2008).

As proposed in (Hayashi & Baranauskas, 2013), the Socio-Technical perspective might contribute to the dialogical approach to involve careful listening and understand one another, as well as awareness of each other’s values, emotions, and interests (Hayashi & Baranauskas, 2013). The design of the Socio-Technical system is based on the underlying concept and premise that a work unit or an organization is a combination of both social and technical elements (Appelbaum, 1997). Both social and technical elements should work together to accomplish the ultimate goal (Appelbaum, 1997). Thus, the work system develops and produces both physical products and social/psychological outcomes (Appelbaum, 1997). The positive outcome called “Joint Optimization” is the key success factor for these two elements of the social and technical (Appelbaum, 1997). Thus, the Socio-Technical system for Proactive Model should involve all social groups from software engineers and the employees to consumers at all levels. Moreover, the “Affectability” concept which is proposed by (Hayashi & Baranauskas, 2013), for the Socio-Technical perspective in the context of the educational technology, is another key success factor for the Proactive Model.

The Joint Optimization and Affectability of the Socio-Technical Plan of the Proactive Model are implemented through the integration of all systems involved such as the technical system, social system, medical system, governance system. The integration and the communication of these systems to work together in harmony is a critical requirement for the Proactive Model. The Affectability of the Socio-Technical Plan will be demonstrated in the final product of the Proactive Model such as ease of use, user acceptance, and user trust. The proposed Socio-Technical Plan for the Proactive Model involves not only people and technology but also other domains such as medical as it plays a significant role in guiding health activities, diets, and energy. Figure 4 illustrates the proposed Four-Runner Socio-Technical Plan for the Proactive Model which is based on the IoT technology and Semantic Web.

Figure 4. The Proposed Four-Runner Socio-Technical Approach for the Innovative Proactive Model.

The innovation is not about developing new products, but it is about reinventing business process and building entirely new markets to meet untapped customer needs (Albarran, 2013). For some businesses, innovation is deliberative and planned, while for others innovation is the direct result of a triggering event such as a change in external market conditions or internal performance which forces a change in business strategy (Gershon). Three main types of innovations: product innovation, process innovation, and business model innovation. This innovative Proactive Model is not only about product innovation, but also process innovation and business model innovation. The product innovation is reflected in the final product which provides the health and financial benefits to people and organization. The process innovation and business model innovation reflect the integration of all systems to generate Joint Optimization and Affectability for the Socio-Technical plan and the product of the Proactive Model. The process model is illustrated in Figure 5.

Figure 5: Proposed Scenario Planning Model for the Innovative Proactive Model.

The key benefit of this proposed Scenario Planning process is to reveal the different strategy of the future based on which more flexible and more thoughtful and better decision can be made. The process innovation model begins with the analysis of the external and internal forces such as technical complexity for the product, culture challenges, communication with other involved parties’ challenges. The second phase of this innovative process is about the uncertainty analysis. The third phase of this process involves strategic planning which contains all scenarios from best-case scenarios to the worst-case scenarios. The fourth phase involves the opportunities and strategy alternative, followed by the last phase of the strategy selection.

The proposed business model innovation involves new departments which are not a conventional department in organizations. Human Resources Department, Financial Department, Marketing Department, Information Technology, and Sales Departments are good examples of the conventional and traditional department in the organization. However, the innovative business model involves other departments such as medical department, governance department, and so forth in the organization. The Join Optimization and the Affectability of the Socio-Technical plan of the Proactive Model require the integration of these parties. Embedding new departments such as medical department and governance department in the organization can ensure the success of the process innovation and the product innovation. Figure 6 illustrates the business model innovation for the Proactive Model.

Figure 6. Innovative Business Model for the Innovative Proactive Model.

Analytical Plan

As illustrated in the proposed Scenario Planning, the analysis begins with the internal and external forces such as communication and integration between these units of technology, medical, governance, and people. The analysis should also cover the technical complexity and the current algorithm and machine learning. The analysis can reveal the need for the new algorithm, or new models. The analysis plan should also include the uncertainty factors which can have a negative impact on the implementation of the Proactive Model. One major uncertainty factor is the acceptance of users, which needs to be analyzed and measured regarding population, age, profession, income, and so forth.

The analytical plan for this Proactive Model includes the proto-type analysis which is the first product pre-release to ensure the product is implemented in accordance with the design specifications and requirements. The proto-type analysis can take between 1-3 months analysis based on the model of the device, simple model, medium model, and complex model. The analysis plan will also include more comprehensive analysis based on a survey on the acceptance of the product, ease of the product, and trust of the product.

As indicated in (Wu, Zhao, Zhu, Tan, & Zheng, 2011) understanding the reasons for the acceptance or rejection of a new product is very challenging. However, the Technology Acceptance Model (TAM) is regarded to be the most powerful theory to analyze the explain the technology usage behavior and whether the product acceptance is based on ease of use, trust or other factors. This model has been extended to include the trust factor as indicated in (Wu et al., 2011). This comprehensive analysis plan includes the TAM model as illustrated in Figure 7. The analysis covers the relationship among the identified variables, and the direct effect of the variables to provide insight into the central tendencies of the relationships. Thus, statistical analysis will be used such as coefficient, correlation, ranges, central tendencies, and analysis of variances ANOVA.

The Innovation Diffusion Theory (IDT) is another well-established theory to analyze the user adoption (L.-d. Chen, Gillenson, & Sherrell, 2004). The innovation diffusion is achieved through the acceptance of the users and the use of new ideas or things such as the Proactive Model-based device. As indicated d in (L.-d. Chen et al., 2004), the relative advantages, the compatibility, complexity, “triability,” and observability were found to explain 49 to 87 percent of the variance in the rate of its adoption. Other studies found that relative advantage, compatibility, and complexity were found consistently related to the rate of innovation adoption (L.-d. Chen et al., 2004). Thus, these critical variables will be used in the comprehensive analysis of the innovative Proactive Model which is based on IoT technology, and the Joint Optimization of the proposed Socio-Technical plan.

Figure 7. The Proposed Model for the innovative Proactive Model based on TAM and IDT Model to Analyze and Evaluate the User Acceptance, and all other Variables.

Anticipated Results

The organizations involved in this innovative Proactive Model represent diverse industries such as IT, Health, Policy Makers and Governance. Other organizations such as Financial, and insurance may get involved to shape additional features of the Proactive Model. Tremendous efforts are expected to be exerted at all organizations level. The commitment and the determination of these organization must drive this innovation because it will change the way we live our daily lives. The initial reaction of users might not be completely positive. It might receive rejection or resistance from users and medical industry, as it might be threatening to the medical field. This innovation is to enhance the medical field and health insurance. The initial user interface might be challenging. However, the user interface is expected to be advanced and more intuitive to all users. The cost factor will play another role in the adoption of the new innovative Proactive Model. Until the cost goes down, only the users who can afford it will be able to enjoy the benefits of such innovation. The anticipated result also involves the impact of the culture and religion on the adoption of this innovation. It might not get adopted completely in certain communities due to culture and certain practices. This innovation is not expected to celebrate success completely for several decades. However, afterward, this innovation will be embedded into our lives and will become invisible as it is expected to be part of our lives.

Conclusion

The internet has changed the way we live our lives today, and how we communicate with each other, and how we perform our work. The interaction between people is completely different today than the interaction between people a few decades ago. The human innovation has moved along from sending messages using birds which arrive in several days or months, to sending messages using smartphone which arrives at the receiver instantaneously.

Technology without social consideration will be hard to sell. Organizations and businesses such as Blockbuster and Yahoo are good examples for those organizations which did not consider the social system and did not lean to the users’ requirements, besides the lack of strategic scenario planning. Thus, the researcher proposed an innovative Proactive Model which is based on the IoT, Web 4.0 and Semantic Web with a proposed Socio-Technical system to ensure the success of this innovation. The purpose of the Proactive Model is not only to save lives but also to live healthy lives. The current model is reactive waiting until a catastrophic event happens to react and save the life of people. The IoT technology and Semantic Web have the potential to add a new dimension to our lives, and how we live our daily lives and how not only people communicate with each other but also how devices will communicate with each anywhere and anytime.

This project also covered the external and internal forces which can have a positive and negative impact on the implementation and adoption of the Proactive Model. The analysis of these factors provides a good insight into the anticipated results of the innovation and the timeframe for full implementation. The project also discussed the methods to be implemented to ensure communications and sound decisions from the involved experts are made using Delphi method and the Think Tank approach as well.

For the analytical plan, the analysis will start with the external and internal forces to overcome any challenges at that level. The analysis plan includes the uncertainty associated not only with the diffusion of innovation but also with the underlying concept of the Proactive Model to live smart life. After the consideration of the Socio-Technical plan and the strategic scenario planning, the first release will be analyzed and evaluated using the prototype for one to three months. Any modification to the original design and all involved components such as sensors are implemented, a more comprehensive analysis plan will be conducted. The comprehensive analytical plan applies the concept of TAM and IDT models to evaluate and analyze the acceptance of users, the ease of use, and trust, which can lead to the behavioral intention for use. The anticipated result based on the historical records for innovation indicate the minimal use at the beginning of the product release. However, after few years, the product will be mature enough and known enough to be used ubiquitously as it is expected to be embedded and invisible in our lives. Our today’s life will be described by the future generations as a primitive generation, the same way we now describe the “stone age” generation.

Areas of Future Research

The IoT is a promising domain which requires future research. It is part of the Web 4 and Semantic Web. The Proactive Model is based on these advanced technologies. There are several areas for more research using this technology. Examples of these areas of future research include robotic models such as robotic taxi (Atzori et al., 2010), robotic assistant, robotic teachers, and robotic cars which can be on the road without drivers. The smart environment requires more research in different areas such as comfortable homes and offices for all people with automated systems with minimum cost. The industrial plant is another area for more research to integrate robotics and automation at a higher level using IoT. As indicated in (Atzori et al., 2010), the machine/robot can help in improving the automation in industrial plants with a massive deployment of RFID tags associated with the production parts. Interconnection of various systems to develop a smart city can provide ubiquitous services to improve the quality of life in the city by making it easier and more convenient for people to find information of interest (Al-Fuqaha et al., 2015).

The underlying concept behind the areas for future research is the IoT technology and the Semantic Web technology which involves Artificial Intelligence. The key element is the intelligence and how we can turn all systems to be smart to be under the human services. The future innovations are anticipated to involve smart and intelligent robotics devices and systems. The implications of these robotic innovations are not trivial. However, if these innovations do not sound feasible today, they might be very much feasible and embedded into the human lives several decades from today.

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Case Study: Business with Good Plans Turned out Wrong

Dr. Aly, O.
Computer Science

Introduction: The purpose of this discussion is to discuss and analyze a business that had a good plan, but something went wrong because of circumstances which are beyond their control. These circumstances can be new technology, market changes, innovative competitors and so forth. The discussion also addresses an example to illustrate a potential impact for the socio-technical plan which will be proposed in later discussions.

There are various examples for some of the businesses that had good plans, but something went wrong. These examples include Netscape, AOL, AltaVista, MySpace, Yahoo, Sun, Sony, Sun Microsystem, and more (Newman, 2010). The focus of this discussion is limited to Yahoo.

Yahoo: Yahoo was founded in 1994 two Stanford University graduates Jerry Yang and David Filo (Thomas, 2016). For the past two decades, Yahoo strove to develop one of the most visited sites and a robust online display advertising business (Dwoskin, 2016; Thomas, 2016). Yahoo was a “veritable web titan,” a leader in email, online news, and search during the 1990s and early 2000s (Thomas, 2016). It was described as “one-stop shop bringing together news and other services for users lacking expertise in navigating the internet” (Thomas, 2016).

However, after the competition with Google and Facebook, Yahoo could not survive (Dwoskin, 2016; Forbes, 2016; Thomas, 2016). Google viewed the core challenge as an algorithmic problem to find the best Web sites through automated indexing instead of using manual indexing as the case with Yahoo. Moreover, Google and Facebook attracted the best engineers and employed a leaner operation to remain flexible and sensitive to what prospective customers want. Thus, the lessons learned from Yahoo which could not survive in the face of Google and Facebook include several factors: Focus on one thing and become the best at it, effective automation, quality hiring, and lean operation (Forbes, 2016). Moreover, Google realized the importance of acquisition and made some strategic acquisitions such as YouTube, whose team developed Android OS, and recently DeepMind. Yahoo, on the other hand, was not good or did not pay attention to the acquisition strategies and their integration. The last but not least, planning and optimization for large-scale data processing, engineering, business, and branding ensures the continuing innovation and help in capturing new markets (Forbes, 2016).

Internet of Things Semantics and Socio-Technical: Internet of Things (IoT) is a novel paradigm which is rapidly gaining ground in modern wireless telecommunications domain (Atzori, Iera, & Morabito, 2010). The underlying concept of the IoT is the pervasive presence of a variety of things or objects around us such as Radio-Frequency Identification (RFID) tags, sensors, actuators, mobile phones, and so forth. The RFID and sensor network technologies will meet the challenge where information and communication systems are invisibly embedded in the environment around us (Gubbi, Buyya, Marusic, & Palaniswami, 2013). The IoT and Internet of People are term which can indicate the increassing interaction between humans and machiens. The IoT is regarded as “one of the most promising fuels of Big Data expansion” (De Mauro, Greco, & Grimaldi, 2015). IoT is the core component of Web 4.0. The current technology, as indicated in TED’s video of (Hougland, 2014), can assist people to save lives in case of unexpected health issues such as the hearth attack or stroke, by wearing a band in hand. There are also other tools for the elder people to save them when they fall, and they need help while living alone by themeselves with no assistance. These tools of the current technologies are reactive tools which can asist after the fact. The question is “Can the “Web of Intelligence Connections” be intelligent enough to be proactive and provide u with useful information on a daily basis?” The researcher is proposing an innovative model called “Proactive Model” which will change how we live our lives. This new model will introduce advanced tools through which people will communicate daily about what to eat, when to exercies, what to drink, and basically what to do to live healthy and to avoid any unexpected catastrophic event such as stroke or heart attack. The existing tools will still be available. However, the innovative approach of the Proactive Model will be pervasive and embedded into our daily lives and the use of the reactive models will be minimum.

As proposed in (Hayashi & Baranauskas, 2013), the Socio-Technical perspective might contribute to the dialogical approach to involve careful listening and understand one another, as well as awareness of each other’s values, emotions, and interests (Hayashi & Baranauskas, 2013). The design of the Socio-Technical system is based on the underlying concept and premise that a work unit or an organization is a combination of both social and technical elements (Appelbaum, 1997). Both social and technical elements should work together to accomplish the ultimate goal (Appelbaum, 1997). Thus, the work system develops and produces both physical products and social/psychological outcomes (Appelbaum, 1997). The positive outcome called “Joint Optimization” is the key success factor for these two elements of the social and technical (Appelbaum, 1997). Thus, the Socio-Technical system for IoT should involve all social groups from software engineers and the employees to consumers at all levels. Moreover, the “Affectability” concept which is proposed by (Hayashi & Baranauskas, 2013), for the Socio-Technical perspective in the context of the educational technology, is another key success factor.

Thus, the potential impact of the Socio-Technical plan in the context of the IoT technology will involve not only the positive outcome and impact reflected in the “Joint Optimization,” but also in the “Affectability” of the system, which will lead to better innovation, better performance, and a better dialogical approach involving careful cognitive awareness of values, emotions, and interests of all social groups. The Socio-Technical plan will consider forces and factors which can affect the innovation such as complexity, acceptance, ease of use, culture, trust, security, privacy and so forth.

References

Appelbaum, S. H. (1997). Socio-technical systems theory: an intervention strategy for organizational development. Management decision, 35(6), 452-463.

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. Paper presented at the AIP Conference Proceedings.

Dwoskin, E. (2016). Behind Yahoo’s downfall: Bad bets and failure to adapt. Retrieved from http://www.chicagotribune.com/bluesky/technology/ct-behind-yahoos-downfall-20160420-story.html, Washington Post.

Forbes. (2016). Where And When Did Yahoo Go Wrong? Retrieved from https://www.forbes.com/sites/quora/2016/07/26/where-and-when-did-yahoo-go-wrong/#66c8f002cc6e.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation computer systems, 29(7), 1645-1660.

Hayashi, E. C., & Baranauskas, M. C. C. (2013). Affectibility in educational technologies: A socio-technical perspective for design. Journal of Educational Technology & Society, 16(1), 57.

Hougland, B. (2014). What is the Internet of Things? And why should you care? [Video file]. TED Talks: Retrieved from https://www.youtube.com/watch?v=_AlcRoqS65E.

Newman, R. (2010). 10 Great Companies that Lost Their Edge. Retrieved from https://money.usnews.com/money/blogs/flowchart/2010/08/19/10-great-companies-that-lost-their-edge.

Thomas, D. (2016). Yahoo – where did it all go wrong? Retrieved from http://www.bbc.com/news/technology-35243407.

Serendipity, Errors and Exaptation

Dr. Aly, O.
Computer Science

Introduction: The purpose of this discussion is to discuss and analyze three terms: Serendipity, Errors, and Exaptation. The discussion begins with some basic definitions, followed with some examples for each term.

Serendipity: As indicated in (De Bonte & Fletcher, 2014), Serendipity can happen anytime anywhere. It can happen through a flyer, or a new perspective or new insight from people you talk to which can provide new ideas hidden in your mind. The new perspective can provide unexpected inspiration. The key factor is to be open to new opportunities, new ideas and ignore the fear that is associated with these new opportunities and ideas as they can lead to discovering an innovation. In (Copeland, 2017), the term Serendipity is used to describe discovery in science which happens at the intersection of opportunity and wisdom.

In (Crampton, 2017; Holubar, 1991), the term Serendipity was coined by Horace Walpole (1717-1797) in 1754 in allusion to an ancient oriental legend of the “Three Princes of Serendip.” Serendip is an old name for the country known today as Sri Lanka (Crampton, 2017). The story described how three traveling princes made discoveries about things that they did not plan to explore or that supposed them during their travel (Crampton, 2017). Thus, Walpole created the word “Serendipity” to indicate and refer to the accidental discoveries (Crampton, 2017). The term “Serendipity” today can mean the ability to make discoveries not intentionally or purposely searched for, and the greater the knowledge, the more likely the discovery (Holubar, 1991). It also infers to a happy and unexpected event which happen due to the chance when searching for something (Crampton, 2017).

There are several serendipitous discoveries in science such as Penicillium which was discovered to make the Penicillin. This serendipitous discovery happened in 1928 by Alexander Fleming, when he found a clear area around the mold (Crampton, 2017). Instead of ignoring the clear area, he investigated and found out that the mold was making an antibiotic which killed the bacteria around it. He identified the mold as Penicillium and named the antibiotic as Penicillin (Crampton, 2017).

Exaptation: Exaptation is defined in (Bonifati, 2013) as a result of a process through which an initial attribution of new functionality to existing outcomes leads to new outcomes. Exaptation is also known as “Pre-Adaptation” (Feltrinelli & Del Garda, 2009). Although Exaptation is regarded to be the most important evolutionary technique in the history of species, technologies, and ecosystems, it is yet little studied and largely unknown outside the field of evolutionary biology (Feltrinelli & Del Garda, 2009). Darwin proposed the “Pre-adaptation” as the solution to explain how gradual process such as natural selection can evolve complex organs whose fitness contribution become positive only when the organ is complete (Feltrinelli & Del Garda, 2009). The term of “Pre-adaptation” is expanded by Gould and Vrba, as cited in (Bonifati, 2013; Feltrinelli & Del Garda, 2009), and coined the term with “Exaptation” for a non-natural selection-driven evolutionary process (Feltrinelli & Del Garda, 2009). In (Gould & Vrba, 1982), the “Exaptation” was proposed for the operation of useful character which is not built by selection for its current role as an effect but evolved for other usages and later “co-opted” for their current role (Gould & Vrba, 1982). Exaptation is described as a central concept in several fields such as technological change, and evolutionary biology (Feltrinelli & Del Garda, 2009). As cited in (Feltrinelli & Del Garda, 2009), Stuart Kauffman states that Exaptation “is one of the most creative forces in the eco- and techno-sphere” (Feltrinelli & Del Garda, 2009).

Examples of Exaptation include the feathers and flight-sequential exaptation in the evolution of birds whose original purpose was to regulate temperature, but over time they were used to aid flight (Barve & Wagner, 2013; Gould & Vrba, 1982; Kastelle, 2010). Another example is the lens crystallins, which are a light-refracting protein which originated as enzymes (Barve & Wagner, 2013). Another example of Exaptation is the phonograph to which Edison attributed the functionality of serving as a dictating machine. However, Edison specified nine other possible uses of the phonograph in a published article in 1878 (Bonifati, 2013). Within the context of technology, Exaptation can suggest that businesses can accumulate technological knowledge without anticipation of its subsequent usage (Andriani & Cattani, 2016). Moreover, Exaptation can emerge from processes through which an initial attribution of new functionalities to existing artifacts or organizations can lead to new artifacts and eventually to new markets in the socio-economic innovation processes (Bonifati, 2013).

Error: The historical and technological records contain various and numerous examples of innovation (Buchanan, 2013). Some of these innovations were by mistake. Examples of the past business mistakes which proved to be brilliant include personal copiers, selling via pet stores, ATMs, credit card for students, organic food, and more.

References

Andriani, P., & Cattani, G. (2016). Exaptation as source of creativity, innovation, and diversity: introduction to the Special Section. Industrial and Corporate Change, 25(1), 115-131.

Barve, A., & Wagner, A. (2013). A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature, 500(7461), 203.

Bonifati, G. (2013). Exaptation and Emerging Degeneracy In Innovation Process. Economics of Innovation and New Technology, 22(1), 1-21.

Buchanan, B. (2013). Alex Mesoudi, Kevin N. Laland, Robert Boyd, Briggs Buchanan, Emma Flynn, Robert N. McCauley, Jürgen Renn, Victoria Reyes-García, Stephen Shennan, Dietrich Stout, and Claudio Tennie. Cultural Evolution: Society, Technology, Language, and Religion, 193.

Copeland, S. (2017). On serendipity in science: discovery at the intersection of chance and wisdom. Synthese, 1-22.

Crampton, L. (2017). Serendipity: The Role of Chance in Making Scientific Discoveries. Retrieved from https://owlcation.com/stem/Serendipity-The-Role-of-Chance-in-Making-Scientific-Discoveries.

De Bonte, A., & Fletcher, D. (2014). Scenario-Focused Engineering: A toolbox for innovation and customer-centricity: Microsoft Press.

Feltrinelli, P., & Del Garda, G. (2009). Exaptation as a Source of Innovation, Creativity, and Diversity in Evolutionary Sciences.

Gould, S. J., & Vrba, E. S. (1982). Exaptation-A Missing Term in the Science of Form Paleobiology, 8(1), 4-15.

Holubar, K. (1991). Serendipity–its basis and importance.

Kastelle, T. (2010). Innovation Through Exaptation. Retrieved from http://timkastelle.org/blog/2010/05/innovation-through-exaptation/

Case Study: The Impact of Relying only on Standard Forecasting instead of Using Proper Scenario Planning

Dr. Aly, O,
Computer Science

Introduction

Scenario planning first emerged for application to businesses in a company set up for researching new forms of weapons technology in the RAND Corporation (Chermack, Lynham, & Ruona, 2001). Kahn of RAND corporation pioneered a technique titled “future-now” thinking (Chermack et al., 2001). Scenario planning encourages organizational leaders to think the unthinkable (Chermack et al., 2001). It has been identified as a useful means of conducting strategic organization planning (Chermack et al., 2001). With a focus on long-term and short-term future, scenario planning forces the organizational planners to consider paradigms which challenge their current thinking (Chermack et al., 2001).

In (Wade, 2012), scenario planning is described as a productive, creative, and existing way to develop the groundwork for a strategic plan which does not bet the future on the company on a single most likely scenario. Scenario planning challenges the idea of a single future but an array of possible future which could potentially unfold (Wade, 2012). The outcome of the scenario planning process is a portfolio of future scenarios, each of which represents a different way the business landscape could look in a few years, and not just the landscape, but also the players who involve in the business such as competitors, suppliers, customers, employees and other stakeholders (Wade, 2012). Scenario planning is considered to be a critical tool for anyone who is not just managing, but also leading (Wade, 2012). It enables the leader to create a realistic vision for the future and craft the strategies which will make the leader successful (Wade, 2012).

The good scenario planning goes beyond just high-low projections (Schoemaker, 1991). In (Peterson, Cumming, & Carpenter, 2003), scenario planning is described as a systematic method for thinking creatively about possible complex and uncertain futures. The underlying concept of the scenario planning is to consider a variety of possible futures which include many of the important uncertainties in the system instead of focusing on accurate prediction of a single outcome (Peterson et al., 2003). There are many approaches to scenario planning such qualitative approach,

The applications of scenario planning can be organized by their use of qualitative or quantitative methods and their approach toward the uncertainty (Peterson et al., 2003). Most scenario planning incorporates both qualitative and quantitative details, and the relative mix of these two aspects distinguish different scenarios exercises (Peterson et al., 2003). Some scenario planning is intended to facilitate the management uncertainty, while others are used to discover it (Peterson et al., 2003). Three examples of scenarios which have been used to approach problems which were beyond the read of the traditional predictive methods include “Shell Oil,” “Monte Fleur, South Africa,” and “Northern Highland Lake District, Wisconsin” (Peterson et al., 2003). In the Shell Oil, the traditional forecasting was found inappropriate, and scenario planning was used to allow well-defined actor “Shell” with a clear goal to maximize shareholder value to develop a strategy for an uncertain future (Peterson et al., 2003). In Monte Fleur, scenario planning is used by bringing a group of disconnected people with divergent goals together to create a shared understanding of the uncertainties surrounding the transition to democracy (Peterson et al., 2003). In Northern Highland Lake District in Wisconson, a team of scientists created an initial set of scenarios to begin a scenario-planning process among a broad group of stakeholders (Peterson et al., 2003). These examples demonstrate that scenario planning can be modified in a multitude of ways to fit a particular context (Peterson et al., 2003).

Scenario Planning Could Have Saved Blockbuster

Blockbuster is a very good example of the business which did not do the proper scenario-type planning and only relied on standard forecasting. Blockbuster Inc. was an American-based DVD and video game rental service. It was founded by David Cook, who used his experience with managing large database networks as the foundation for the retail distribution model of the Blockbuster (Albarran, 2013). In 2009, Blockbuster had an estimated 7,100 retail stores in the US with additional locations in 17 countries worldwide, and had over 60,000 employees in the US and worldwide (Albarran, 2013). The headquarter of the company was in McKinney, Texas (Albarran, 2013). Blockbuster filed for bankruptcy just prior its 25^th anniversary on September 22, 2010, and on April 11, Blockbuster was acquired by satellite television service provider Dish Network at an auction price of $233 million and the assumption of $87 million in liabilities and other obligations (Albarran, 2013).

The innovation is not about developing new products, but it is about reinventing business process and building entirely new markets to meet untapped customer needs (Albarran, 2013). For some businesses, innovation is deliberative and planned, while for others innovation is the direct result of a triggering event such as a change in external market conditions or internal performance which forces a change in business strategy (Gershon). Three main types of innovations: product innovation, process innovation, and business model innovation. Blockbuster followed the traditional forecasting model without paying attention to any innovation and the impact of the technology on its business. If Blockbuster had implemented scenario-typed planning in their Business Process, it would not have failed. The Blockbuster retail model was going to be difficult to sustain in the presence of advancing technology (Albarran, 2013). Figure 1 illustrates a Scenario Planning Model.

Figure 1: Scenario Planning Model

Forces Driving Blockbuster Out of Business

The Internet made the future of e-commerce and “disruptive technologies” possible. Netflix is one of these “disruptive technologies” which took the form of a unique business process innovation (Albarran, 2013). Netflix is an online subscription-based DVD rental service founded by Reed Hastings in 1997during the emergent days of electronic commerce (EC) when companies like Amazon and Dell Computer were starting to gain prominence (Albarran, 2013). Netflix provides greater value to the consumer when compared to the traditional video rental store which charges by the individual DVD rental unit, by offering two to three DVDs per week for a fixed monthly price (Albarran, 2013). Moreover, Netflix offers greater convenience in the form of “no late fee.” (Albarran, 2013). The success of Netflix is the direct result of personalized marketing which involves knowing more about the particular interest and viewing habits of the customers (Albarran, 2013). Netflix utilizes the power of the Internet to promote a proprietary software recommendation system (Albarran, 2013). This recommendation system solved the common complaints with Blockbuster when renting an unfamiliar movie, and the customer gets dissatisfied with the viewing experience later. With this recommendation system, Netflix offers suggestions of other films that the customer might like based on the past selection and the brief evaluation filled by the customers (Albarran, 2013).

Blockbuster failed to react to the competition and revise its business model (Albarran, 2013). Blockbuster could have re-position itself strategically as early as 2011 (Albarran, 2013). However, Blockbuster could have acquired Netflix or modified its strategy by duplicating many of the same EC efficiencies which Netflix’s business model had already demonstrated (Albarran, 2013). Blockbuster chose to ignore the competitive threat posed by Netflix.

Scenario Planning could have saved Blockbuster. Scenario Planning offers a framework for resilient conservation policies when faced with uncontrollable, irreducible uncertainty (Peterson et al., 2003). Scenario Planning consists of using a few contrasting scenarios to explore the uncertainty surrounding the future consequences of a decision. Blockbuster could have benefitted from a few contrasting scenarios to explore the uncertainty that is resulted from the competition, the technology, and the presence of the Internet and e-Commerce. The key benefit of the Scenario Planning process is that it reveals different ways of the future based on which more flexible and more thoughtful and better decision can be made today (Wade, 2012). An additional benefit of scenario planning includes the increased understanding of key uncertainties, the incorporation of alternative perspectives into conservation planning, and greater resilience of decisions to surprise (Peterson et al., 2003).

In summary, the traditional forecast model did not help Blockbuster stay in business. On the contrary, the traditional forecast model did not forecast the future of Blockbuster. The forces such as the Internet, the emerging technologies supporting e-commerce, recommendation systems for customers, monthly rental for two to three DVDs, and no late fee have driven Blockbuster out of business. Blockbuster could have saved itself if it has applied scenario planning strategically to protect itself from the uncertainty of the future, instead of following the same traditional forecasting model which worked perfectly in the past but became not applicable any longer in the presence of the Internet and other emerging technologies and innovations driven by these technologies.

References

Albarran, A. B. (2013). Media management and economics research in a transmedia environment: Routledge.

Chermack, T. J., Lynham, S. A., & Ruona, W. E. (2001). A review of scenario planning literature. Futures Research Quarterly, 17(2), 7-32.

Gershon, R. A. MEDIA INNOVATION: Disruptive Technology and the Challenges of Business Reinvention: Kalamazoo, Western Michigan University.

Peterson, G. D., Cumming, G. S., & Carpenter, S. R. (2003). Scenario Planning: a Tool for Conservation in an Uncertain World. Conservation Biology, 17(2), 358-366.

Schoemaker, P. J. H. (1991). When and How to Use Scenario Planning: A Heuristic Approach with Illustration. Journal of Forecasting, 10(6), 549-564.

Wade, W. (2012). Scenario planning: A field guide to the future: John Wiley & Sons.

Concepts of Forecasting and Predictions

Dr. Aly, O.
Computer Science

Purpose:

The purpose of this discussion is to research the concepts of forecasting and predictions in a business or innovation context. The discussion will identify and document one infamous prediction that actually came true.

Discussion

Prediction: The goal of prediction is to obtain a significant estimate of what the value of the dependent variable will be by known independent variable value (Bateh & Heyliger, 2014). However, as indicated in (Garrett, 2013), the prediction is rough and is subject to a large error of the estimate. Prediction means different things to different technical disciplines and different people (Peterson, Cumming, & Carpenter, 2003). The prediction is understood to be the best possible estimate of future conditions (Peterson et al., 2003). The less sensitive the prediction is to drivers the better (Peterson et al., 2003). Whereas scientists understand that predictions are probabilistic conditional statements, non-scientists often understand them as things that will happen no matter what they do (Peterson et al., 2003).

The historical and technological records contain various and numerous examples of predictions that came true (Dreher, n.d.; Sterbenz, 2013). Some of these predictions that came true include the following as indicated in (Dreher, n.d.; Sterbenz, 2013).

Jules Verne predicted a man on the moon in 1865.
Ray Bradbury foretold earbuds in 1953.
Edward Bellamy envisaged the debit card in 1888.
Robert Boyle predicted organ transplants in the 1660s.
Arthur C. Clark imagined the iPad in 1968.
Nikola Tesla predicted personal wireless devices in 1909.
H. G. Wells predicted the atomic bomb in 1914.
Roger Ebert predicted video-on-demand services Netflix and Hulu in 1987.
Isaac Asimov predicted the use of the Internet for learning in 1988.

For this discussion, the focus is on the prediction of Nikola Tesla for the personal wireless devices in 1909.

Nikola Tesla’s Prediction of Personal Wireless Devices in 1909: In 1891, Nikola Tesla developed a type of resonant transformer called the Tesla coil, which achieved a major breakthrough in his work by transmitting 100 million volts of electric power wirelessly over a distance of 26 miles to light up a bank of 200 light bulbs and run one electric motor (Bhutkar & Sapre, 2009). Tesla claimed to have achieved 95% efficiency, but the technology had to be shelved because the effects of transmitting such high voltages in electric arcs would have been disastrous to humans and electrical equipment in the vicinity (Bhutkar & Sapre, 2009). This technology has been neglected in obscurity for several years. However, the advent of portable devices such as mobiles, laptops, smartphones, MP3 players brought this technology into life and Tesla’s prediction of the wireless in 1909, which came true.

Tesla was called as a visionary as well as “charlatan” (Lumpkins, 2014). However, many attest that his early vision of alternating current transmission systems and wireless power were the precursors of today’s energy-harvesting technology (Lumpkins, 2014). He is regarded as a prolific inventor (Marincic, 1982). Tesla believed that by transmitting waves of alternating radio-frequency (RF) energy, devices such as electric vehicles and even flying dirigibles, could reuse this energy for consistent operation (Lumpkins, 2014). Tesla experimented with large-scale wireless power distribution by building the world’s first power station in Long Island, New York (Xie, Shi, Hou, & Lou, 2013). He planned to use the power station called Wardenclyffe Tower to transmit not only signals but also wireless electricity (Xie et al., 2013). However, due to its large electric fields, which significantly diminished the power transfer efficiency, Tesla’s invention was not successful and was never put into practical use (Xie et al., 2013).

The field of wireless power has been growing over the past sixty years, from conceptual ideas such as collecting solar power in space and “beaming” it back to Earth-based collectors, like a Dyson sphere, to the reality of charging Philips Sonicare electric toothbrush with an inductive charger (Lumpkins, 2014). The impact of the Tesla’s innovation and prediction is observed in every minute of our lives.

References

Bateh, J., & Heyliger, W. (2014). Academic Administrator Leadership Styles and the Impact on Faculty Job Satisfaction. Journal of leadership Education, 13(3).

Bhutkar, R., & Sapre, S. (2009). Wireless energy transfer using magnetic resonance. Paper presented at the Computer and Electrical Engineering, 2009. ICCEE’09. Second International Conference On.

Dreher, B. (n.d.). 9 Incredible Historical Predictions That Came True. Retrieved Jan 27, 2018, from https://www.rd.com/culture/historical-predictions-that-came-true/.

Garrett, M. (2013). Traditional Forecasting Leads to Traditional Results … Failure. Retrieved Jan 27, 2018, from https://www.forbes.com/sites/matthewgarrett/2013/08/22/traditional-forecasting-leads-to-traditional-results-failure/#4a0c95e0bebc, Forbes.

Lumpkins, W. (2014). Nikola Tesla’s Dream Realized: Wireless power energy harvesting. IEEE Consumer Electronics Magazine, 3(1), 39-42.

Marincic, A. (1982). Nikola Tesla and the wireless transmission of energy. IEEE Transactions on Power Apparatus and Systems(10), 4064-4068.

Peterson, G. D., Cumming, G. S., & Carpenter, S. R. (2003). Scenario Planning: a Tool for Conservation in an Uncertain World. Conservation Biology, 17(2), 358-366.

Sterbenz, C. (2013). 16 Of The Most Impressive Predictions Of All Time. Retrieved Jan 27, 2018, from http://www.businessinsider.com/predictions-from-the-past-that-came-true-2013-9.

Xie, L., Shi, Y., Hou, Y. T., & Lou, A. (2013). Wireless power transfer and applications to sensor networks. IEEE Wireless Communications, 20(4), 140-145.

Scenario Planning vs. Traditional Forecasting

Dr. Aly, O.
Computer Science

Purpose

The purpose of this discussion is to compare and contrast the concepts of scenario planning versus traditional forecasting.

Traditional Forecasting: A forecast is described as the best estimate of a particular method, model, or individual (Peterson, Cumming, & Carpenter, 2003). The view toward the forecast is that a forecast may or may not turn out to be true (Peterson et al., 2003). Forecast is uncertain (Peterson et al., 2003). Prediction, on the other hand, means different things to different technical disciplines and different people (Peterson et al., 2003). The prediction is understood to be the best possible estimate of future conditions (Peterson et al., 2003). Forecast and prediction are closely tied to the notion of optimal decision making (Peterson et al., 2003). The optimal decisions maximize the expected net benefits and minimize the expected net losses (Peterson et al., 2003).

Traditional forecasting has different models and methods as indicated in (DSG, 2011). The traditional forecasting methods involve trending, extrapolation and curve fitting methods, which are used when forecast time frame is short to medium term, and there is sufficient evidence that forecast inflection points do not exist in the time frame (DSG, 2011). It also involves the Adoption and Penetration Model which is used when there is sufficient evidence that the historical and forecast time frame of the model will include inflection points (DSG, 2011). The scope of these Adoption and Penetration Models is usually total market, and the objectives are to predict market penetration, the location of the inflection points, and take-up times (DSG, 2011). The traditional forecasting methods also include the “Casual and Multivariate” methods which are used when multiple known causal influences are driving or constraining the market (DSG, 2011). The two most common form of this approach include the Total Market models and the Market Share methods (DSG, 2011). Other traditional forecasting models include Time-Series Analysis, Agent-Based Models, and Trackers & Bottom-Up Models (DSG, 2011). As indicated in (Garrett, 2013), traditional forecasting leads to traditional results and may be failures. Thus, the models of the traditional forecasting do not have to be applied in every business scenarios.

Scenario Planning: Scenario planning first emerged for application to businesses in a company set up for researching new forms of weapons technology in the RAND Corporation (Chermack, Lynham, & Ruona, 2001). Kahn of RAND corporation pioneered a technique titled “future-now” thinking (Chermack et al., 2001).

Scenario planning encourages organizational leaders to think the unthinkable (Chermack et al., 2001). It has been identified as a useful means of conducting strategic organization planning (Chermack et al., 2001). With a focus on long-term and short-term future, scenario planning forces the organizational planners to consider paradigms which challenge their current thinking (Chermack et al., 2001).

As indicated in (Chermack et al., 2001), scenario planning has been defined in several ways. Scenario planning was defined as “an internally consistent view of what the future might turn out to be -not a forecast, but one possible future outcome.” Scenario planning is defined as “a tool for ordering one’s perceptions about alternative future environments in which one’s decisions might be played out” (Chermack et al., 2001). It was also defined as “that part of strategic planning which relates to the tools and technologies for managing the uncertainties of the future” (Chermack et al., 2001). The major themes of the scenario planning include history, scenarios as stories, the theory of scenarios, the effect of scenarios on decision-making capabilities, creating “future memory” from scenarios, scenarios as tools for organizational learning, and the evaluation of scenario projects (Chermack et al., 2001). Scenario planning offers a framework for developing more resilient conservation policies when confronted with uncontrollable, irreducible uncertainty (Peterson et al., 2003).

In (Wade, 2012), scenario planning is described as a productive, creative, and even existing way to develop the groundwork for a strategic plan which does not bet the future on the company on a single most likely scenario. Scenario planning challenges the idea of a single future but an array of possible future which could potentially unfold (Wade, 2012). The outcome of the scenario planning process is a portfolio of future scenarios, each of which represents a different way the business landscape could look in a few years, and not just the landscape, but also the players who involve in the business such as competitors, suppliers, customers, employees and other stakeholders (Wade, 2012). Scenario planning is considered to be a critical tool for anyone who is not just managing, but also leading (Wade, 2012). It enables the leader to create a realistic vision for the future and craft the strategies which will make the leader successful (Wade, 2012).

The good scenario planning goes beyond just high-low projections (Schoemaker, 1991). In (Peterson et al., 2003), scenario planning is described as a systematic method for thinking creatively about possible complex and uncertain futures. The underlying concept of the scenario planning is to consider a variety of possible futures which include many of the important uncertainties in the system instead of focusing on accurate prediction of a single outcome (Peterson et al., 2003). There are many approaches to scenario planning such qualitative approach,

There is no one-size-fits-all approach to scenario planning (Wade, 2012). Six steps are proposed by (Wade, 2012) as a general guideline for scenario planning. These six steps include: framing the challenge, gathering information, identifying the driving forces, defining the future’s critical uncertainties, generating the scenarios, and fleshing them out and creating storylines. Four more steps follow these six steps including the validation, the implication assessment, the identification of signposts, and the monitoring and updating the scenario as the time moves (Wade, 2012). In (Peterson et al., 2003), a similar guideline is provided. However, in (Peterson et al., 2003), the approach is described as a qualitative approach, presenting the process as a linear process, with iteration, where system assessment leads to a redefinition of the central question, and testing can reveal blind spots which require more assessment. The process begins with the identification of a focal issue, followed by the assessment, identification of alternative, building scenarios, testing scenarios, and policy screening (Peterson et al., 2003).

The key benefit of the scenario planning process is that it reveals different ways of the future based on which more flexible and more thoughtful and better decision can be made today (Wade, 2012). An additional benefit of scenario planning includes the increased understanding of key uncertainties, the incorporation of alternative perspectives into conservation planning, and greater resilience of decisions to surprise (Peterson et al., 2003).

References

Chermack, T. J., Lynham, S. A., & Ruona, W. E. (2001). A review of scenario planning literature. Futures Research Quarterly, 17(2), 7-32.

DSG. (2011). Traditional forecasting and modeling methods. Retrieved Jan 27, 2018, from http://www.danielresearchgroup.com/WhatWeDo/ForecastsandMarketModels/TraditionalForecasting.aspx, Daniel Research Group: Understanding the Future.

Peterson, G. D., Cumming, G. S., & Carpenter, S. R. (2003). Scenario Planning: a Tool for Conservation in an Uncertain World. Conservation Biology, 17(2), 358-366.

Schoemaker, P. J. H. (1991). When and How to Use Scenario Planning: A Heuristic Approach with Illustration. Journal of Forecasting, 10(6), 549-564.

Wade, W. (2012). Scenario planning: A field guide to the future: John Wiley & Sons.

Accidental Inventions and Game-Changing Ideas

Dr. Aly, O.
Computer Science

Innovation can refer to a successful novel variant, a novel variant, or any variant (Buchanan, 2013). It can also refer to the ideas underlying an invention or its first implementation (Buchanan, 2013). Innovation can also refer to both the process by which variants are generated and the product (Buchanan, 2013). Innovation introduces new cultural variation into the population through copying error, novel invention, refinement, recombination, and exaptation (Buchanan, 2013). Thus, innovation is not a synonym of variation, as the variation entails a broader category which encompasses diverse forms only some of which are novel (Buchanan, 2013).

Game-changing ideas are the “transformational magic” which takes the organizations from ordinary to exceptional (Myatt, 2012). Game changers focus on pursuing a game-changing idea (Myatt, 2012). They never get satisfied with the ordinary or mundane (Myatt, 2012). They are described as relentless, persistent, committed to pursuing that idea that is hunting them (Myatt, 2012). Moreover, the game changers are originals, and they refuse to allow their organizations to adopt conventional orthodoxy and bureaucracy (Myatt, 2012). They challenge the norm, break the conventions, and encourage diversity of thoughts (Myatt, 2012). They have a clear purpose, and they understand the value of serving something beyond themselves (Myatt, 2012). In (Myatt, 2012), six steps called SMARTS for finding and implementing game-changing ideas; Simple-Meaningful-Actionable-Relational-Transformational-Scalable (Myatt, 2012).

Every game-changing ideas and innovation have driving forces that either supported them or were against them. The driving force is described by (Wade, 2012) as “something with the potential to bring about significant change in the future.” Some of the driving forces include uncertainty, potential impact, stability, risks, benefits, culture (Wade, 2012).

The historical and technological records contain various and numerous examples of innovation (Buchanan, 2013). Some of these innovations were accidental. Some of these accidental inventions include the microwave, Saccharin, Slinky, Play-Doh, Super Glue, Teflon, Bakelite, Pacemaker, Velcro, X-Rays, Stainless Steel, Plastic, Teflon, Corn Flakes (Biddle, 2010; Cyran, 2012). The discussion in this project is limited to two of these accidental inventions, and the driving forces that supported them. The driving forces can be culture, religion, technical complexity, technology and so forth.

Pacemaker

In 1959, the engineer Wilson Greatbatch and the cardiologist Chardack developed the first fully implantable pacemaker (Haddad & Serdijn, 2009). The accidental innovation of the pacemaker happened when Greatbatch took 1-megaohm variety instead of picking a 10,000-ohm resistor out of a box to use on a heart-recording prototype (Biddle, 2010). The resulting circuit produced a signal which sounded for 1.8 milliseconds, and then paused for a second – a dead ringer for the human heart (Biddle, 2010). Greatbatch realized that the precise current of the resulting circuit could regulate a pulse, overriding the imperfect heartbeat of the person who has an issue with the heartbeat (Biddle, 2010). The pacemaker before this accidental innovation was large and was attached to the person from the outside. However, after this accidental innovation, the effect of the resulting circuit can lead to a small circuit which can be implanted into the person’s heart (Biddle, 2010). Pacemakers have become smaller and lighter over the years (Haddad & Serdijn, 2009).

The pacemaker evolved with time. The complexity and reliability in the modern pacemaker have increased because of the developments in the integrated circuit design (Haddad & Serdijn, 2009). For instance, the early pacemakers did not have the capability of electrogram sensing pacing the ventricles asynchronously (Haddad & Serdijn, 2009). However, the modern devices, called “demand mode pacemakers,” included a sense amplifier measuring cardiac activity, thereby avoiding competition between paced and intrinsic rhythms (Haddad & Serdijn, 2009). The demand pacemaker functional block involves power source, a sense amplifier, timing control, output driver, and electrode, while the earlier pacemaker functional block involved only power source, pulse generator and electrodes (Haddad & Serdijn, 2009).

Since pacing stimuli were only delivered when needed, longevity increase by the introduction of demand pacemakers (Haddad & Serdijn, 2009). In 1963, the pacemakers were introduced to have the capability to synchronize ventricular stimuli to a trial activation (Haddad & Serdijn, 2009). Since that time, the clinical, surgical and technological developments have proceeded at a significant pace providing the highly reliable, extensive therapeutic and diagnostic devices that are available today (Haddad & Serdijn, 2009). Today, the modern pacemaker technologies are extremely complex and include an analog part, comprising the sense amplifier and a pacing output state, and a digital part consisting of a microcontroller, and some memory, implementing diagnostic analysis of sensed electrograms, adaptive rate response and device programmability (Haddad & Serdijn, 2009).

X-Ray

In 1895, the German physicist Wilhelm Roentgen was performing a routine experiment involving cathode rays (Biddle, 2010; Cyran, 2012; NASA, n.d.). He observed that a piece of fluorescent cardboard was lighting up from across the room (Biddle, 2010; NASA, n.d.). A thick screen was placed between his cathode emitter and the radiated cardboard, demonstrating that particles of light passed through a solid object (Biddle, 2010). He discovered it through arms and hands created detailed images of the bones inside (NASA, n.d.). He experimented with cathode-ray tubes. Glass tubes with the air sucked out and a special gas pumped in (Cyran, 2012). When he ran the electricity through the gas, the tube would glow. However, something strange happened after he surrounded the tube with blackboard. When he turned on the machine, a chemical few feet away started to glow (Cyran, 2012). He was surprised because the cardboard should have prevented any light from escaping (Cyran, 2012). He found out that cathode-ray tube had been sending out more than just visible light (Cyran, 2012). It was sending out invisible rays which could pass right through paper, wood, and even skin (Cyran, 2012). He captured X-Ray images, and the first of the skeletal images was his wife’s hand (Biddle, 2010; Cyran, 2012; NASA, n.d.).

X-Rays have much higher energy and much shorter wavelength than the ultraviolet light. Scientists refer to X-Rays regarding their energy instead of their wavelength, because they have very small wavelengths, and some of them are no bigger than a single atom of many elements (NASA, n.d.). Due to the benefits of the X-Rays, they have been used in many domains such as dental, any part of our body, and even the universe (NASA, n.d.). In the area of radiography, X-Rays have used on dental, chest, mammography which is recommended for early detection of breast cancer. These tests utilize short bursts of X-Ray beams and post little risk (NRPB, n.d.). X-Rays benefit extended to fluoroscopy a technique that uses X-Rays to produce a moving image on a TV screen. More sophisticated method of using X-Rays is found in the computed tomography (CT) scan to produce 3-D pictures of the patients (NRPB, n.d.). Although X-Rays provided many benefits to our lives, they expose some risks as they are a form of electromagnetic radiation, just like light waves and radio waves (NRPB, n.d.). X-Rays can cause damage to cells in the body, which in turn can increase the risk of developing cancer with the increasing number of X-Rays tests (NRPB, n.d.).

In summary, game-changing ideas and innovation can also be accidental. The key success factor and forces for any innovation and game-changing ideas rely heavily on the person to process persistently, patiently, wisely with great commitment to go against the conventional and the traditional process and be bold. Game changer leaders have these common attributes which make them game changers, leaders, and innovators.

References

Biddle, S. (2010). Whoops! The 10 Greatest (Accidental) Inventions of All Time. Retrieved from https://gizmodo.com/5620910/whoops-the-10-greatest-accidental-inventions-of-all-time.

Cyran, P. (2012). The 20 Most Fascinating Accidental Inventions. Retrieved from https://www.csmonitor.com/Technology/2012/1005/The-20-most-fascinating-accidental-inventions/X-ray-images.

Haddad, S. A. P., & Serdijn, W. A. (2009). Ultra-low-power biomedical signal processing: an analog wavelet filter approach for pacemakers: Springer Science & Business Media.

Myatt, M. (2012). 6 Steps for Creating a Game Changer. Retrieved January 30, 2018, from https://www.forbes.com/sites/mikemyatt/2012/10/10/how-great-leaders-create-game-changers/#43ee8019558b, Forbes.

NASA. (n.d.). X-Rays. NASA, Retrieved January 30, 2018, from https://web.archive.org/web/20121122024930/http://missionscience.nasa.gov/ems/11_xrays.html.

NRPB. (n.d.). X-Rays – Benefits and Risks. National Radiological Protection Board, Retrieved January 30, 2018, from http://www.radiology.ie/wp-content/uploads/2012/01/X-Rays-Benefits-and-Risks.pdf.

Wade, W. (2012). Scenario planning: A field guide to the future: John Wiley & Sons.

Think Tank Methods

Dr. Aly, O.
Computer Science

Purpose

The purpose of this discussion is to research some of the think tank concepts and methods that are deliberate and foster innovation. The discussion will address some key points about each method.

Discussion

Think Tank is also known as “Reflection Pool” (Caliva & Scheier, 1992). It was developed to assist in addressing the needs to expand the process of thinking without restriction (Caliva & Scheier, 1992). The traditional way for solving problems and learning include conferences, workshops and so forth (Caliva & Scheier, 1992). However, with Think Tank, the techniques go beyond the traditional method to include simulating creativity in the field, developing holistic thinking patterns, providing powerful tools to deal with complex and long-term problems, challenging restlessly creative leaders, and renewing the participants’ spirit (Caliva & Scheier, 1992).

There is no consensus on the definition of the Think Tank, despite the considerable efforts of the academic literature to define Think Tank and to establish its topology (Kelstrup, n.d.). Think Tank is defined in various studies and journals. The term “Think Tank” is defined as a structure for a group that focuses on providing a solution to a particular problem in the technology and science domain (Caliva & Scheier, 1992). However, it is regarded as a process rather than a structure by (Caliva & Scheier, 1992). Thus, the term can be defined as “a process for in-depth consideration of issues and challenges whose relevance reaches beyond the individual or program and the immediate time frame.” (Caliva & Scheier, 1992). In (Shaw, Russell, Greenhalgh, & Korica, 2014), Think Thank is described as “a civil society organization specializing in the production and dissemination of knowledge related to public policy” (Shaw et al., 2014). In (Whittenhauer, n.d.), the Think Tank is described as “an organization that assembles experts with the sole purpose of coming together to think—more specifically, to think of ideas on how to solve a particular problem” (Whittenhauer, n.d.). In (Hauck, 2017) Think Tanks are described as “organizations that have to proliferate, playing more and more the role of very relevant actors on the political scene in clashes over interests, preferences, and ideas for the formation of public policies” (Hauck, 2017). In (Kelstrup, n.d.), Think Tanks are described as agents established globally in public policy across different levels of governance (Kelstrup, n.d.).

Some indicated that the first proliferation wave of Think Tanks began in the United States and the United Kingdom at the turn of the twenty century (Hauck, 2017). Most of the Think Tanks in the United States are funded by the government or political advocacy groups (Whittenhauer, n.d.). However, some of the Think Tank are established as for-profit organizations which sell their intellectual property or ideas to businesses and government (Whittenhauer, n.d.). In Western Europe, the government finances 75% of German Think Tanks, to include public organizations in the studies (Hauck, 2017). As indicated in (Shaw et al., 2014), around 6500 Think Tanks are operating across 169 countries and representing a range of organizations and interests (Shaw et al., 2014). The role of the Think Tanks is increasing in healthcare domain worldwide through the work of organizations such as “Commonwealth Fund” in the US, the King’s Fund in the UK, and the Health and Global Policy Institute in Japan. These organizations support health services research and policy analysis such as surveying trends in health coverage and communicating their work through media briefings and research seminars to shaping the health policy and planning (Shaw et al., 2014).

Two Major Concepts of Autonomous and Influence: The two major concepts of Think Tank are the autonomy and influence (Kelstrup, n.d.). These two concepts are drawn from existing literature on the definitions and description of the Think Tank (Kelstrup, n.d.). Thus, the general definition of Think Tank is “Organizations that claim autonomy from and attempt to influence public policy” (Kelstrup, n.d.). Based on these two underlying concepts, two dimensions are formed to include demand-driven and supply-driven (Kelstrup, n.d.). Two main perspectives are categorized under each of these two dimensions; “political policy world,” and “administrative policy world” (Kelstrup, n.d.). The “political policy world” perspective include two main sub-categories; the “political advisor” under the demand-driven dimension, and the “instrumental” under the supply-driven dimension (Kelstrup, n.d.). Under the “political advisor,” which is the demand-driven approach, Think Tank meet the demand for biased knowledge (Kelstrup, n.d.). Under the “instrumental,” which is the supply-driven approach, Think Tank supply normative knowledge by stakeholder interests (Kelstrup, n.d.). The “administrative policy world” perspective include two main sub-categories; the “administrative, institutional” and “network” (Kelstrup, n.d.). Using the “administrative institutional” that is the demand-driven approach, Think Tank meet the demand for cognitive knowledge (Kelstrup, n.d.). In the “network,” which is the supply-driven approach, Think Tank supply cognitive knowledge to public administration (Kelstrup, n.d.).

Two Major Models of “one roof” and “without roof”: There are two models for the Think Tank; the “one roof” Think Tank model and “without a roof” Think Tank model (Whittenhauer, n.d.). In the “one roof” Think Tank Model, the diversified group comes in one place “under one roof” and interacts together face to face (Whittenhauer, n.d.). Before the “one roof” model, the participants of the Think Tank communicated through phones and written correspondences (Whittenhauer, n.d.). The costs that are associated with “one roof” model such as travel was a factor in decreasing the interaction among the Think Tank members (Whittenhauer, n.d.). This model of “one roof” is regarded to be an effective Think Tank approach when immediate interactive conservation facilitates the intensified thought process (Whittenhauer, n.d.). In 2009, the second model of “without roof” Think Tank model is used by organizations which do not have to fund the “one roof” model (Whittenhauer, n.d.). The “without a roof” Think Tank model is more effective than the “one roof” because it does not require the funding that is required by the “one roof” on travel costs and so forth. The “without roof” Think Tank model spends most of the money on research and the required costs for computers and utilities are not paid by think tank organization using this model (Whittenhauer, n.d.).

Five Think Tank Techniques: In (Penttila, 2007), there are five Think Tank techniques that enhance innovation: “combine ideas,” “think backward,” “do rapid prototyping,” “Create an internal incubation fund,” and “take it online” (Penttila, 2007). Example of the “Combine Ideas” technique is the interactions between ideas and the methods to merge them which is used by Xerox (Penttila, 2007). Example of “Think Backward” technique is the innovation method of McDonald by “backcasting” the product to see the end product first and work towards that end product (Penttila, 2007). Example of “Do Rapid Prototyping” is McDonald which puts ideas through fast prototyping for a short period such as one day (Penttila, 2007). Example of the “Create an internal incubation fund” is Xerox which sets aside funds that encourage employees to network and chase ideas that otherwise would not have a budget (Penttila, 2007). For the “Take it online” Think Tank technique, as cited in (Penttila, 2007), Anthony Warren, the director of the Farrell Center for Corporate Innovation and Entrepreneurship at Penn State states that “Everybody can contribute all the time” (Penttila, 2007).

The Most Influential Think Tanks: In (TBS, 2015), there are fifty most influential Think Tanks in the United States. However, for this Discussion Board, the researcher is covering only the first five of these most influential Think Tanks in the United States. The first Think Tank in the US that has great influence is “Belfer Center for Science and International Affairs” established in 1973 to analyze arms control and nuclear threat reduction (TBS, 2015). The “Earth Institute” is the second influential Think Tank in US established in 1995 focusing on addressing important global issues such as sustainable development and the needs of the world’s poor (TBS, 2015). The third most influential Think Tank is “Heritage Foundation” established in 1973 (TBS, 2015). The focus of the Heritage Foundation is to track the yearly growth of federal spending, revenue, debt and deficit, and entitlement programs, which it then publishes as the Budget Chart Book and distributes free to the public. The fourth most influential Think Tank is “Human Rights Watch” established in 1978 with the goal to conduct research and advocacy on human rights (TBS, 2015). Kaiser Family Foundation is one of the first five most influential Think Tank founded in 1948 focusing on major health care issues in the US and the world (TBS, 2015).

References

Caliva, L., & Scheier, I. H. (1992). The Think Tank Techniques. Retrieved from http://academic.regis.edu/volunteer/ivan/sect03/sect03b.htm, The Center for Creative Community(Santa Fe, New Mexico).

Hauck, J. C. R. (2017). What are ‘Think Tanks’? Revisiting the Dilemma of the Definition *. Brazilian Political Science Review, 11(2), 1-30. doi:http://dx.doi.org/10.1590/1981-3821201700020006

Kelstrup, J. D. (n.d.). Four Think Tank Perspectives. Retrieved from http://www.lse.ac.uk/europeanInstitute/pdfs/Kelstrup_EILS.pdf.

Penttila, C. (2007). 5 Big Biz Think Tank Techniques. Retrieved from https://www.entrepreneur.com/article/174688.

Shaw, S., Russell, J., Greenhalgh, T., & Korica, M. (2014). Thinking about Think Tanks in Health Care: a call for a New Research Agenda.

TBS. (2015). The 50 Most Influential Think Tanks in the United States. The Best Schools: Retrieved from https://thebestschools.org/features/most-influential-think-tanks/.

Whittenhauer, K. (n.d.). Effective Think Tank Methods. Retrieved from http://classroom.synonym.com/effective-think-tank-methods-5728092.html.

Think Tank Methods

Dr. Aly, O.
Computer Science

Purpose

The purpose of this discussion is to discuss the research group decision-making methods. The discussion will include the Delphi technique, and at least two methods with a comparison among these methods.

Discussion

There are different techniques in group decision-making. These techniques include Brainstorming, Normal Group Technique, Delphi Method, Dialectical Inquiry (Sarkissian, 2002). The techniques in group decision-making also include the “Plop” Method” (Ozcan, Misir, & Kheiri, 2013; Schwartz, 1994), Decision by Authority Rule (Schwartz, 1994), Decision by Authority without Consultation (Minnesota, 2007), and Decision by Authority after Consultation (Minnesota, 2007). Moreover, the group decision-making techniques also include Average of Group Member Opinion (Minnesota, 2007), and Decision by Minority Rule (Minnesota, 2007; Schwartz, 1994). The decision by Majority Rule (Minnesota, 2007; Schwartz, 1994) also known as “Voting and Polling” (Schwartz, 1994), Decision by Experts (Minnesota, 2007), and Consensus (Minnesota, 2007) are also group decision-making techniques. The two group decision-making techniques for this DB are limited to the Delphi method, and to the Plop Method.

The Delphi method is described as “a general way of structuring the group communication process and making it effective enough to allow a group of individuals, functioning as a whole, to deal with complex problems (Saizarbitoria Iñaki, Arana Landín, & Casadesús Fa, 2006). It is also described as a systematic process attempting to obtain group consensus resulting in much more open and in-depth research as each member of the group has a unique contribution to identify a new aspect of the problem for more research (Saizarbitoria Iñaki et al., 2006). The Delphi method is also described as “a panel of experts is asked individually to provide forecasts in a technical field, with their views summarized and circulated for iterative forecasting until a consensus is reached” (Ritchie, Lewis, Nicholls, & Ormston, 2013). The Delphi method a commonly used technique for research in the fields of medicine or sociology (Saizarbitoria Iñaki et al., 2006). The techniques of Delphi are rooted in the social representation more than in statistics representation. This social representation is based on views of experts in the field of the research and investigation (Saizarbitoria Iñaki et al., 2006). The key factors to this type of research are the selection of the members of the panel which should be based on their knowledge, capabilities, and independence (Saizarbitoria Iñaki et al., 2006). It is highly recommended that the panel should include at least seven members and at most thirty members (Saizarbitoria Iñaki et al., 2006). Studies show that when the panel has a large group of experts, many of them do not demonstrate sufficient knowledge or capabilities, and accordingly, they withdraw from the panel prematurely increases (Saizarbitoria Iñaki et al., 2006). To minimize such premature withdrawal from the panel, it is critical that the experts must be selected carefully and receive the information about the objective of the study (Saizarbitoria Iñaki et al., 2006). The selected experts should be notified of the estimated time required for their participation, and the potential of the research and possible benefits they can obtain by participating in such a study (Saizarbitoria Iñaki et al., 2006). Delphi method minimizes the danger of dominant influence of any of the panel members by not identifying the members when expressing their opinions (Saizarbitoria Iñaki et al., 2006). Another success factor for Delphi method is rooted in the writing of the questions to be included in the different questionnaires (Saizarbitoria Iñaki et al., 2006).

The “Plop” method as a group decision-making technique works by providing different ideas about a subject and arguing them until the group reaches consensus on one of them (Ozcan et al., 2013; Schwartz, 1994). It is described to be simple and commonly used technique (Ozcan et al., 2013). However, it is not regarded to be appropriate for all types of group decisions (Ozcan et al., 2013). In (Ozcan et al., 2013), the “Plop” method is described similar to (Ozcan et al., 2013). However, (Schwartz, 1994) elaborated on the technique indicating that a member from the group proposes an idea before anyone else in the group, followed by another member proposes another idea until the group eventually finds one and agree upon it to act on (Schwartz, 1994). The result in shooting down the original idea before it is considered and the rejection of all other ideas, the members who proposed these rejected ideas feel their proposals have “plopped” (Schwartz, 1994). The member feels ignored and possibly rejected (Lauby, 2015). In (Rebori, NA) the “Plop” method is described as “no decision” technique where members avoid making decision consciously or unconsciously and thus make the decision not to decide (Rebori, NA). In this techniques member jumping from one topic to another, allowing the member to shift the topic before a decision is reached and by the “plop” (Rebori, NA). The plop is a board decision by “omission” (Rebori, NA). Thus, it is a decision not to decide (Rebori, NA). While the “Plop” method is common, it is the least visible technique for group decision making (Ozcan et al., 2013). The “Plop” method can be very useful when a person just refuses to believe the idea has any merit (Lauby, 2015).

References

Lauby, S. (2015). Essential Meeting Blueprints for Managers: Packt Publishing.

Minnesota, U. O. (2007). Typical Methods of Group Decision Making. Retrieve from http://www.minneapolismn.gov/www/groups/public/@ncr/documents/webcontent/convert_274389.pdf.

Ozcan, E., Misir, M., & Kheiri, A. (2013). Group decision making hyper-heuristics for function optimisation. Paper presented at the Computational Intelligence (UKCI), 2013 13th UK Workshop on.

Rebori, M. K. (NA). Community Board Development: Series 5. University of Nevada, Retrieved from https://www.unce.unr.edu/publications/files/cd/other/fs9856.pdf.

Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (2013). Qualitative research practice: A guide for social science students and researchers: Sage.

Sarkissian, A. (2002). Different Techniques in Group Decision-Making. Retrieve from https://yourbusiness.azcentral.com/different-techniques-group-decisionmaking-17366.html.

Schwartz, A. E. (1994). Group decision-making. The CPA Journal, 64(8), 60.

Think Tank Methods

Dr. Aly, O.
Computer Science

Purpose

The purpose of this discussion is to discuss a technology and a key trend from this Web site: https://www.nmc.org/nmc-horizon/. The discussion will analyze at least two forces that impact the trend and the technology.

Note: For additional information on the sociotechnical process, review this Web site: http://horizon.wiki.nmc.org/

Discussion

This discussion is about “Think Tank.” Let us begin with the definition of “Think Tank” before we get to the NMC. The term “Think Tank” is defined as a structure for a group that focuses on providing a solution to a particular problem in the technology and science domain (Caliva & Scheier, 1992). However, it is regarded as a process rather than a structure by (Caliva & Scheier, 1992). Thus, the term can be defined as “a process for in-depth consideration of issues and challenges whose relevance reaches beyond the individual or program and the immediate time frame.” (Caliva & Scheier, 1992). In (Shaw, Russell, Greenhalgh, & Korica, 2014), Think Thank is described as “a civil society organization specializing in the production and dissemination of knowledge related to public policy” (Shaw et al., 2014). In (Whittenhauer, NA), the Think Tank is described as “an organization that assembles experts with the sole purpose of coming together to think—more specifically, to think of ideas on how to solve a particular problem” (Whittenhauer, NA). Most of the Think Tanks in the United States are funded by the government or political advocacy groups (Whittenhauer, NA). However, some of the Think Tank are established as for-profit organizations which sell their intellectual property or ideas to businesses and government (Whittenhauer, NA). There are two models for the Think Tank; the “one roof” Think Tank model and “without a roof” Think Tank model (Whittenhauer, NA). The “without a roof” Think Tank model is more effective than the “one roof” because it does not require the funding that is required by the “one roof” on travel costs and so forth. The “without roof” Think Tank model spends most of the money on research and the required costs for computers and utilities are not paid by think tank organization using this model (Whittenhauer, NA).

NMC Horizon Report 2017 Higher Ed Edition: This is a collaborative effort between NMC and the EduCause Learning Initiative (ELI) (NMC, 2018). This Edition is the fourteenth edition to describe the annual findings from the NMC Horizon Project. The purpose of this project is to identify and describe emerging technologies likely to have an impact on learning, teaching, and creative inquiry in education. The key trends for accelerating technology adoption in Higher Education include three modes of trends: long-term, mid-term and short-term (NMC, 2018). The main objective of the long-term trends is to drive ED Tech adoption in higher education for five or more year. The advancing cultures of innovation and deeper learning approaches are the two forces that are required to achieve the long-term trends (NMC, 2018). The main objective of the mid-term trends is to drive Ed Tech adoption in higher education for the next three to five years (NMC, 2018). The growing focus on measuring learning and redesigning learning spaces are the two forces that are required to achieve the mid-term trends (NMC, 2018). The main objective of the short-term trends is to drive Ed Tech adoption in higher education for the next one to two years. Two forces are required to achieve short-term trends which are the blended learning designs, and collaborative learning (NMC, 2018).

For the long-term trends, the advancing cultures of innovation force include many areas of higher education that are spreading innovation, including the advancing cultures of entrepreneurial thinking and designing new forms of artificial intelligence (AI) (NMC, 2018). It is considered to be the vehicle for driving the innovation. The focus of this trend has moved from understanding the value of fostering the exploration of new ideas to finding ways to replicate it across a span of diverse and unique learning institutions (NMC, 2018). The main element to enhance this force is to encourage higher education to modify its status quo to accept failure as an important part of the learning process. The integration of the entrepreneurship in the higher education is an important step in realizing that big ideas usually begin from somewhere (NMC, 2018). Students and faculties should be equipped with tools that are required to spark the real progress in that domain. Thus, the institution must evaluates and examine the curriculum and implement the required changes to remove any barriers that limit the development of new ideas (NMC, 2018). There is a need for policies that can assist institutions to better finance revolutionary practices encouraging the nations to be more strategic in the allocation of funds to invest in efforts that enhance the completion of programs and the attainment of degree (NMC, 2018).

The deeper learning approach force is defined by William and Flora Hewlett Foundation as “the mastery of content that engages students in critical thinking, problem-solving, collaboration, and self-directed learning” (NMC, 2018). The connection between the coursework and the real world is required to help student remain motivated. The deeper learning has proved that it is effective for improving the rates of the graduation in schools (NMC, 2018). The trend of the deeper learning approach force has been growing and is continuing to new developments. The active learning approach has two strategies of inquiry-based learning; the problem-based learning where students solve real challenges and project-based learning where they create completed products (NMC, 2018). There are no explicit policies that mandate project-based learning or other deeper learning approached in the universities or colleges. However, there is an effort from entities such as the Knowledge Alliances in Europe which represent projects intending to bring together post-secondary institutions and businesses to solve common problems (NMC, 2018). The emphasis is on to develop innovation using multi-disciplinary approaches to education, and simulating entrepreneurial skills in higher education; and exchanging knowledge (NMC, 2018). In the US, there are efforts from entities such as the Improving Career and Technical Education for the 21^st Century Act is to assist Americans to receive the skills that are required to compete for in-demand jobs. The purpose of these efforts is to support students to get involved in work-based learning opportunities and promote the use of new types of credentialing (NMC, 2018).

References

Caliva, L., & Scheier, I. H. (1992). The Think Tank Techniques. Retrieved from http://academic.regis.edu/volunteer/ivan/sect03/sect03b.htm.

NMC, H. P. (2018). NMC Horizon Report: 2017 Higher Education Edition. Retrieve from https://www.nmc.org/publication/nmc-horizon-report-2017-higher-education-edition/.

Shaw, S., Russell, J., Greenhalgh, T., & Korica, M. (2014). Thinking about Think Tanks in Health Care: a call for a New Research Agenda.

Whittenhauer, K. (NA). Effective Think Tank Methods. Retrieved from http://classroom.synonym.com/effective-think-tank-methods-5728092.html.