Neo Strategic Management: Conceptual and Operational Foundations of Tomorrow's Strategic Thinking 3031372077, 9783031372070

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Neo Strategic Management Conceptual and Operational Foundations of Tomorrow’s Strategic Thinking M. S. S. El Namaki

Neo Strategic Management

M. S. S. El Namaki

Neo Strategic Management Conceptual and Operational Foundations of Tomorrow’s Strategic Thinking

M. S. S. El Namaki VU School of Management Neuchâtel, Switzerland

ISBN 978-3-031-37207-0    ISBN 978-3-031-37208-7 (eBook) https://doi.org/10.1007/978-3-031-37208-7 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Introduction: Scope, Coverage, Approach, and Relevance  1 2 T  omorrow’s Business Environment  5 3 T  omorrow’s Advancing Technologies 15 4 Tomorrow’s New Strategic Management Concepts 43 5 T  omorrow’s Managerial Functions 63 6 New Paradigm: The Top Management Context 77 7 S  elf-awareness 79 8 V  ision 87 9 Th  inking 97 10 C  ognitive Predictions105 11 P  lasticity-Driven Decision Making119 v

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12 I ntelligence129 13 B  ehavior137 14 S  trategic Intent145 15 S  trategic Control151 16 The Propensity to Lead157 17 The Propensity to Enterprise165 18 New Paradigm: The Organization’s Context175 19 D  ata-Driven Strategic Thinking177 20 C  ognitive Computing187 21 Implicit Learning and Innovation195 22 The New Paradigm: From Concepts to Operations207 23 C  oncluding Remarks229 I ndex245

About the Author

M. S. S. El Namaki  is an authority in strategic management and management applications of artificial intelligence. He is the founder and past dean of Maastricht School of Management (Netherlands) and is currently the dean of VU (Switzerland). He has taught and consulted Worldwide. He performed at key MNCs such as Philips and Time Inc., and at international organizations such as the World Bank, UNDP, UNIDO, and the EU.  He has taught at recognized institutions such as MSM (Netherlands), Sheffield (UK), Kellogg (USA), and Jiao Tong (China). He has published 10 books and more than 100 articles. His recent publications focus on artificial intelligence and include pioneering work on strategic thinking in the age of artificial intelligence. He has received awards from several governments including the government of Malaysia and the government of the People Republic of China.

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List of Figures

Fig. 2.1

AI Ops platform. (Source: Gartner® Market Guide for AI Ops Platforms—ScienceLogic, 2022) 8 Fig. 2.2 Concentration level in selected US industries 2018. (Source: ComScore 2018a, 2018b (search engines and smartphone). Fierce Wireless 2018 Wireless Carriers), DHL 2018 Delivery Services), Infirmity 2018 (Pay TV), Marketing Charts 2016 (social media), Bureau of Transportation Statistics 2018a (airlines. All accessed via statistical.com).) 9 Fig. 2.3 Key traits of tomorrow’s executives 12 Fig. 3.1 Tomorrow’s key emerging technologies 16 Fig. 3.2 System structure of artificial intelligence 19 Fig. 3.3 Biological neuron structure 22 Fig. 3.4 Artificial neural network 22 Fig. 3.5 Artificial neuron structure 2 23 Fig. 3.6 A speculative view of current and prospective state of artificial intelligence system flows 27 Fig. 3.7 The life cycle of the AI capability framework 29 Fig. 3.8 Speculative view of synthetic versus real data today and tomorrow30 Fig. 3.9 Data analytics model. (Source: Gartner March 2012) 32 Fig. 3.10 AI system construct 33 Fig. 3.11 Building stones of IoT 35

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List of Figures

Fig. 3.12 Global analog integrated circuit CAGR market growth 2017−2022. (Source: IC insights) Fig. 3.13 Global analog integrated circuit market 2019−2024. (Source: https://www.mordorintelligence.com/industry-­reports/ analog-­integrated-­circuit-­market) Fig. 4.1 Domains of disruption Fig. 4.2 Driving forces of artificial intelligence. (Source: Chethan Kumar GN, Artificial Intelligence: Definition, Types, Examples, Technologies, Medium, And Aug 31, 2018. https://chethankumargn.medium.com/artificial-­intelligence-­ definition-­types-­examples-­technologies-­962ea75c7b9b) Fig. 4.3 Shifts in strategic thinking concepts Fig. 4.4 Technology adoption lags. (Source: Indermit Gill, Whoever leads in artificial intelligence in 2030 will rule the world until 2100, Brookings, Friday, January 17, 2020) Fig. 4.5 Neo-industry life cycle Fig. 4.6 Competitive intelligence. (Source: https://www.crayon.co/ blog/what-­is-­competitive-­intelligence-­terms-­and-­concepts-­ you-­need-­to-­know?) Fig. 4.7 How AI could change the job market. (Source: https://www. crayon.co/blog/what-­is-­competitive-­intelligence-­terms-­and-­ concepts-­you-­need-­to-­know?) Fig. 4.8 The conceptual model Fig. 4.9 The emerging span of AI-derived visions Fig. 4.10 Implicit versus explicit learning Fig. 4.11 Data-strategy link Fig. 5.1 Neo-managerial function perspective Fig. 5.2 From data to modeling and application Fig. 5.3 Product function. (Source: Belu et al. 2011) Fig. 5.4 Strategy implementation tools. (Source: Sull et al “Turning Strategy into Results,” MIT Sloan, September 28, 2017) Fig. 5.5 The outcome of potential triggered opportunities and strategic control parameters Fig. 6.1 Technology-driven demands on top management Fig. 7.1 Areas of self-awareness Fig. 7.2 Drucker’s key self-management parameters Fig. 7.3 The four common self-management syndromes. (Source: Sunday Post, January 11, 2009)

38 39 44

46 47 48 49 50 52 53 56 59 60 64 67 68 72 73 78 80 82 82

  List of Figures 

Fig. 8.1 Fig. 8.2 Fig. 9.1 Fig. 9.2

Fig. 9.3 Fig. 10.1

Fig. 11.1 Fig. 11.2 Fig. 12.1 Fig. 12.2 Fig. 13.1 Fig. 13.2 Fig. 13.3 Fig. 14.1 Fig. 14.2 Fig. 15.1 Fig. 16.1 Fig. 17.1 Fig. 18.1 Fig. 19.1 Fig. 19.2 Fig. 19.3 Fig. 19.4 Fig. 20.1

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The emerging driving forces of AI-derived visions 91 Leading with vision 91 Neuron system construct 100 Neurons and action. Soma/cell body: Keeps cell alive and functioning. Dendrite: Receive info from another neuron and conduct it toward cell body. Axon: Passes message through its terminal branches to neuron, muscle, or glands. (Source: Watsonnov J (2017) “Neuroscience Basics: The Neuron” https://owlcation.com/) 101 Competency-based learning. (Source: https://www.pinterest. com/pin/668714244648679675/)103 Key elements of cognitive psychology. (Source: How Cognitive Psychology Can Improve Blog Content, By Melanie Sovann | June 4, 2019, https://returnonnow. com/2019/06/how-­cognitive-­psychology-­can-­improve-­blog-­ content/)108 Systems framework of the neural plasticity force field 120 Brain memory storage. (Source: How Are Memories Stored and Retrieved? Science ABC, Ishan Daftardar. Last Updated On Jul 2022) 123 Components of emotional intelligence 132 AI-driven decision-making 134 Determinants of locus of control 139 Behavioral competencies 141 The interdisciplinary character of game theory 142 The elements of intent 147 The making of strategic intent 148 A possible configuration of a set strategic fitness test 155 A graphic presentation of the propensity to lead 162 Induced influence of AI on key parameters of entrepreneurship170 Demands on the organization 175 Data analytics cycle 179 Data prediction flow 180 Data-induced strategic thinking 181 Data-induced strategic thinking patterns 183 Cognitive computing flow 188

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List of Figures

Fig. 21.1 Implicit and explicit memory. (Adapted from: Stangor and Walinga 2014, p. 366) Fig. 21.2 Explicit vs. implicit learning Fig. 21.3 Implicit learning system Fig. 22.1 Top management operating competencies Fig. 22.2 What is algorithm? Fig. 22.3 Vision, strategy, and goals Fig. 22.4 Data-driven decision stages Fig. 22.5 Key organization-wide competency generation programs Fig. 23.1 The BCG segments Fig. 23.2 AI and data BCG matrix substitute Fig. 23.3 Porter’s five force analysis Fig. 23.4 Porter’s five force substitute Fig. 23.5 Semiconductor industry concentration 2017 (Not including foundry sales). (Source: IC Insights)

197 198 200 208 210 211 215 217 231 234 236 239 240

1 Introduction: Scope, Coverage, Approach, and Relevance

Emerging technologies led by artificial intelligence (AI) and data sciences have driven strategic management and strategic thinking into uncharted waters. This has resulted in serious disruptions especially in the contents and approaches to strategic thinking. Many of the early, and for many years, familiar concepts have run out of steam. They failed at accommodating and reflecting the realities of the new technologies. And they lost relevance. Emerging business models and business driving forces changed the contents and the modes of strategic thinking. They also lost the link to other supporting sciences from economics and psychology to biology and even physics! Neurology, psychology, and even biology penetrated the strategic thinking domain. Artificial intelligence led the process of structural change. Data sciences followed. Descriptive, diagnostic, perspective, and predictive analysis of a wide variety and categories of data has unmasked new unexplored dimension in strategic thinking and practice. Both have led to a serious adjustment of the profile and functions of top management as much as a restructuring of the very modes of performance of organizations. Venturing beyond the familiar boundaries of strategic management became, therefore, a necessity. And this is the focus of this book. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_1

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The book starts with an analysis of disruptive forces within contemporary and future environments. It proceeds to an analysis of the emerging forces of technology. This is followed by an analysis of the impact of those disruptive and innovative forces on the concept of strategy. And this is supplemented by a revisiting and reformulation of managerial functions. Derived analysis follows with an assessment of the implications of those forthcoming events to top management profile and performance as well as organization’s structure and tasks. Change in top management competencies is dealt with as much as organization-wide compatible instruments. A concluding chapter deals with the translation of all those conceptual premises into applied and operational tools. Those are tools essential for the translation of the implementation of the underlying forces of the new paradigm. The book is quite unusual within the management literature arena as it draws upon concepts and tools belonging to other sciences such as neurology, psychology, and even physics. Yet this will mark future developments in the area and a sound understanding of that is essential for future conduct and performance of the strategic thinking function within the business environment. The book relies heavily on recent research in areas as wide as artificial intelligence, neurology, and psychology. It also refers to the author’s own work on AI, strategic thinking, and data sciences. Book contents will appeal and be useful to a wide span of audience. The start will be those involved into strategic thinking whether as an academic endeavor or an applied effort within consulting or industry. It will also appeal to those in medical fields such as neurology if they are edging toward emerging technologies and their relevance to brain studies. Those involved in psychology again as an academic issue or a practice will find the psychology stream running through the text involving. The treatment of subjects as locus of control and self-awareness could prove quite relevant to contemporary research and practice of psychology. And finally those dealing with economic theory will find traces of pricing, competition, and demand analysis quite relevant. The book offers plenty of room for the executive training industry. The last chapter dealing with the operational dimensions of the new paradigm provides specific areas for executive competency generation. They relate to

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almost all of the novel areas touched upon by this new paradigm from awareness and self-management to cognitive prediction and strategic intent. And finally those interested in advanced research on new boundaries of management whether as a concept or as a practice will come across enough triggers of research. Current instruction is running out of steam, and it is high time that a genuine substitute is “discovered”! Last but not least, the book is analytical in approach and not empirical. Outcomes of empirical research done elsewhere are included, however. Hypothesis generation is a common mode that runs throughout the text.

2 Tomorrow’s Business Environment

1 Technology Future shifts in technology will be vast in scale, deep in reach, and wide in scope. It will touch almost every aspect of business as known today. The following is an analysis of some of the most prominent as highlighted by current research.

1.1 Applied AI Advanced phases of AI will be used to solve and augment productivity and competencies. Applied AI is the branch of artificial intelligence that moves AI to the real world, enabling execution of tasks and solving of problems. Applied AI services provide several support options to help creating intelligent applications within broad business arenas.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_2

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1.2 Advanced Connectivity Technologies, such as 5G/6G cellular, wireless low-power networks, software-­defined networking (SDN), among others, will support a host of digital solutions enhancing productivity. Advanced connectivity instruments like those will drive new products and services, enable new business models, transform inefficient operating models, or allow for reduced time to market (Deloitte.wsj.com/cfo/2019/02/28/).

1.3 Bioengineering Bioengineering is the application of engineering knowledge to the fields of medicine and biology. Biological and information technologies will converge in order to improve health and human performance, transform food value chains, and create innovative products and services.

1.4 Clean Energy Energy efficiency and renewable are fundamental for achieving climate goals, but there are large portions of emissions that will require the use of other technologies. Innovation is the key to fostering new technologies and advancing existing ones. Innovation is the key to fostering new technologies and advancing existing ones.

1.5 Web3 Web3 is a new iteration of the World Wide Web which incorporates concepts such as decentralization, blockchain technologies. It includes platforms and applications that enable shifts toward a future, decentralized internet with open standards and protocols.

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1.6 Immersive-reality technologies Immersive-reality technologies use sensing technologies and spatial computing to help users “see the world differently” through mixed or augmented reality or “see a different world” through virtual reality.

1.7 Cloud and edge computing The main difference between cloud and edge containers is the location. Edge containers are located at the edge of a network, closer to the data source, while cloud containers operate in a data center. Cloud and edge computing involves distributing computing workloads across remote data centers and local nodes to improve data sovereignty, autonomy, resource productivity, latency, and security.

1.8 Quantum Technologies Quantum technology resorts to the principles of quantum mechanics (the physics of subatomic particles), including quantum entanglement and quantum superposition. It exploits the properties of quantum physics (the physics of subatomic particles) to perform calculations and simulations unreachable through non-quantum machines. The power of the quantum computer comes from the fact that it’s not limited to binary bits (the ones and zeros of traditional computer processors). Instead, it uses quantum bits or qubits to provide an exponential increase in computational performance. https://www.paconsulting.com/insights/what-­ is-­quantum-­computing-­performance-­applications-­evolution.

1.9 Next-generation Software Development Next-generation tools aid in the development of software applications, improving process and software quality. Tools include AI-enabled development and testing and low-code or no-code platforms (What is the future of business, R Shawn McBride, The Startup, June 23, 2019?) (Fig. 2.1).

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Fig. 2.1  AI Ops platform. (Source: Gartner® Market Guide for AI Ops Platforms— ScienceLogic, 2022)

2 The Corporation The corporation of tomorrow will differ in marked terms from that of today. The difference will relate to four dominant features: concentration, risk as a product, business model, and mission (Mayer 2021).

2.1 Concentration Concentration connotes the existence of a few major competitors within a given industry. Concentration ratio, or the ratio of sales by the four largest firms in the industry to aggregate industry sales, is one of the adopted measures of this dominance and level of competition within the respective industry. Another measure of concentration is the Herfindahl index, or the sum of the squares of the market shares for each firm within the industry and is always less than one (HHI  =  s12  +  s22  +  s3 2 + … + sn2).

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Strategic concentration behavior is likely to mark the future of corporations. There will be absolute concentration, partial concentration, reversible concentration, permanent concentration, dynamic concentration, and static concentration. Absolute concentration is a state of ultimate dominance within the respective industry. Absolute concentration has been, and still is, the favorite strategy for many multinational corporations. It could take any form from capital resource and scale concentration to brand and market share concentration. Partial concentration is concentration based on a specific strategic competitive advantage, whether it is the product, the technology, the market segment, or the market area (El Namaki, Strategic Thinking for Turbulent Times). Concentration levels in the United States industries reached high level as the following figure shows. This is especially evident in industries such as search engines, wireless carriers, and smartphones as the following figure reveals (Fig. 2.2).

Fig. 2.2  Concentration level in selected US industries 2018. (Source: ComScore 2018a, 2018b (search engines and smartphone). Fierce Wireless 2018 Wireless Carriers), DHL 2018 Delivery Services), Infirmity 2018 (Pay TV), Marketing Charts 2016 (social media), Bureau of Transportation Statistics 2018a (airlines. All accessed via statistical.com).)

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Similar trends could be traced in other countries such as South Korea and Japan.

2.2 Risk as a Product Risk is becoming a commodity and corporations are increasingly focusing on this transaction. Pandemics, as the one we have been going through in 2021 and 2022, and past global financial crises, as those of 1997 and 2008, are prominent illustrations of the “risk” dimension of business in our times. The 2008 financial crisis arose from the widespread practice of turning risk into a commodity through, among others, mortgage-backed securities. Recent pandemics illustrate the high-risk-quick returns formula triggered by a global disease and a consequent disruption. In an interconnected world, risk is produced locally and amplified globally. Politics and increasingly science are creating “risk” products. Those enhance uncertainties, inequalities, and anxiety leading to corporate windfalls.

2.3 Elusive Business Models Corporate business models are undergoing fundamental change. Today’s corporations are becoming distant, difficult to regulate, and increasingly responsible toward geographically and institutionally broader audiences. Online portals stand in for organizations; outsourcing practices minimize reliance on employees and tangible assets. Electronic transactions are substituting for cash and removing parties away from direct contact. Remote working via digital platforms blurs controls and client–corporation relationship.

2.4 Quasi Political Mission Nationalizations and government bailouts are frequently emerging as a result of a crisis be it medical or financial. It is a response to governments facing uncharted disruptions. Faced with an unknown crisis situation,

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governments have oscillated between libertarian and interventionist positions. Political leaders in different parts of the world have resorted to what could be labeled, at times, as democracy (where present) in different ways and with varying outcomes. These new modes of organizing will have important consequences for the construction of trust-based relationships (purpose) and future forms of internal and external accountability (The Future of the Corporation, Journal of Management Studies December 2020).

3 The Players and the Process Many of today’s management concepts and functions are, to all appearances, out of date. Let us recall that managerial functions emerged as an outcome of work done decades ago by Henri Fayol (Fayol 1949) followed by others as Max Weber and Frederik Taylor. Fayol identified, in his work “General and Industrial Management” which was published in 1949, five managerial functions: planning, organizing, command, coordination, and control. They reflected the realities of those decades and embodied, more or less, images of business conditions as they once existed. Vast change especially that of the disruptive genre has undermined many of these old premises. Early concepts emphasis on productivity and control no longer suffice in the forthcoming business environment where creativity and innovation underpin and drive the process. With this in mind, it’s now time to formulate a scenario for the way managerial functions are going to look in the days to come. A new management paradigm would imply: • A redefined profile and role of leaders and managers • A trend toward flat structures or holacracy • A dynamic organization chart with distributed leadership and “matrix” teams • A dynamic ability to absorb new technologies and enter new industries. The profile and task role of leaders and managers within an organization may provide the most challenging aspect of those prospective

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developments. Continuous disruption will change the profile and very texture of the “leader” and “manager” and the way he or she will be contributing to the management process. It is more likely than not that those future actors will possess a thorough understanding and a high measure of competency at technology. They will possess a high measure of understanding technologies and appreciating the immediate or distant potential of each. There is also the issue of locus of intervention among followers. They will most likely be following from the front, that is, develop a responsibility to remove obstacles from the paths of his followers. They will be viewing his role as that of making followers fulfill conducive goals and achieve identified end results, and demonstrate an understanding for the dynamic processes of work and how the concept of work is changing (Fig. 2.3).

Technology

multi disciplinary

future orientation

Fig. 2.3  Key traits of tomorrow’s executives

Lead from behind

innovation

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4 Summary and Conclusions Today’s business environment is witnessing substantial disruptions. Tomorrow will bring about a lot more. Actually tomorrow’s shifts will be as dramatic as to trigger fundamental disruptions in all dimensions of business from the concept to the players and the outcomes. There is the future of technology, the future of the corporation, the future of the managers, and the future of the management processes. Each and every “future” has premises and implications. Some premises and implications are congruent with each other and some others are contradictory. This chapter dealt with the specifics of each “future” and casts a view as to the congruence and dissonance of the elements. Future shifts in technology will be vast in scale, deep in reach, and wide in scope. It will touch almost every aspect of business as known today. The corporation of tomorrow will differ in marked terms from that of today. The difference will relate to four dominant features: concentration, risk as a product, business model, and the very mission. And last but not least, continuous disruption will change the profile and very texture of the “leader” and “manager” and the way he or she will be contributing to the management process. Change in players and process will be paramount. There will be a redefined profile and role of leaders and managers, a trend toward flat structures or holacracy, a dynamic organization chart with distributed leadership and “matrix” teams, and a dynamic ability to absorb new technologies and enter new industries. Analysis relates to today’s status quo but the future may induce far more radical shifts than projected here.

References ComScore 2018, 2018b (search engines and smartphone). Fierce Wireless 2018 Wireless Carriers), DHL 2018 Delivery Services), Infirmity 2018 (Pay TV), Marketing Charts 2016 (social media), Bureau of Transportation Statistics 2018a (airlines). All accessed via Statistical.com.

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Fayol H (1949) “General and Industrial Management” London: Pitman and Sons, Ltd. Gartner® Market Guide for AIOps Platforms – ScienceLogic. Mayer C (2021) The Future of the Corporation and the Economics of Purpose. Journal of Management Studies Finance Working Paper No. 710/2020. McKinsey and company, Mckinsey to automate activities Technology Trends, outlook 2022, Applied AI, August 2022). Shawn McBride R (2019) what is the future of business. The Startup June 23 2019.

3 Tomorrow’s Advancing Technologies

1 Tomorrow’s Emerging Technologies Several technologies are going to leave a long-term tangible impact on management whether it is strategic or broader than that. Prime among those are artificial intelligence and data sciences. Others include Internet of Things (IoT) and Cognitive Computing. Each is built around a complex structure with distinctive components and unique system relationships (Fig. 3.1). The two prime technologies, artificial intelligence and data sciences, are segmented according to a variable. In the case of artificial intelligence the variables are capabilities, time, and finally, system structure. In the case of the data sciences, the variables are boundaries, data segmentation, and data analytics.

2 The First Prime Emerging Technology: Artificial Intelligence Artificial intelligence is a computing technology that helps computers to learn. The objective is to enable a computer to simulate human-like activities. The Oxford Dictionary defines Artificial Intelligence (AI) as “the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_3

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Emerging technologies

I

II

Intelligence

Data Sciences

Artificial

Capabilitiy Framework

Narrow General Super

Time Framework

System Framework

Present Future

Input Transformation

Conceptual boundaries

III Internet of Things IoT

IV Cognitive Computing

Data framework

Output Feedback

Fig. 3.1  Tomorrow’s key emerging technologies

theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception” (https:// www.oed.com/viewdictionaryentry/Entry/271625). AI software makes computers “learn” from an inflow of a massive volume of data. The artificial intelligence concept could become opaque at time. Scope and content could vary according to the context and the ultimate goal of the discussion. The following is an analysis of three AI frameworks: a time/functionality framework, a capability framework, and a system framework.

2.1 Time/Functionality Framework of AI Artificial intelligence can be viewed in terms of functionality over time be it the present or the future time.

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The Present Present-day intelligent systems are systems able to handle massive volumes of data but lack the analytical and independent self-awareness element. They are either reactive or limited memory. • Reactive. These are configurations that trace change, their own and their opponent’s, and choose the most strategic move. They do not have the ability either to form memories or to use past experiences in order to guide current decisions. The computer’s perception of the world is direct and it acts according to what it “sees.” • Limited memory or corrective and predictive AI. This configuration uses past experience in order to influence future decisions. Past information is, however, only transient and is not saved as part of a library or a learning experience (The Conversation, November 14, 2016). Limited memory AI makes use of historical and observational data. Depending on this data it can make predictions and perform complex classification tasks. It extracts knowledge from previously learned information, facts, and stored data with the help of machine learning.

The Future This AI segment does not only form an image of its own world, but also of other agents or entities in the world at large. It does not only understand consciousness, but has it. • Theory of mind. Theory of mind is the ability to attribute mental states—beliefs, intents, desires, emotions, and knowledge—to ourselves and others. Having a theory of mind is important as it provides the ability to predict and interpret the behavior of others and their impact upon their decisions. This kind of AI does not exist yet. (Charlotte Ruhl 2020) Computers equipped with Theory of Mind AI will infer the objectives of entities around them from visible cues, will answer simple “what if ” questions about potential actions that entities around them might

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undertake and will be able to simulate the consequences of their actions. (https://www.devteam.space/blog/theory-­of-­mind-­ai/) • Self-awareness. In this category, AI systems have a sense of self and consciousness. Self-awareness is an awareness of one’s own personality or individuality. Machines with self-awareness understand their current state and can use the information to infer what others are feeling. Conscious beings are aware of them, know about their internal states, and are able to predict feelings of others. This type of AI does not exist yet (The Conversation, November 14, 2016).

2.2 Capability Framework of AI AI capabilities could be narrow or broad. The outcome is an artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. • Artificial Narrow Intelligence (ANI)/Weak AI. ANI is an Artificial Intelligence vehicle that is applied to narrow tasks within e-commerce sites. It is goal-oriented version of AI designed to better perform a single task. • Artificial General Intelligence (AGI). AGI is an artificial intelligence vehicle with an ability to think and make decisions. AGI systems can think, comprehend, learn, and apply their intelligence to solve problems much like humans would for a given situation (Forbes, July 16, 2021). • Artificial Super Intelligence (ASI). Hypothetically, this concept sees AI developing human cognitive competencies, that is, emotions, beliefs, needs, and desires. Thinking, making judgments, and making decisions belong to ASI competencies. It is an intelligence that greatly surpasses the cognitive performance of humans in virtually all domains (Forbes 2021).

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2.3 Systems Framework of Artificial Intelligence AI fragments could acquire a coherent whole if put within a systems theory framework. AI is pictured, then, as a system with inputs, transformations, outputs, and a feedback loop. Data, raw and otherwise, biological neurons as well as artificial neural subsystems provide the inputs. Learning (machine and otherwise) and analysis (diagnostic, predictive, and otherwise) provide the transformation. Insights, technologies as well as derived subsystems constitute the output. Feedback loop conveys system outcome compatibility with identified goals (Fig. 3.2).

Inputs An Artificial Intelligence system is a system that recognizes two inputs: data and neurons.

Data Data constitute a prime input into AI systems.

• Neurons (Biological and/or Artifical) • Data

• •

Learning Analysis • • • •

Fig. 3.2  System structure of artificial intelligence

Insights Sub-systems Strategic thinking Visions

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Data is essentially “information in raw or unorganized form (such as alphabets, numbers, or symbols) that refer to, or represent, conditions, ideas, or objects … Data is limitless and present everywhere in the universe. To computers, data include symbols or signals that are input, stored, and processed by a computer, for output as usable information” (Business dictionary 2017). Most data can be categorized into four basic types from a machine learning perspective: numerical data, categorical data, time series data, and text data. Data could also be segmented according to different criteria: useless, nominal, binary, ordinal, and count. Also as time, interval, image, video, audio, and text-related (Jeff Hale 2018). Data could be logged or collected. Data logging, according to Technopedia, is the process of collecting and storing data over a period of time in order to analyze specific trends or record the data-based events/ actions of a system, network, or IT environment. A data logger is an electronic device designed to measure and store data values, often independently of a PC. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. As AI technology advances it will develop an autonomous ability to seek, classify, and validate data. Data could also be synthetic. This is data that is generated using algorithms simulating the statistical properties of real data. The reasons that use of synthetic data is on the rise is a need to tackle the bias that is present in smaller “real” datasets. It has, however, several complications. It could emulate the distribution and characteristics of the original data but could also, fail at real-world simulation. It often does not represent the quality and the amount of variability that is present within real-world data. Reference “real” data may also be biased and incomplete leading to a biased outcome. Moreover, real-world data consists of outliers, which might be useful for some of the models. Generating accurate synthetic data is a challenge given the requirement of the process from expertise and resources to accuracy and fitness test. Even small errors in the generation process can lead to significant inaccuracies.

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Neurons Neurons provide the second primary input into AI systems. Neurons could be biological or artificial. Artificial neurons are attempts at simulating the biological neurons.

Biological Neurons Biological neurons are the basic information processing structures within the human CNS or “Central Nervous System” and PNS or “Periphery Neural System.” Neurons are the cells that pass chemical and electrical signals along the pathways in the brain. There are motor neurons, for conveying of motor information; sensory neurons, for the conveying of sensory information; and interneurons for conveying information between different types of neurons (Stufflebeam 2008). Neurons are connected to each other through synapses, sites where signals are transmitted in the form of chemical messengers. At the synapse, electrical impulses are converted into chemical signals. Human nervous systems process information in three stages: sensory input, integration, and motor output. Sensors detect external stimuli and internal conditions and transmit information along sensory neurons to the central nervous system and motor neurons carry impulses from the CNS to effector organs (Fig. 3.3).

Artificial Neurons An artificial neuron is a digital construct that seeks to simulate the behavior of biological neurons. Artificial neural networks are mathematical models for information processing based on how neurons and synapses work in the human brain. Using the human brain as a model, a neural network connects simple nodes to form a network of nodes—thus the term “neural network” Artificial neurons differ from biological neurons in several ways but the key difference relate to size, speed, and learning. Human brain contains about 86 billion neurons and more than 100 trillion synapses

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Dendrite Axon

Nerve ending

Fig. 3.3  Biological neuron structure Input Layer

Hidden Layer

Output Layer

Fig. 3.4  Artificial neural network

(connections). Biological neurons can fire around 200 times a second on average. Brain fibers grow and reach out to connect to other neurons. Neuroplasticity allows new connections to be created or areas to move and change function. “We do not understand how brain learns” (Nagy 2018) (Fig. 3.4). A solid artificial neural network is dependent on the connections between neurons. Good connections improve the efficiency of the

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inputs

Parameters

23

outputs

Fig. 3.5  Artificial neuron structure 2

network and the opposite is true. The process of making those connections is called training, and it is similar to what human brains do within a learning mode (Fig. 3.5).

Transformation Transformation takes place through two processes: learning and analysis.

Learning Learning is the process of the acquisition of knowledge or skills through experience, study, or instruction. Artificial intelligence resorts to two types of learning machine learning and deep learning. Machine learning refers to the ability of software to independently find solutions to problems by recognizing patterns in databases. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time. Machine learning could be supervised learning, unsupervised learning, and/or reinforcement learning. Cloud providers such as Google, Microsoft, Amazon Web service, and IBM have now created services for machine learning. Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine learning approaches (although it requires a larger amount of data to do so). In deep learning, interconnected layers of software-based calculators or

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“neurons” form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then determine the data, learn if its determination is correct, and use what it has learned to make determinations about new data. Deep learning multilayered neural network have a special architecture designed to extract increasingly complex features of the data at each layer to determine the output. Deep learning recognizes what is known as convolutional neural networks. Those are classes of deep neural networks that are most commonly applied to analyzing visual imagery.

Analysis Data analytics are approaches to raw data analysis that could lead to parameters and conclusions earmarking the raw data universe. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of raw information accessed in the first place. This information can then be used to formulate efficiency-optimizing processes within the respective business or system. Data analytics is usually broken down into four basic types starting with the descriptive and ending with the prescriptive. Descriptive analytics describe what has happened over a given period of time. Diagnostic analytics attempt to reveal the cause or trigger of the symptom. Predictive analytics moves toward projecting possible change within the short term. And, finally, prescriptive analytics suggests a course of action. Predictive analytics, a key element, focuses on interpreting existing data in order to make informed future predictions Tools include regression or the determination of the relationship between a dependent and an independent variable. Also classification or the establishment of shared characteristics of a dataset and the determination of the category of a new piece of data based on its characteristics. And, finally, clustering or the mapping of relationships between data that can then be applied to predict the status of future data. ­(https://www.investopedia.com/terms/d/data-­analytics.asp)

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Outputs Artificial intelligence systems produce a variety of outputs with the following as the most prevalent.

Insights and Abilities Artificial intelligence processes could lead to insights or a capacity to gain an accurate and deep intuitive understanding of individuals and issues. The medium are abilities to solve problems, through logical deduction or reasoning, to set and achieve goals, to understand spoken and written language or communication and infer things about the world via sounds, images, and other sensory inputs. Those abilities are expressed in many present-day applications such as medical diagnosis, autonomous vehicles, and surveillance among others (https://www.hackerearth.com/blog/ developers/applications-­of-­artificial-­intelligence).

Subsystems Artificial intelligence has the potential to penetrate industries where data are prevalent. Subsystems congruent with the specific conditions of that industry would, then, emerge and blend with the operating flows of the industry. Early symptoms of this penetration could be seen in a wide variety of industries from healthcare and banking to retailing, logistics, and communication. Present-day banking subsystems, for example, include fraud detection and credit analysis while government subsystems include facial recognition and smart cities. Health and life sciences subsystems include predictive diagnostics and biomedical images. Several other subsystems will soon emerge in manufacturing, logistics, marketing, and probably above all, security and defense.

Derived Technologies A wide array of AI-derived technologies is emerging. They vary in penetration but some are already identifiable. Those include robotic process

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automation, biometrics, speech recognition, virtual agents’ decision management, text analytics, and natural language processing (NLP) (https://www.forbes.com/sites/gilpress/2017/01/23/top-­1 0-­h ot-­ artificial-­intelligence-­ai-­technologies/#5edab2b81928).

The Feedback Loop Feedback occurs when outputs of a system are fed back as inputs within the systems loop. It is an element of a control mechanism that allows for self-correction and that adjusts its operation according to differences between the actual and the desired or optimal output. Feedback is essential for AI systems. The transformation process could enhance inputs and may even alter some of the basic underlying variables. Feedback information flows take many forms including an ultimate information system that could feed into the input segment of the AI system (https://www. thefreedictionary.com/feedback+loop).

3 Artificial Intelligence Tomorrow’s Outlook, a Set of Hypotheses It is the author’s contention that components of the system of artificial intelligence will go through a life cycle that would take them from entry to growth, maturity, and eventual decline. Research and application efforts will drive this life cycle. The following table reflects the author’s hypotheses with regards to the stage of this lifecycle that each system component is going through right now. A speculative view of the future is also added (Fig. 3.6). One can derive a prime hypothesis from this table: AI is an infant science and maturity is a long way down the road. The following curve provides another way of looking at the issue of artificial intelligence penetration and application. It projects an AI capability framework life cycle that begins with the reaction phase and ends with the self-awareness phase. The curve projects the current state of adoption and application of the different concepts. It suggests that

3  Tomorrow’s Advancing Technologies 

AI System component

Component building block

Today’s state of research and business application

Tomorrow’s medium to long term outlook

inputs

Data

Early growth

Rapid growth

Biological neurons

Entry

Slow entry

Artificial neural networks

Entry

Rapid entry

Learning

Entry/ Early growth

Rapid growth

Analysis

Entry Early growth

Rapid growth

Insights

Entry

Growth

Technologies

Entry

Growth

Sub-systems

Entry

Growth

Transformation

Output

27

Fig. 3.6  A speculative view of current and prospective state of artificial intelligence system flows

reaction phase is being fully adopted while the limited memory phase is being explored but, in the authors view, is a long way from reaching full adoption. It shows the reaction phase as a typical entry phase that should lead, upon concept acceptance, to the limited memory phase and, eventually, to the theory of mind and awareness phases.

3.1 Cognitive AI Cognitive AI is another future dimension of today’s AI. It integrates technologies such as speech recognition, computer vision, machine learning, natural language processing (NLP), video analytics, and robotics into a

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single architecture to offer new levels of functionality. Yet, cognition is, until today, largely human. Many AI efforts aim at simulating the cognitive functions of the human brain. Attempts are made at recreating some of these cognitive functions as the ability to focus on a specific task and reassigning the others or resort to episodic memories to remember autobiographical events, or the ability to learn new tasks without forgetting previous knowledge or continual learning (Rodrigues, 2020). Yet the outcome is nowhere near the cognitive performance of the human brain. Many of the current deep learning applications address perception tasks related to object recognition, natural language processing (NLP), translation, and broad data correlation. Artificial neural networks cannot, today, properly provide cognition, reasoning, and interpretation akin to those of the human brain. Deep learning lacks the cognitive mechanisms essential for the performance of human intelligence. It lacks competencies as abstraction, context, causality, interpretation, and intelligible reasoning. Deep learning capabilities could be elementary, advanced and brain like and contemporary competencies do not go beyond the elementary. Those elementary capabilities possess intuitive, fast, unconscious, and habitual features and contrast with the advanced capabilities implying slow, logical, sequential, conscious, and algorithmic. “Mind-like” capabilities show perpetual adaptation, abstraction, reasoning, and interpretation features (Singer 2021).

4 The Second Prime Emerging Technology: Data sciences 4.1 What Is data, and What Are Data Concepts? As we said earlier, data are sets of qualitative or quantitative attributes of variables related to persons or objects. It could constitute a collection of

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facts, numbers, words, measurements, and other observations compatible with computer specific software. There is a wide variety of data. • Human accessible data. Unstructured data that only humans can interpret and study. • Machine accessible data. Structured information that computer software can process. • Personal data. Personal data is person proper information. • Transactional data. Operation- or process-related information. • Web data. Internet provided information. • Sensor data. Information gained through sensors. Data could be real or synthetic. Synthetic data is a novel genre of data where data is artificially created, fully or partly, rather than being generated by actual events (Fig. 3.7). The purpose is preserving privacy, testing systems, or creating training data for machine learning algorithms. One of the prime purposes of creating synthetic data, however, is simulating not yet encountered conditions for which no real data is available (Dilmegani, July 19, 2021) (Fig. 3.8). Synthetic data is a class of data that is artificially generated. It is in contrast with real data which is directly drawn from real-world events. Time

Theory of mind

Awareness

Limited Memory

Reaction

Maturity Decline Growth

Introduction State of Artificial Intelligence technologies

Fig. 3.7  The life cycle of the AI capability framework

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Real Data

Today

Synthetic Data

Tomorrow

Fig. 3.8  Speculative view of synthetic versus real data today and tomorrow

Problems with real data may lead to resort to synthetic data. Real data could be expensive, difficult to access, unreliable, or even beyond legal access norms. When combined with real data, synthetic data creates an enhanced dataset that can mitigate the weaknesses of the real data. Synthetic data can be used for hackathons, product demos, and internal prototyping to replicate a set of data with the right statistical attributes. For example, banks and financial services institutions use synthetic data by setting up multi-agent simulations to explore market behaviors (such as pension investments and loans), to make better lending decisions or to combat financial fraud. Retailers use synthetic data for autonomous check-out systems, non-cash stores, or analysis of customer demographics (Linden 2022). Data could also be big, mass, or implicit. Big data are “large datasets” that are too large to be reasonably processed by or stored within the traditional computing soft and hardware. The “three Vs of big data” describe some of the characteristics that make big data processing different from other data processing. There is first

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volume or the fact that big data sets are larger in magnitude than traditional data sets. There is then velocity or big data being processed in real time to gain insights and update the current understanding of the system. And there is finally variety or big data’s coverage of a wide range of quality sources. “Mass Data” are data sets that combine a variety of sources including science fiction, data lakes, and implicit data among others. Science fiction segment could include soft and hard science fiction data. “Implicit data is information that is not provided intentionally but gathered from available data streams, either directly or through analysis of explicit data” (hatis.techtarget.com/definition/implicit-­data)

4.2 Database A database is a volume of electronically stored structured data that are controlled by a database management system (DBMS). A database system combines the database management system with the relevant applications. Most common types of databases in operation today are typically modeled in rows and columns in a series of tables to make processing and data querying efficient. The data can then be easily accessed, managed, modified, updated, controlled, and organized. Most databases resort to structured query language (SQL) for writing and querying data. Today’s large enterprise databases are expected to deliver nearly instant responses to complex queries. They have to deal with sizable data volume, data security, and accommodation of demand growth requirements.

4.3 Data Analytics Data analytics refers to the process of collecting, organizing, analyzing, and transforming any type of raw data into a piece of comprehensive information with the ultimate goal of increasing the performance of a business or organization. At its very core, data analytics is an intersection

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How can we make it happen?

Value

What will happen? Why did it happen? What happened?

Prescriptive Analytics

Predictive Analytics

Diagnostic Analytics

Descriptive Analytics

ion izat m i t Op ht esig r o F

t igh

Ins

ht tion indsig ma r H o Inf

Difficulty Fig. 3.9  Data analytics model. (Source: Gartner March 2012)

of information technology, statistics, and business. Further on, it is a multiple staged process that breaks down into phases such as data grouping, data collection, data organization, and data cleaning and preparation. The process of analysis could be descriptive, diagnostic, predictive, or prescriptive. The descriptive phase looks at events during the period under consideration. No consequent analysis. The diagnostic phase focuses on the root cause of a particular cause. Predictive analysis, on the other hand, addresses the possible flow of events and what will happen in the near future or how a process will develop. Prescriptive analysis leads to possible courses of action and their implications (Fig. 3.9).

5 The Third Emerging Technology: Cognitive Computing Cognition within Artificial Intelligence is closely associated with the system structure of the process.

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AI is built, as a system, around a flow of inputs, transformations, outputs, and a feedback loop. Data, raw and otherwise, as well as artificial neural subsystems constitute the inputs. Learning (machine and otherwise) and analysis (diagnostic, predictive, and otherwise) provide the transformation. Insights, technologies as well as derived subsystems constitute the output. Feedback loop conveys outputs to the input and transformation segments and triggers essential adjustments (El Namaki 2019). One can hypothesize that the cognition within artificial intelligence is the output of the learning and analysis processes that take place within the transformation segment of the AI system. Cognitive computing is the medium. Cognitive computing is a type of computing that focuses on reasoning and conceptual analysis often analogous to human cognition. It deals with symbolic and conceptual information rather than pure data or sensor streams (Computer World, March 3, 2016). It resorts to computerized models to simulate the human cognition process and to find solutions in complex situations. It involves technologies that power cognitive applications such as expert systems, neural networks, robotics, and virtual reality (VR) (Fig. 3.10). The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. Human cognition involves real-­ time analysis of environment, context, and intent, among many other variables that inform a person’s ability to solve problems. A number of AI

Fig. 3.10  AI system construct

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technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, and NLP and sentiment analysis (Rouse 2020). Cognitive computing is a mixture of computer science and cognitive science—that is, the understanding of the human brain and how it works. By means of self-teaching algorithms that use data mining, visual recognition, and natural language processing, the computer is able to solve problems and thereby optimize human processes (IBM, November 20, 2017). Vast amounts of structured and unstructured data are fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions.

6 The Fourth Emerging Technology: Internet of Things (IoT) The Internet of Things technology connotes the concept of connecting internet-connected devices to each other. It is the aggregate collection of network-enabled devices. Types of network connections can include Wi-Fi connections, Bluetooth connections, and near-field communication (NFC). The IoTA could include “smart” appliances, computer peripherals, and wearable technology. The IoTA is a giant network of connected things and people—all of which collect and share data about the way they are used (Clark 2016). These devices use Internet protocol (IP), the same protocol that identifies computers over the world wide web and allows them to communicate with one another. Internet of things aims at improving efficiency through real-time communication between devices, thus bringing important information to the surface more quickly than a system depending on human intervention. It has great promise, yet business, policy, and technical challenges must be tackled before these systems are widely embraced.

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interfaces

Cloud

internet of things

analycs

Devices

Fig. 3.11  Building stones of IoT

The global Internet of Things market is projected to grow from $478.36 billion in 2022 to $2465.26 billion by 2029, at a CAGR of 26.4% in forecast period. Read More at: https://www.fortunebusinessinsights.com/industry-­reports/internet-­o f-­t hings-­i ot-­m arket-­1 00307 (Fortune) (Fig. 3.11).

7 A Note on AI and the Integrated Circuit Industry Technology Technology progression whether it is artificial intelligence or data sciences–related is dependent, in many ways, on the integrated circuit industry. Technical developments in that industry have direct influence on the capacity, speed, and processes involved in artificial intelligence and data science applications.

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The semiconductor industry is the aggregate of companies engaged in the design and fabrication of semiconductors and semiconductor devices, such as transistors and integrated circuits. It all started in the 1960s but grew rapidly with the global semiconductor industry projected to reach the trillion-dollar mark by 2030. Whether it is TSMC, NVIDIA, Intel, AMD, or any other global operator that makes its own silicon chips, they follow the same process. All of these chip makers use wafers sliced into thin layers, packaged and programmed according to their ultimate purpose. Two key global operators, Advanced Semiconductor Materials Lithography (ASML), a Dutch technology leader, and TMSL, a Taiwan “colleague,” play a key role in the manufacturing of chips-enabled applications such as virtual reality and on-device artificial intelligence (AI) as well as gains in data transfer such as 5G connectivity. They are also behind algorithms such as those used in deep learning. The ultraviolet lithography technology that is used by all these chipmakers is owned by ASML. Also, the machinery that chipmakers use to physically slice and cut the wafers, is manufactured by ASML. A lithography system is essentially a projection system where light is projected through a blueprint of the pattern that will be printed (known as a “mask” or “reticle”). The blueprint is four times larger than the intended pattern on the chip. With the pattern encoded in the light, the system’s optics shrink and focus the pattern onto a photosensitive silicon wafer. ASML’s latest technology to slice silicon wafers is called EUV or Extreme Ultraviolet lithography. ASML is the only company that has the technology for building chip manufacturing machinery with extreme ultraviolet lithography, and as chip manufacturers have claimed, EUV is the future of slicing wafers. ASML sells its EUV chip-slicing machinery for about $200 million a set, and all major chip makers, including Intel, NVIDIA, and TSMC, have to buy these machines for their core functionalities. This new development by ASML is expected to be based on a new technology called the “High-NA” version of EUV which is essential for chipmakers such as Intel, Samsung, TSMC, and others to build their next generation of dense chipsets with very high-power efficiency. The scanner made by ASML is the lithography machine that turns chip designs into physical chips on silicon wafers. With semiconductors

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in short supply, these lithography machines are hard to come by and are highly demanded commodities. (The semiconductor monopoly: How one Dutch company has a stranglehold over the global chip industry, Mehul Reuben Das, First Post, and January 23, 2023?) The other manufacturer, TSMC, is a Taiwanese multinational semiconductor contract manufacturing and design company. It has the world’s largest dedicated independent semiconductor foundry. TSMC has a global capacity of about 13 million 300 mm-equivalent wafers per year as of 2020 (Abrams 2013). Taiwan’s TSMC has a special arrangement with ASML because they used to purchase their machinery much before Intel, Samsung, and other major chip makers. Because of this, TSMC and companies that used their silicon had a slight generational advantage over their rivals. Semiconductor architectural improvements are needed to address data use in AI-integrated circuits. Improvements in semiconductor design for AI will be less about improving overall performance and more about speeding the movement of data in and out of memory with increased power and more efficient memory systems. One option is the design of chips for AI neural networks that perform like human brain synapses. Instead of sending constant signals, such chips would “fire” and send data only when needed. Artificial intelligence chip, a comprehensive silicon chips incorporating AI technology and used for machine learning, is a step in this direction. The need for more productive systems to solve mathematical and computational problems is becoming critical, owing to the increased volume of data and this chip provides an answer. Moreover, the arrival of quantum computing and increased implementation of AI chips in robotics steer the growth of the global artificial intelligence chip market (Chetan Arvind Pati 2020). Artificial neural networks are yet another development. Those are specialized AI algorithms based on the human brain. These neural networks are capable of interpreting sensory data and delivering patterns in large amounts of unstructured data. Neural networks find use in predictive analysis, facial recognition, targeted marketing, and self-driving cars. And they require AI accelerators and multiple inferencing chips, all of which

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the semiconductor industry will supply (Semiconductors and Artificial Intelligence, International Roadmap for Devices and Systems [IRDS™]). Chip shortage has been observed for a few years despite the capacities of all those involved. This shortage is unlikely to be resolved in the near future, partly because of the complexities of the semiconductor production process and political undercurrents. The label “chip war” emerged in several contexts. Production lead times, on the other hand, can exceed four months for products that are already well established in a manufacturing line. Increasing capacity by moving a product to another manufacturing site usually adds another six months (even in existing plants). Integrated circuit industry market is projected to grow from USD 573.44 billion in 2022 to USD 1380.79 billion in 2029, exhibiting a CAGR of 12.2% during the forecast period (Figs. 3.12 and 3.13). Quantum computers will add another dimension to the chip industry. Quantum computing is a rapidly emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers. Traditional computer processors work in binary—the billions of transistors that handle information on laptop or smartphone are either on (1) or off (0). Using a series of circuits, called “gates,” computers perform logical operations based on the state of those switches.

Analog

6.6%

Logic

5.4%

Memory

5.2%

Total ICs

5.1%

Microcomponents

3.9% 0%

1%

2%

3%

4%

5%

6%

7%

Fig. 3.12  Global analog integrated circuit CAGR market growth 2017−2022. (Source: IC insights)

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Fig. 3.13  Global analog integrated circuit market 2019−2024. (Source: https:// www.mordorintelligence.com/industry-­reports/analog-­integrated-­circuit-­market)

Classical computers are designed to follow specific inflexible rules. This makes them extremely reliable, but it also makes them ill-suited for solving certain kinds of problems—in particular, problems where you’re trying to find a needle in a haystack. Quantum computers do this by substituting the binary “bits” of classical computing with something called “qubits.” Qubits operate according to the mysterious laws of quantum mechanics: the theory that physics works differently at the atomic and subatomic scale. Quantum computer chips will introduce a revolutionary dimension in the picture (Scientific American, How Does a Quantum Computer Work? By Michael Tabb, Andrea Gawrylewski, Jeffery DelViscio on July 7, 2021). It is worth noting here that China is among the leading countries producing semiconductors and related devices. Although local analog IC design and manufacturing is still behind, favorable industry regulations and increased focus of various industry stakeholders are expected to facilitate industry’s growth.

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8 Summary and Conclusions Advanced technologies from artificial intelligence and data sciences to Internet of Things and cognitive computing are assuming a key role in the technology and economic environments of today. Two among those are prime: Artificial Intelligence and data sciences. The artificial intelligence concept could become opaque at time. Scope and content could vary according to the context and the ultimate goal of the discussion. Definitions, therefore, could spell out a variety of elements and a plethora of shadow concepts. This author has attempted to pierce through this fog by segmenting AI into three categories: a time category, a function category, and a capability category. Analysis went further by drawing a systems framework for the concept. A system that envisaged the existence of inputs, transformation mechanisms, outputs, and feedbacks. All elements are variables that took different shapes as time progressed and AI thinking advanced. It is the author’s contention that the transformation component of the system of artificial intelligence will go through a life cycle that would take them from entry to growth and maturity stages. Research and application efforts drive this life cycle. Application of Artificial Intelligence technologies resorts to two prime tools: learning and analysis. Learning is the process of the acquisition of knowledge or skills through experience, study, or instruction. Artificial intelligence resorts to two types of learning machine learning and deep learning. Data analytics are approaches to raw data analysis that could lead to parameters and conclusions earmarking the raw data universe. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of raw information accessed in the first place. Technology progression whether it is artificial intelligence or data sciences–related is dependent, in many ways, on the integrated circuit industry. Technical developments in that industry have direct influence on the capacity, speed, and processes involved in artificial intelligence and data science applications. A key player and a strategic pivot in the industry is advanced semiconductor materials lithography (ASML), the Dutch semiconductor technology leader. It pioneered the ultraviolet lithography technology that is

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used by nearly all chipmakers for more than a decade. ASML’s new more advanced versions have overtaken ASML’s earlier technology and are the source of global political conflict. ASML manufactures the waver printing machines as well as some advanced chips as well. Quantum computing will add a new dimension to the industry but this will take some time.

References Abrams R (2013) Asia Semiconductor Sector (Sector Review). Asia Pacific Equity Research, Credit Suisse, pp. 1, 3. Business dictionary (2017). Chetan Arvind Pati (2020) The Race for AI Semiconductor Chips Dec 13, 2020. Clark J (2016) What is the Internet of Things (IoT)? IBM Business Operations Blog. November 17, 2016. Davies A What is Theory of Mind AI? https://www.devteam.space/blog/theory­of-­mind-­ai/. Davis a is Theory of Mind AI? https://www.devteam.space/blog/theory-­of­mind-­ai/. Dilmegani C (2021) What is Synthetic Data? 19 Jul 2021 https://research. aimultiple.com/synthetic-­data. El Namaki MSS (2019) A Systems Approach to the Artificial Intelligence Concept. NSP Natural Sciences Publishing Corp. August 2019. Forbes (2021) The Future Of Artificial General Intelligence, 16 Jul 2021). Hale J (2018) Seven Data Types: A Better Way to Think 7 Data Types. Pdf – 7 Data Types: A Better Way to Think. https://www.coursehero.com/ file/173306845/7-­Data-­Typespd. https://www.hackerearth.com/blog/developers/applications-­o f-­a rtificial­intelligence https://www.oed.com/viewdictionaryentry/Entry/271625 https://www.thefreedictionary.com/feedback+loop https://www.oracle.com/database/what-­is-­database/ Linden A (2022) Q&A with Alexander Linden Is Synthetic Data the Future of AI? Gartner 22 Jun 2022. Nagy R (2018) The differences between Artificial and Biological Neural Network. Medium· Sep 4, 2018 https://medium.com/@sedthh.

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Reuben Das M (2023) The semiconductor monopoly: How one Dutch company has a stranglehold over the global chip industry. First Post 23 Jan 2023. Rouse W B (2020) AI as Systems Engineering Augmented Intelligence for Systems Engineers. INSIGHT. 23. 52–54. https://doi.org/10.1002/ inst.12286 Mar 2020. Ruhl C (2020) Theory of Mind. Simple Psychology Aug 07 Aug 2020. Singer G (2021) Towards Data Science. Thoughts and Theory. The Rise of Cognitive AI, Medium, Apr 6, 2021. Stufflebeam D (2008) Biological neuron model. Journal of neurophysiology. Tabb M, Gawrylewski A, Delviscio J (2021) How Does a Quantum Computer Work? Scientific American 7 Jul 2021. What is Database https://www.oracle.com/database/what-­is-­database/.

4 Tomorrow’s New Strategic Management Concepts

1 Triggers of Change 1.1 Disruption Disruption is an occurrence that interrupts events, processes, systems, or paradigms. It is a violating force. Disruption of an event, a system, or a process is tantamount to discontinuity and a suspension or even a reversal of what is considered a normal flow. Roots of disruption, in management, is Christensen’s work on innovation (Christensen 1997). He introduced the idea of “disruptive innovation” or a process of rapid anticipation of future needs and equally rapid development of congruent products, services, and processes. In the process, he separates new technology into sustaining and disruptive, with sustaining technology resorting to incremental improvements to an already established technology, while disruptive technology reflects anticipation of a different set of parameters. The anticipation and adjustment would often lead to market shake-ups and the eventual replacement of dominant operators by nimble often small innovators. The term, however, quickly took on a life of its own (Fig. 4.1). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_4

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Disruption doman

The generic framework of disruption

The functional arena

Firm locus

Fig. 4.1  Domains of disruption

Functional disruption is a force that undermines one or the other aspect of system-related functional performance. One can think of it in terms of four segments of functional disruption: a technology segment, an economic segment, a political segment, and a sociology segment, with each segment having its own driving forces. • Technology: the fourth industrial revolution Technologies that significantly alter the way that businesses or entire industries operate are labeled disruptive and seem to be a leading source of functional disruption (Schwab 2016). • Politics: Neo-globalization Premises of a new paradigm for globalization are challenging traditional frameworks and introducing disruption to international economic policies, strategies, and institutions (El Namaki 2018).

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• Economics: Extreme capitalism Capital markets provide the core and the driving force of capitalism and, also, the prime source of far-reaching economic disruption (El Namaki 2014). • Socio-culture: Social media Social media or software-based technologies that facilitate the creation and sharing of all forms of expression via virtual communities and networks is proving to be a potent disruption vehicle (Forbes, October 23, 2016).

1.2 The Role of artificial intelligence Analysis as described above suggests that strategic thinking stands at a crossroads. A myriad of disruptive forces are undermining almost each and every conceptual and operational premise. Technology qualifies as the most penetrative of these disruptive forces. Artificial intelligence (AI) technologies are emerging as potent drivers of change. They are introducing the human brain as a template for managerial thinking, data as seeds of decision making, machines as learning instruments, computing software as a source of cognition, and machine learning as a mode of managerial analysis. AI is having a far-reaching impact on virtually every industry. The combination of huge volumes of data and access to high-level analytical computing software rendered that feasible. It has acted as the main driver of emerging technologies from big data and robotics to Internet of Things. AI technologies from mass data inputs to machine learning, deep learning, and yet-to-emerge derivative transformations could induce insights that may go far beyond “familiar” visions. It is more likely than not that AI-induced insights could take analysis to boundaries far beyond the commonly accepted parameters of the identifiable environment. Visions, then, do not only reflect the probable but they could cross that boundary and project the improbable! Strategic thinking, then, would have to identify exogenous variables not encountered earlier within the industry or the arena.

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Fig. 4.2  Driving forces of artificial intelligence. (Source: Chethan Kumar GN, Artificial Intelligence: Definition, Types, Examples, Technologies, Medium, And Aug 31, 2018. https://chethankumargn.medium.com/artificial-­intelligence-­ definition-­types-­examples-­technologies-­962ea75c7b9b)

Brains conduct cognitive functions from perception to conception; they perceive external stimuli and pass them to neurons for conception. It is a systems flow. Inputs, or sensory stimuli, are converted through neurons into an output being the memory. Data passing along these lines can be related to other data and develop “associations” or what we may describe as insights (Fig. 4.2).

2 Induced Strategic Thinking Shifts Technology, especially AI, is having and will continue to have a far-­ reaching impact on fundamental strategic thinking premises and processes. The impact is wide ranging and could include the following areas (El Namaki 2021a):

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2.1 From Product to Function AI frameworks will very likely lead to a shift from strategic product and market focus to a function focus. Function will determine whether the instrument (a product or a service) is congruent with business environment conditions. Function analysis derived from big data will contrast with need analysis drawn from market parameters (Karakašić et al. 2016). Rather than relying on customers to tell a business what they want from a product, data analysis will point to the ultimate function-­ fulfilling medium. It is the author’s connotation that this AI-induced product function link will lead to four states of strategic positioning (Fig. 4.3): A: Today’s products for today’s functions B: Future products for future AI-induced functions C: Future AI-induced products for today’s functions D: Future AI-induced functions for today’s products

2.2 Change in Time Perspective Time is a measure of events and their sequence. The duration of events and the intervals between them mark the lapse of time. The shorter the

B AI induced innovation

Fig. 4.3  Shifts in strategic thinking concepts

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Fig. 4.4  Technology adoption lags. (Source: Indermit Gill, Whoever leads in artificial intelligence in 2030 will rule the world until 2100, Brookings, Friday, January 17, 2020)

duration and the faster the flow of events, the greater the conscious “feel” of time. AI will, more likely than not, lead to a greater number of threshold events and a faster flow of those events. Event flow and sequence will constitute a yardstick to measure business strategies and their ultimate outcome. Long and short term, in conventional analysis, will give way to event fulfillment and the speed of this fulfillment (Fig. 4.4). “Based on the evidence, the time between invention and widespread use was cut from about 80 years for the steam engine to 40 years for electricity, and then to about 20 years for IT (Fig. 4.1). There are reasons to believe that the implementation lag for AI-related technologies will be about 10 years” (Indermit 2020).

2.3 Creating a Different Shape of Industry Structure Long-cherished industry structure drivers, such as power of buyers, power of suppliers, entrance, and substitution (Porter 1979), will give way to factors such as industry function fit, industry life cycle slope, and industry overlap. AI penetration of industry will be wide and deep. The result

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Fig. 4.5  Neo-industry life cycle

will be deep and far-reaching restructuring of industries such as banking, urban management, health sciences, communication, energy, and retailing. Visible flaws in what constitutes an industry analysis today are leading to the emergence of novel foundations or parameters of “industry” analysis. To those belong the market, the technology, the concentration, and the capital market (see Fig. 4.5). Each exists within a scale that represents the positive and the negative attribute of the variable. A combination of high market and high technology attributes creates strategic opportunity conditions. A combination of high industry concentration and high capital concentration could lead to strategic decline conditions.

2.4 Replacing Competitive Advantage with Competitive Intelligence Concepts of competition will assume parameters that differ from those recognized in contemporary economics and management frameworks. Future competition among firms will likely depend more on innovative intelligence rather than competitive advantage. Businesses that extract “intelligence” from data will have a competitive edge within an

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environment driven by AI forces. The quality of intelligence can make the difference between competing firms (Fig. 4.6). It is worth noting here that the concept of competitive intelligence roots lie, most likely, in Ansoff’s “silent signals” (Ansoff 1980). It is essentially the process of monitoring the firm’s industry and market in order to identify forces of competitive advantage and take better-informed decisions. It could be tactical or strategic. Tactical intelligence is shorter-term focusing on narrow issues such as market share and revenues. Strategic intelligence focuses on the longer term with shifts in underlying technology and capital markets taking central position. It covers the entire competitive environment, not just the competition. Competitive intelligence outputs should ideally reveal trends and insights. Those could include industry structure, competitor profile, client context, key company and industry performance parameters and technology trends. It may also include competitive benchmarking; early warning signals, market or industry trends; supply chain elements, economic/and political forces; and executive profiles (Calof and Wright 2008).

Tier 1: Top Direct Competitors

Tier 2: Direct Competitors in Target Industries Tier 3: Indirect Well-Known Competitors

Competitive Enablement Assets: Real-time alerts Battlecards Comparison one-sheets New hire sales training Product deep-dives Monthly newsletters Quarterly analyses Competitive Enablement Assets: Battlecards New hire sales training Monthly newsletters Quarterly analyses Competitive Enablement Assets: Competitor profiles Quarterly analyses

Fig. 4.6 Competitive intelligence. (Source: https://www.crayon.co/blog/ what-­is-­competitive-­intelligence-­terms-­and-­concepts-­you-­need-­to-­know?)

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2.5 Redefining CEO Profile An AI-compatible profile of CEOs will soon emerge. AI will drive a new cultural paradigm in which automation and data-driven facts trump opinions and where probabilities are used to address uncertainties. This is an era of human–machine collaboration which will require a rethink of traditional human operating models, role definitions, individual success measures, and career progress. Conventional management strategy involving planning, certainty, hierarchies, functional silos, incremental innovation and execution will give way to new modes and approaches.

2.6 Redefining Work AI would boost economic growth but this will vary by industry. This will also have impact on the content as well as the level of competency required. Recent research reveals that emerging technology impact on jobs and job markets could take two dimensions. The first is a continued rise in jobs requiring emerging technologies competencies and, in parallel, a growth in competencies requiring a human “intervention.” Technology-­based jobs like software engineers and data analysts, along with technical skills such as cloud computing, mobile application development, and software testing are on the rise in most industries and across all regions. Industries with intensive AI input in their processes and in their workforce competencies are also the fastest-changing industries (WEF 2018) (Fig. 4.7).

3 Neo-strategic Thinking Concept: The Ultimate Framework 3.1 The Conceptual Model An ultimate strategic thinking model is illustrated in the following figure (Fig. 4.8). The figure represents a system’s structure of a strategic thinking concept where an input leads to a transformation and, ultimately, an output.

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Fig. 4.7  How AI could change the job market. (Source: https://www.crayon.co/ blog/what-­is-­competitive-­intelligence-­terms-­and-­concepts-­you-­need-­to-­know?)

Input Data will constitute the prime input into future strategic thinking models. The data will gradually evolve from the “big” to the “mass.” Mass data, as we said earlier, are datasets that contain data from a variety of sources, including science fiction, data lakes, and implicit data. The science fiction segment will include soft and hard science fiction data. Soft science fiction relies on “soft” sciences while “hard” science fiction relates to accurate, exact, and logic sciences.

Transformation Transformation will be done through learning and analytics. The medium is machine learning and deep learning software.

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Input Transformation Output

Big Data Data lakes

Learning

Science fiction

Analysis

Implicit data

Insights Visions Novel functions Novel arenas

Fig. 4.8  The conceptual model

Algorithms will be derived and inter-variable relationships will be revealed. This data analysis should deliver new business functions and new industry arenas. Much will depend on the quality, scope and span of data inputs, and on the continuous monitoring of inputs in order to ascertain novel parameters and updated attributes.

Output Outputs will include sub-system structures, novel functions, and insights. Subsystems relate to the industry or field where AI technologies were put to use. Novel functions spell out dynamics and arenas that were invisible to the operators prior to the introduction of AI. Insights are the most proclaimed end result as they relate to discoveries and innovations.

3.2 Deviation from Traditional Paradigms This model departs from traditional strategic thinking paradigms in several ways.

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The Trigger First, there is the trigger or the starting point of the strategic thinking process. Traditional analysis puts emphasis on an environmental scanning that could reveal “opportunities and threats”; a scanning that cuts across all functional segments of the environment from the economic and political to the social and cultural. The suggested model’s starting point is enhanced big data or a mass of information with a wider scope and broader cover. Traditional scanning is replaced by databases that encompass almost every existing and possible variable that could have a relevant impact on this environment.

The Conception Second, there is the conceiving of visions. Past visions were indeed based on a perception of futures to come. Visions of the enhanced big data era will be based on big data analysis and learning. These processes might allow these visions to go beyond the recognizable and familiar. They may address the unimaginable and the blurred, the distant and the far reaching.

The Tools Third, there are the tools of the analysis. Again, traditional analysis resorted to instruments common within economic and social science domains. Figure 4.8 suggests a reliance on advanced tools resting on a foundation of data analytics. Diagnostic analytics and predictive analytics will provide a strong impetus to the strategic thinking process. They will sketch a horizon that was unreachable before. Fourth, is the earmarking of an arena or a field of business combat? The suggested paradigm leaves the door open to encounters that never happened before; competitive encounters within uncharted arenas. Competition, in this sense, is replaced by either synergy or destruction by substitution. All in all, strategic thinking will assume different dimensions within this novel framework.

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3.3 The Premises The new strategic thinking concept featuring above has top management-­ related premises and organization-related premises. The following is an analysis of each.

Top Management-Related Premises Several other chapters of this book will cover, in detail, top management-­ related premises and the following is a brief analysis of the issues.

Awareness Neo-strategic thinking will require a high measure of top management awareness of both self and environment. This will constitute an indispensable top management trait. Self-awareness refers to the realistic and accurate perception of one’s interests, values, skills, competencies, and limitations. Environment awareness constitutes accurate and realistic perception of opportunities, constraints, and challenges. Awareness or “to sense” can be described as something that occurs when the brain is activated in certain ways, such as when the color red is what is seen once the retina is stimulated by light waves. Awareness renders, for strategic thinking, a sense of scanning, tracking, urgency, and induction. It could also imply a propensity to initiate, enhance and reflect.

Vision Neo-strategic thinking will render top management visioning more crucial than ever. Let us recall that a vision is a mental perception of the kind of environment an individual or an organization aspires to create within a broad time horizon and the underlying conditions for the actualization of this perception. It is a description of something; an organization, a corporate culture, a business, a technology, or an activity in the future. It could also be a concept for a new and desirable future reality that can be

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Emerging technology induced product function arenas

Dynamic resource profiles

Intelligence driven competitive behavior

Fig. 4.9  The emerging span of AI-derived visions

communicated throughout the organization. Individuals perceive their visions and do not ask themselves whether they have one (El Namaki 1992). Disruptive forces are creating an unstable environment that having a vision implies involvement, commitment, and total immersion. All efforts stem from it and all forces and structures are seen in terms of their relevance to its existence. Issues such as rest and reward become secondary as attention is focused on the prime target, that is, vision fulfillment. Visions are the outcome of three processes: creativity, competitive advantage, and pertinent resources (El Namaki 1992) (Fig. 4.9).

Behavior Behavior reflects people’s response to events and stimuli. And top management’s reaction to the strategic dimension of the unfolding disruptions stands at the heart of the neo-strategic thinking concept. Behavior, in this case, represents actions and mannerisms made by top management in conjunction with themselves and/or their environment. Cognitive factors, environmental factors, and personality factors will play a role here. Rules for effective top management behavior are abundant. Among the most quoted and stressed are clarity of vision, result orientation, empowerment, and most importantly perhaps, having sufficient technological competencies that could allow a measure of technological support.

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Intent An intent is the mental driver of an action. Common synonyms of intent are aim, design, end result, goal, and purpose. Top management intent stands at the heart of the new strategic thinking concept. It provides a philosophical base of the strategic management process and projects what the organization desires to attain in future. It reveals a perspective of ways and means of achieving a conceived vision. Strategic intent inspires, motivates, and provides clear direction to stakeholders whether they are employees, investors, or customers. And to achieve that it captures the essence of winning, it sets ultimate goals worth the effort and the investment, and it maintains a measure of challenge over time.

Locus of Control Locus of control (Rotter 1966) is central to the neo-strategic thinking paradigm. It reflects top management’s belief that they, as opposed to external forces (beyond their influence), have control over the outcome of pertinent events. The concept provides an aspect of personality psychology. A person’s “locus” is conceptualized as internal (a belief that one can control one’s own life) or external (a belief that life is controlled by outside factors). Individuals with a high measure of internal locus of control believe to have a great deal of control over the direction of their own and the relevant environment … The concept provides a key strategic thinking premise. It relates the concept to strategic control. It could help providing an answer to the strategic control question of “are we where we should have been”! This is a question that operational and managerial controls do not answer.

Organization-Related Premises Emerging technologies will have far-reaching impact on the structure, the business model, and the operating processes of organizations. Those aspects will be explored further in the following chapters of this book.

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Cognitive Computing Cognitive computing is a type of computing that focuses on reasoning and conceptual analysis often analogous to human cognition. It deals with symbolic and conceptual information rather than pure data or sensor streams (Computer World, March 3, 2016). It resorts to computerized models to simulate the human cognition process and to find solutions in complex situations. It involves technologies that power cognitive applications such as expert systems, neural networks, robotics, and virtual reality (VR). The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. Human cognition involves realtime analysis of environment, context, and intent, among many other variables that inform a person’s ability to solve problems. A number of AI technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, and NLP and sentiment analysis. Organizations should possess this capacity and should be able to put it to use in order to answer central questions related to scenario building and strategy formulation.

Implicit Learning The brain engages in two types of learning: explicit and implicit. Explicit learning is learning that one has conscious awareness of and is able to articulate what he or she is taught (Schendan 2003). Implicit learning is the opposite: it is the kind of learning that one has conscious access to, but cannot really articulate (Curran and Schacter 1996). Implicit learning relies on two aspects of implicit memory: perceptual and conceptual priming. Perceptual priming refers to brain’s ability to recognize stimuli, while conceptual priming is an ability to assess meaning of events and/or facts based on available semantic information (Curran and Schacter 1996; Ulman 2004; Xie et  al. 2019). It is also linked to category and sequence teaching (Schendan 2003). In general, implicit learning relies on the brain’s ability to engage its different regions based on the type of the processed information, and it is modifiable via experience. Implicit learning’s knowledge is also claimed to lie at the very

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•Unintentional learning

Aggregate Learning

Explicit learning

•Intentional lerning

Fig. 4.10  Implicit versus explicit learning

core of creativity. “To engage in a creative act (is) to struggle to make tacitly acquired and held knowledge conscious and communicable” (Michael Polanyi 1967; Reber and Reber 2001). Organizations could possess own implicit thinking competency (Fig. 4.10).

Data Analysis Business strategic thinking will increasingly be driven by how well a business can leverage data, apply data analytics, and implement new data technologies. In effect, every business, regardless of size, will need a solid data foundation. Data-strategy synergy requires penetrative analysis. Strategic behavior will, to a great measure, focus on insights revealed by data and the direction those insights point to. Visions and strategies should relate to what data analytics reveal as a prospect and longer-term outlook. The relationship is reflected in the following figure. The Y axis represents data segments while the X axis represents strategy drivers. Strategy drivers are those forces inducing strategic behavior. Those are either existing or evolving. Existing drivers are present-day inducers of strategic behavior including the search for competitive advantage. Evolving drivers are those inducers resulting from insights as much as the disruptive forces of, among others, technology (Fig. 4.11). The ultimate outcome are four strategy formulation modes. Existing strategy drivers link with perspective insights in order to deliver a product development strategy mode. The same existing strategy drivers would

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Data analytics

Predictable/Prescriptive insights

Descriptive/diagnostic outcome

Product

Function

development

arenas

Portfolio

Concentration

Existing

evolving Strategy drivers

Fig. 4.11  Data-strategy link

relate to descriptive and diagnostic data analysis outcomes and produce a portfolio mode for strategy formulation. Evolving strategy drivers relation to predictable insights could deliver new Drivers function arenas. Evolving strategy drivers may also relate to descriptive outcomes and produce a concentration strategy mode.

4 Summary and Conclusions AI technologies will induce a fundamental shift in strategic thinking. Data and neural cognition processes will lead to insights and visions. A new strategic thinking model will accommodate and operate with those premises. It will follow a system’s construct with data inputs leading to a transformation process and ultimate insights and feedback. This is a model that works with inputs, transformations, outputs, and feedbacks that differ from yesteryear’s models. Inputs will rely heavily on data whether big, enhanced, or mass data. Data will provide a vision and a silhouette of the determination parameters. Transformation will resort to learning and analysis through AI technologies from machine learning to deep learning. The output will convert those transformation outcomes into strategies.

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Business strategic thinking will increasingly be driven by how well a business can leverage data, apply data analytics, and implement new data technologies. In effect, every business, regardless of size, will need a solid data foundation. Interaction of data analytics and strategy drivers would lead to four strategy formulation modes. Existing strategy drivers link with perspective insights in order to deliver a product development strategy mode. The same existing strategy drivers would relate to descriptive and diagnostic data analysis outcomes and produce a portfolio mode for strategy formulation. Evolving strategy driver’s relation to predictable insights could deliver new function arenas. Evolving strategy drivers may also relate to descriptive outcomes and produce a concentration strategy mode.

References Christensen, CM (1997) The Innovator’s Dilemma When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Cambridge. Curran T, Schacter DL (1996) Affiliation 1 Case Western Reserve University, Cleveland, USA. PMID: 9156090 DOI: 10.1080/741941153. El Namaki MSS (2021a) Fundamental Shifts in Strategic Thinking Concepts and their Teaching Implications. Scholedge International Journal of Management & Development ISSN 2394-3378, Vol.08, Issue 02 (2021), Pg 4–13. El Namaki MSS (1992) Creating a corporate vision. Long Range Planning, Vol. 25, No. 6, pp. 25 to 29, 1992 https://www.sciencedirect.com/science/article/ pii/002463019290166Y. El Namaki MSS (2018) Disruption in Business Environments: A Framework and Case Evidence. International Journal of Management and Applied Research, Vol. 5, No. 1, pp. 1–7. https://doi.org/10.18646/2056.51.18-­001. El-Namaki MSS (2014) How damaged are investment capital markets today. Vol 2, No 5 (2015) Scholedge International Journal of Management & Development. Vol 2 Issue 5 May 2015 ISSN-2394-3378. Forbes, Oct 23 (2016). https://www.crayon.co/blog/what-­i s-­c ompetitive-­i ntelligence-­t erms-­a nd­concepts-­you-­need-­to-­know? Igor Ansoff H (1980) Strategic Issue Management. Indermit G (2020) whoever leads in artificial intelligence in 2030 will rule the world until 2100. Brookings, Friday, 17 Jan 2020.

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Karakašić M et al (2016) Matrix of function and functionality as a tool in product development processes. Tehnički Wjesnik [Technical Gazette] vol. 23, 1295–1300. Polanyi M (1967) The Tacit Dimension, New York: Anchor Books 1966. Porter M (1979) Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press 1980. Reber A, Reber E (2001). The penguin dictionary of psychology. London: Penguin. Rotter JB (1966) Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs 80 (1), 1–28). Schendan HE (2003) Sequence Learning: What’s the Hippocampus to Do? Neuron, Volume 37, Issue 6 Mar 2003. Schwab K (2016) The Fourth Industrial Revolution, Geneva: World Economic Forum. Ulman M (2004) Contributions of memory circuits to language: the declarative/ procedural mode. Cognition 92 (1–2), 231–270. https://doi.org/10.1016/ j.cognition.2003.10.008 WEF (2018) How artificial intelligence is shaking up the job market, September 17. Xie, T.T.; Wang, T.Z.; Wei, Y.P. and Ye, E.C. (2019), “Declarative memory affects procedural memory: The role of semantic association and sequence matching”, Psychology of Sport and Exercise, Vol. 43, pp. 253-260. https:// doi.org/10.1016/j.psychsport.2019.03.009

5 Tomorrow’s Managerial Functions

1 Today’s Challengeable Managerial Functions Managerial functions emerged as an outcome of work done decades ago by Henri Fayol (Fayol 1949) followed by others such as Max Weber and Frederik Taylor. He identified, in his work “General and Industrial Management” which was published in 1949, five managerial functions: planning, organizing, command, coordination, and control. They reflected the realities of those decades and embodied, more or less, images of business conditions as they once existed. Variable degrees of change especially the disruptive genre have undermined many of these old premises. A blurred vision of those functions gradually took place and a strong need for a reconsideration emerged (Fig. 5.1). Let us examine the current status and the structural shifts that took place in those prime functions: planning, organizing, and controlling.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_5

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Neu strategic thinking

Strategic control

Strategy fulfillment

Feedback Fig. 5.1  Neo-managerial function perspective

1.1 Planning Business planning went through reiterations and a present-day genre is called strategic thinking. A vast majority of corporate strategic planning efforts fails, up to 67% actually, according to a source (Inc. October 23, 2020). Reasons, according to this research, varied but the essence is a traditional way of thinking that failed to adjust too many of the realities of the emerging era. A gap came to exist between strategic planning (analysis) and strategic thinking (synthesis). Mintzberg referred to this failure in some early writings where he stressed the three fallacies of strategic planning. The first was those executives who assumed that prediction is possible, the second related to those who assumed that strategists can be detached from the subjects of their strategies, and, the third and above all, are those who believed that the strategy-making process can be formalized (Mintzberg, HBR, January–February 1994). Today’s strategic planning “cum thinking” concepts and applications do not, however, deviate, much, from the early frameworks. They, as we have underlined elsewhere, have a long way before actually

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accommodating the elements of artificial intelligence or the driving forces of data sciences that are dominant at this very hour and more likely to be more dominant in the future.

1.2 Organizing Fayol, the “father” of management science, introduced organizing as one of the five prime functions of management. The essence of his theories is still traceable today. The core of organizing as he has defined it centers on division of work, authority and responsibility, and unity of command. He believed that division of work improves the productivity, efficiency, accuracy, and speed of the workers. He also stated that authority and responsibility are the two key aspects of management. Authority facilitates the management to work efficiently, and responsibility makes them responsible for the work done under their guidance or leadership. And he adds elsewhere that there should be a unity of command or that individual workers should have one line of responsibility. Organizing today connotes different demands from Fayol’s. It demands novel concepts such as leading from behind, knowledge workers, agility, continuous learning, and several others. The concept has a long way to go before reflecting those developments.

1.3 Controlling Problems with control were highlighted in recent research. The implementation of management control systems in organizations has, over time, displayed a wide variety of shortcomings from the magnitude of change and the effectiveness of feedback to the applied standards, the volume of feedback information, and, ultimately, the level of feedback resistance. There exists a view that management control systems are designed to cope with changes of a limited magnitude which is in stark contrast with the volume and scope of current change especially in technology. Applied standards are also not always in line with the measure of change. Time gap between events and feedback varies and could lead to delayed or perfunctory response.

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Add to that macro variables as hyper-competition, rapidly changing technology, deregulation, oscillating globalization norms and virtual modes of conduct and the decline picture is complete (Schibler 2012). Several of these issues led to a measurable decline in the effectiveness of management control. All these developments have limited the scope and usefulness of conventional managerial control and rendered the search for other modes timely.

2 Tomorrow’s Perspective Managerial Function 2.1 Tomorrow’s First Managerial Function: Neo-strategic Thinking Neo-strategic thinking is tantamount to positioning of the organization within a rather blurred distant environment, building a malleable resource profile, identifying modes of ultimate reach, and dynamic process reconfiguration in response to techno-economic driver shifts. Failure of many key strategic thinking concepts coupled with the rapid emergence of new technologies is leading to the rise of new or novel thrust concepts. The process is driven by a myriad of previously unknown forces from artificial intelligence and data dynamics to Internet of Things and cognitive computing. The emerging premises include:

Scenario Building: Predictive Analytics Predictive analytics are providing valid substitute to open-ended environmental scanning inherent in the traditional process of scenario building. Predictive analytics is a form of technology that makes predictions about certain unknowns in the future. It draws on a series of techniques to make these outcomes, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics. It extracts information

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from data and uses it to predict trends and behavior patterns. Predictive analytics uses statistics and modeling techniques to project future performance resorting to models such as decision trees, regression, and neural networks. These models trace relationships, patterns, and structures in data. Those can be used to trace correlations between data and possible outcomes (SAS, Predictive Analysis) (Fig. 5.2).

Strategy Formulation: Data-Driven Strategies Data-driven strategy formulation models will become essential element of the new strategic thinking framework. Those are models relying on an analytical data-based foundation supportive of corporate strategic directions. It is analytics and not the logic or intuition of the past. Data is viewed as an asset with a clear link to a business impact. And as a mode for better strategies and better business decisions. They predict and optimize business outcomes. All of that assumes access to the right data and to a data resource beyond limited company reach, for example, social media or data flows from sensors, monitored processes, and external sources. It also assumes an ability to build models that could predict and optimize business outcomes within a data culture. Data access

Model Application

Model validation

Data training

Model development

Fig. 5.2  From data to modeling and application

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Strategic Thinking Models: Function-Specific Arenas Technology disruption is inducing a radical shift in product development, a shift from the product to the function (Fig. 5.3). Product function connotes product mission within an environment. Product function analysis develops a function structure or an abstract model of the product, or product concept, without the material features of shape, dimension, or material. It provides a link with the environment where the product is born, used, and abandoned (Belu et  al. 2011). Disruptive technology will lead to a shift from product to function. Strategy formulation models as BCG’s will have to develop new parameters as a consequence. “We need to think of strategy and competition in terms of competitive arenas, not industries. An arena represents a chunk of resources controlled by different stakeholders—customers, certainly, but others too” (McGrath 2019). Product physics

Product concept Specific technology and technical functions Generic functions

Big data

Fig. 5.3  Product function. (Source: Belu et al. 2011)

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Top Management Competencies: Competitive Cognition Competitive cognition will constitute a novel element in top management competencies. The term “competitive cognition” refers to the framework within which competitive knowledge is continuously acquired, used, and retained. It is the process of making sense of the competitive environment (Walker et al. 2005). Through repeated exposure to rivals, executives learn the attributes and strategies of competitors and form mental representations of a given rival, then assigns the target competitor to a category, using that classification as a guide to direct future actions. Blind spots in competitive cognition and outmoded mental models can explain empirically observable phenomena such as industry overcapacity, the failure of new entries, and acquisition overpayment. Industries are actually created through a shared interpretation of reality among business rivals. Rather than defining competitors on an individual basis, executives assign themselves to a competitive category (Porac and Thomas 1990).

Problem Solving: Diagnostic Data Analysis Diagnostic data analysis will provide a firm foundation for problem identification and problem solving. Diagnostic analytics constitute a thorough penetration of data in order to search for constraints and identify insights. It explores possible link between outcomes and possible drivers. Put differently it relates problem areas to identifiable symptoms. Diagnostic analytics is usually performed using such techniques as data discovery, drill-down, data mining, and correlations. In the discovery process, analysts identify the data sources that will help them interpret the results. Drilling down involves focusing on a certain facet of the data or particular widget. Data mining is an automated process to get information from a massive set of raw data. And finding consistent correlations pinpoint the parameters of the investigation (https://www. sisense.com).

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Strategic Behavior: Competitive Synergy Dynamic synergy analysis is a process whereby future driving forces of two companies are juxtaposed in order to identify areas of synergy and create a foundation for cross-company strategic behavior. Synergy connotes, in this case, interaction between two or more forces in a way that leads to a combined output that is greater than the sum of the individual components. Future driving forces could be capital related, technology related, or productivity related. Capital could become a driving force if capital markets are mature enough to create a dynamic force. Capital market maturity here is measured in terms of instruments, institutions, players, policies, and flows. Technology could become a driving force if company parameters of technological innovation measure up to industry innovation standards. Those standards could relate to the volume of patents and patent product and process conversion. Productivity is a depended function depending on capital and technology inputs.

Strategic Control: Implicit Learning Implicit learning will provide a medium for strategic control or control against the potential as opposed to control by historical standards. The key question in strategic control is: are goals in line with potential? Many goals are deduced or extrapolated and bear, in reality, little relevance to the “true” potential of the organization. There are several ways to identify this potential. One of them is implicit learning. Implicit human long-term memory performs “implicit learning,” a form of learning that occurs without the individual’s awareness (Curran and Schacter 1997). Could business organizations develop an ability to learn implicitly and derive creative strategies from this implicit learning? One could hypothesize that in very much the same way that the human brain resorts to implicit learning to enhance cognitive competencies, executives and corporations could resort to implicit learning to enhance the scope, depth, and reach of strategic thinking. And identify the potential. And in very much the same way that human brain enhancement of cognitive capacities comes through the growing of new neurons, executives and

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corporations could enhance their strategic thinking potential by enhancing their organizations’ implicit memory or exposure to silent signals and stimuli originating from beyond the organizations’ immediate periphery (El Namaki 2020).

2.2 Tomorrow’s Second Managerial Function: Strategic Fulfillment Strategic fulfillment is the process of mobilization of resources, identification of strategic priorities, and conduct of goal-fulfilling actions. It implies identifying resources and resource goal relationships and prioritizing resource use and focus. It also implies keeping the organization aligned. Financial analysis can provide a view as to how funding sources and financing strategies could be used to support key goals. It reveals the limits of need and the new resource reach. It reveals gaps in current funding and how resources can be more effectively coordinated, maximized, or secured. It also discloses new funding sources that could be leveraged to support goal efforts. And it maximizes funding opportunities through improved coordination and matching and blending of funds. Strategic priorities should be forward-looking and action-oriented and should focus on choices that matter most to the organization’s success over a time horizon. And they are strategic, in the sense that they describe specific actions that will help the company execute its strategy, as opposed to financial targets or corporate values. Restricting the number of strategic priorities has several advantages including ease of absorption, communication throughout the organization. Strategic priorities act as a bridge between long-term aspirations, embodied in a vision or mission, and shorter-term objectives. The types of initiatives that have the biggest impact (e.g., building data analytics capabilities, integrating online and physical stores, or entering a new market) typically take a few years. In general, three to five strategic priorities that can be accomplished in three to five years are advisable (Turning Strategy into Results MIT Sloan, September 28, 2017) (Fig. 5.4).

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Fig. 5.4  Strategy implementation tools. (Source: Sull et al “Turning Strategy into Results,” MIT Sloan, September 28, 2017)

2.3 Tomorrow’s Third Managerial Function: Strategic Control Strategic control is the process of comparing achievements to potential. The measure is what we may term “the strategic fit.” An organization’s potential is achieved if it has a “strategic fit.” Strategic fitness could relate to the industry fitness, the resource fitness, the competency fitness, and the outlook fitness (El Namaki, Have you lost your control? Capital Magazine April 2006) (Fig. 5.5). The road to strategic control centers on the concept of “fitness.” An organization should fit within an industry, have goals that fit its potential, and have core competencies that fit new future demands and resources that would fit future dynamics. Fitness is measured by trying to find objective answers to a number of key questions (El Namaki). • The “potential” fitness test. The key question here is, goal alignment with potential? Many goals are deduced or extrapolated and bear little relevance to the true potential of the organization. The following questions may help in finding an answer. What is the “true” revenue potential? What is the “true” market share potential and how far away are you from that? Are you exploring all possible potential-enhancing

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Potential / Opportunities

Hi

failing

Conducive

Lo

Weak

Deficient

Lo

Hi

Strategic control parameters Fig. 5.5  The outcome of potential triggered opportunities and strategic control parameters

technological opportunities? Are there venues for internal or external synergy that might increase? Would a strategic alliance enhance potential within your industry? • The “industry” fitness test. The key issue here is whether the company is in the right industry and whether this is the right place for the foreseeable future. Questions to be asked include the rate of growth of current industry, shape of industry life cycle, product life cycle, recent entrants, industry concentration, technology shifts, and if there are end-game players in the industry. • The “core competency” fitness test. The main question here is the existence of a solid core competency. Again answers to some questions could throw light on this parameter. What real core competency is there? how does core competency provide a competitive edge? How durable is this core competency? Is it subject to wear and tear, any other core competencies in the making, and, last but not least, is company core competency transferable across industries? • The “resource” fitness test. The final question is about resource compatibility with evolving conditions. Consider issues as: is there equity capital flexibility? is the organization creditable enough to allow for an expansion in the debt base? does top management have the capacity, the strategic fit, and the degree of dependability that a change of direction may require? is organization culture open and liable to change?

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3 Summary and Conclusions It is the contention of the author that classic managerial functions starting with planning and proceeding to staffing, directing, and controlling have, as a result of the dynamics analyzed in this book, lost ground. Forces of change in the corporation, the managers, the technology, and the environment as a whole have induced grass root change. This change has rendered the classic tenants of the managerial function obsolete. What could emerge, in this author’s view, is a managerial process based on three tenants: strategic thinking, strategic fulfillment, and strategic control. Each of those constitutes a concept of its own and each of those relates to the other within a systems relationship. Strategic thinking leads to strategic fulfillment and strategic control, in that sequence. A feedback process completes the cycle. Neo-strategic thinking is tantamount to positioning of the organization within a rather blurred distant environment, building a malleable resource profile, identifying modes of ultimate reach and dynamic goal and resource reconfiguration in response to change in techno-economic drivers. Strategic fulfillment is the process of mobilization of resources, identification of strategic priorities, and conduct of goal-fulfilling actions. Strategic control is the process of comparing achievements to potential with potential fitness, industry fitness, core competency fitness, and resource fitness test. The outcome of those fitness tests is a repositioning of the organization within emerging arenas.

References Belu et al, 2011 Teaching mechanics with maple, January 2011. Clark, D. A., Beck, A. T., & Alford, B. A. (1999). Scientific foundations of cognitive theory and therapy of depression. John Wiley & Sons Inc. Curran T, Schacter DL. Implicit memory: what must theories of amnesia explain? Memory. 1997 Jan-Mar;5(1-2):37-47. https://doi.org/10.1080/ 741941153. PMID: 9156090. El Namaki M, “Have you lost your control”, Capital Middle East, April, 2006.

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El Namaki, M “Could Business Organizations Simulate the Brain’s Implicit Learning Process? And Apply That to Strategic Thinking?” The International Journal of Management 7 (2020): 72-80. HBR, The Fall and Rise of Strategic Planning, by Henry Mintzberg,, January– February 1994. Henri Fayol, “General and Industrial Management” London: Pitman and Sons, Ltd., 1949. Predictive Analytics: What it is and why it matters | SAS. Schibler, John J. (2012) ““The End of Managerial Control?” by Joseph A. Raelin,” Organization Management Journal: Vol. 9: Iss. 1, Article 11. Sull D, Turconi S, Sull C, and Yoder J “Turning Strategy into Results”, MIT Sloan, September 28, 2017. Tanya Prive, Why 67 Percent of Strategic Plans Fail, Inc. Oct 23, 2020. https:// www.inc.com/tanya-prive/why-67-percent-of-strategic-plans-fail.html Turning Strategy into Results MIT Sloan, September 28, 2017 Donald Sull, Stefano. Turconi, Charles Sull, and James Yoder September 28, 2017. Walker et al, 2005.

6 New Paradigm: The Top Management Context

1 Introduction Emerging technologies led by AI and data sciences induce a strategic thinking paradigm that creates demands on top management and the organization as a whole. This chapter deals with demands on top management. Novel top management competencies and traits will underline the new concept. Those could spread along a wide front but include in the first place awareness, visioning, thinking, behavior, plasticity, intelligence, and intent. Management competencies are those underlying characteristics, traits, and behaviors essential for the successful performance of a leading managerial function. They identify the key skills and abilities of top management performers and bring those in line with the demands of the dynamic environment. They cut across several disciplines and sciences. There are those that relate, distinctly, to neurology as self-awareness, predictive cognition, and vision and there are those that relate to psychology as locus of control and intent. On the other hand, there are those that cut across several areas as

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Awareness

Intelligence

Vision

Strategic thinking Intent

Plasticity

Behavior

Cognitive predictions

Fig. 6.1  Technology-driven demands on top management

intelligence. The reader will be traveling through these scientific domains and will, ultimately, blend the elements and phenomena (Fig. 6.1). Many of those are novel and seldom treated in terms of top management competencies. Issues such as awareness, cognitive prediction, plasticity, and behavior were seldom dealt with in management literature and if done it was within a very narrow scope. The treatment in this chapter expands the scope and coverage of those issues. Awareness, for example, is treated within the neurological and psychological senses and the overlapping of those conceptual domains with strategy and strategic thinking. Cognitive prediction is also dealt with within the psychological and brain science domains and related to the predictive essence of the function of the chief executive officer. Several top management-related issues are projected through the eyes of neurology and psychology run through the analysis.

7 Self-awareness

1 Concept of Awareness Awareness could be defined as a comprehensive and accurate perception of the state and characteristics of self and the environment. Self-awareness refers to the realistic and accurate perception of one’s interests, values, skills, limitations, and lifestyle preferences. Environment awareness is characterized by an accurate and realistic perception of opportunities, constraints, and challenges relevant to the individual’s function and environment. The two types of awareness are relevant to many managerial processes from decision-making to strategy formulation (Mascetta 2022). Awareness is a relative concept. It may focus on an internal state, such as a visceral feeling, or on external events by way of sensory perception. It is analogous to sensing something, a process distinguished from observing and perceiving. Awareness or “to sense” can be described as something that occurs when the brain is activated in certain ways, such as when the color red is what is seen once the retina is stimulated by light waves (Hussain et al. 2008; Locke 2002). Awareness is also associated with consciousness in the sense that it connotes an experience, a feeling or intuition that accompanies exposure to an event or a phenomenon (Miller 2020). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_7

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2 Awareness of “self” Self-awareness extends over a wide front and acquires a trait inherent in the situation. The following are a few of the commonly recognized categories of self-awareness (Fig. 7.1): • Informal self-awareness is the sense of physical presence of others and their intentions. • Social self-awareness is the information maintained within a social or conversational context. • Group-structural self-awareness is an understanding of group dynamics and individual-group interrelationships. • Workspace self-awareness connotes workspace’s influence, particularly the location, the activity, and the objects within the workspace. • Context self-awareness and location awareness refer to information a computer system might need in a particular situation. • Private and public self-awareness. Private self-awareness is a propensity to reflect and observe the inner nature and spirits of a person. Public self-awareness is a mindfulness of the personality as it is observed by other individuals. Both private and public selves are regarded as character qualities, which are comparatively steady over time ­ (Psychology Writing 2022).

Fig. 7.1  Areas of self-awareness

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3 “Managing self” Managing self could be considered an extension of self-awareness. The concept has roots in a 1999 original contribution by Peter Drucker and some consequent research. His message was that one has to understand and be able to manage himself if he is to contribute, effectively, to himself, to the organization, and to the environment around him. Drucker set a number of criteria for successful self-management and formulated them as questions. The first was “What are my strengths?” a question aiming at identifying specific individual points of strength that can produce results. Drucker stresses that very few people actually know—let alone take advantage of—their fundamental strengths. The second was “How do I perform?” And with this the focus is on the propensity to learn and the ability to participate and to lead. Then there was the issue of values, that is, recognizing individual values and measuring compatibility with them. The following was the issue of belonging and values. And last but not least is the image that one develops for the second half of one’s life (Drucker 2005). Drucker approach to the issue is a “challenge” approach. Answers to the questions should reveal issues that need attention and require an effort. Don’t try to change yourself, Drucker cautions. “Instead, concentrate on improving the skills you have and accepting assignments that are tailored to your individual way of working. If you do that, you can transform yourself from an ordinary worker into an outstanding performer” (Drucker 2005) (Fig. 7.2). There are self-management syndromes, though. Some of those feature in the following figure (Fig.  7.3). The issue there is striking a balance between perspective and control. A high perspective combined with measured control seems to be the most conducive.

4 Locus of Control Self-awareness helps controlling emotions and applying emotion in a manner conducive to personal relationships or managing conflict. It avoids letting emotions having the upper hand or making decisions that

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strengths and weaknesses

own set of values

own performance

the second half of my life

responsibility for relaonships

Fig. 7.2  Drucker’s key self-management parameters

Fig. 7.3  The four common self-management syndromes. (Source: Sunday Post, January 11, 2009)

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are not in line with own values (Mind by design, June 13, 2021). It could also lead to a strong internal locus of control. Individuals with a strong internal locus of control tend to be more independent, taking responsibility for their actions; tend to be less influenced by the opinions of others, have a strong sense of self-efficacy; and tend to put effort into achieving identified goals.

5 Communications In a communication setting, awareness could convey knowledge created through the interaction of an individual and its environment. In this setting, awareness is meant to convey how individuals monitor and perceive the information surrounding their environment. It is knowledge about the state of some specific element of the environment taking into consideration that environments are continually changing and an awareness knowledge must be constantly maintained. Effective communication goes beyond exchanging information and conveys an understanding of the message behind the information.

6 Attempts at Measuring Self-awareness Several attempts were made at measuring self-awareness. They vary from the elementary to the focused. An example is the personal self-concept questionnaire (PSQ) and the self-concept questionnaire (SCQ). The first (Goni et al. 2011) was developed in 2011 in response to the plethora of self-concept tools available at that point in time. The objective was creating a shorter measure with a degree of accuracy. The questionnaire is made up of 22 statements, divided into 4 subcategories: self-fulfillment, autonomy, honesty, and emotional self-concept. Participants use a scale of one to five to respond to the statements, with one being “Totally Disagree” and five being “Totally Agree.” The second (Saraswat 1981) “Self-Concept Questionnaire” was initially developed in 1984 and seem to be one of the more popular questionnaires when it comes to measuring self-concept. The questionnaire

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itself contains 48 statements that aim at measuring self-concept across six different aspects: physical, social, temperamental, educational, moral, and intellectual. Participants rate their answers to the statements on a scale from 1 to 5, with one being “very unlike me” and five being “very true of me.” A higher score indicates a high sense of self-concept. Several other approaches are accessible with a variety of focus and assessment method.

7 Awareness as a Top Management Competency Today’s increasingly dynamic and complex business environment dictates a high measure of self-awareness. This applied to those leading organizations and their followers. Organizational complexities make it all the more critical that executives understand what their competences are, how to enhance them, and how to apply them to the process of strategic design. Self-awareness, complemented by several other competencies, are essentials under today’s strong demands of the market and the equally strong driving forces of the competitive environment. Self-awareness is critical to the artificial intelligence and data sciences– derived new genre of strategic thinking. This includes awareness of a wide range of internal and external currents that could influence the future direction of the organization. One way of doing that is by absorbing information, identifying signals even the silent ones, and tracking significant analogies between different impulses (Bradford 1999). Top management awareness competencies could be measured by what one may label as a “propensity to sense.” As we stated earlier, self-­awareness is a state in which the concept of the self-reacts to social and environmental cues. This reaction could be termed “propensity to sense” implying a keen awareness of individual specific driving forces and their impact on varieties of behaviors.

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8 Summary and Conclusions A multiple of characteristics have been attributed to top management in traditional management literature. Leadership, initiative, and sense of direction are among them. Artificial intelligence has introduced a novel characteristic: awareness. Top management has to demonstrate all common characteristics but before all awareness. Awareness precedes all other because of the dynamic disruptions and emerging need for a continuous assessment of environmental change and the consequences of that to the industry, the organization, and those conducting the managerial function. Awareness has been defined as a relatively complete and accurate perception of individuals’ qualities and the characteristics of their environments. It can be of two types: self-awareness and environment awareness and our analysis has focused on self-awareness. It has far-reaching impact on the process of strategic thinking, the process of managing self, and the process of control. Managing self could be considered an extension of self-awareness. The concept has roots in a 1999 original contribution by Peter Drucker and some consequent research. His message was that one has to understand and be able to manage himself if he is to contribute, effectively, to himself, to the organization, and to the environment around him. Several approaches to the measurement of self-awareness are accessible.

References Bradford R (1999) This is How to Develop Strategic Awareness. Center for Simplified Strategic Planning, Inc. Michigan 1999–2022 https://www.cssp. com/strategic-­awareness-­a-­critical-­skill-­in-­strategic-­thinking. Drucker P (2005) Managing Oneself. Harvard Business Review January 2005 https://hbr.org/2005/01/managing-­oneself. Duncan C https://destinysodyssey.com/the-­odyssey/self-­discovery/self-­aware ness/personal-­identity-­workshops/self-­awareness-­worksheet. Goni E et al (2011) Structure of the Personal Self-Concept (PSC) Questionnaire. September 2011, International Journal of Clinical and Health Psychology 11(3):509–522.

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Hussain A et al. (2008) Brain Inspired Cognitive Systems. New York: Springer Science Business. Media. pp. 298 ISBN9780387790992. Locke D (2002) Perception: And Our Knowledge of the External World. Volume 3. London: Psychology Press. p. 27. StudyCorgi (2022) Two Types of Self-Awareness: Private and Public (26 Sep 2022 Retrieved from https://studycorgi.com/two-­types-­of-­self-­awareness­private-­and-­public/. Mascetta P (2022) Two Types of Self-Awareness: Private and Public. Retrieved from https://studycorgi.com/two-­types-­of-­self-­awareness-­private-­and-­ public/ 26 Sep 2022 Miller KD (2020) Using Self-Awareness Theory and Skills in Psychology. 7 Jan 2020 Positive psychology.com. Psychology Writing (2022 Mar 24). How Situational Awareness Affects Decision Making. Retrieved from https://psychologywriting.com/how-­situational­awareness-­affects-­decision-­making/. Saraswat RK (1981), the Self Concept Questionnaire. Mind by design.io (2021) 10 Reasons Why self-awareness is Important in communication. Blog 13 Jun 2021. Sunday Post, January 11 Jan 2009.

8 Vision

1 What Is Vision? Vision is a mental perception of the kind of environment an individual or an organization aspires to create within a broad time horizon and the underlying conditions for the actualization of this perception. It is a description of something: an organization, a corporate culture, a business, a technology, or an activity in the future. It could also be a concept for a new and desirable future reality that can be communicated throughout the organization. Individuals perceive their visions and do not ask themselves whether they have one (El Namaki 1992). Having a vision implies involvement, commitment, and total immersion. All efforts stem from it, and all forces and structures are seen in terms of their relevance to its existence. Issues such as rest and reward become secondary as attention is focused on the prime target, that is, vision fulfillment (Collins and Porras 1996; Nutt and Backoff 1997).

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2 The Buildup of Vision Visions are formulated by explicitly identifying a domain for competitive behavior, or an arena; a set of sources of strategic competitive advantage, and a resource capability profile. An arena is a delineated boundary for combat—a boundary for competitive behavior, be it a set of industries, a segment of an economy, a decision-making function, or a new technology. This demarcation of a competitive domain is instrumental in identifying the thrust of future developments and the scope of the products, markets, constructs that will, or will not emerge as a result of this vision. Organizational competitive strength could rest on a unique strategic competitive advantage be it technology, cost, positioning, scale or competency-related. Visions imply a capability construct. Capabilities could rest on a managerial competency, a logistic framework, a technological profile, a financial resource, or all of them. A vision-based capability profile is thrust-specific in the sense that each type of strategic thrust calls for a different configuration of attributes. As a vision may imply a radical and far-reaching change in the capability profile of the organization, it is important to remember that for each strategic thrust there is a combination of capability attributes which is most effective for supporting the thrust (El Namaki 1992). Visions are the products of change. Fundamental change triggered successful business visions in the past and contemporary change is triggering others. To the former belong McDonald’s Kroc. And to the latter belongs, certainly, Haier’s Zhang Ruimin of China. Ray Kroc, McDonald’s founder, envisioned a new type of fast food, making use of Henry Ford’s assembly line idea. He also resorted to input and process standardization ensuring a standardization in output, whether a consumer is in New York or Tokyo. Both were revolutionary ideas for the food-serving industry and the vision turned into a reality. Haier’s Zhang Ruimin saw in the malfunctioning fridge factory that he was asked to manage in 1984 an opportunity and went on to conceive a vision making quality household appliances, a global brand, and a global marketing strategy.

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3 What Kills a Vision? Visions may be killed at different stages of their conception and implementation. Conception-related mortality could relate to individual behavior, and the way individuals approach their environment and react to external stimuli. Among those factors are the following: • • • • • • • • •

Fear of mistakes. Inability to tolerate ambiguity. Preference for judging ideas instead of creating them. Inability to incubate or “sleep on it.” Lack of challenge or identification of problems that engage interest. Excessive zeal or excessive motivation to succeed quickly. Lack of access to areas of imagination. Lack of imaginative control and inability to focus on one idea. Inability to distinguish reality from fiction.

To the second category belong an equally wide and diverse set of factors though most of them relate to the way organizations respond to visions and deal with them. The most critical among those factors are basic corporate culture norms that could blur a vision and retard effectiveness as well as implementation failure to get strategic commitment (El Namaki 1992).

4 Conditions for Effectiveness of Visions There are generic conditions for a vision to be effective. And effectiveness here implies ability to translate into tangible goals and strategies as much as the ability to have followers who believe in the conceived vision and are ready to pursue the identified track. Those generic conditions include • Being realistic and feasible; simple and clear. • Providing a challenge for the whole organization.

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Mirroring the goals and aspirations of the constituents. Far but close, in terms of time span and organizational commitment. Able to focus the attention with respect to scope and time. Translatable into goals and strategies. Endorsed and frequently articulated by top management. Derived from a sense of direction (El Namaki 1992).

5 The Emerging Span of AI-derived Visions A vision implies a capability construct. This capability construct is built around three prime components: technology, capital, and managerial competence. Artificial intelligence technology-related dimension represents the prime component of this construct. This extends over a wide front that include, among others, cognitive computing, machine learning, deep learning, predictive APIs, natural language processing, image recognition, and speech recognition. And those will touch some of the basic foundations of industries from healthcare, marketing, finance, and security to logistics and communication. Capability constructs will constitute a fundamental element of future visions. There is for each vision-­ induced strategic thrust a capability construct conducive to that specific vision (Hintze 2016; Ransbotham et al. 2017). Visions within an AI era will be the outcome of a blend of novel arenas, intelligent competition, and technology-rooted capabilities. The three forces will interact within a cause and affect pattern. It is more likely than not that AI-induced technology will take the lead with novel arenas emerging as a consequence and the capability construct acting as a dependent variable (Lewis et al. 2011) (Figs. 8.1 and 8.2). To be specific AI-driving forces will lead to the following shifts.

5.1 Arena Shift AI technologies will lead to a fundamental restructuring and the emergence of new arenas.

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Dynamic resource profiles

Emerging technologyinduced arenas Fig. 8.1  The emerging driving forces of AI-derived visions

Conceive the vision

Lead Create the drive Fig. 8.2  Leading with vision

An arena is a delineated boundary for combat. Within a business context it would describe a boundary for competitive behavior among a set of related industries or a segment of the economy. Artificial intelligence will disrupt this segmentation leading to the emergence of novel industries as much as a reconfiguration of existing industries and the emergence of new business arenas. Intelligent manufacturing software. Emerging future arenas most quoted today include Internet of Things (IoT), mobility, digital logistics, 3D printing, robotics, advanced life sciences, cyber security, and big data among others. Business visions will have to reflect this dynamic shift and the ultimate shape of the business environment.

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5.2 Competitive Advantage AI technologies will change the essence of competitive advantage. AI technologies will reconfigure competitive behavior. There is first demand generation. Consumer identification of products and markets will be running through cyber domains. Data analysis will also allow better learning from the past to predict—or change—the future. Historic data would lead to the unraveling of future opportunities and risks. Data-­ driven analytics will lead to data-driven marketing. Privileged access to data will become the focus of rivalry among corporations. Parameters of competition will undergo fundamental shifts and, with them, the driving forces of business visions.

5.3 Capability Construct AI technologies will change the resource dynamics of firms. A vision implies a capability construct. This capability construct is built, in the first place, around a technology capability. Artificial intelligence technology-related dimension will go all the way from cognitive computing and machine learning to deep learning, predictive APIs, natural language processing, image recognition among others. And those will touch some of the basic foundations of industries from healthcare, marketing, finance, and security to logistics and communication. Capability constructs will constitute a fundamental element of future visions. Each vision-induced strategic thrust will induce a capability construct relevant to that specific vision.

6 Novel Technologies and Management Visionary Competencies Novel technologies are inducing massive change in almost all aspects of management as we have known them for decades (El Namaki 2017, 2018). It is disruptive change altering many of the stable premises of business and the concept of strategy. A radically different view of the

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consumer, the market, the corporation, and top management is emerging. Top management competencies will, more likely than not, go beyond the familiar traits repeated over decades. These competencies should express an ability to see beyond the tangibles of the immediate present. They should pierce through the fog of the future and the inherent almost science fiction-like picture of this future. Visions are inherent in this process. Demands of tomorrow’s ever-changing environment will require visions to be dynamic. Dynamic visioning is the ability to see ultimate goals while the individual, the organization, and the environment are in motion. It is also the ability to mentally project an image or scene, consistently recalling and adjusting what has been projected to the emerging realities, recognizing the significant parts of what is derived, and then acting on it. Predictive and prescriptive data analysis provide a key to dynamic visioning. They support top management by providing contours of a future of the environment and the emerging state of the organization. These images could be rather sharp at the edges but blurred at the core. Dynamic visioning is a competency that will require thinking outside the box or pointing to a new direction that contrasts sharply with past practice. It is taking the organization beyond familiar grounds. Resorting to a series of shifting panels projecting different states of future comfort. It also demands Zeal! Zeal implies persistence and consistency in the pursuit of visionary aspirations. Constraints and eventual temporary failures do not undermine either the will to achieve or the intent to break new grounds. And it finally demands exploring the opportunity of the unknown as dynamic visions constitute a search for opportunities within an amorphous environment. It is the skill of tracing contours of opportunities within unstructured situations. And it is the ability to pierce the fog of the future and reach out to possible venues for achievement.

7 Summary and Conclusions Vision is a depiction of a future state an organization, a corporate culture, a technology, or an activity may aspire to realize, and the relative position of the organization within that time-space continuum. It is a

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conceptualization of a new and desirable future reality that can induce achievement and motivate followers. Conceiving a vision has never been as critical as today. The era of data sciences and artificial intelligence is upon us and the disruptive implication of those forces calls for a significant shift in the content of visions and the pertinent visionary leadership. Artificial intelligence will influence the process of vision conception as well as the ultimate shape of the vision itself. Conventional building stones of visions, that is, arenas, competitive advantage, and resources will give way to a new AI-induced era of dynamic arenas, intelligent competition, and dynamic resource construct. Demands of tomorrow’s ever-changing environment will require visions to be dynamic. Dynamic visioning is a competency that will require thinking outside the box or pointing to a new direction that contrasts sharply with past practice.

References Collins JC, Porras J I (1996) Building Your Company’s Vision. Harvard Business Review, Vol. 74, No. 5, pp. 65–78. El Namaki MSS (1992). Creating a Corporate Vision Long Range Planning, Vol. 25, No. 6, pp. 25–29. https://doi.org/10.1016/0024-­6301 (92)90166-­Y. El Namaki MSS (2017). Disruption and the Changing Concept of Leadership. International Journal of Management and Applied Research Vol. 4, No. 2, pp. 122–129. https://doi.org/10.18646/2056.42.17-­010. El Namaki MSS (2018) Disruption in Business Environments: A Framework and Case Evidence. International Journal of Management and Applied Research, Vol. 5, No. 1, pp. 1–7. https://doi.org/10.18646/2056.51.18-­001. Hintze A (2016) Understanding the four types of AI, from reactive robots to self-aware beings. The Conversation, [Online] Available from: http://theconversation.com/understanding-­the-­four-­types-­of-­ai-­from-­reactive-­robots-­to-­ self-­aware-­beings-­67616 [Accessed on 1 March 2019]. Lewis PR et al (2011) A Survey of Self-Awareness and Its Application in Computing Systems. in: 2011 Fifth IEEE Conference on Self-Adaptive and

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Self-Organizing Systems Workshops, https://doi.org/10.1109/SASOW. 2011.25. Nutt PC, Backoff RW (1997) Crafting Vision. Journal of Management Inquiry, Vol. 6, No. 4, pp. 308–328. https://doi.org/10.1177/105649269764007. Ransbotham S et al (2017) Reshaping Business With Artificial Intelligence. MIT Sloan Management Review, Vol. 59, No. 1, pp. 1–17.

9 Thinking

1 The Brain and the Nervous System The human brain is a complex organ. One of the sources of complexity is the different media of communication. The brain sends and receives chemical and electrical signals. Different signals control different processes, and the brain interprets each differently. Some messages are kept within the brain, while others are relayed through the spine and across the body’s vast network of nerves to distant extremities. To do this, the central nervous system (CNS) relies on billions of neurons. The nervous system can be divided into two major regions: the central and peripheral nervous systems. The central nervous system (CNS) is the brain and spinal cord, and the peripheral nervous system (PNS) is everything else. The peripheral nervous system is on the periphery—meaning beyond the brain and spinal cord. The brain is the center of the human nervous system. The brain can be said to have three main parts: the brain stem, the cerebrum, and the cerebellum. The cerebrum is associated with information storage and processing; the cerebellum is responsible for balance, posture, and coordination of movements; and the brain stem plays a vital © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_9

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role in controlling breathing and heart rate along with some other important body processes. There are two ways to consider how the nervous system is divided functionally. First, the basic functions of the nervous system are sensation, integration, and response. Secondly, control of the body can be somatic or autonomic—divisions that are largely defined by the structures that are involved in the response (Basic Structure and Function of the Nervous System, Open Stax College).

2 Neurons as Pivots Nervous tissue contains two basic types of cells: neurons and glial cells. The neuron is the more functionally important of the two, in terms of the communicative function of the nervous system. Neurons are cells referred to as a process. There is one important process that every neuron has called an axon, which is the fiber that connects a neuron with its target. Another type of process that branches off from the soma is the dendrite. Dendrites are responsible for receiving most of the input from other neurons. Neuron can be considered as the basic unit of the nervous system, which processes and transmits information by means of electrochemical signals. Sensory neurons respond to external stimuli that affect the sensory organ cells. Motor neurons, on receiving signals from the central nervous system, bring about responses at the target organs. Interneurons act as the connectors between neurons. Neurons are of different shapes and sizes The human brain contains 86.1 billion neurons.

3 Neurons and Synapses Most neurons send signals via their axons. Neural signals propagate along an axon in the form of electrochemical waves called action potentials, which produce cell-to-cell signals. Synapses may be electrical or chemical. Electrical synapses make direct electrical connections between neurons, but chemical synapses are much more common, and much more diverse in function.

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Basically, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. In fact, it is a “simple” device that receives data at the input and provides a response. First, the neural network learns to correlate incoming and outgoing signals with each other—this is called learning. And then the neural network begins to work—it receives input data, generating output signals based on the accumulated knowledge. Most likely, the initial evolutionary task of a neural network in nature was to separate the signal from noise. “Noise” is random and difficult to build into a pattern. A “signal” is a surge (electrical, mechanical, molecular), something that is already by no means random (Jividen 2022).

4 Types and Functions of Neurons Neurons vary in structure, function, and genetic makeup. They could perform a sensory, motor, or interneuron function. Sensory neurons help the tasting, smelling, hearing, seeing, and feeling functions. They are triggered by physical and chemical inputs from the environment. Sound, touch, heat, and light are physical inputs. Smell and taste are chemical inputs. Motor neurons influence voluntary and involuntary movements. There are lower and upper. Lower motor neurons carry signals from the spinal cord to the smooth muscles and skeletal muscles. Upper motor neurons carry signals between the brain and the spinal cord. Interneurons are neural intermediaries found in the brain and spinal cord. They’re the most common type of neuron. They pass signals from sensory neurons and other interneurons to motor neurons and other interneurons (Fig. 9.1).

5 Neurons and Thinking Neurons are the keys to thinking. They can detect external stimuli, or information about the outside world, and can transmit that information to other nerve cells. It’s the transmission of information or

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Fig. 9.1  Neuron system construct

“communication” among cells that is the fundamental physical basis for thinking. Neurons are the pivot here as they transport chemicals essential for the “communication” and, consequently, the thinking process. These neurons communicate via chemical synapses. Most of what humans consider “thought” is a motor process involving motor neurons used for forming speech. Thinking a word uses the same brain process as saying a word, but the motor signal from the brain is inhibited before it hits the nerves responsible for moving tongue, throat, and lip muscles. A given neuron receives hundreds of inputs. These inputs add and subtract in a constantly evolving pattern, depending on what the brain is thinking. This is a process called synaptic integration, which determines whether a neuron becomes active. Neural pathways are links between neurons that “wire” the brain so that the brain can control different body functions and thinking processes (www.toridawnselden.com/all-­about-­ habits/what-­is-­a-­neural-­pathway-­to-­a-­new-­habit) (Fig. 9.2).

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Fig. 9.2  Neurons and action. Soma/cell body: Keeps cell alive and functioning. Dendrite: Receive info from another neuron and conduct it toward cell body. Axon: Passes message through its terminal branches to neuron, muscle, or glands. (Source: Watsonnov J (2017) “Neuroscience Basics: The Neuron” https://owlcation.com/)

6 From Thinking to Reasoning and Learning Thinking is a complex process that relates to reasoning learning and awareness. Reasoning and thinking relate when reasoning implies taking facts and evidence perceived by the senses and combining it with thinking to draw conclusions. There is a wide variety of reasoning. The most common types include inductive and deductive reasoning. Inductive reasoning refers to the process of starting from specifics and expanding the concepts to cover a range of observations. Deductive reasoning implies starting from a general rule and moving to a specific item. Learning relates to thinking when it is the outcome of the thinking process. Learning by thinking refers to the construction of cognitive artifacts (such as conceptual models and theories about the physical world) and mental models of both simple and complex systems. They aim at the explanation and simulation of transactions in a complex system and thus lead to thought experiments. Learning by thinking transcends immediate

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experiential and associative learning and is closely related to inferential learning and reasoning (Seel 2012). Awareness, as we said earlier, relates to thinking of a situation or situations observed by senses. The mind creates, through awareness, thoughts about a situation observed or sensed (https://warriorsway.com › thinking-­ vs-­awareness, November 19, 2009). Thinking often has to do with the past or the future while awareness can only occur in the present moment, and generally has very little to do with words that are not being said or images that are not being seen. Awareness involves an invitation to see, hear, smell, taste, and feel while thinking invites comparison to past occurrence or an expectation of what is to come.

7 Thinking and Learning as a Managerial Competency Fulfillment of a managerial function requires thinking and learning. Thinking and learning are emerging as key managerial competencies. The core is “learning to learn,” defined as the ability to pursue and persist in acquiring knowledge, and competencies, and to organize one’s own learning through, among others, effective management of time and information. Executives that are open to learning are better able to conduct awareness, think, and comprehend change (Jarvis 2006). Learning and thinking competencies are related to what is known as competency-based learning. Competency-based learning is a technology developed in order to enhance learning as a mode of competency generation. Participants understand the competencies they need to master in order to achieve their goals. They progress through learning processes without time constraints, explore diverse learning opportunities, collaborate in learning activities with communities of peers and mentors, and reflect on their own learning achievements by seeing what they’ve mastered, what they still need to accomplish, and where to improve (E learningindustry.com/key-­concepts-­of-­competency-­based-­learning).

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Competencies

Competency model Competency based learning processs Role based learning paths Learning acons

Fig. 9.3 Competency-based pin/668714244648679675/)

learning.

(Source:

https://www.pinterest.com/

This could prove conducive to the absorption, adoption, and adaptation of novel technology-related competencies as predictive data analysis, for example (Fig. 9.3).

8 Summary and Conclusions Thinking is one of the most complex and most demanding cognitive competencies. It is a system with inputs, transformation processes, and ultimately, an output. Inputs are drawn from different strata of the brain. Transformation relies on a multiple neural and synaptic flows and output demands a multiple of cognitive competencies. The chapter starts with a broad analysis of the brain and the human nervous system. This is followed by an analysis of the position and role of neurons and the derived thinking. Thinking involves motor process created by motor neurons in order to form speech. The chapter concludes with an exploration of the relationship between thinking and reasoning thinking and learning and thinking and awareness.

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Fulfillment of a managerial function requires thinking and learning. Thinking and learning are emerging as key managerial competencies. Competency-based thinking and learning provide an instrument for technology-derived competency generation.

References Queensland Brain Institute How do neurons work? How do neurons work? https//qbi.uq.edu.au/brain-basics/brain/brain-physiology/how-doneurons-work. Jividen S (2022) What Are Neurons? Verywell Health. 06 Apr 2022 https:// www.verywellhealth.com/neurons-­5217652? Seel NM (2012) Learning and Thinking. In: Seel, NM (Eds) Encyclopedia of the Sciences of Learning. Springer Boston MA. https://doi.org/10.1007/ 978-­1-­4419-­1428-­6_584. Jarvis P (2006) The Lifelong Learning and the Learning Society. Trilogy Volumes 1–3, March 2010. https://www.pinterest.com/pin/668714244648679675 Watsonnov J (2017) Neuroscience Basics: The Neuron https://owlcation.com/. www.toridawnselden.com/all-­about-­habits/what-­is-­a-­neural-­pathway-­to-­a­new-­habit.

10 Cognitive Predictions

1 Cognition and the Cognitive Functions of the Brain 1.1 What Is Cognition? Cognition is a term that refers to the mental processes involved in gaining knowledge and comprehension. It is defined as “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.” Cognitive processes include thinking, knowing, remembering, judging, and problem solving. They also involve higher-level functions of the brain as language, imagination, perception, and planning (Rodriguez 2020). Roots of the word “cognition” lie in the Latin word “cognoscere,” which is to “get to know.” Basic cognitive processes of perception, attention, and memory could lead to creativity. Creative cognition involves perceptive original interpretation of experiences or associations with support from memory and stored information. Creative cognition may also be metacognitive and

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tactical, that is, it allows the individual to exert a degree of control over his own thinking and direct individual cognition to the generation of original and useful ideas, insights, and solutions.

1.2 The Prime Cognitive Functions of the Brain Cognitive functions of the brain spread over a wide span with memory and perception at the forefront.

Memory As we stated earlier, memory is the process by which knowledge is encoded, stored, and retrieved. It could be short term or working memory or long term. Short-term memory and working memory terms are often used interchangeably. There are scholars who claim that some kind of manipulation of remembered information is needed in order to qualify the task as one of working memory (Atkinson 1968). Senses are involved in memory creation. Visual memory involves the ability to store and retrieve previously experienced visual sensations and perceptions when the stimuli that originally evoked them, are no longer present. Auditory memory, on the other hand, involves the skills of attending, listening, processing, storing, and recalling. Sequential memory requires items to be recalled in a specific order. Visual sequential memory is the ability to remember things seen in sequence, while auditory sequential memory is the ability to remember things heard in sequence (Atkinson 1968) Sensory memory is the shortest-term element of memory. It is the ability to retain impressions of sensory information after the original stimuli have ended. It acts as a kind of buffer for stimuli received through the five senses of sight, hearing, smell, taste, and touch, which are retained accurately.

Perception Perception is the interpretation of what is sensed. Sensation is the absorption of information by a sensory receptor. Visual perception refers to the

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brain’s ability to make sense of what the eyes see, while auditory perception is the ability to identify, interpret, and attach meaning to sound. Lack of experience may cause a person to misinterpret what he has seen or heard. In other words, perception represents our apprehension of a present situation in terms of our past experiences. “We see things not as they are but as we are” (Immanuel Kant 1781).

Attention Attention is the ability to actively process specific information in the environment to the exclusion of others. It is an ability to choose and concentrate on relevant stimuli. Attention is a basic component of human biology. Orienting reflexes help determine which events in environment need to be attended to, a vital process. There are, however, different segments of attention. One of those makes a distinction between arousal, focused, sustained, selective, alternating, and divided attention (Sohlberg and Mateer 1987, 1989). Another classifies attention as sustained, alternating, selective, and focused and limited attention. Perception and attention are related to each other by the outcome that an individual experiencing the stimuli assigns his/her awareness to the object he/she identifies (Bayne and Montague 2011).

Logical Reasoning Logical reasoning is the process of using a rational, systematic series of steps based on sound mathematical procedures and given statements to arrive at a conclusion. In logic, there are two broad methods of reaching a conclusion, deductive reasoning, and inductive reasoning. Deduction begins with a major premise followed by a minor premise. In inductive reasoning, broad conclusions are drawn from specific observations.

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1.3 Cognitive Psychology Cognitive psychology connotes the notion that individuals do not respond to the external events, but to their interpretation of those events. They receive external information and convert it into a behavior. Put differently the focus is on external stimuli leading to knowledge, understanding, and thinking about the environment and how their thoughts influence their behaviors. The cognitivists emphasize is on the study of mental processes used in learning, perceiving, thinking, remembering, deciding, and problem solving. These mental processes help us understand and react to our environment (Fig. 10.1).

1.4 Flexible Cognition? Cognitive flexibility is a skill that enables individuals to switch between different concepts and the accommodation of demands of stimuli. It is essentially about learning to learn and being able to be flexible about the way you learn. It is also about the ability to restructure knowledge in multiple ways depending on changing situational demands (Spiro, Human Intelligence Perception

Language Cognitive Psychology Thinking & problem solving

Attention Memory

Fig. 10.1  Key elements of cognitive psychology. (Source: How Cognitive Psychology Can Improve Blog Content, By Melanie Sovann | June 4, 2019, https:// returnonnow.com/2019/06/how-­c ognitive-­p sychology-­c an-­i mprove-­b log­content/)

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Feltovich, Jacobson and Coulson 1995). Cognitive flexibility provides the ability to see that pursued venues are not fulfilling identified goals and a need for course adjustment is there (Cognitive Flexibility’ Is Key to Learning and Creativity, Neuroscience, ·June 26, 2021). Flexible cognition is related to what is known in cognition as “Executive functions” (EFs). Those include high-order cognitive abilities such as working memory, inhibitory control, cognitive flexibility, planning, reasoning, and problem solving. They allow for goal achievement as well as accommodation of disruptions. Cognitive flexibility connotes several aspects (Prahalad 2016) including the process of transition of “stream-of-thoughts” and attention, the updating of beliefs, multi-faceted observation as well as deconstructing thoughts. Several behavioral interventions enhance the process. Those include paying attention to one’s thoughts; organizing experiences by creating mental categories for information and situations and aligning encoding and retrieval cues; recording relevant experience and dumping irrelevant data; understanding in order to remember; learning new skills leading to mental flexibility; and being open to new stimuli in order to promote mental flexibility (https://www.opencolleges.edu.au/informed/features/ worrymuch-­brain-­scientists-­say-­expressive-­writing-­can-­help/). Others undermine the process. Those include memory, confirmation bias, salience, myopia, low latent inhibition, information bottleneck, rigid thinking, and reinforcement (Briggs 2018).

1.5 Biased Cognition Confirmation bias is the tendency to seek out or interpret evidence in such a way that supports our own strongly-held beliefs or expectations. This means that, given access to the same set of data and information, different people can come to wildly differing conclusions. Feeding into confirmation bias can lead us to make ill-informed choices or even reinforce negative stereotypes. For this reason, it is important to remember to seek out information that both confirms and contradicts your presumptions about a certain topic. Sampling bias is one of

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those biases. It is a kind of bias that allows for faulty conclusions based on inaccurate sample groups or data. Generally, the cause of sample bias is in poor study design and data collection.

2 The Cognition Framework As we stated earlier, cognition is the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses. Cognitive functions are brain-based skills one needs in order to carry out any task from the simplest to the most complex. Human cognition can be conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language).

2.1 Cognitive Stimuli and Sensory Receptors Cognitive competencies of the brain depend on stimuli. A stimulus is a trigger that evokes a specific functional reaction in an object or provokes an action or response. In physiology, a stimulus is a detectable change in the physical or chemical structure of an organism’s internal or external environment. An organism’s ability to detect external stimuli is called sensitivity. The human brain has sensory receptors that can receive information and elicit a reflex via stimulus transduction (Biga et al. 2019). Sensory receptors are primarily classified as chemoreceptors, thermoreceptors, mechanoreceptors, or photoreceptors. Chemoreceptors detect the presence of chemicals. Thermoreceptors detect changes in temperature. Mechanoreceptors detect mechanical forces. Photoreceptors detect light during vision. These sensory receptors perform countless functions. During vision, rod and cone photoreceptors respond to light intensity and color. Taken together those receptors can induce a predictive function based on imagination, planning, and forecasting. A major role of sensory receptors is to help learn about the task and the general environments of the individual. A variety of stimuli originating from varying sources are received and converted into nervous system

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electrochemical signals through sensory transduction. The process starts with a detection of a stimulus by a receptor which generates a graded potential in a sensory neuron. If strong enough, the graded potential causes the sensory neuron to produce an action potential that is relayed into the central nervous system (CNS), where it is integrated with other sensory information—and sometimes higher cognitive functions—to become a conscious perception of that stimulus.

2.2 Process of Prediction Several works have hypothesized that the brain could indulge into a process of cognitive future event prediction. There exists a view that events are predictable by the brain if they occur in a non-random fashion, allowing the brain to extract either deterministic or probabilistic regularity of the relationship between different events. Those predictive formulations can be based on knowledge gained through long-term experience (Bar 2007) or learning triggered by short-term exposure to non-random patterns (Schubotz 2007). Predictions generated within non-random contexts could be, however, the outcome of learning and identified associations, especially temporal dependencies between events (Butz et al. 2003; Bar 2007). Evidence supports, however, the proposition that the brain may still employ similar predictive strategies in an attempt to extract a pattern from random inputs (Schubotz 2004) or relate the novel input to familiar knowledge by generating analogies, thus facilitating the processing of new stimuli (Bar 2007). Predictions could cover the short or long “terms.” The brain could predict events which are expected to occur within seconds in contrast to those which may occur in the distant future. Long-term prediction is usually used “offline” and is not necessarily coupled with any immediately relevant or running process in contrast to short-term prediction which is more likely to be used “online” for regulating the ongoing behavior. It goes without saying that predictions can lead to faster recognition, interpretation, and possible response to emerging events within an environment (Bar 2007). Anticipatory or predictive processing reflects one of the core, fundamental functions of the brain. Prediction in cognitive and

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neural processing allows us to direct our behavior toward the future, while remaining well-grounded and guided by the information pertaining to the present and the past. But where do predictions take place? There are different approaches to the process of conceptualizing and differentiating the role of different brain areas in prediction. Holistic models as the predictive coding model allow the brain to operate as a “Bayesian inference machine,” (based on Bayes theory or the probability of an event based on prior knowledge of conditions that might be related to the event) constantly building models of the environment and the body, allowing the brain to predict their respective future states (Friston and Stephan 2008). Importantly, such general nature of brain processing can then account for many phenomena across domains and processes, for example, perception, attention, action, or learning (Friston and Stephan 2008).

2.3 Conceiving of Visions As we stated earlier vision is a mental perception of the kind of environment an individual, or an organization, aspires to create or emerge within a broad time horizon and the underlying conditions for the actualization of this perception. And a prospective visualization of the position of the individual or the organization within that emerging environment. It could also be a “concept for a new and desirable future reality.” A vision belongs to what we may term the process of direction setting, or the identification of a point in the future, often the distant future, and a strategy for getting there. Creating this direction requires challenging conventional wisdom and analytically looking for patterns that answer very basic questions about the future. Individuals perceive their visions and do not ask themselves whether they have one (El Namaki 1992). The key to visioning may be found in the argument that prediction in cognitive and neural processing allows a futuristic behavior guided by present-day information and yesterday’s knowledge. This prediction can take place on different temporal scales. It can relate to knowledge gained through long-term experience (Bar 2007) or learning triggered by

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short-­term exposure to non-random patterns (Schubotz 2007). This could lead to the hypothesis that predictive events could constitute a vision. They could constitute a vision if they project a sense of direction, a disruptive innovation, an ultimate goal, a resource base, and a strategy all within a novel distant environment marked by a blurred boundary and disruptive contours. The essence of a vision is the identification of an attribute to each of those variables and the integration of all attributes within a mental view or a most likely scenario and a projection of the position of the individual or the organization within this construed texture. “The brain does not only receive data from the sensors and organs, it also delivers continuous forecasts to them” (Muckli 2018). It is the nearest thing to a prediction mechanism that is optimizing its own predictions of the environment it is navigating through (Muckli 2018). Put differently, the brain is essentially a “prediction machine” that is constantly busy comparing new input from the environment with predictions generated by its own internal models. This is the only way that a human brain is able to adapt to ever new situations and environments (Neuroscience, March 2, 2018). Cognitively conceived predictive events will have to meet certain criteria to qualify as “vision.” They will have to tie the future to the present and the past, the implicit to explicit memory, and the managerially contemplated to strategic intent. The construed “vision” will have to be “reachable,” have a far but close time span, and constitute a managerial and technological challenge. It should form a managerial point of view, able to focus the attention and translate into goals and strategies. A predicted vision may imply a radical and far-reaching change in the capability profile of the organization. The essence of a vision is the identification of an attribute to each of those variables and the integration of all attributes within a mental view or a conceived prediction. Predicted events may not lead to a vision, however, if aberrations abort conception. Those aberrations could arise from the way individuals approach their environment and react to external stimuli or from their inability to tolerate ambiguity, to incubate, to access areas of imagination and to distinguish reality from fiction (El Namaki 1992).

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2.4 Role of Science Fiction Science fiction is one of the possible illustrations of brain predictions. Science fiction is “fiction dealing principally with the impact of actual or imagined science on society or individuals …” (Merriam Webster, 2020). The content is imaginative based on brain predictions. It relies heavily on hence untackled boundaries of science in order to configure novel settings, characters, themes, plots, and visions. It blends scientific predictions with projected elements of sociology, psychology to philosophy. Hard science fiction predictions rely on prospective change in sciences exploring the workings of the natural world as physics, chemistry, and biology while soft science fiction forecasts prospective human behavior, interactions, thoughts, and feelings (Helmenstine 2019; El Namaki 2020). Predicted visions whether of the hard or soft genre pave the way toward a reflection on prospective human interactions with each other, with technology, with environment, and with the future.

3 The Cognitive Competencies of AI Cognition is, until today, largely a human proper. Many AI efforts aim at simulating the cognitive functions of the brain. AI has started trying to recreate some of these cognitive functions of the brain through, for example, the ability to focus on a specific task and ignore the rest of the environment. Resort to episodic memories to remember autobiographical events is yet another. Continual learning or the ability to learn new tasks without forgetting previous knowledge is yet another (Rodriguez 2020). Yet the outcome is nowhere near the cognitive performance of the human brain. Many of the current deep learning applications address “perception” tasks related to object recognition, natural language processing (NLP), translation, and broad data correlation. Its results are based on differential programming and sophisticated models for data correlation. These models are, however, challenged when encountering situations sparsely sampled in the training dataset, or even absent from the training data.

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Artificial neurons cannot, also today, properly provide abstraction, reasoning, and interpretation competencies akin to those of the human brain. Deep learning lacks the cognitive mechanisms essential for the performance of human intelligence as it lacks abstraction, context, causality, interpretation, and intelligible reasoning. If one is to segment deep learning capabilities into three segments: elementary, advanced, and brain-like, one may conclude that we are going through the elementary phase right now. Elementary capabilities possess intuitive, fast, unconscious, and habitual features. Advanced capabilities are, however, slow, logical, sequential, conscious, and algorithmic. And “Mind-like” capabilities provide contextual adaptation, abstraction, reasoning, and interpretation. Let us stress, however, that an AI with higher cognitive capabilities could change some basic premises of business conduct if only because it will help navigating through the “shared values” of human and the machine. This is, however, a long way down the road.

4 Cognitive Prediction as a Top Management Competency Training aiming at the creation or enhancement of cognitive competencies could provide controversy. Yet training the brain to conduct specific cognitive function is plausible. There are claims that cognitive training could take place if one is to pursue certain venues. Research suggests that creative practices from painting and instrument playing to autobiographical writing and language learning could improve cognitive capacities. “A 2014 study in Gerontologist reviewed 31 studies that focused on how these specific endeavors affected older adults’ mental skills and found that all of them improved several aspects of memory like recalling instructions and processing speed” (Train your brain, February 15, 2021, Harvard Health Publishing, https://www.health.harvard.edu/mind-­and-­mood/ train-­your-­brain). For that to take place the exercise should pause challenge, complex, and learning-driven. Challenging the brain leads to brain growth; complex activities force the brain to work on specific thought

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processes like problem solving and creativity; and learning requires a level of constant practice. Brain plasticity—the brain’s ability to grow and change shape in response to environmental pressures—could be conducive to the process of enhanced cognitive prediction. Prediction, on the other hand, allows future orientation of behavior toward the future, while remaining well-­ grounded and guided by the information without losing the present or the past. Predictive processing, let us stress, represents one of the key features of many cognitive functions (Bubic et al. 2010).

5 Summary and Conclusions This chapter dealt with the predictive competencies of the brain and whether artificial intelligence and other emerging technologies could help gaining this competency. Put differently, would the cognitive functions of the brain lead to a measure of predictive competency and whether the emerging technologies of artificial intelligence and data science allow for “simulated” cognitive competencies that would include predictive competency? Answer to the first question is addressed in the early pages. It addresses the basic question of what is cognition and how does the brain perform a cognitive function. These functions depend on several sources including the sensory stimuli drawn from visual, auditory, olfactory, gustatory, and somatosensory systems. Stimuli that may relate to the past or the present and, occasionally, the future. And the question becomes could a stimuli induce a predictive pulse? Put differently can the brain, in response to stimuli, predict events? Answer to the second question is addressed in the following part of the chapter. It explores the potential for artificial intelligence-derived cognitive functions that may lead to a quasi-human brain cognitive performance. The question of artificial intelligence relevance to predictive competencies is addressed and a guarded conclusion states that existing state of the art does not lead to this possibility. Artificial intelligence lacks issues of context, abstraction, and interpretation essential for this process.

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Brain plasticity—the brain’s ability to grow and change shape in response to environmental pressures—could be conducive to the process of enhanced cognitive prediction. This is however an area that will be explored in another chapter of this book.

References Bar M (2007) The proactive brain: using analogies and associations to generate predictions. Trends Cogn. Sci. 11, 280–28910.1016/j.tics.2007.05.005 [PubMed] [CrossRef ] [Google Scholar] 9. Bayne T, Montague M (eds.) (2011) Cognitive phenomenology. Oxford University Press. pp. 35. Oxford University Press. 1 Dec 2011. Biga L et al (2019) Anatomy & Physiology – Simple Book Publishing, open. Oregonstate.education 2019. Bubic A et al (2010) Prediction, cognition and the brain. Front Hum Neurosci. 2010 Mar 22. Butz M et  al (2003) Anticipatory Behavior in Adaptive Learning Systems. Springer-Verlag Berlin Heidelberg. El Namaki MSS (2020) How an Integration of Artificial Intelligence and Science Fiction Could Change the Premises of Strategic Thinking”, Journal of Knowledge Management Application and Practice, 1 Aug 2020. El Namaki MSS (1992) Creating a corporate vision, Long Range Planning, Volume 25, Issue 6, December 1992. Friston K, Stephan K, (2008) Free Energy and the Brain. PubMed. Helmenstine AM (2019) What Is the Difference Between Hard and Soft Science? Thoughtco 29 Nov 2019. Muckli L (2018) Theory of predictive brain as important as evolution. Horizon, 29 May 2018. Neuroscience (2018) How the Brain Makes Predictions. Neuroscience. Neuroscience Goethe University Frankfurt Mar 2, 2018. Rodriguez J (2020) Beyond Neurons: Five cognitive functions of the human brain that we are trying to recreate with AI. The Startup, July 22, 2020. Schubotz RI (2004) Human Premotor Cortex: Beyond Motor Performance. Leipzig: Max Planck Institute for Human Cognitive and Brain Sciences [Google Scholar].

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Schubotz RI (2007) Prediction of external events with our motor system: towards a new framework. Trends Cogn. Sci. 11, 211–21810.1016/j. tics.2007.02.006 [PubMed] [CrossRef ] [Google Scholar]. Sohlberg MM, Mateer CA (1987) “Effectiveness of an attention training program”. Journal of Clinical and Experimental Neuropsychology, 9 (2), 117–130 4 published on line 4 Jan 2008.

11 Plasticity-Driven Decision Making

1 What Is Neural Plasticity? Neuroplasticity, also called brain plasticity, refers to the capacity of the brain to change and adapt in structure and function in response to learning and experience. Put differently it is the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. Neuroplasticity significance lies in two driving forces: learning and memory. The first theoretical notions of neural plasticity were developed in the nineteenth century (The Principles of Psychology, William James, 1890). In the twentieth century, others proposed that neurons in adults break down and rebuild (Fuchs and Flügge 2014). Scientists now think that neuroplasticity occurs throughout all life stages, from childhood onwards. The brain can rearrange itself in terms of the functions it carries out, as well as in terms of the basic underlying structure (Roland and Zilles 1996).

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2 The Systems Approach to Neural Plasticity Neural plasticity could be looked at as a force field with system constituent elements, that is, inputs, outputs, and transformation mechanisms. Inputs are delivered through learning, memory, and experience. Transformation occurs through the functional and structural processes of the phenomenon. Outputs could be function induced or structure induced (Fig. 11.1).

2.1 The Input Memory Let us start by distinguishing different types of memory. Functionally, memory could be explicit or implicit. Explicit (declarative) memory includes semantics (words) and episodic (events) memory. Implicit (non-­ declarative) memory includes, primarily, nonverbal and motor memory.

Input

•Memory •Experience •Learning •Trauma

Transformation plasticity

•Function •Structure

Output

•Function- related •Struccture- related •Strategic thinking related

Fig. 11.1  Systems framework of the neural plasticity force field

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A temporal perspective of memory can divide memory into working, short term and long term. The working memory is a cognitive system with a limited short-term capacity that holds information temporarily (less than 30  seconds). Information can then be temporarily stored “online” in the brain, so that it is available for further cognitive processing. Short-term memory provides the capacity for holding, but not manipulating, a small amount of active, readily available information for a short period of time. Long-term memory may last a lifetime, but can be modified. Structural plasticity performs several memory-related tasks. It can increase storage efficiency in sparsely connected neural networks and it could also increase stability of long-term memories.

Learning Neuroplasticity underlies the capacity for learning as it enables mental and behavioral flexibility. Learning induces the brain to create new synapses or gaps between one neuron cell and another that carry electrochemical information between neurons. To learn is to accumulate and process knowledge and information through experiences Neuroscientists stress that if one performs a task or recalls some information that causes different neurons to fire in concert, it strengthens the connections between those cells. Over time, these connections become prime routes that link various parts of the brain—and stimulating one neuron in the sequence is more likely to trigger the next one to fire (Bernard 2010). Learning and new experiences cause new neural pathways to strengthen whereas neural pathways which are used infrequently become weak and eventually die. This process is called synaptic pruning. Mature brains continue to show plasticity as a result of learning.

Experience Two types of experience-related neural plasticity have been described in literature. One is experience-expectant, and the other is experiencedependent.

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Experience-dependent neural plasticity allows the nervous system to incorporate other types of information from environmental experiences that are relatively unpredictable and idiosyncratic. These experiences are unique to the individual and depend on the context in which development occurs, such as the physical, social, and cultural environment (Donna J. Cech DHS, PT, PCS, Suzanne “Tink” Martin MACT, PT, in Functional Movement Development Across the Life Span (Third Edition), 2012). Experience-Expectant Plasticity describes the normal, generalized development of neuron connections that occur as a result of common experiences that all humans are exposed to in a normal environment. These early universal experiences are visual stimulation, sound (specifically voices), and bodily movement.

2.2 The Transformation Transformation takes place through functional or structural plasticity. Functional plasticity connotes the brain’s ability to move functions from a damaged area of the brain to other undamaged areas. Functional plasticity moves functions across the brain areas. Structural plasticity connotes the brain’s ability to actually change its physical structure as a result of learning. This happens through processes such as axonal sprouting, homologous area adaptation, crossmodal reassignment, map expansion, and compensatory masquerade. Structural plasticity may be required to form novel or to delete existing synapses. Brain structural plasticity is an extraordinary tool that allows the mature brain to adapt to environmental changes, to learn, to repair itself after lesions or disease, and to slow aging. Developmental plasticity is yet another transformational process whereby undamaged axons grow new nerve endings to reconnect the neurons whose links have been severed (Fig. 11.2).

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al

ea Reh rs

Sensory input

Sensory register

Short-term memory

Long-term memory

g ttin rge Fo

g ttin rge Fo

Fig. 11.2  Brain memory storage. (Source: How Are Memories Stored and Retrieved? Science ABC, Ishan Daftardar. Last Updated On Jul 2022)

2.3 The Output Fundamentally, the nervous system needs to rearrange itself in order to adapt to the unfolding situation that it faces. The genes program the body to have neuroplasticity so that one can survive in unpredictable environments. There is a functional response and a structural response. Functional plasticity output could cover a wide range including homologous area adaptation and map expansion. Structural Plasticity deals with brain’s ability to change its physical structure as a result of learning, involving reshaping individual neurons (nerve cells). Both impact brain’s task, trauma, and memory (Gamma, E. (2021, March 24). What is brain plasticity? Simply Psychology. www.simplypsychology.org/brain-­plasticity.html).

3 Plasticity Contribution to Managerial Competencies Plasticity impacts upon four prime managerial competencies: strategic thinking, vision conception, learning curve composition, and strategic control.

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3.1 Strategic Thinking Strategic thinking is a cognitive process that delivers visions and goals and ways and means of their fulfillment. Today’s complex and uncertain environment demands compatible strategic thinking processes. It is the author’s contention (hypothesis) that neural plasticity accommodates those environmental disruptions and, as a result, induces three strategic thinking-related cognitive competencies: the conceiving of visions, the conversion of learning data into learning curves, and the resort to implicit memory data in order to create strategic control parameters.

3.2 Vision Conception “Visions are mental perceptions of the kind of environment an individual, or an organization, aspires to create within a broad time horizon and the underlying conditions for the actualization of this perception.” It could also be a “concept for a new and desirable future reality that can be communicated throughout the organization”. Visions are the outcome of three processes: creativity, competitive advantage, and pertinent resources. Neuroplasticity leads to better-diffused thinking, that is, connecting different regions of the brain to get an insight. Many of these complex concepts involving different areas of the brain induce those insights. It is the author’s contention (or hypothesis) that visions could be derived from interrelated insights (Ayk Martirosyan August 23, 2021 Neuroscience).

3.3 The Learning Curve The learning curve theory proposes that a learner’s productivity in the conduct of a task improves with the repeated performance of the task, over time. Learning curve models are derived from the basic premise that individuals acquire knowledge, and competency, by the repeated conduct of an effort. Experience-derived repetition induces relatively permanent

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changes in individual behavior. Experience-based learning could, then, be closely correlated with enhanced output. “Experience and Learning Curves.” (Encyclopedia of Management 2022). The author should like to hypothesize that the experience and learning inputs or driving forces of plasticity could induce learning curve symptoms. Neural plasticity, in this situation, enhance and stimulate the cumulative knowledge associated with the learning curve phenomenon. Learning associated with neural plasticity could provide a stimulus to the cumulative knowledge base of the learning curve (Bronfman et al. 2014; Galván 2010).

3.4 Strategic Control Control is an essential element of the process of strategic thinking. Yet different types of control require different inputs and deliver different outcomes. Operational and managerial control, the prime instruments, could provide an answer to the question “have we reached the once identified goals” but could fail at providing an answer to the strategic control question of “are we where we should have been!” It is the author’s contention (hypothesis) that the three driving forces of neural plasticity: memory, experience, and learning could help generating an answer to the core question of strategic control: are we where we should have been and if not how to get there? Learning may be the most instrumental element here as it calls upon insights and juxtaposes those to extrapolations based on experience and memories. Implicit memory could play an important role here.

4 Summary and Conclusions Plasticity is the change force within the human brain. It is the biological, chemical, and physical capacity for the brain to reorganize its structure and function. It induces shifts in functions and function instrument allocation. It is vital for the process of accommodating brain environmental change as well as brain trauma accommodation. It is also a key to brain

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function restructuring as well as the management of brain “behavior.” This role is spelled out in many contexts from learning to memory formation, yet the process of strategic thinking is seldom addressed within the overall framework of plasticity. Strategic thinking demands, after all, the creation of scenarios, choice between multiple scenarios as much as the isolation of proper modes of strategic behavior. All are inner brain processes. How does this vital element of brain performance take shape? The chapter defines plasticity and identifies plasticity driving forces as a point of start. It then proceeds to relate plasticity driving forces to the cognitive processes of memory and learning, being key pivots of strategic thinking. The outcome is a set of hypotheses relating plasticity to the core of the strategic thinking concept, that is, vision, learning, and strategic control.

References Bernard S (2010) Neuroplasticity: Learning Physically Changes the Brain. Edutopia 1 Dec 2010. Bronfman ZZ, Ginsburg S, Jablonka E (2014) Shaping the learning curve: epigenetic dynamics in neural plasticity. Front Integr Neurosci. 2014 Jul 7; 8:55. https://doi.org/10.3389/fnint.2014.00055. PMID: 25071483; PMCID: PMC4083220. Encyclopedia of Management (2022) Experience and Learning Curves. Retrieved 21 Mar 2022 from Encyclopedia.com: https://www.encyclopedia. com/management/encyclopedias-­almanacs-­transcripts-­and-­maps/experience­and-­learning-­curves. Fuchs E, Flügge G (2014) Adult neuroplasticity: More than 40 years of research. Neural Plasticity. Galván A (2010) Neural plasticity of development and learning. Hum Brain Mapp. 2010 Jun; 31(6):879–90. https://doi.org/10.1002/hbm.21029. PMID: 20496379; PMCID: PMC6871182. https://www.encyclopedia.com/management/encyclopedias-­a lmanacs-­ transcripts-­and-­maps

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Roland PE, Zilles K (1996) Functions and Structures of the Motor Cortices in Humans. Current Opinion in Neurobiology, 6, 773–781. The rewiring brain, a computational approach to structural plasticity in the adult brain 2017, pages 361–386, Chapter 17—Impact of Structural Plasticity on Memory Formation and Decline. Wikipediamaps Baddeley’s model of working memory  – Wikipediamaps/ experience-­and-learning-curves, en.wikipedia.org/wiki/Baddeley’s.

12 Intelligence

1 What Is Intelligence? Today, intelligence is generally equated to many processes. The majority ­consider it an ability to understand and adapt to the environment by using inherited abilities and learned knowledge. Also the capacity for learning, ­reasoning, understanding, and similar forms of mental activity; as well as aptitude in grasping truths, relationships, facts, meanings, and so on. And a third meaning equates it to manifestation of a high mental capacity and the faculty of understanding (https://www.dictionary.com/browse/intelligence). Intelligence within a managerial environment could have those and other connotations. The following is a review of those.

2 The Connotations of Intelligence Within a Managerial Environment 2.1 Human Intelligence Human intelligence is generally equated to the ability to acquire and apply knowledge and skills (Legg and Hutter 2007). The subject is © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_12

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heavily researched and is controversial (Cherry 2020). Almost all schools suggest that intelligence connotes the ability to learn (acquisition, retention, and use of knowledge), the ability to recognize environment-related problems, and, finally, the ability to come up with a useful solution to an identified problem. The author has opted, therefore, to confine the analysis to the following two schools of thought. Louis L. Thurstone’s school (1955) views intelligence as a function of seven different primary mental abilities that include verbal comprehension, reasoning, perceptual speed, numerical ability, word fluency, associative memory, and spatial visualization. Triarchic theory, on the other hand, relates intelligence to three traits: analytical, creative, and practical (Sternberg 1985). Analytical intelligence refers to intelligence that is applied to analysis of dimensions, valuing of problems, and arriving at solutions. Creative intelligence is the ability to go beyond what is given to create novel and interesting ideas. This type of intelligence involves imagination, innovation, and problem solving. Practical intelligence is the ability to adapt to the demands of the environment by either utilizing knowledge gained from experience to purposefully change oneself to suit the environment (adaptation), changing the environment to suit oneself (shaping), or finding a new environment in which to work (selection) (Ruhl 2020, July 16). In summary human intelligence connotes a blend of cognitive capacities going all the way from analysis and conception to creativity and expression.

2.2 Artificial Intelligence Human intelligence revolves around exploring the environment using a combination of several cognitive processes. Artificial intelligence focuses on developing software that tries to mimic human behavior. AI software reach has, however, serious limitations. It is able to identify or record aberrations from a “given” standard and re-establish a conducive state of a specific event. It is neither able to simulate the brain’s analytical potency nor graduate to the process of self-awareness and/or managing of self. One can, more specifically, identify the following areas of contrast:

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• Nature of Existence. Human intelligence revolves around adapting to the environment using a combination of several cognitive processes. Artificial intelligence focuses on finding answers to narrow and delineated problems through a software that can mimic human behavior. • Memory usage. Humans use content memory and thinking whereas, AI software confines itself to predefined built-in instructions. • Learning process. Human intelligence is based on the variants they encounter in life and responses they get which may result in millions of functions for every aspect in their lives. However, Artificial intelligence is defined or developed for specific tasks only and its applicability to other tasks may not be easily possible (Pedamkar 2022).

2.3 Hybrid Intelligence Although powerful learning algorithms are now able to extract and establish models and patterns from large-scale data sets, problems of interpretability are there. The opaque nature of some learning algorithms creates this bottleneck. There is also the gap between algorithm-based knowledge and insights and problem solving or the capabilities of reasoning and inference. Existing AI techniques are still struggling with reasoning and cognitive inference, an area in which humans significantly outperform machines (Chen et al. 2020). There is also AI-assisted Human Interaction where the driver of the interaction is a human agent, and the user’s perception is that they are interacting with a person. The role of the AI here is to provide assistance to the human agent in order to optimize and enhance their performance. Several companies have recently explored the application of sequence-­ to-­sequence models using Deep Neural Networks to formulate a response or multiple responses that an agent can adopt or edit. One of the great advantages of this setting for applying new machine learning algorithms is reduced risk of failure as the human agent maintains the final say on whether to adopt the suggested response or use another (Human and Artificial Intelligence Harmony | Interactions August 9, 2017).

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2.4 Emotional Intelligence Emotional intelligence refers to the individual’s ability to perceive, control, and evaluate emotions. The ability to express and control emotions as well as the ability to understand, interpret, and respond to the emotions of others. A key element of emotional intelligence is empathy. Emotional intelligence is essential for good interpersonal communication (Kendra Cherry 2020). Emotional intelligence can be helpful in many situations as in giving and receiving feedback, meeting deadlines, dealing with challenging work environment, dealing with change and shift to new work configuration, among others. Emotional intelligence could also help improve the quality of work relationships, build a sense of responsibility, and enhance leadership qualities (Fig. 12.1).

2.5 Cross-Cultural Intelligence There exists a notion that one’s culture affects the person’s interpretation of intelligence (Sternberg 1985; Colson 2019; Kawar 2012). Several studies support the notion that human intelligence carries different meanings across cultures. Most North American view of

Emotional intelligence

Motivation

Social competencies

Self management

Fig. 12.1  Components of emotional intelligence

Empathy

Self awareness

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intelligence equates intelligence to creativity. Many “Eastern” cultures relate intelligence to social roles and responsibilities. A Chinese conception of intelligence would, in one variety, define intelligence as the ability to empathize with and understand others. Several African communities see intelligence through a social lens. And Eastern cultures relate intelligence to social responsibilities.

3 Could AI Influence Human Intelligence? AI could complement or impact upon human intelligence and there are several areas where this is explicit. Take for example precision medicine. AI is currently in use to understand how individual genetics, environment, and lifestyle can help in disease treatment. Digital therapeutics, custom-designed drugs, and improved diagnosis are the instruments. Take also implantable. Brain-machine interfaces will eventually enhance human intelligence thus helping in solving many complex medical conditions (Vassev 2021). In general today’s advanced computers are considered by many as “intelligent” because they have the potential of learning and make informed decisions. This is a different ability, however, from what humans possess. AI has the quality to identify informational patterns relevant to an object or a function. It is, however and contrary to humans, not limited by the physical limits of its components and processes.

4 Intelligence as an AI-Driven Top Management Competency It is rather awkward to state that intelligence must constitute a top management competency. The concept of intelligence has, as we highlighted above, many connotations and a myriad of interpretations. It is reasonable to hypothesize, however, that symptoms of specific aspects of intelligence as emotional implications and cross-cultural manifestations should be

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Data

AI

Outcomes

Human judgment

Decisions

Fig. 12.2  AI-driven decision-making

demonstrated by top management and, to that extent, become a top management competency. Also that an appreciation of the far-­reaching managerial implications of artificial intelligence should be demonstrated. This is specially the case in decision-making and strategy formulation. Decision intelligence is a set of enabling technologies to improve decision-­making. It is a data-driven process that enables faster and more accurate decision-making by leveraging a comprehensive information landscape, enabling data-driven decisions to be made faster and more accurately. Decision intelligence combines artificial intelligence, machine learning, contextual intelligence, and automation to specifically support analysts in the data gathering and data analysis phases, in order to generate concrete and actionable outputs that can be applied the specific decisions contextualization and the customer needs analysis, in order to understand the need for decision support creating business value and speeding up time-consuming processes. Decision intelligence includes a feedback loop (also known as closed-­ loop learning) in order to retrain and improve the system over time (Telsy SpA Unico Socio Gruppo TIM 2022) (Fig. 12.2).

5 Summary and Conclusions Human intelligence is a source of many a competency. Through intelligence, humans possess the cognitive abilities to learn, to form concepts, to understand issues, and to apply logic and reason and also the ability to recognize patterns, to plan, to innovate, to solve problems, to make decisions, to retain information, and to use language to communicate. General intelligence captures human ability to find adaptive solutions to all types of problems.

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The chapter relies on the very extensive research done on intelligence and its implications. It also contrasts human intelligence with artificial intelligence and underlines future horizons and potentials. Intelligence is also viewed from a cross-cultural perspective. The chapter deals finally with decision intelligence. Decision intelligence is a set of enabling technologies to improve decision-making. It is a data-driven process that enables faster and more accurate decision-­ making by leveraging a comprehensive information landscape, enabling data-driven decisions to be made faster and more accurately.

References Chen L et al (2020) Hybrid Human-Artificial Intelligence. In Computer, vol. 53, no. 8, pp.  14–17, Aug. 2020, https://doi.org/10.1109/ MC.2020.2997573. Cherry K (2020) What is emotional intelligence? Verywell Mind Updated 3 Jun 2020. Colson E (2019) What AI-Driven Decision Making Looks Like? Harvard Business Review 8 Jul 2019. https://www.dictionary.com/browse/intelligence/ © 2023 Dictionary.com, LLC. https://www.dreamstime.com/components-­e motional-­i ntelligence-­f ive-­ components-­emotional-­intelligence-­image 157443575. Kawar TI (2012) Cross Cultural Differences in Management. International Journal of Business and Social Science Vol.3 no. 6 Princess Sumaya University for Technology Mar 2012. Legg S, Hutter M (2007) A Collection of Definitions of Intelligence. Technical Report IDSIA-07-07. Pedamkar P (2022) Artificial Intelligence vs Human Intelligence. © 2022—EDUCBA. Ruhl C (2020) Intelligence: definition, theories and testing. Simply Psychology 16 Jul 2020 www.simplypsychology.org/intelligence.html. Sternberg R (1985) Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press. Telsy SpA Unico Socio Gruppo TIM, 2022. Vassev N (2021) Artificial Intelligence and the Future of Humans. Forbes 5 May 2021.

13 Behavior

1 The Essence of Behavior and Behavioral Competencies Behavior connotes people’s responses to events and stimuli. It is the actions and mannerisms made by individuals, organisms, systems, or artificial entities in conjunction with themselves or their environment. Three determinants determine human behavior: cognitive factors, environmental factors, and personality factors (https://en.wikipedia.org/wiki/Behavi or#:~:text=Behavior). Behavioral competencies, on the other hand, are those behavioral patterns and expressions impacting upon performance of a function. There are individual competencies, interpersonal competencies, motivational competencies, executive competencies, and analytical competencies. Individual competencies relate to characteristics such as decisiveness, initiative, and reasoning. Interpersonal competencies demonstrate collaboration, communication, and conflict resolution. Motivational competencies include motivation and role model profile. Executive competencies connote abilities to manage. And analytical competencies cover data management and numeracy skills (­https://www.talenteria.com/ news/defining-­measuring-­and-­assessing-­behavioral-­competencies). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_13

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Measuring behavioral competencies is based on three parameters: a people-based parameter, a task fulfillment parameter, and a managerial skill parameter. People-based parameters refer to empathy and communication; task-based parameters relate to display of effective decision-­ making and task-managing abilities; and task-based parameters connote inclusiveness and positive reinforcement (Talenteria, February 04, 2021). The following analysis will focus on the impact of emerging technologies on cognitive behavior competencies especially those related to the performance of a managerial function.

2 Emerging Technologies and Management-Related Behavioral Cognitive Emerging technologies could have tangible influence on a number of behavioral cognitive traits related to the performance of a managerial function. Those could include strategic behaviors, communication behaviors, and control behaviors.

2.1 Strategic Behavior Strategy as such could be viewed as an attempt at allocating resources according to a rational set of criteria (Horwath, 2020). Strategic behavior provides a response to this premise. It could be seen as a conscious behavior seeking market uncertainty avoidance. Its essential feature is the recognition of the direct interdependence between one’s behavior and that of others. It can also be seen as individual behavior aiming at influencing the structure of a market. In traditional economics, such situations as monopoly or oligopoly were seen as the outcome of technological conditions and the state of mind of those managing the process. Emerging technologies could lead to rather unusual patterns of individual and corporate strategic behaviors. Data perspectives may point to a rapid decline of an industry and induce a need for rapid search for one

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mode or the other of exit. Alternative modes of exist may induce, on their turn, innovative and creative behaviors or cooperative or hostile behaviors never contemplated before.

2.2 Locus of Control Behavior Locus of control (Rotter 1966) refers to an individual’s beliefs about the extent of control that they have over things that happen to them. It is the degree to which people believe that they, as opposed to external forces (beyond their influence), have control over the outcome of events in their lives. The concept’s roots lie in Rotter’s work and have since become an aspect of personality psychology. A person’s “locus,” Latin for “place” or “location,” is conceptualized as internal (a belief that one can control one’s own life) or external (a belief that life is controlled by outside factors). People with a high internal locus of control believe to have a great deal of control over the direction of their lives. In contrast, individuals with a high external locus of control believe to have no control over the direction of their lives (Fig. 13.1). Technology could influence the locus of control in either way. Technology-induced shorter attention span could reduce problem-­ solving competencies as well as productivity and induce internal focus. Technology-induced faceless interaction and relative distancing from people and events could increase internal locus of control. But it could also generate belief in the frameworks put together far beyond his scope and a state of surrender to those distant “wisdoms.” Internal Control

Locus of control External control

Fig. 13.1  Determinants of locus of control

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3 Communication Behavior Communicative behavior is psychological constructs that influence individual’s expressions of feelings, needs, and thoughts. Indirect messages are the mode. A behavior may be judged as communicative if it intends to convey an indirect message be it aggression, assertion, or passivity or else. Technology impacts communicative behavior in many ways and one of those is shorter attention spans. Attention spans are reduced as a result of intensive interaction with a variety of electronic communication modes as the mobile phone, for example. Shorter attention spans lead to lower concentration, weaker problem-solving attitudes, and ultimately lower productivity. This contrasts with early years of technology when it was more for productivity rather than entertainment and personal gratification. Another dimension of communicative behavior is faceless interaction. Faceless computer screen interaction eases the process of expression of hostility and exclusionary behavior. Communicating online replaces face-to-face interaction for users, reducing time spent on human interaction and inducing possible negative connotations (Zohuri et  al. 2019) (Fig. 13.2).

4 Illustration: Game Theory Behavior Game theory is one way of expressing individual behavior under changing technology and market conditions. Game theory is the study of individual’s strategic behavior when environmental driving forces, including technology, oscillate and decisions are interdependent. Players, strategies, and payoffs constitute game elements. A strategy is a course of action which either player can adopt. Payoff refers the net benefit or loss that accrues to each player from carrying out his strategy. Individuals know they do not make decisions in a

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Analytical

Behavioral Behavioral competencies competencies

interpersonal

Motivational

Fig. 13.2  Behavioral competencies

vacuum and are aware that there is mutual interdependence whereby outcomes of a decision depend not only on what the individual does, but on what others do as well. Games vary. And individual strategic behavior varies as well. A simultaneous-­move game is a game in which both players must choose their strategies at the same time. A sequential game is a game in which one player chooses its strategy before the other. A static game is a game which is both simultaneous-move and one-off while a repeated game is a game which is played over and over again. A cooperative game is a game in which both players can communicate and cooperate, and a non-­ cooperative game is a game in which no cooperation is possible (Obaidullah Jan, 2019) (Fig. 13.3). Each variety of game reflects an individual strategic response to three variables starting with technology and proceeding through psychology to economics.

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Psycology

Technology

Market/ Economics

Fig. 13.3  The interdisciplinary character of game theory

5 The Aggregate Picture: Technology-­Rooted Behavioral Competencies Emerging technologies have far-reaching impact on the managerial side of behavior and behavioral competencies. Behavioral competencies are those behavioral patterns and expressions impacting upon the conduct of a function. There are the individual, interpersonal, motivational, executive, and analytical competencies. Technology impacts upon those competencies, and technology-rooted behavioral competencies are emerging. Those are competencies that could help addressing and formulating an approach to several management-related issues such as communication, conflict, time, and self (Zohuri et al., 2019). The impact of technology and the emergence of technology-influenced behavioral competencies could relate to people, task, and managerial performance (Talenteria, February 4, 2021).

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6 Summary and Conclusions Emerging technologies, whether artificial intelligence or data sciences related, will have an impact on behavior, whether at individual or organizational levels. It will, at the individual level, impact upon individual culture, individual strategic attitudes, individual communication patterns, individual perceptions, and individual approaches to control. It will, at the organizational level, have an impact on the structure, the culture, and the environmental dynamics. All in all it is more likely than not that emerging technologies will deliver a different individual and organizational behavioral pattern from what we have experienced over the past decades. Top management competencies will belong to these dynamics. This chapter will deal with the different dimensions of technology-­ influenced behavior and impact on managerial competencies. Management games are given as example for an interaction of technology, behavior, and economics. Behavioral competencies emerge as those behavioral patterns and expressions impacting upon performance of a job or a function. They could be broken down into five distinct categories: individual, interpersonal, motivational, executive, and analytical. Individual competencies include characteristics such as decisiveness, initiative, and reasoning. Interpersonal competencies relate to collaboration, communication, and conflict resolution. Motivational competencies include motivation and role model profile.Executive competencies connote abilities to manage.

References Horwath R “What is Strategy?” Strategic Thinking Institute, Sept 23, 2020. https://en.wikipedia.org/wiki/Behavior#:~:text=Behavior; https://www. slideshare.net/SumaVenkatagiri/behavioral-­competencies; https://www.talenteria.com/news/defining-­m easuring-­a nd-­a ssessing-­b ehavioral-­ competencies. 4 Feb 2021

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Obaidullah A T M (2019), Institutionalization of Parliament in Bangladesh: A Study of Donor Intervention for Reorganization and Development (Singapore: Palgrave Macmillan. Rotter, J B (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. Zohuri, B., et al. (2019) The Impact of Technology on Human Behavior. https:// doi.org/10.1201/9781003000662-­9)

14 Strategic Intent

1 What Is Intent and Strategic Intent? There are many ways to look at intent and strategic intent. An intent is the mental driver of an action. Intent or common synonyms of intent are aim, design, end, goal, intention, objective, object, and purpose. While all these words mean “what one intends to accomplish or attain,” a strategy component could be there suggesting a philosophical base of the strategic management process. It implies the organizations’ purpose or the ultimate endeavor it is aiming at. Strategic intent serves to inspire, motivate, and provide clear direction to an audience be it the employees, the investors, or the ultimate consumers. Strategic intent is a term that projects aspiration, purpose, and contemplated direction of vision-rooted efforts. The strategic intent of an organization could also describe how the firm’s energy and resources are channeled into a focused and unified overall goal (Daft 2010; Hamel and Prahalad 2005). It is the strategic direction and destiny to be pursued by the company (Landrum and McDuffie 2010). To summarize, it is a statement that provides a perspective and a road to an identified vision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_14

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2 The Strategy Context of Intent Intent becomes strategic if it fulfills certain primary and secondary criteria. To the primary criteria belong the following: • A sense of urgency by amplifying silent environmental signals calling for an effort. • A competitive environment focus based on competitive intelligence. • A clear milestone and review mechanisms leading to a measure of strategic control. • A desired leadership position. To the secondary criteria belong the following: • A sense of direction or an ultimate vision of where the organization is heading and what could, at times, be a blurred light at the end of the tunnel. • Sense of discovery or an appeal to creativity and innovation. • A sense of competitive stride or a readiness to enter the competitive battle with zeal and determination. • Sense of destiny or an emotional touch to reach that ultimate distant but promising goal. Formulating a strategic intent requires, in the final analysis, three inputs: a vision, a sense of direction, and a desired end result (Fig. 14.1).

3 Statement of Intent The strategic intent statement typically contains the vision statement, the mission statement, the framework of corporate goals, and a time dimension. There exists a wide variety of statement of intent. This one deals with intent within an educational context. “The Board and the UFF fully support all laws intended to protect and safeguard the rights and opportunities of each faculty member, staff member, and student to work and learn in an environment free from any form of unlawful discrimination or

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Drive and sense of Direction Desired end result

Strategic intent Fig. 14.1  The elements of intent

unlawful harassment.” Yet another one, “In recognition of the mutual interests of both parties in supporting the exploration of high quality, pedagogically sound distance education opportunities, the parties are committed to attracting interested faculty to pursue initiatives in distance education, training and professional development in this area, and to the utilization of appropriate technologies in delivering quality distance education” https://www.lawinsider.com/clause/statement-­of-­intent.

4 Strategic Intent as a Top Management Competency Let us recall that strategic intent has been and will continue to constitute an essential top management competency. The novel aspect is that advanced phases of artificial intelligence do harbor an element of intent. One can hypothesize that early phase as the limited memory phase connotes a resort to memories that incorporate intentions at different stages of fulfillment. The theory of mind phase, on the other hand, implies the development of analytical competencies where intent is a common

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Vision

Sense of direction

Strategic intent

Fig. 14.2  The making of strategic intent

denominator. And the awareness phase assumes an intent-rooted response to stimuli. All in all, artificial intelligence phases and processes perform against a background of intent, strategic and non-strategic. Strategic intent has three attributes, all of them belong to the heart of executive competencies. There is first the sense of direction. It is a distant point in the future where the organization wants to flow. It is a point defined by the technology, the market, the industry, and the economy. And there is then the sense of discovery or the “gut” feeling that there is a light at the end of the tunnel. And there is last but not least the sense of destiny or the emotional edge or the feeling that the distant end result is perceived to be inherently worthwhile. It is all part of the competency structure of top management. It is part of the emotional attachment to the desired end result (Fig. 14.2).

5 Summary and Conclusions Strategic intent is a useful concept in accounting for purpose and continuity of goals in an organization adapting to internal and external developmental pressures. It is an inspirational statement that reflects visions, values, goals, and ultimate outcomes.

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Strategic thinking has roots in strategic intent. Research revealed this relationship some time ago (Hamel and Prahalad 2005). For a strategic intent statement to be valid there are primary and secondary conditions. Most important is the existence of a vision, a sense of direction, a sense of urgency, and an element of strategic control. Yet, strategic intent is an opaque concept with roots in psychology, neurology, and, of course, management. Advanced artificial intelligence phases rely on brain-like behavior, brain-like analysis, and brain-like awareness. All are intent-inducing, intent-expressing, and intent-­ driving forces. Strategic intent will become a top management competency demanded for progression, continuity, and scoring.

References Daft, R.L. (2010) Organization Theory and Design. 10th Edition, South-Western Cengage Learning, Mason, USA. Hamal G and Prahalad C K (2005) Strategic Intent, HBR (Jul–Aug 2005). https://www.lawinsider.com/clause/statement-­of-­intent Landrum T and McDuffie K (2010) Learning Styles in the Age of Differentiated Instruction, Exceptionality, 18:1, 6–17, https://doi.org/10.1080/0936 2830903462441

15 Strategic Control

1 The Essence of Strategic Control Control is an essential element of the process of strategic thinking. Yet, different types of control require different interventions and deliver different outcomes. Operational and managerial control could fail at times, especially when it comes to the repositioning of the corporation or search for congruence with environmental shifts. They provide an answer to the question “where we plan to be” instead of “where we ought to be.” Many a corporation made strategies focused on improving an established position instead of the search of a better “fit” within an emerging arena. A measure of strategic control would have taken them in the right direction. It is the author’s contention that “strategic control” is a “measure of the dynamic compatibility between the organization and the environment, over a foreseeable future time horizon.” Strategic control could, within the authors’ framework, be exercised by measuring two variables: company attributes and environmental change over time. Strategic control relates tomorrow’s desired attributes to tomorrow’s evolving conditions. The following table underlines the difference between strategic control and management control (Table 15.1). It reveals that strategic control has © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_15

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Table 15.1  Strategic control versus management control Variable

Strategic control

Management control

Time Core competencies Roots Outcome

Borderless time horizon Future environment Potential

Vision time horizon Present Benchmarks Achievable

Source: El Namaki M. (2014). Contemporary Strategic Control. In: Strategic Thinking for Turbulent Times. Palgrave Macmillan, London. Pp 158–163

a borderless time horizon, focuses on the future, monitors the future, and views the longer term potential. Management control, in contrast, is limited by the time horizon of the conceived vision, focuses on present-­day competencies, and views the achievable within a benchmark framework.

1.1 The Potential Fitness Test The key question here is the existence of a measure of congruence between company goals and the true potential of the corporation. Key questions relate to: • The gap between the “true” revenue potential of the corporation and the achieved one. • The “true” market share potential as contrasted with the actual. • Possible potential-enhancing technologies and synergies reachable by the organization. • Merger, acquisitions, or strategic alliances that could enhance potential (increase concentration).

1.2 The “Industry” Fitness Test The key issue here is whether the corporation exists within the right industry and whether the industry itself provides a valid choice. Key questions involve: • The rate of growth of the industry. • Shape and stage in industry life cycle.

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Stage in the product life cycle. Recent entrants into industry and their entry strategy. Concentration level of the industry. Technology shifts within the industry. Product technology coefficients. Relative market share and whether that changes over time. Existence of end-game players in the industry.

1.3 Core Competency Tests The main question here is whether the corporation possesses a core competency, and is it durable? Examining the following issues may help providing an answer. • What is your real core competency? Have you been testing that lately? • How does your core competency provide a competitive edge over the longer term? • How “durable” is your core competency? Is it subject to wear and tear?? • Are you developing other core competencies? • Is your core competency transferable across industries? • Are you prone to learning or de-learning?

1.4 The Core Competency Fitness Test The main question here is whether the corporation possess a core competency and is it durable? This involves: • Flexibility of equity input. • Is the organization creditable enough to allow for an expansion in the debt base? • Does top management have the capacity, the strategic fit, and the degree of “dependability” that a change of direction may require? • Is middle management of the right capacity and potential for upward mobility?

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• Routes to new technology and access to other strategic competitive advantages. • Is organization culture open and liable to change if there is a high measure of change? (El Namaki 2014)

2 Strategic Control and Strategic Thinking Control is an essential element of the process of strategic thinking. Yet, different types of control require different interventions and deliver different outcomes. Operational and managerial control could fail at times, especially when it comes to the repositioning of the corporation or search for congruence with environmental shifts. They provide an answer to the question of “where do we plan to be” instead of “where should we be.” Blackberry and Kodak went through this difficult test. Blackberry failed at matching emerging technologies, and Kodak was late in doing so. Both are cases in which strategies focused on improving an established position instead of searching for a better “fit” within an emerging arena. The road to strategic control goes through the concept of “strategic fitness.” An organization should “fit” within an industry, have goals that “fit” its potential, have core competencies that “fit” new future demands, and have resources that would “fit” future dynamics. One could measure this fitness by trying to find objective answers to a number of key questions. The “potential” fitness test. The key question here is the existence of a measure of congruence between company goals and the true potential of the corporation. The “industry” fitness test. The key issue here is whether the corporation exists within the right industry and whether the industry itself provides a valid choice. The core competency fitness test. The main question here is, whether the corporation possesses a core competency, and is it durable? Examining the following issues may help providing an answer: The core competency fitness test. The main question here is, whether the corporation possess a core competency, and is it durable? Projecting the outcome of each of the fitness test on a scale could lead to the following diagram (Fig. 15.1).

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A The potential test

A B C The resource fitness test

B The arena fitness test

D The competency test Fig. 15.1  A possible configuration of a set strategic fitness test

3 The Strategic Control Competency Emerging technologies will mandate a strong element of strategic control. The dynamics of artificial intelligence and data sciences will render environmental scanning and rapid strategic response essential. Data flows may reveal fundamental shifts in industry foundations and an urgent need for a repositioning. They may also unmask incompatibility between the organizational resources and the alternative future resources dictated by new technology demands. Top management strategic control competencies should allow for this monitoring and open the door for rapid strategic response.

4 Summary and Conclusions It is the author’s contention that “Strategic control is a measure of the dynamic compatibility between the organization and the environment, over a foreseeable future time horizon.” This contrasts with many of the traditional definitions given to strategic control over time. Some place the issue within the management control framework (Anthony 1998). Others position it within a “balanced score card” framework implying that the balanced score card provides a

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“strategic control systems that measure efficiency, quality, innovation and customer response” (Kaplan and Norton 1992). Some others make it even simpler by stating that strategic control is “the process by which managers monitor the ongoing activities of an organization and its members and take corrective action to improve performance when needed” (Hill and Jones 2004). None of these definitions tackles the core issue of dynamic change and organization fitness within a new set of realities. The road to strategic control goes through this concept of “fitness.” Fitness of resources, of industry, of management competencies, and of strategies.

References Anthony R (1998) Management Control System. McGraw-Hill School Education Group, ISBN. 0256131554, 9780256131550 El Namaki MSS (2014) Contemporary Strategic Control. In: Strategic Thinking for Turbulent Times. Palgrave Macmillan, London. pp 158–163 Hill C and Jones G (2004) Strategic Management Theory, An Integrated Approach, 6th ed., Houghton Kaplan R and Norton D (1992) The Balanced Scorecard—Measures That Drive Performance, Harvard Business Review

16 The Propensity to Lead

Conceptual blur is not only the outcome of the great diversity of schools of thought but also a very fundamental shift in the building stones of the concept itself. All three building stones, the leader, the followers, and the environment, have been changing content, relationship, and boundaries. Disruption is setting in.

1 The Shifting Determinants It is the author’s contention that leadership performance today is a function of two key variables: perspective and control.

1.1 Determinant One: The Perspective Perspective is the ability to perceive things in their actual interrelations or comparative performance. It is a state induced by individual experience, ability to identify the core from the periphery, as well as a strong element

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of cognition. A sound perspective is essential for a process of leadership. This prospective is built around two elements: conceived vision and achievement motivation.

Conceived Vision As we stated earlier, a vision is a mental perception of the kind of environment an individual or an organization aspires to create within a broad time horizon and the underlying conditions for the actualization of this perception (El Namaki 1992). Visions are inherent in the process of direction setting or the identification of something in the future, a vision, often the distant future, and the development of a strategy for getting there. Visions require commitment and total immersion with time, people, and structures seen in terms of their relevance to their existence (El Namaki 1992). Leaders perceive visions and do not wonder whether they have one.

Achievement Motivation The concept of achievement motivation is derived from McClelland’s “Achieving Society,” a classic work where he developed the “Achievement Motivation Theory” commonly referred to as need achievement or N-Ach Theory (McClelland 1961). He equates achievement motivation to the urge to accomplish; to master things, people, or ideas; and to attain high standards. Leadership cognitive roots, according to McClelland, lie in human motivation theories where individuals are assumed to have one of three main driving motivators: achievement, affiliation, and/or power. The motivation to achieve arises when an individual knows that he is responsible for the outcome of an effort, when he anticipates explicit knowledge of the results that will define his success or failure, and when there is some degree of risk connoting uncertainty about the outcome of his effort. The goal of achievement-oriented activity is to succeed and to perform well in relation to a standard of excellence or in comparison to competitors (McClelland 1961; Atkinson 1964). Leadership conveys a high measure of motivation to achieve.

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1.2 Determinant Two: From Managing Self to Recognizing the Locus of Control Measuring outcomes against a predetermined goal, or a probable potential, or both connotes control. The process assumes setting of goals, scanning of outcomes, identifying areas of non-fulfilment, and introducing corrective action. This implies an ability by those who aspire leadership to go all the way from setting end results to checking progress and inducing course adjustment. To do that one should be able to manage himself within a proper locus domain.

Managing Self As we stated earlier, managing self is a process whereby an individual sets a context for his performance and develops modes for performance monitor and adjustment (Drucker 2005). The concept rests on the argument that those who have achieved, in a managerial sense, have always managed themselves. Several key “probes,” some intrinsic and some environmental, provide the context and the monitor framework. Intrinsic include questions related to the strengths and weaknesses of the individual in question as well as his level of performance. To the environmental belongs the individual’s view of his contribution to this environment and responsibility for the associated relationships. The essence is that a leader should possess a deep understanding of what is going on within himself and learn how to manage that when it comes to interaction with his environment (Drucker 2005; El Namaki 2010)

Locus of Control Leadership connotes an ability to attribute decisions to one’s own reasoning and decision making or external driving forces dictating a course of action or the other. This is labelled the locus of control (Rotter 1954). A locus of control could be internal or external. A leader possesses, according to research, a strong internal locus of control. Put differently, internal

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locus is an ability to attribute decisions to one’s own reasoning process while an external locus relates the outcome to forces within the environment. Individuals with a strong internal locus of control believe in their ability to influence events and formulate ultimate decisions. People with a strong external locus of control tend to relate their decisions to forces beyond their own decision-­making competencies (Rotter 1966). Research, again, makes us believe that individuals in top management tend to have a high measure of internal locus and this has, obviously, far-­ reaching implications to the conduct of leadership.

2 The Outcome: The Concept of the Propensity to Lead A blend of perspective and control could lead to what we may term the propensity to lead or a measure of an individual’s ability to perform a leadership function. High propensity is synonymous with a strong perspective and conducive control. An individual with a high propensity to lead is most likely to be able to conduct certain functions including the following: • Conceive a dynamic vision that mirrors the goals and aspirations of the constituents, reflects environmental disruptions, and is derived from a sense of direction. • Have high achievement motivation coefficient allowing for a desire to excel, a setting of ambitious goals, a measurement of performance, as well as an openness to feedback. • Maintain a balance between internal and external controls, thus allowing external forces to have an impact without undermining intrinsic judgment. • Managing self in a manner conducive to the conduct of the leadership function and, equally, the expectations of his followers, that is, keenly aware of his strengths and weaknesses, is assuming responsibility for relationships and have an eye to others’ expectations (El Namaki 1992).

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3 How to Measure the Propensity to Lead? Earlier work by several, including McClelland, led to the development of instruments measuring determinant attributes. The medium was an adapted questionnaire that explored the underlying premises and placed the outcome within a scale. The scale varied per parameter. The potential for conceiving a vision, to start with, could be deduced from response to an array of questions (El Namaki 1992). Those focus on the potential for proposing unusual ideas applying familiar approaches within different contexts, deriving broad principles from specific occurrences, pursuing independent track rather than following the herd, and proposing ideas based on conceptual assumption. Achievement motivation, on the other hand, was probed through a set of queries that are responded to by an affirmative or ejective response (McClelland 1961). Original work included a wide range of questions. Some of those relate to inclination to plan ahead for career moves, getting restless when time is wasted, preference to work with a congenial but incompetent partner rather than with a difficult but highly competent one, and whether days often go by without an activity. Locus of control questions try to deliver a picture of the roots of the internal or external individual decision making. Twenty questions should be marked as either true or false and should lead to a placement along a decision-making scale (Rotter 1966). Issues relate to a variety of dimensions starting with attitude toward life, where he stands with other people, his belief or disbelief in chance, his ability to convince others to do things his way, his conviction that people must master their own fate, role of random events in his life, and role of others in his life’s events (Fig. 16.1). Another approach is Drucker’s self-management questions. Those include the following (Drucker 2005): • What are my strengths, can I recognize and segment those in terms of competencies and attitudes? • How do I perform? I know the level of my performance. • What are my values? I can recognize and identify with those.

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Perspective High

Propensity to Lead

Low

Low

High

Control Fig. 16.1  A graphic presentation of the propensity to lead

• Where do I belong? I have a clear segmentation of domains of performance. What I should contribute. Have a clear picture of the expectations of the followers. • Who is responsible for my relationships? Sharing of responsibility between self and others. • What about the second half of my life. I have contours and intent. Answers to these questions could be converted into a quantitative scale that reflects the degree of fulfillment of the determinants. Determinant scores could be aggregated and converted into a single score reflecting the level of the propensity.

4 The Application Measuring the propensity to lead could deliver tangible human resource management outcomes. It could be quite conducive to the process of top management competency development, talent identification, talent progression, and career mobility and rewards.

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At individual level, the propensity levels could induce different responses. A high level could induce a search for pertinent opportunities that would allow this uncommon competency to come to fruition. The respective individual could, for instance, draw the attention of those around him to this uncommon competency, seek situations where his leadership qualities could be demonstrated, or track leadership bottlenecks and try to fill the emerging gaps. He could also seek alternative venues where his competencies are better utilized. A low level would require other interventions. Those may vary between competency enhancement measures toward a search for a better function fit. Vision-related shortcomings would require specific creativity and imagination-based training programs that could induce scenario building and longer-term positioning. Achievement motivation deficiencies are usually addressed through achievement-motivation exercises and achievement-­motivation stimulants. Locus of control biases are addressed through awareness and effective feedback. And management of self is managed through executive personality adjustment and career planning.

5 Summary and Conclusions Leadership is a catch-all concept and the subject attracts intensive attention. Yet, the concept, as it stands today, is obsolete. The prime parameters of people and tasks have lost content with people performance parameters going through palpable change and the fundamentals of the task seriously disrupted. Those developments are rendering concepts of leadership in their current professional and conceptual framework blurred. Gamely and fashion-prone colorful presentation of “leadership” is taking over. A serious search into the heart and soul of leadership and a formulation of building blocks is, therefore, needed. It should be serious and should be conceptual and operationally cogent. This chapter provides an attempt at that. The chapter starts with a critique of current concepts and proceeds to suggest a framework that accommodates contemporary disruptions. Two

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parameters are identified as essential to a leadership function under contemporary conditions of disruption: perspective and control. Perspective and control take the issue out of the traditional single track focus on the leader’s behavior or traits to the broader context of the leader, as an individual, the followers, as players and the event, and as a disruptive environmental force. The chapter further defines perspective in terms of vision and desire to achieve. It also defines control in terms of locus and management of self. An operational medium, the propensity to lead questionnaire, is suggested as a mode of testing competencies and drawing relevant conclusions. A strong achievement motivation may encourage progression and challenge. A weak locus of control may suggest alternative functions. The propensity to lead could prove to be a potent force in identifying leadership competencies.

References Atkinson, J.W. (1964). An introduction to motivation. Van Nostrand. Drucker, P. (2005). Managing Oneself, HBR, January 2005 El Namaki MSS (2010) Leaders who failed the Drucker test in Peter F. Drucker’s Next Management: New Institutions, New Theories and Practices—Göttingen: Sordon, ISBN 978-3-9810228-6-5 El Namaki, M. (1992), “Creating a Corporate Vision”, Long Range Planning, Vol. 25, No. 6, pp. 25–29. McClelland DC (1961) Achieving Society. Simon & Schuster 1 Feb 1967 Rotter JB (1954). Social Learning and Clinical Psychology. Prentice-Hall, New York Rotter JB (1966) Generalized expectancies of internal versus external control of reinforcements. Psychological Monographs: General and Applied, 80(1), 1–28

17 The Propensity to Enterprise

1 The Essence of Entrepreneurship Entrepreneurship is the process of identifying opportunities and exploiting them. Entrepreneurship is widely regarded as an integral player in the business culture of American life, particularly as an engine for job creation and economic growth (McClelland 1976). But who is an entrepreneur? Views vary: Some are rational and others border on the romantic! It, most commonly, connotes the conversion of an opportunity into a running business venture. Several scholars played a role in conceiving the concept and giving it its intellectual content. Four did that with a distinction, each with his own unique views, conceptual tenants, and underlying premises. Those include Joseph Schumpeter, Peter Drucker, David McClelland, and Henry Mintzberg. Several others complemented the analysis, and the understanding of entrepreneurship owes a lot to the work of all those authors.

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1.1 Creativity and Creative Destruction: Schumpeter For Schumpeter and the Austrian School of economics (Schumpeter 1943), an entrepreneur is a person who is willing and able to convert a new idea or invention into a successful business. Entrepreneurship, according to this school of thought, forces “creative destruction” across markets and industries, simultaneously creating new products and business models, and killing others (Schumpeter 1943). This so-called creative destruction is largely responsible for the dynamism of capitalism and the long-term economic growth associated with it. Schumpeter placed the entrepreneur at the heart of the capitalist economic system to the extent that the eventual disappearance of the concept would lead to a collapse of the system itself. Fundamental to Schumpeter is innovation, meaning new products, new methods of production, new markets, new sources of raw material, new markets, and or new organizations (Schumpeter 1984).

1.2 Innovation: Drucker Drucker’s work seems to build on Schumpeter’s innovation framework. He states: “what we need is an entrepreneurial society in which innovation and entrepreneurship are normal, steady and continual. Innovation and entrepreneurship have to become an integral life sustaining activity in our organizations, our economy, and our society” (Drucker 1985). By stating that entrepreneurship is part and parcel of the workings of an economy and society, he places the process at the heart of economic systems, policies, and strategies. He also pierces through to the future and paints a picture of the role of entrepreneurship in the years to come.

1.3 Achievement Motivation: McClelland David McClelland addressed entrepreneurial traits and driving forces within the individual. He is most noted for describing three types of motivational needs: The need for achievement, the need for affiliation, and the need for power (McClelland 1961). These needs are found, to

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varying degrees, in all individuals within an environment and their relative weight and mix characterizes a person’s behavior, both in terms of being motivated and in terms of managing and motivating others. The “need to achieve” (n-ach) measures the individual’s desire to achieve. A high n-ach person is “achievement motivated” and therefore seeks achievement and attainment of realistic but challenging goals. He possesses a strong need for an achievement and progress feedback as well as a need for a sense of accomplishment.

1.4 Management Competency: Mintzberg Henry Mintzberg addressed the managerial role of the entrepreneur and how different that is from “classic” managerial roles within organizations. He also states that one of the executive roles of managers within organizations is to enterprise. As such, the manager acts as an initiator, designer, and stimulator of change and innovator. He searches for new opportunities and explores the potential for change (Mintzberg 1989).

2 Enter Artificial Intelligence Artificial intelligence is a novel technology-rooted force that is changing the foundations and premises of many concepts and practices, entrepreneurship included. To understand that let us identify the contours and scope of artificial intelligence and explore their impact on the concept and practice of enterprise. Merriam-Webster defines artificial intelligence as “A branch of computer science dealing with the simulation of intelligent behavior….” As well as “The capability of a machine to imitate intelligent human behavior” (Forbes, Feb 14, 2018). In other words, AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction (adjusting prospective actions to current outcomes). AI relates to many sciences from

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computing and data to psychology, philosophy, and linguistics, among others. AI fragments could acquire a coherent whole if put within a systems theory framework. AI is pictured, then, as a system with inputs, transformations, outputs, and a feedback loop. Data, raw and otherwise, as well as artificial neural sub-systems constitute the inputs. Learning (machine and otherwise) and analysis (diagnostic, predictive, and otherwise) provide the transformation. Insights, technologies, as well as derived sub-­ systems constitute the output. A feedback loop conveys outputs to the input and transformation segments and triggers essential adjustments. AI outputs could extend over a wide front that would include insights, novel sub-system structures, and derived and dependent technologies. 1. Insights AI processes could lead to insights or a capacity to gain an accurate and deep intuitive understanding of individuals and issues. Data insights could lead to abilities to solve problems, through logical deduction or reasoning; to set and achieve goals; to understand spoken and written language or communication; and to infer things about the world via sounds, images, and other sensory inputs. These abilities are expressed in many present-day applications, such as medical diagnosis, autonomous vehicles, and surveillance. 2. Novel sub-systems structures AI has the potential to penetrate industries where data are prevalent. Sub-systems congruent with the specific conditions of such an industry would, then, emerge and blend with the operating flows of the industry. Early signs of this penetration can be seen in a wide variety of industries from healthcare and banking to retail, logistics, and communication. Present-day banking sub-systems, for example, include fraud detection and credit analysis, government sub-systems include facial recognition and smart cities, and health and life sciences sub-systems include

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predictive diagnostics and biomedical images. Several other sub-systems will soon emerge in manufacturing, logistics, marketing, and, probably above all, security and defense. 3. Derived and dependent technologies A wide array of AI technologies is emerging. They vary in penetration but some are already identifiable. These include robotic process automation, biometrics, speech recognition, virtual agents’ decision management, text analytics, and natural language processing; these AI technologies are gaining situational significance All in all, artificial intelligence induces, ultimately, what we may term “generic disruption.” Generic disruption is a force or a bundle of forces that cut across systems and reconfigure constituent elements. Generic disruption cuts across industries, markets, organizations, and functions. It does not arise from competitors in the same industry or even from companies with a remotely similar business model but from distant and previously unidentified driving force. It blends forces drawn from separate, seemingly unrelated strands of technology, primarily, in order to create dramatic value enhancing and rule changing propositions (El Namaki 2019a).

3 Induced Influence of AI on Premises and Performance of Enterprise Artificial intelligence will influence enterprise and top management performance of the concept in three ways: the opportunity horizon, the trait configuration, and the performance outcomes. Artificial intelligence, whether narrow or broad, is having far-reaching impact on business and the industry and consumers behind. This places the entrepreneur at a junction where there is no turning back. His only option is to look forward and position himself within the new arena. The author should like, therefore, to formulate the following hypotheses (Fig. 17.1):

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Artificial Inteligence

Opportunities

Traits

Performance Strategy innovation dark side

Fig. 17.1  Induced influence of AI on key parameters of entrepreneurship

3.1 Hypothesis One: Artificial Intelligence Will Widen Opportunity Horizon Artificial intelligence will deliver three types of opportunities: input-­ related opportunities, process-related opportunities, and output-related opportunities. Input-related opportunities will relate to data in terms of scope, scale, type, sourcing, processing, storing, and conditioning. Add to that the emergence of quasi and shadow data as well as substitute data. Process-related opportunities will relate to approach and methods of data analysis as well as the learning processes related to that data. Finally, artificial intelligence insights could deliver tangible product and function opportunities leading to the emergence of new industries and industrial arenas.

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3.2 Hypothesis Two: Artificial Intelligence Will Alter Entrepreneurial Trait Profile One may hypothesize that artificial intelligence processes and technologies could pose a challenge or even alter some of the traditional traits attributed to the entrepreneur. This challenge could relate to three traits: achievement motivation, risk taking, and visionary impulses. Artificial intelligence processes from data management to learning may pose a challenge to the entrepreneurs’ achievement motivation drive and induce a strong desire to meet the forthcoming challenge. The entrepreneur’s tendency to cope with moderate risk taking may also be stimulated by the artificial intelligence disruptive pattern of technology change. Artificial intelligence insights finally could trigger what one may refer to as “dynamic visioning” or the shifting scenarios within “infant” visions as a result of the ever-evolving disruption.

3.3 Hypothesis Three: Artificial Intelligence Will Induce Entrepreneurial Innovation Innovation is inherent in the process of enterprise. It also, equally, constitutes an output within the artificial intelligence system. A prime function of an entrepreneur is to innovate in terms of products, industries, technologies, and markets, among other things. Artificial intelligence supports this process through data analysis and induced learning. AI technologies will, more likely than not, lead to a fundamental restructuring of industries and the emergence of new arenas. AI frameworks will very likely lead to a shift from strategic market focus to function focus. Functions will determine the instrument, being a product or a service, congruent with business environment conditions. Function analysis derived from big data will contrast with “need analysis drawn from market parameters” (Karakasic et al. 2018). Rather than relying on customers to tell a business what they want from a product, data analysis will point to the ultimate function-fulfilling medium (El Namaki 2019b).

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The entrepreneur will more likely than not stand at the heart of this dynamic process.

3.4 Hypothesis Four: Artificial Intelligence May Enhance the Dark Side of the Entrepreneur The entrepreneurial function has dark sides, and artificial intelligence can have an impact on those too. Key dark sides include a high sense of distrust, a dislike of feedback, resistance to technology change, and high internal locus of control. Artificial intelligence technologies could enhance some of those dark sides. The most likely, from the author’s point of view, could be the resistance to change. Most entrepreneurs embark upon a business where they have gained insight and competency through paid employment. Technologies implying a departure from the tried and trusted elements of past experience are, according to research, frowned upon and even resisted. Artificial intelligence brings along a lot of those technologies (De Vries 1985).

4 Top Management Technology-Driven Propensity to Enterprise Top management exposure to emerging technology could lead to an enhancement of what we may refer to as the propensity to enterprise or the expression of entrepreneurial initiative as an outcome of specific driving forces. This propensity could be, if one is to consider it from an operational point of view, measured. Prime measures cover a wide range but key parameters would include variables as the relationship of self-­ employment to the total workforce of a country or the community in question, the contribution of family workers to total workforce within a firm, the existence of a culture of enterprise within the family, volume of sickness and recovery among small firms, and updating of technology in terms of both products and processes. A key parameter in firms is “entrepreneurship” or demonstration of entrepreneurial initiative within firms. Employees in this case identify

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opportunities, including those created by new technology, and embark upon an entrepreneurial initiative be it on their own or within the company as a whole. Top management could be encouraged to endorse or practice what we may call “enterprise-triggering events.” Other chapters will deal with the implications of this proposal.

5 Summary and Conclusions But artificial intelligence is inducing massive change in another area that is seldom discussed in current AI contexts: Entrepreneurship. AI is changing the nature of entrepreneurial function, altering the demands of the entrepreneurial task, introducing new parameters for opportunity identification, reshaping entrepreneurial thinking, and throwing new light on the dark side of the entrepreneur. Top management exposure to emerging technology could lead to an enhancement of what we may refer to as the propensity to enterprise or the expression of entrepreneurial initiative as an outcome of specific driving forces.

References Drucker PF (1985) Innovation and Entrepreneurship: Practice and Principles. University of Illinois at Urbana-Champaign’s Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship, Available at SSRN: https://ssrn.com/abstract=1496169 El Namaki MSS (2019a) Conceiving a Vision within Artificial Intelligence Environments. International Journal of Management and Applied Research, Vol. 6, No. 1, pp. 41–47 https://doi.org/10.18646/2056.61.19-­003 Kets de Vries MFR (1985) The Dark Side of Entrepreneurship. University of Illinois at Urbana-Champaign’s Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship Available at SSRN: https://ssrn.com/abstract=1505242 Nov 1985 McClelland DC (1976) The achieving society. Irvington Publishers, New York

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Mintzberg H (1989) Mintzberg on Management: Inside our Strange World of Organizations. Free Press/Collier Macmillan New York and London McClelland, D.C. (1961). The achieving society. Van Nostrand. Forbes Feb 14, 2018 · The capability of a machine to imitate intelligent human behavior. El Namaki, M. S. S. (2019b), “Conceiving a Vision within Artificial Intelligence Environments”, International Journal of Management and Applied Research, Vol. 6, No. 1, pp. 41–47. https://doi.org/10.18646/2056.61.19-­003 Schumpeter, J. A. (1943). Capitalism, Socialism, and Democracy (6th ed., pp. 81–84). London and New York: George Allen & Unwin. Karakasic M et al (2018), The Matrix of Function and Functionality in Product Development Process, International Journal of Simulation Modelling 17 (3), 391–404

18 New Paradigm: The Organization’s Context

Cognitive computing

Demands on the organization

Implicit innovation

Data driven decision making

Fig. 18.1  Demands on the organization

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19 Data-Driven Strategic Thinking

1 Organization-Wide Data Systems 1.1 Data, Big Data, Implicit Data, Mass Data, and Synthetic Data We may recall, and as we said in earlier chapters, that data are sets of qualitative or quantitative attributes of variables related to persons or objects. It could constitute a collection of facts, numbers, words, measurements, and other observations compatible with computer-specific software (DiFranza 2018). Data could also be real or synthetic. Synthetic data is a novel genre of data where data is artificially created, fully or partly, rather than being generated by actual events. The purpose is preserving privacy, testing systems, or creating training data for machine learning algorithms. One of the prime purposes of creating synthetic data, however, is simulating not yet encountered conditions for which no real data is available (Dilmegani, July 19, 2021). Big data, on the other hand, are real “large datasets” that are too large to be reasonably processed by or stored within the traditional computing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_19

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software and hardware. The “three Vs of big data” describe some of the characteristics that make big data processing different from other data processing. There is first volume or the fact that big data sets are larger in magnitude than traditional data sets. There is then velocity or big data being processed in real time to gain insights and update the current understanding of the system. And there is finally variety or big data’s coverage of a wide range of quality sources. “Mass Data” are data sets that combine a variety of sources including science fiction, data lakes, and implicit data, among others. Science fiction segment will include soft and hard science fiction data. “Implicit data is information that is not provided intentionally but gathered from available data streams, either directly or through analysis of explicit data” (whatis.techtarget.com/definition/implicit-­data). Synthetic data is a class of data that is artificially generated. It is in contrast with real data which is directly drawn from real-world events. Problems with real data may lead to resort to synthetic data. Real data could be expensive, difficult to access, unreliable, or even beyond legal access norms. When combined with real data, synthetic data creates an enhanced dataset that can mitigate the weaknesses of the real data. Synthetic data can be used for hackathons, product demos, and internal prototyping to replicate a set of data with the right statistical attributes. For example, banks and financial services institutions use synthetic data by setting up multi-agent simulations to explore market behaviors (such as pension investments and loans), to make better lending decisions, or to combat financial fraud. Retailers use synthetic data for autonomous check-out systems, non-cash stores, or analysis of customer demographics (Gartner 2022).

1.2 Data-Rooted Strategic Thinking Data is revolutionizing the way companies view themselves, their industry, and their future. And data-rooted strategic thinking is emerging as a new driving force within the strategic thinking arena. It is a process that

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problem

data

model building

model performance

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outcome

Feedback Fig. 19.1  Data analytics cycle

involves data collection and consequent extraction of facts, patterns, and insights. It also involves utilizing those findings in order to develop insights as well as business strategy inducing inferences. Data, then, is seen as a strategic premise complementing, and at times replacing, intuition and observation. Data-driven strategic thinking could deliver answers to relevant strategy-­related questions and constraints. Findings could take the form of descriptive, inferential, or predictive information (Fig. 19.1).

1.3 Data-Based Predictive Analysis As we stated earlier, predictive analytics is the use of data to project future flow of events. It resorts to historical data in order to formulate potential scenarios that can help drive strategic decisions. The predictions could be for the near or the distant future. The analysis can be manual or by resorting to machine-learning algorithms. Either way, historical data is used to make assumptions about the future. An analyst’s role in predictive analysis is to assemble and organize the data, identify which type of mathematical model applies to the case at hand, and then draw the necessary conclusions. Relevant software is developed by statisticians and programmers in order to carry out these processes (Predictive Analytics: What It is & Why It’s Important, Ashley DiFranza | February 17, 2021) (Fig. 19.2). The use of predictive analysis helps organizations forecast future outcomes based on past experiences.

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New Data

Data

s Prediction

Fig. 19.2  Data prediction flow

2 Data Foundations of Strategic Thinking 2.1 The Broad Framework Strategic thinking is a cognitive process that delivers visions and goals and ways and means of their fulfillment. Today’s environment is both complex and uncertain. These conditions demand compatible business strategic thinking processes allowing for perspective navigation of turbulent environments. Complex situations with shifting platforms require strategic insight and innovation. “In complex systems, one cause can create multiple effects. Reactive systems using previously learnt behavior miss out on insight”. So, environments are evolving and the challenge of setting a strategy within an evolving environment is the need to understand that there is no single future but multiple futures each with its own parameters and premises. AI has provided a measure of response to the disrupted environment of strategic thinking. It has induced considerable shifts in the concept, the process, the vision, the insights, the competencies, and the fulfillment efforts. The strategic thinking process became an AI underlined system with inputs converted through strategic drivers into an output or fulfilled goals (Fig. 19.3).

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input

• Big Data • Mass data • science fiction • Sybthetic data • implicit data

• Descriptive, • predictive • perscriptive analytics

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Output

• insights • visions • novel functions • novel indsutry arenas • novel demands of functions

Feedback

Fig. 19.3  Data-induced strategic thinking

Inputs Data will constitute the prime input into future business strategic thinking models. This data will extend over the entire range from the real to the synthetic and from the big to the mass.

Transformation Transformation will, thanks to data analytics from the descriptive to the prescriptive and predictive analytics, lead to the emergence of novel business arenas and derived instruments.

Outputs Output will include three varieties: sub-system structures, novel functions, and revealing insights. Insights might turn out to be the most significant outcome here.

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Feedback Feedback data will introduce essential system adjustments from inputs to transformation. This model departs from traditional business strategic thinking paradigms in several ways. First, there is the trigger or the point of start of the strategic thinking process. Traditional analysis puts emphasis on an environmental scanning that could reveal “opportunities and threats.” The suggested model’s point of start is enhanced big data or that mass of information with the wider scope and broader cover. Traditional scanning is replaced by databases that encompasses almost every existing and possible variable of relevance to and of possible impact on this environment. Second, there is the conceiving of visions. Visions of the past were indeed based on a perception of futures to come. Visions of the enhanced big data era will be based on big data analysis and learning. A process that might allow these visions to go beyond the recognizable and familiar. They may address the unimaginable and the blurred, the distant, and the far reaching. Third, there are the tools of the analysis. Again, traditional analysis resorted to the common instruments within economic and social science domains. The above model suggests a reliance on advanced tools resting on a foundation of data analytics. Diagnostic analytics and predictive analytics will provide a strong impetus to the strategic thinking process. They will sketch a horizon that was unreachable before. Fourth is the earmarking of an arena or a field of business combat. The suggested paradigm leaves the door quite open to encounters that never happened before. Competitive encounters within uncharted arenas. Competition, in that sense, is replaced by either synergy or destruction by substitution.

2.2 The Link and the Emerging Patterns Strategic behavior will, to a great measure, focus on insights revealed by data and the direction those insights point to. Visions and strategies

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Data analytics Predictable/ Prescriptive insights Descriptive

Diagnostic

Product development

Function arenas

Portfolio

Concentration

Existing outcomes

evolving outcomes

Strategy drivers Fig. 19.4  Data-induced strategic thinking patterns

should relate to what data analytics reveal as a prospect and longer term outlook. The relationship is reflected in the following figure. The Y axis represents data segments while the X axis represents strategy drivers. Strategy drivers are those forces inducing strategic behavior. Those are either existing or evolving. Existing drivers are present-day inducers of strategic behavior including the search for competitive advantage. Evolving drivers are those inducers resulting from insights as much as the disruptive forces of, among others, technology. Data is segmented into two states, the diagnostic and the descriptive progressing toward the analytical and the perspective (Fig. 19.4). Several patterns of strategic behavior could emerge from this blend of data and strategy analysis.

Function Arena Strategies Predictive and prescriptive analysis combined with evolving strategy competencies could lead to a shift from products to functions or a comprehensive shift toward function arenas.

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Product Development Predictive and prescriptive analysis combined with existing competencies could lead to product development.

Concentration Diagnostic and descriptive data analysis combined with evolving strategy competencies could lead to a search for concentration.

Portfolio Diagnostic and descriptive data analysis combined with existing strategy competencies could lead to merger and acquisition. Data-rooted business strategic thinking demands prioritizing data gathering, analysis, and interpretation at board and leadership levels. It also requires the bringing of data to life with a data culture that induces rich insights (Mck Digital, March 1, 2013).

3 Summary and Conclusions The world of data has changed dramatically over the past decade. Massive volumes are being generated with data storage, extraction, loading, and transformation assuming new dimensions. Data is revolutionizing the way companies view themselves, their industry, and their future. And data analysis has become a premise to businesses regardless of size or industry. Data-rooted strategic thinking is becoming a pivot. Analysis conducted in the chapter leads to the identification of four data-rooted strategic thinking patterns: function arena strategies, product development strategies, concentration strategies, and portfolio strategies. Data-induced strategic thinking patterns are explored and contrasted with traditional approaches.

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This analysis represents a departure from many historical as much as present-day strategic thinking frameworks. And it provides an attempt at developing a mosaic for data-rooted future strategic thinking concepts and patterns.

References 7 Elements of a Data Strategy Analytics 9, July 9, 2021 Dilmegani C (2021) The Ultimate Guide to Synthetic Data: Uses, Benefits & Tools AI Multiple 19 Jul 2021 DiFranza A (2018) Predictive Analytics: What is data, and why is it important, Import-io. 28 Jun 2018 Gartner (2022) Q & A with Alexander Linde, Is Synthetic Data the Future of AI? 22 Jun 2022

20 Cognitive Computing

1 What Is Cognitive Computing? Cognitive computing is a blend of computer science and cognitive science that aims at developing a measure of understanding of the human brain and how it works. By means of self-teaching algorithms that use data mining, visual recognition, and natural language processing, the computer is able to solve problems and thereby optimize human processes (IBM, November 20, 2017). Vast amounts of structured and unstructured data are fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions. It is a type of computing that focuses on reasoning and conceptual analysis often analogous to human cognition. It deals with symbolic and conceptual information rather than pure data or sensor streams (Computer World, 3 March 2016). It resorts to computerized models to simulate the human cognition process and to find solutions in complex situations. It involves technologies that power cognitive applications as expert systems, neural networks, robotics, and virtual reality (VR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_20

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The term cognitive computing is typically used to describe AI systems trying to simulate human thought. Human cognition involves real-time analysis of environment, context, and intent, among many other variables that inform a person’s ability to solve problems, and cognitive computing provides a move in that direction. A number of AI technologies are required for a computer system to build these cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, and natural language processing (NLP) and sentiment analysis. Cognitive systems can analyze structured and unstructured data from diverse information sources. At the same time, these systems are able to take context into account and consider conflicting information, which enables them to formulate optimal solutions to questions and problems. These capabilities are ideal for optimizing the promise of adaptive learning. Cognition within Artificial Intelligence is closely associated with the system structure of the process. As we said earlier, AI is built, as a system, around a flow of inputs, transformations, outputs, and a feedback loop. Data, raw and otherwise, as well as artificial neural sub-systems constitute the inputs. Learning (machine and otherwise) and analysis (diagnostic, predictive and otherwise) provide the transformation. Insights, technologies, and derived sub-systems constitute the output. Feedback loop conveys outputs to the input and transformation segments and triggers essential adjustments (El Namaki 2019) (Fig. 20.1). Machine learning Deep learning Cognitive computing

Fig. 20.1  Cognitive computing flow

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One can hypothesize that the cognition within artificial intelligence is the output of the learning and analysis processes that take place within the transformation segment of the AI system. Cognitive computing is the outcome.

2 Human and Machine Cognition There is a measure of congruence between human and machine cognitions. The cognitive drivers of strategic thinking seem to run parallel to the underlying forces of cognitive computing. The input is different but the output dimension of cognition could be comparable. The strategic thinking delivers three prime concepts: The concept of vision, the concept of strategy, and the concept of corrective action. All of them are creative concepts derived from cognitive processes conducted by the human brain. Cognitive computing produces conceptual frameworks mimicking the cognitive processes of the human brain as well. Cognitive computing resorts to technologies as machine self-learning, natural language processing, data mining, and others in order to deliver cognition. Processes that simulate the human cognition process in order to solve complex and at times amorphous problems. It is computing focusing on reasoning often in a manner that is analogous to human cognition or at least inspired by it. It deals with symbolic and conceptual information rather than data or sensor streams (Noyes 2016). It also resorts to more advanced algorithms to be able to understand naturally spoken or written human communications. Cognitive computing is also combined with analytics to produce computing analytics or a combination of analytics and cognitive computing technologies which is used to help humans make smarter decisions. Cognitive analytics enables users to apply human-like intelligence to various tasks, for example, it understands not only the words in a text but the full context of the written or spoken material and also recognizes objects among a large amount of information in an image. This could induce business vision conception and innovation.

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3 Implications to Organization-Wide Strategic Thinking 3.1 General Strategic thinking, as we stated earlier, comes with specific demands. It requires a systems perspective or a holistic view of the environments, the events, and the players within the business context. It appeals to intent (Hamel and Prahalad 2005). The focus that allows individuals within an organization to leverage energy, focus attention, and concentrate on goal achievement. It works with a broad time horizon that holds past, present, and future in equal view. “Strategy is not driven by future intent alone. It is the gap between today’s reality and intent for the future that is critical” (Hamel and Prahalad 2005). Finally, the strategic thinking process requires a hypothesis-driven sense of direction that ensures the incorporation of both creative and critical thinking into strategy making. Cognitive psychology is the scientific investigation of human cognition or human mental abilities from perception, attention, learning and memory to concept formation, reasoning, judgment, decision-making, problem solving, and language processing. It deals with processes that link mental stimulus (input) to mental response (output). Cognitive psychology plays a role in the emergence of cognition-rooted strategies (Dumper et al. 2019; Sieff 2014). Cognition leads to opportunities and opportunities lead to strategies. Cognition is a prime source of strategic thinking. It works through associative thinking or the creation of new mental models based on new technology association. Associative thinking occurs when the brain is free to “associate,” or link up ideas, thoughts, observations, sensory input, memory of existing knowledge, and subconscious. These associations could relate to products, processes, or insights. Product applications embed the technology in a product or service to provide end-customer benefits. Process applications embed the technology in an organization’s workflow to automate or improve operations. And insight applications use cognitive technologies—specifically advanced analytical capabilities such as machine learning—to uncover insights that can inform operational and strategic decisions across an organization.

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Associative thinking requires a radical departure from past mental framework. Top management’s perception of their industry, their organization’s perception of itself, and third-party perception of the organization go through a vast process of change (Blanding 2013).

3.2 Specific Cognitive computing prime contribution to strategic thinking could relate to its ability to evaluate hypotheses based on present-day knowledge, integrate fresh data, and reach new findings. It can also look at information from multiple perspectives, provide several solutions, and order findings according to confidence in inputs (Market Leadership Journal, October 20, 2016). Product and process innovations are a case in point. • Product and service innovation Cognitive technologies could prove to be a powerful tool in the strategic shift from existing products to novel function-fulfilling instruments. Those could be derivatives of existing products, that is, more effective, convenient, safer, faster, distinctive, or otherwise more valuable products. They can also bring about entirely new classes of products and services that can create new markets and generate large gains for inventors. Automakers are, for instance, using computer vision and other cognitive technologies in order to develop self-driving cars, a radically different instrument for a function, that is, transportation. (El Namaki 2019) • Processes and insights Cognitive technologies could improve processes and create insight. Natural language processing techniques, for instance, make it possible to analyze large volumes of unstructured textual information that has not yielded to other techniques. Machine learning can draw c­ onclusions from large, complex data sets and help make high-quality predictions from operational data. (Schatsky et al. 2015)

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Current cognitive computing platforms introducing one form or the other of those cognitive innovations include IBM’s Watson platform. Watson constitutes a new era of computing based on an ability to interact in natural language, process vast amounts of disparate forms of big data, and learn from each interaction. And there is also Microsoft AI infusing apps, websites, and bots with intelligent algorithms to see, hear, speak, understand, and interpret user needs through natural methods of communication. And there is SAS’s contribution providing NLP, text analytics, and data mining solutions based on structured data.

4 Adoption and Reach Several major corporations are adopting cognitive computing. Major players as Cisco Systems, Hewlett Packard, IBM, Accenture, and Microsoft are among the major operators in the global cognitive computing market. The companies are involved in several growth and expansion strategies to gain a competitive advantage within the industry. The market could be segmented in several ways. If considered by technology, the segments include natural language processing, machine learning, automated reasoning, and others. If considered by application, the segments include healthcare, retail, IT and telecom, government and defense, security, and others.

5 Summary and Conclusions Cognitive computing, an AI-derived paradigm that focuses on reasoning and understanding at a quasi-abstract level, seems to enhance the cognitive processes inherent in business strategic thinking. It is built around a combination of multiple technologies as machine learning, machine reasoning, natural language processing, speech recognition, computer vision, and so on. It is built to work in sync with humans, and it will be nurtured by feeding more information to it. Cognition which is an output of processes within the human brain delivers creativity and innovation. Cognitive computing delivers concepts, insights, and innovations too. The two “systems” seem to synergize

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with cognitive computing having the potential of supporting executive cognition demonstrated in the course of a vision, a strategy, or an innovation. It seems, also, to support the strategic thinking processes by an ability to evaluate conflicting present-day hypotheses and updating those through novel information or data. Cognitive technologies could, in this and other ways, deliver product and process innovations and shifts.

References Blanding M, “The Psychology of Strategy”, Tuck School of Business, April 26, 2013

Cognitive Computing Market Global Demand, Opportunities, Trends, Analysis and Forecast to 2030, Market watch, Jan. 19, 2023. Dumper K, Jenkins W, Lacombe A, Lovett M, and Perimutter M. Introductory Psychology. Pressbooks.

El Namaki M, “A System’s approach to the Artificial Intelligence concept”, Journal of Knowledge Management Application and Practice an International Journal, 1 Aug 2019. Gary Hamel, Prahalad G., “Strategic Intent”, HBR, July–August 2005 Noyes K, “5 things you need to know about AI: cognitive, neural and deep, oh my”, March 3, 2016. Schatsky D, Muraskin C and Gurumurthy R, “Cognitive technologies, The real opportunities for business”, Deloitte Review 2015 Sieff G, The psychology of strategy, IC Growth/ Info choice Growth 9PTY) Ltd, 2014. “5 things you need to know about A.I.: Cognitive, neural and deep, oh my!”, Computer World, March 3, 2016 “Artificial Intelligence, Machine Learning and Cognitive Computing”, IBM, November 20, 2017 “Can Cognitive Computing Provide Your Business an Edge?” Market Leadership Journal, October 20, 2016

21 Implicit Learning and Innovation

1 What Is Learning? Learning is about acquiring knowledge or skills via studying or experimentation and is a longitudinal process. It is often defined as a relatively lasting change in behavior that is the result of experience (Curran and Schacter 2001). Learning is an ongoing process that takes place throughout one’s life. It may occur as part of education, personal development, or any other informal or formal training process. It is also a continuous life-­ long process. Individuals constantly learn, unlearn, and relearn through experiences in order to express ideas, to inform people, to communicate, to create, and a myriad of other activities (Gross 2014).

1.1 What Is Implicit Learning? Learning is a key cognitive function of the human brain. It is a process of acquiring or modifying knowledge, behaviors, skills, values, or preferences (Curran and Schacter 2001; Gross 2014; Xie et al. 2019). Learning takes place when neurons transmit sensory information through synapses and store it temporarily in a volatile region of the brain: The short-term © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_21

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memory. Once information is processed, it goes via neural pathways to the long-term memory. A comparison with existing memories follows and storage in the long-term memory ensues (Curran and Schacter 2001; Pavlik and Anderson 2005; Xie et al. 2019). In reference to Curran and Schacter (2001: 7237), “Learning is the acquisition of new information that leads to lasting changes in behavior, and memory reflects the storage and retrieval of learned information.” In the case when new information contradicts the previously known facts or beliefs, this conflict is typically resolved via cognitive process, resulting in new information either being rejected or confirmed and stored for the later use. Neurons, the prime building block of human brains, undergo change through “plasticity” (Pavlik and Anderson 2005), which is a learning-­ rooted process of creating and strengthening some neurons and neuronal connections and weakening or eliminating others, depending on the type of learning that the brain goes through. It is a core, life-long feature of the brain with long-term learning having the most profound impact. Propensity to learn varies over time, however, according to the type of stimuli the brain is exposed to. It is grounded into experiences of early years when sounds and speech have stronger impact than later in life, when learning tends to be dominated by vocabulary acquisition and experimentation (Pavlik and Anderson 2005; Winter et al. 2018; Ullman 2004). Altogether, learning is about acquisition of new information or experience with some bits being forgotten, whereas most striking elements can be remembered for a long time.

1.2 Implicit and Explicit Memory There are two main sub-categories of the long-term memory: the implicit memory and the explicit memory (Fig.  21.1). They differ in terms of memory content, content retrieval mode, and what part of the brain structure they make use of. Implicit memory is an unconscious memory that is acquired and put to use without awareness. It can affect thoughts and behaviors. Explicit memory is a conscious memory whose prime

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Fig. 21.1  Implicit and explicit memory. (Adapted from: Stangor and Walinga 2014, p. 366)

function is the intentional recollection of factual information as well as previous experiences and concepts (Curran and Schacter 2001). Therefore, explicit memory is often referred to as declarative memory and being described as having episodic and semantic aspects (Xie et al. 2019). Implicit memory has procedural and priming sub-sets, with the former one being helpful in performing usual tasks automatically that is guided previous experiences. Another smaller subset of implicit memory is priming or the act of associating stimuli to assist in recognizing an object or a concept. An example would be to think of the color green to remember the word grass. We are “primed” by experiences (Curran and Schacter 2001; Ullman 2004; Xie et al. 2019).

1.3 Explicit vs. Implicit Learning The brain engages in two types of learning: explicit and implicit. Explicit learning is learning that one has conscious awareness of and able to articulate what he or she taught (Schendan et al. 2003). Implicit learning is the opposite: It is the kind of learning that one has conscious access to but cannot really articulate (Curran and Schacter 2001).

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Implicit learning relies on the two aspects of implicit memory: perceptual and conceptual priming. Perceptual priming refers to brain’s ability to recognize stimuli, while conceptual priming is an ability to assess meaning of events and/or facts based on available semantic information (Curran and Schacter 2001; Ullman 2004; Xie et  al. 2019). It is also linked to category and sequence teaching (Schendan et al. 2003). In general, implicit learning relies on the brain’s ability to engage its different regions based on the type of the processed information and it is modifiable via experience. Implicit learning’s knowledge is also claimed to lie at the very core of creativity (Fig. 21.2). Neural plasticity could relate to the ability to learn and the explicit and implicit memory function. Plasticity, as such, is the capacity to shape and mold and neuroplasticity is the ability of the brain to create new neurons and build new networks and by doing that adjust functions and structure. Enhancing neuroplasticity and the consequent learning could emerge as a result of different types of learnings. Those could cover a wide scope going all the way from language learning to music learning. What the brain does is creating new synapses and inducing an enhancement of intellect. A synapse, the gap between one neuron cell and another, carries electrochemical information between neurons. Neuroplasticity: Rewiring The Brain in 2023 (n.d.) (declutterthemind.com).

Implicit

Fig. 21.2  Explicit vs. implicit learning

Explicit

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2 Could Business Organizations Develop Implicit Learning Competency? A business organization could find itself in an implicit learning situation when it uncovers objects, actions attributes, or even strategies whose roots were hence unknown. Consequent analysis would reveal that those have emerged from an exposure to hence invisible stimuli. Stimuli originating from immediate or distant force fields within the organization’s own periphery or within distant and unrelated arenas. “Silent signals” emitted from within those peripheries were sensed by the organization’s receptors (Heilmann et al. 2020), information systems, for example, disregarded but stored into the organization’s implicit long-term memory. An implicit learning exercise ensued when the organization sensed a need for a creative alternative to existing practices. It is a process that combines several competencies. Prime among those are data identification, access and assembly, data processing, data analysis, and, last but not least, data-­ derived learning. It is this data-triggered analysis and learning that could create a state of implicit learning and induce a novel approach to strategic thinking. Artificial intelligence (AI) could provide a medium here. AI systems resort to data as an input and to learning as a transformation process. AI systems collect and are exposed to torrents of data. AI systems also conduct data analysis and learning along machine learning and deep learning constructs within system flows. AI-supported organizations are more likely than not to enhance implicit memory (of the organization) by developing algorithms and sub-technologies that are not immediately relevant to the organization’s today’s functions. Those are stored, then, in the organization’s implicit memory and may become dormant or even unconscious until the day the organization faces silent signals from distant peripheries. Organizations resort to what is termed the “implicit knowledge management process” (Arena et  al. 2018), a process that employs tools, techniques, and methodologies that capture these previously elusive outcomes and processes and make them more generally available to the organization.

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3 Could Implicit Learning Competency Boost Innovation? The answer is a hypothetically affirmative. One could hypothesize that the potential for an impact is there. In very much the same way that the human brain resorts to implicit learning to enhance cognitive competencies, executives and corporation could resort to implicit learning to enhance the scope, depth, and reach of innovation. And in very much the same way that human brain enhancement of cognitive capacities comes through the growing of new neurons, executives and businesses could enhance their innovation potential by enhancing their implicit memory construct. Implicit organization memory could be enhanced by greater exposure to silent signals and stimuli originating from beyond the organization’s immediate periphery. Systems theory can provide a framework for the process. There are inputs, transformations, and outputs. Inputs are technology, market, and industry data as well as silent signals. Transformation is the process of implicit learning. Outputs are product, service, and sub-system innovations. A feedback supplies corrective actions. The following figure illustrates this process (Fig. 21.3).

Fig. 21.3  Implicit learning system

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This paradigm could have far-reaching implications for strategic thinking. Conventional approaches to the concept rely, to a very large extent, on present-day, and at times yesterday’s, knowledge. Silent signals and stimuli do not seem to have a significant bearing on the strategic thinking process or the outcome of that process. Strategic moves seem to be incremental rather than “revolutionary!” One has only to follow Microsoft’s Windows software to identify evidence.

4 Case Evidence 4.1 Case One: Brands Crossing Industries Brands are cutting across industrial branches and markets reflecting an implicit learning process. Traditional brand strategy that created differentiation around a business model is giving way to novel business models making brands constantly relevant. The outcome is a type of compelling competition emerging from invisible corners, hidden from sight, impossible to predict, hard to pre-empt, and difficult to challenge (Brodie et al. 2017; Burmann et al. 2017; Castro and Giraldi 2018). Take the case of Philips and Nike. Back in the 2000s, Philips and Nike once introduced a range of portable devices—such as radios, MP3, and CD players— designed specifically to be worn on the body during athletic activity (Viksnins 2004). The alliance sought a function that goes beyond the music function of MP3 to wearable technology. This could see the applications of smart fabric for health monitoring (Aziz and Chang 2018), such as incorporating GPS tracking systems in shoes (Yang and Kels 2016).

4.2 Case Two: Businesses Reinventing Core Competencies Businesses develop core competencies but these could mature and eventually die. Some seek an innovative extension of existing life or a new life altogether. Their search of an enhancement of existing core competency or the configuration of a new competency altogether could take them

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down the implicit learning route. They could “sense” market and technology shifts and implicit learning helps reveal new arenas and novel competencies. Cambridge Analytics and Facebook provide an example here. US Facebook users were subjected to media interventions during a presidential campaign with the ultimate aim of expressing preference to specific candidates. Millions of Facebook profiles of US voters were harvested and accessed in the process (Venturini and Rogers 2019). A repeat performance was done in the Brexit campaign (Berghel 2018). Facebook Analytics or the medium to “insights into business” followed and Facebook entered a new business arena.

4.3 Case Three: Data Creating Industries Big data is crossing industry boundaries and assuming the key role in new industry creation. It reflects the case when data no longer serves its initial and somewhat limited purpose. Instead, data is being aggregated and combined with a purposeful analysis toward innovative solutions and, hence, may create even new industries. Consider the case of IBM and Twitter. IBM and Twitter have joined hands in order to sell analytical information to corporate clients. IBM analyzes Twitter’s data and combines it with other public and business sources in order to help businesses “to tap into billions of real-time conversations to make smarter decisions,” according to Glenn Finch (2015), Global Leader Data and Analytics (Pham 2015). This partnership rests on companies’ abilities to leverage their core competences: IBM utilizes its analytical capacity, while Twitter aggregates large volumes of contextual data.

4.4 Case Four: Technology Crossing Industries Technology drives the continuous change of almost all industries. The music industry is not an exception to this rule. Terrestrial radio has been marginalized by the rise of streaming and digital distribution. The next level was the emergence of cloud-based services and opportunities provided by Big Data analysis performed by artificial intelligence. In parallel to that, social media enabled new ways of music personalization and distribution (Maasø 2018).

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5 Implicit Learning as a Top Management Competency A measure of implicit learning within a managerial context could be developed through “reflection.” Put differently, implicit learning could take different routes and one of those is through the process of gaining insight and thinking analytically about one’s own thoughts. Executive development of a measure of reflection competency could lead to an equal measure of self-learning as one analyzes and understands why a decision was made, rather than simply examine the outcome of the decision. Management development of an implicit learning competency through reflection could take place through reflection cases, situations, and exercises. Thinking about one’s own thinking or metacognition can boost implicit learning, especially about new knowledge and competencies (Fleming 2021). It leads to metacognition, a process that begins with forming beliefs about how best to learn and where one should focus attention (Fleming 2021).

6 Summary and Conclusion The human brain learns ubiquitously and it does that in many ways. Cognition, sensing, and memory play key roles in this process. Both long-term memory and short-term memory shape the conditions of information processing rendered via implicit long-term memory and explicit long-term memory. Implicit long-term memory is used unconsciously and can affect thoughts and behaviors. Implicit long-term memory performs “implicit learning,” a form of learning that occurs without the individual’s awareness. It evolves from continuous exposure to events and stimuli and relies on different brain systems that are consciously controlled or “explicit” learning (Curran and Schacter 2001). Could business organizations develop an ability to learn implicitly and derive creative strategies from this implicit learning?

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Analysis provided in this chapter could lead to the hypotheses that the potential for an impact is there and that the key to that is implicit learning. In very much the same way that the human brain resorts to implicit learning to enhance cognitive competencies, executives and corporations could resort to implicit learning to enhance the scope, depth, and reach of strategic thinking. And in very much the same way that human brain enhancement of cognitive capacities comes through the growing of new neurons, executives and corporations could enhance their innovative potential by enhancing their organization’s implicit memory. Implicit organization memory could be enhanced by greater exposure to silent signals and stimuli originating from beyond the organization’s immediate periphery.

References Arena D et  al (2018) Human resource optimization through semantically enriched data. International Journal of Production Research Vol. 56, No. 8, pp. 2855–2877. https://doi.org/10.1080/00207543.2017.1415468 Aziz S, Chang SH (2018) Smart-fabric sensor composed of single-walled carbon nanotubes containing binary polymer composites for health monitoring. Composites Science and Technology Vol. 163, pp. 1–9. https://doi.org/10.1016/j. compscitech. 12 May 2018 Berghel H (2018) Malice Domestic: The Cambridge Analytica Dystopia. Computer, Vol. 51, pp. 84–89. https://doi.org/10.1109/MC.2018.2381135 Burmann C et al (2017) The Foundations of Identity-Based Brand Management. In: Burmann, C.; Riley, N.; Halaszovich, T. and Schade, M. (Eds), Identity-­ Based Brand Management, Wiesbaden: Springer Gabler, pp 1–16. https://doi. org/10.1007/978-­3-­658-­13561-­4_1 Brodie RJ et al (2017) Branding as a dynamic capability: Strategic advantage from integrating meanings with identification. Marketing Theory, Vol. 17, No. 2, pp. 183–199. https://doi.org/10.1177/1470593116679871 Castro V, Giraldi J (2018) Shared brands and sustainable competitive advantage in the Brazilian wine sector. International Journal of Wine Business Research, Vol. 30 No. 2, pp. 243–259. https://doi.org/10.1108/IJWBR-­04-­2017-­0019 Curran T, Schacter DL (2001) Implicit Learning and Memory: Psychological and Neural Aspects. in: Smelser, N. J. and Baltes, P. B. (Eds.), International

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Encyclopedia of the Social & Behavioral Sciences, USA: Pergamon, pp. 7237–7241. https://doi.org/10.1016/B0-­08-­043076-­7/03513-­0 Finch G (2015) Global Leader Data and Analytics at IBM. The Global Experience Summit 2020 Gross R (2014) Themes, Issues and Debates in Psychology. 4th Ed., UK: Hodder Education Heilmann P et al (2020) Agile HRM practices of SMEs. Journal of Small Business Management, https://doi.org/10.1111/jsbm.12483 Maasø A (2018) Music Streaming, Festivals, and the Eventization of Music. Popular Music and Society Vol. 41, No. 2, pp.  154–175. https://doi.org/1 0.1080/03007766.2016.1231001 Muraskin J et al. (2016) Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players. Human Brain Mapping, Vol. 37, No. 12, pp. 4454–4471. https://doi.org/10.1002/hbm.23321 Pavlik PI, Anderson JR (2005) Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect. Cognitive Science, Vol. 29, No. 4, pp. 559–586. https://doi.org/10.1207/s15516709cog0000_14 Pham P (2015) ‘The Impacts Of Big Data That You May Not Have Heard Of ”, Forbes [Online] Available from: https://www.forbes.com/sites/peterpham/2015/08/28/the-­i mpacts-­o f-­b ig-­d ata-­t hat-­y ou-­m ay-­n ot-­h ave-­ heard-­of/ Accessed on 2 Feb 2020 Schendan HE et al (2003) An FMRI Study of the Role of the Medial Temporal Lobe in Implicit and Explicit Sequence Learning Neuron, Vol. 37, No. 6, pp. 1013–1025. https://doi.org/10.1016/S0896-­6273(03)00123-­5 Stangor C, Walinga, J (2014) Introduction to Psychology. Canada: BC Campus. Winter et al (2018) Vision dominates in perceptual language: English sensory vocabulary is optimized for usage. Cognition Vol. 179, pp. 213–220. https:// doi.org/10.1016/j.cognition.2018.05.008 Ullman M (2004). Contributions of memory circuits to language: the declarative/procedural model. Cognition, Vol. 92, No. 1–2, pp. 231–270. https:// doi.org/10.1016/j.cognition.2003.10.008 Venturini T and Rogers R (2019) API-Based Research or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica Data Breach. Digital Journalism, Vol. 7, Vol. 4, pp.  532–540, https://doi.org/10.1080/21670811.2019.1591927 Viksnins R (2004) Philips Nike Review. CNET [Online] Available from: https:// www.cnet.com/reviews/philips-­nike-­mp3-­max-­review/ [Accessed on 2 February 2020]

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Xie TT et al (2019) Declarative memory affects procedural memory: The role of semantic association and sequence matching. Psychology of Sport and Exercise, Vol. 43, pp. 253–260. Yang YT and Kels CG (2016) Does the Shoe Fit? Ethical, Legal, and Policy Considerations of Global Positioning System Shoes for Individuals with Alzheimer’s Disease. Journal of the American Geriatrics Society, Vol. 64, No. 8, pp. 1708–1715 https://doi.org/10.1111/jgs.14265 Neuroplasticity: Rewiring The Brain in 2023 (n.d.) (declutterthemind.com) Fleming SM (2021) Know thyself: The science of self-awareness. Basic Books 27 Apr 2021

22 The New Paradigm: From Concepts to Operations

1 The New Paradigm: Top Management Operating Competencies Operationalization of concepts within the top management context will require the following areas of exposure. Those are executive competency generating programs leading to the acquisition of specific strategic or operational competencies. To these programs belong the following: 1. Advanced top management competencies 2. Algorithm-derived control 3. Dynamic visioning 4. Data strategies 5. Leading with technology 6. Competitive cognition 7. Managing business arena shifts 8. Data-driven decision-making 9. Enhancing the propensity to lead 10. Enhancing the propensity to enterprise © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_22

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Algorithm derived control

Advanced top management competencies Competitive cognition

Data strategies

Dynamic visioning

Data driven decision making

propensity to lead

Leading with technology Managing Business arena shifts

propensity to enterprise

Fig. 22.1  Top management operating competencies

The following is an outline of the framework and the structure of these key top management competency generation programs (Fig. 22.1).

1.1 Advanced Top Management Competencies Generations of today’s top management are the product of traditional view of management functions and attitude toward strategy and organization. Novel or advanced technologies are challenging those worn-out

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profiles and competencies. The contents and the modes of conduct of a top management function tomorrow will deviate, considerably, from past practice. Future incumbents will have to be conversant with artificial intelligence system premises and the possible outcome of those. Central to this conversance would be a realization of the fact that awareness, for example, is a potent driving force, that visionary dynamics are inducers of a sense of direction setting, that intent is an underlying zeal, that intelligence is a dynamic trait, and so on. Today’s top management will have to undergo a process of “re-­learning” whereby the tenets of the new technology are absorbed and incorporated into their performance and total approach to the function. This will constitute a process whereby the new competencies are embedded and the obsolete “competencies” are allowed to die. Novel competencies could be included into “exposure” sessions that would cover the following areas: • • • • •

Managing awareness Conceiving visions Searching and formulating intent Measuring strategic control Measuring own locus of control

1.2 Algorithm-Derived Control Traditional management control rests on a setting of achievements against desired end result. Both desired achievements and achieved end result reflect, in the majority of cases, earlier experiences and observed practices within the firm or within the industry as a whole. What is novel here is that the entire process from setting desired achievements to ultimate outputs will depend on data. Input data will determine what could be reached and also what has been reached or made concrete. The process of dynamic data flows and analysis will, through analytical tools of machine and deep learning, lead to algorithms. Change in data structure, be it structured or unstructured data or even simulated data, will exercise control over the ultimate output of the process. Control, in this situation,

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1

Data inputs

2

Rules for deriving outputs from input data

3

Algorithm

Fig. 22.2  What is algorithm?

will assume a dynamic nature leading to a span of judgmental outcomes reflecting data scope, quality, reach, and dynamics among other things. The analysis will lead to information that is dynamic, timely, and reflective of events. It could also reflect the potential or the predictive behavior of key variables (Fig. 22.2). Competency generating programs in this area would include the following: • • • • • •

What is an algorithm? Defining control algorithms Deriving control algorithms from data Incorporating control algorithms into information systems Updating control algorithms Segmenting control algorithms

1.3 Dynamic Visioning Visions represented, for some time, an outcome of a pierce through the future. It, as we said earlier, represented an amalgam of entrepreneurial insight, accessible resources, and drive. Visions could be blurred or sharp depending on multiple variables including insight and intuition. Data science analytics will provide a rich foundation for visioning. Rich data whether structured, unstructured, factual or simulated will open the door

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and widen the scope of visioning. They may even open the door for a wide variety of scenes and a myriad of visionary images. Data drawn from unusual sources as science fiction could broaden the scope, depth, and reach of visions. This could lead to visions transcending the limits of today’s sciences. It could also lead to images of products, processes, and industries exceeding the tangible realties of today. This type of visioning will require a special type of top management “conditioning” that is very unlikely today. Some aspect of this “training” may very well touch upon the very possibilities of converting tomorrow’s scientific perspective into products and industries (Fig. 22.3). Competency generating programs in this area would include the following: • • • • • •

What constitutes a vision Sources of visionary scenes Science fiction as a source of visionary scenes Technology dimension of visions From visions to strategies Dynamic visioning

1.4 Data Strategies Including Data Simulation Data input is both a complex and comprehensive process. One of the areas that will require special attention is data simulation being a potent and, concurrently, complex tool. As we said earlier, data simulation is the vision

goals Fig. 22.3  Vision, strategy, and goals

strategy

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process of simulating real-world scenarios or conditions. It is an imitation of real-world processes over time. In machine learning, a simulation is an algorithm that mimics a real-world environment that can be used to test different courses of action. Data simulation enables the creation of comprehensive models of complex, dynamic systems; empowers data-driven decision-making; and helps test hypotheses, understand relationships, and improve predictions. It, moreover, allows the study of phenomena that are difficult or impossible to investigate directly and generates synthetic data that is representative of specific populations or conditions. Data simulation will provide, over time, a powerful management tool and a top management competency. The ability to test millions of scenarios against a simulation has been shown to help machine learning models learn quickly, and even outperform models trained on more limited real-world data. However, simulation is only as accurate as the underlying model it is based on (Gretel 2022). Competency generating programs in this area would include the following: • • • • • • • •

Data sourcing Data training Data analysis tools Data diagnosis. Data analytics Data predictive Synthetic data or data simulation Data processing

1.5 Leading with Technology Recent history of technology reveals that technology is an ever-changing variable with steep life cycle and far-reaching tentacles! Leadership of the past decades relied on traits that did not include explicit awareness of this rapid change in technology or adjustment of leadership functions to the emerging events and phenomena. Top management of tomorrow will demand a continuous awareness of emerging technology trends and their impact on the company, organization, and the industry as a whole.

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Among the emerging technologies with potential far-reaching and longer-term impact on managerial performance, and leadership are things like brain-computer interface, that is, brain directly connected to a computing equipment, 4D printing, and DNA computing, among others. Competency generating programs in this area would include the following: • • • • •

Technology as a leadership trait Technology awareness Cross-industry and arena technology Synergy in technology Cost implications of technology

1.6 Competitive Cognition Competitive cognition will constitute a very novel element in top management competencies. The term “competitive cognition” refers to the framework within which competitive knowledge is continuously acquired, retained, and used (Walker et al. 2005). Repeated exposure to rivals allows executives to learn the attributes and strategies of those competing rivals and place them within a mental framework. Target competitors are then assigned to a category within this hypothetical mental framework. Blind spots in competitive cognition and outmoded mental models can explain empirically observable phenomena such as industry overcapacity, the failure of new entries, and acquisition overpayment (Porac and Thomas 1990). Competency generating programs in this area would include the following: • • • • • •

What is competitive cognition? Competitive knowledge acquisition Converting competitive knowledge into competitive advantage From insights to competitive knowledge Creating competitive benchmarks From competitive cognition to strategic thinking

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1.7 Managing Business Arena Shifts Emerging technologies as well as technology shifts witnessed over the past decade or two and those very likely to occur over the forthcoming decades will expose top management to radical decisions touching upon the continuity of the organization within its present industry and the possible search for other venues or industries. Yesterday’s concepts of industry structure analysis fall short of providing a sufficient base for this strategic move, however. The future will dictate a need for a top management ability to identify symptoms of incumbent industry decline and search for a remedy whether by leaving the industry altogether or migrating to another business arena. Those decisions will not be easy to take and a top management competency to deal with them will be needed. Migration to another business arena may require dealing with barriers from durable and specialized assets to high costs of exit, but the core issue will be the search for an arena with long-term potential and possible relevant synergy (Harrigan and Porter 1983). Competency generating programs in this area would include the following: • • • • • •

Symptoms of industry decline Strategies for industry migration Cross-industry synergy Technology silent signals Science fiction New industry entry strategies

1.8 Data-Driven Decision-Making Data collected today has never been greater or more complex. This makes it difficult for organizations to manage and analyze their data and for top management to manage this process. Data-driven decision-making is tantamount to resorting to facts, metrics, and data to guide strategic business decisions in order to match goals, objectives, and initiatives.

22  The New Paradigm: From Concepts to Operations  • Problem definition • Data collection • Data analysis

data interpretation

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Decision making

Fig. 22.4  Data-driven decision stages

Data analysis could deliver descriptive (factual) outputs, inferential (interpretive) outputs, and predictive (inference of future events based on analysis as performed) outputs. Top management will have to develop a competency at identifying the need for this data-driven effort, managing the introduction of the different instruments, and making effective use of the outcomes. Competency generating programs in this area would include the following (Fig. 22.4): • • • •

Data-driven systems Data-driven outputs Strategic use of data-driven outputs Data-driven decision-making

1.9 Enhancing Propensity to Lead As we stated earlier, leadership is becoming a dynamic concept with a dynamic pattern of traits and a variable propensity. This rather fluid status of leadership requires a continuous monitoring of the requirements of the “hour” and the measure of compliance required. And this is a continuous process. It should be done at every turn of the day and with every sign of change in the immediate or distant environments. • • • • • •

Leading from behind Testing leadership propensities Mending leadership propensities Developing dynamic control parameters Situational propensity adjustment Balancing locus of control.

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1.10 Enhancing Propensity to Enterprise Entrepreneurial initiative belongs to the dynamic state of technology that we are witnessing today and will continue to witness in the future. This requires a keen sense of identification of potential opportunities and a fluid attitude toward structures and strategies. Those will become function of the disruptive waves in environmental variables starting with technology. For that to happen, a number of parameters are needed, and prime among those is the propensity to enterprise. • • • • • •

Dynamic opportunity identification From vision to opportunities Technology-derived opportunities From opportunity to venture Adapting structure and culture to opportunities Dynamic enterprising

2 The New Paradigm: Organization-Wide Operational Competencies Organizations as a whole will have to acquire new competencies that would allow continuity as well as progression in the light of tomorrow’s technology-driven era. Most of those competencies will be, just as the top management genre is, novel and seldom recognized before. Organization-wide instruments would include the following: 1 . Re-inventing organization structure 2. Re-shaping corporate culture 3. Reformulating human resource policies and practices 4. Data-specific industry analysis 5. Database design

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Reinventing organization structure

Data base design

Reformulating human resource policies and practices

Reshaping of corporate culture

Data specific industry analysis

Fig. 22.5  Key organization-wide competency generation programs

The following figure shows an outline of the framework and the structure of key organization-wide competency generation programs (Fig. 22.5).

2.1 Reinventing Organization Structures Organization structures have traditionally followed a hierarchy cum function lines. Managerial functions emerged as an outcome of work done decades ago by Henri Fayol (Fayol 1949) followed by others as Max Weber and Frederik Taylor. He identified, in his work “General and Industrial Management” which was published in 1949, five managerial functions: planning, organizing, command, coordination, and control. They reflected the realities of those decades and embodied, more or less, images of business conditions as they then once existed. Variable degrees of change, especially the disruptive genre, have undermined many of these old premises.

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Organization structures of today and tomorrow are likely to reflect technology-derived dynamic pattern. A flexible data-derived task-based cluster within organization will attend to “issues” and move from one issue to the other. Those issues may demand different competencies and organizational belonging. CEO role will be that of monitoring dynamic goal setting, team–issue-goal fit, strategic monitoring, and control of the process. The organization structure will look, then, more like task bubbles floating towards a goal mesh. In summary, a trend toward flat structures or holacracy will emerge as well as a dynamic organization chart with distributed leadership and “matrix” teams. Competency generating programs in this area would include the following: • • • • •

From product to function Data-derived tasks Task-derived issue teams Control by algorithm Dynamic matrix structures

2.2 Re-Shaping of Corporate Culture Corporate cultures have historically demonstrated a pattern where corporate environments and individual desired positioning lead to a set of endorsed values. Technology change of the proportion that we have been discussing in this book will exercise substantial impact on those underlying premises of the corporate culture. An immediate change element will be the underlying value system. Corporate culture will have to become open and malleable. New operating conditions will dictate cultural values that encourage change and cross-functionality. Competency generating programs in this area would include the following: • Neo corporate value systems • Change-rooted cultural norms

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• Integrating culture in human resource practice • Monitoring corporate culture practices

2.3 Reformulating Human Resource Policies and Practices Traditional reliance on conventional human resource policies and practices will subside under the influence of the emerging waves of technology. Issues such as competencies, placements, promotions, careers, motivation, rewards, and progression will either subside or assume new meanings and connotations. The shifting premises of industries and the associated shifts in structures and appropriate situational competencies will throw a new light at the whole issue of having a workforce and the dynamics of this workforce. Continuous re-education may be the most potent mode if a workforce is to continue contributing within the emerging parameters of the industry and the culture. Competency generating programs in this area would include the following: • • • • • • •

New motivational tools Identifying a talent Dynamic competencies Creativity and innovation Dynamic rewards Managing culture Enterprise

2.4 Data-Specific Industry Analysis Data-specific industry analysis is an assessment tool that would help diagnose contemporary conditions as much as the perspectives. It covers a wide front from demand-supply parameters and competitive profile to longer-term perspectives including industry status and future prospects taking into account technology shifts.

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Data-specific industry analysis lends itself to some industries more than others. Healthcare industry is becoming more efficient in collecting and using big data. The finance industry has access to an enormous volume of data, ranging from banking transactions to analyst projections and stock prices among others and are adopting those in a variety of ways. Insurance industry is also one of the top industries heavily investing in predictive analytics. Competency generating programs in this area would include the following: • • • • •

Industry analytics Cross-industry synergy Industry sickness parameters Managing declining industries Cross-industry technologies

2.5 Database Design Database design is the organization of data according to a database model. It is the design of a collection of steps that help create, implement, and maintain business data management systems. The designer determines what data must be acquired and stored and how the data elements interrelate. This information should feed into the database model. Database design defines the database structure used for planning, storing, and managing information. To ensure data accuracy, a database should only store relevant and valuable information. Competency generating programs in this area would include the following: • • • • •

Data sourcing Data training Data storage Data simulation Data economics

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3 The Structure of Competency Generation Program A competency generation program will have to resort to nontraditional modes of “instruction” and equally nontraditional contents. Key features could include the following: • Ultimate goal. Developing an ability at conducting a task or group of tasks demonstrating a thorough understanding of the issue at hand and an ability to convey this understanding to others. • Mediums. The event should ideally resort to problem-based learning approach where a problem provides the mode of problem identification and follow up efforts build upon this door opening effort. • Duration. Event duration should become a variable depending on the tackled issue and an estimate of time required to achieve the ultimate goal. • Venue. Top management’s own performance venue should provide the “theater” or the exposure. • Recognition. An evidence of completion and reach of ultimate goal should take the form of a “clinical trial.” All these parameters will demand intensive preparation and an essentially different setup from the “executive training” setups found today. It could actually lead to a reinvention of “executive” education”. Annex A provides an illustration.

4 Summary and Conclusions Earlier chapters provided critical elements of the technology-derived strategic management paradigm. It is however a predominantly conceptual analysis. How will this conceptual framework translate into applications within the emerging technology-laced business environment of today and tomorrow? This is the question that this chapter tries to answer.

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Two types of interventions or operational competency generating interventions are suggested: a top management intervention and an organization-­wide intervention. The scope of each is wide and covers a broad spectrum. The most important with regards to top management are the new competencies starting with awareness and visioning and going all the way toward intent and control. The most important for the organization is adjusting organization structure and culture as well as data systems to the new realities of technology. Competency generation programs belong to each and every area of intervention. These competency generation programs will have to resort to nontraditional modes of “instruction” and equally nontraditional contents. Key features could include problem-based “instruction or learning,” participant-based problem identification, and participant reality tests. Annex A of this chapter provides a “model” for a competency generation program. It deals with strategic arena shift and focusses on a number of contemporary cases including the Metaverse, a new industry entry strategy, and the solar films, a substitute technology to an existing product.

 nnex A: A “Model” Competency A Exposure Program Managing Business Arena Shifts Scope Emerging technologies are changing the premises of many industries. Technology is creating different and novel ways of response to market conditions. New products, new processes, new supply chains, new outlets, and new modes of obsolescence could emerge as a result. The point of start is a sign of industry decline. This decline can take many shapes from a simple demand decline to attrition or the gradual disappearance of premises of the industry as a whole. Tracing those silent signals and searching for a way out of a rising or an imminent end game is a

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contemporary problem. CEOs may gradually trace it or they may wake up one day to see the ground disappearing from beneath his feet. How to manage this process and how to develop a solution is the focus of this competency exposure program. This competency generation program relies on problem-based learning. This approach presents participants with a real-world issue and asks them to come up with a well-constructed answer. The problem could also be of their own. They can tap into online resources, use their previously taught knowledge, and brainstorm in order to conclude with a convincing solution. Contrary to conventional approaches to learning, there might not be just one right answer, but the process encourages creative search of answers. This approach is most appropriate within today’s dynamic technology environments.

Desired Competency • • • • • •

Managing silent signals Identifying symptoms of industry decline Identifying parameters and stages of decline Positioning of own company Developing a “shift” strategy Building a shift transition process

Coverage • • • • • • • •

Symptoms of critical change or decline Scope, pulse, and symptoms of a sick industry Search for new challenge Process of identification of a target horizon Process of developing a shift strategy Managing the exit process Managing the entry process Positioning within the new industry lifespan

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Approach • • • • • •

Fundamentals Participant reflections Problem formulation Problem solving Derived competencies Competency demonstration

Exit Parameters • • • •

Identifying silent signals within the participant’s own industry Developing a scenario for the participant’s own industry Identifying a case within neighboring industries Developing a shift scenario

Resource Persons Resource persons should ideally be a blend of those who are doing research in relevant industry evolution and restructuring areas as well as industry executives dealing with industry shifts. They will, more likely than not, be technology-related and or investment and capital market-­ related. Those operating within capital markets will, very likely, have a sharp sense for the rise and decline of industries as well as the entry and exit parameters.

Duration Variable depending on the type of industry and the strength of the “silent signals.” It could also be intermittent relating exposure to the stage the industry or the company is going through. And it could be physical or virtual depending, again, on conditions and variables.

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Cases Cases could represent different stages of industry shift from emerging technology-driven entrants to subsiding exit cases.

Emerging Industries and Products The Metaverse. The metaverse is the next evolution in social connection and the successor to the mobile internet. It provides mobility within a set of digital spaces that one can move seamlessly between just like the internet. Technically, a metaverse is a collective virtual shared space, created by the convergence of virtually enhanced physical and digital reality. It promises the next level of interaction in the virtual and physical worlds, providing innovative new opportunities and business models. A metaverse is neither device-independent nor owned by a single vendor. It is an independent virtual economy. The litho chip optical lithography technology used in semiconductor device fabrication to make integrated circuits (ICs) is another case. It uses extreme ultraviolet (EUV) optical lithography, a photon-based technique that includes projecting an image into a photosensitive emulsion (photoresist) coated onto a substrate such as a silicon wafer. It is the most widely used lithography process in the high-volume manufacturing of nano-­ electronics by the semiconductor industry (Naulleau 2019). ASML, the developer and leader of the technology, is a Dutch multinational corporation founded in 1984. It specializes in the development and manufacturing of photolithography machines used for computer chips.

Declining Industries and Products Solar cell plates thin-film photovoltaics, a thin-film solar cell, is made by depositing one or more thin layers of PV material on a supporting material such as glass, plastic, or metal. There are two main types of thin-film

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PV. It is designed to convert light energy into electrical energy (through the photovoltaic effect) and is composed of micron-thick photon-­ absorbing material layers deposited over a flexible substrate. The technology is rather old but is continuously improving and is currently growing at a remarkable rate. Several types of thin-film solar cells are widely used because of their relatively low cost and their efficiency in producing electricity. (semiconductorwww.scientificamerican.com/article/solar-­power-­ lightens-­up-­with-­thin-­film-­cells/). Fossil fuel engines. There exists a view that fossil fuel companies are set to face “terminal decline” because of falling demand and higher investment risks caused by competition from clean technologies, government regulations, and energy security targets. The fossil fuel system as a whole is being disrupted by the forces of cheaper renewable technologies and government policies. The decline of the fossil fuel economy poses a significant threat to global financial stability, and a key issue for the industry is whether the energy transition will be gradual or rapid and how much time they have to adjust by gradual decline, exit, or shift to other domains.

References Boskin, M. J. (1987, January 1) Reagan and the Economy: The Successes, Failures, and Unfinished Agenda. Hardcover. Jennings W “Data Simulation: Tools, Benefits, and Use Cases”, Gretel, July 13, 2022 Patrick Naulleau, in Comprehensive Nanoscience and Nanotechnology (Second Edition), 2019) Harrigan K, Porter M (1983) End-Game Strategies for Declining Industries. Harvard Business Review 64, no. 4: 111–20. 1 Jul 1983 Naulleau P (2019) Comprehensive Nanoscience and Nanotechnology (Second Edition). Oxford University Press (1949) Fayol’s (1949) 14 principles of management. https://learninglink.oup.com/static/5d493e568dc66e0010f815d1/ page_02.htm

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Porac JF, Thomas H (1990) Taxonomic mental models in competitor definition. Academy of Management Review 15, 224–240. 1994 Walker et al 2005, Managerial Identification of Competitors, Bruce H. Clark and David B. Montgomery, Volume 63, Issue 3 WebFayol (1949) Fayol’s (1949) 14 principles of management See pages 31–32

23 Concluding Remarks

1 Illustrative Case One: The Return of the BCG Question Mark There was a time when BCG’s Growth Share Matrix broke all records of strategic management instrument popularity. Populist labels became industry standards. There were Stars, Cash Cows, and Question Marks and not to forget Dogs. All came to represent sets of business conditions, company positions, competitive forces, and possible venues for strategic behavior. Those days are gone and superior substitute instruments took over. Yet, a rather unremarkable segment of BCG’s Growth Share Matrix, the “question mark,” could, in the author’s view, assume key role within the emerging disruptive technologies of Artificial Intelligence and Data Sciences. Modern-day dynamics of this “question mark” segment will provide the focus of the following chapter. The point of start is the historical positioning of the “question mark” segment with BCG’s matrix. This is followed by an analysis of modern-­ day driving forces of the segment and how AI-derived technologies and insights could populate this segment with innovative products and strategic business units. The ultimate outcome is a framework, conceptual © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7_23

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and operational, for a new “question mark” segment analysis and a BCG matrix altogether reflecting today’s technologies.

1.1 The “Question Mark” of Yesteryear Business models provide products or services that are profitable today as well as search for viable future substitutes. BCG’s matrix, or the “growth share matrix,” provides a business model for doing that. The matrix analyzes business units and products according to two key variables: rate of market growth and change in market share. The matrix which dates back to the late 60 s was introduced with the aim of helping businesses gain insights on what products can best help them capitalize on market growth opportunities as much as provide future substitutes. Scope and relevance to business was succinctly put by the developer of the concept as follows “A company should have a portfolio of products with different growth rates and different market shares. The portfolio composition is a function of the balance between cash flows. … Margins and cash generated are a function of market share” (Henderson 1970). The four segments of the matrix starting with the dogs and ending up with the cash cows represented different states of market share and market growth rates. The “question mark” was one of those segments. It was a segment identified with a potential for market share but a somewhat murky rate of market growth. This murkiness could arise from several factors ranging from unattractive product features and competitive pricing to low-product quality, poor support services, and high switching costs. “Question Marks” existed within a growing market but have a small relative market share and limited revenues. They require careful analysis to determine whether or not they are worth the investment required to enhance their market share. This may be especially important if the emerging market could replace low potential “dogs” or dying “cash cows.” “Question Marks” are supposed to contrast with what was labelled as Stars or high market share and high market growth strategic business units (Henderson 1970; Reeves et al. 2014).

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Fig. 23.1  The BCG segments

The question in “Question Marks” is the potential and the probability of occurrence of the growth event. A “Question Mark” may assume a measure of market growth and a substantial market share but this may require the advent of new forces and drivers. It is also a probability issue. Could today’s artificial intelligence technologies provide these new driving forces? And improve the probabilities? (Fig. 23.1).

1.2 The New Pivots Powerful forces of artificial intelligence could change the outlook of the “Question Marks” and increase the probabilities of a rapid shift to a “Star” status. Let us consider these forces:

Thinking Functions One of the prime contributions of Big Data is the emergence of novel functions. Novel functions are emerging and with them new concepts of product technology, product innovation, product positioning, new markets, and new market segments all within a strategy construct. They are inducing a strategic shift from products to functions.

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This strategic search for novel function-fulfilling products will induce venturing beyond the familiar boundaries of today’s industries. New arenas will be explored and competitive intelligence will lead the way. Artificial intelligence will thus disrupt traditional industry segmentation leading to “leapfrogging” in the development of new industries, the reconfiguration of existing industries, and the emergence of new business arenas. Internet of things IoT, mobility, digital logistics, 3D printing, robotics, advanced life sciences, cyber security, and big data are all examples of those emerging arenas (Ransbotham et al. 2017). The very essence of the “Question Mark” changes!

Penetrative Disruption Forces of disruption could lead to a radical reconfiguration of technologies, industries, products, and markets, as well as a reformulation of the question mark’s possible course of events! Disruption is an occurrence that interrupts events, processes, systems, or paradigms. It is a violating force. Disruption of an event, a system, or a process is tantamount to discontinuity and a suspension or even a reversal of what is considered a normal flow. Disruption could be generic or functional (El Namaki 2018). Generic disruption is a force or a bundle of forces that cut across systems and reconfigure constituent elements. Generic disruption cuts across industries, markets organizations, and functions. It does not arise from competitors in the same industry or even from companies with a remotely similar business model but from distant and previously unidentified driving forces. It blends forces drawn from separate seemingly unrelated strands of technology, primarily, in order to create dramatic value-enhancing and rule-changing propositions (El Namaki 2018). Functional disruption, on the other hand, is a force that undermines one or the other aspect of system-related elements. One can think of it in terms of function, technology, economic, political, and sociology dimensions. Disruption could give the “Question Mark” a different course of possible events.

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Receding Industries, Emerging Arenas Many industries recede and, at times, get absorbed by what we may call arenas. This could change the very essence of the “Question Mark.” Arenas are spaces where “solutions” are configured as a result of technologies and ever-shifting demand conditions. This shift of focus from industry to arena is critically important because of a rapidly redefining consumer and ultimate user “needs” or “expectations” that are continually evolving in response to a steady stream of technological innovations. This is leading to the emergence of what is now termed transient competitive advantage, or transitional competencies compatible with volatile and uncertain markets. Arenas are technology “black holes” absorbing ailing industries and thrusting substitutions (Rita Gunther McGrath 2019). Industry boundaries do, as a result, fade. Strategy should be formulated, then, in terms of competitive arenas, not industries. And a “Question Mark” changes texture!

Alternative Visions Vision is a mental perception of the kind of environment an individual, or an organization, aspires to create or emerge within a broad time horizon and the underlying conditions for the actualization of this perception. Visions within an AI era will be the outcome of a blend of novel arenas, intelligent competition, and technology-rooted capabilities. The three forces will interact within a cause and affect pattern and change the trigger and structure of visions. And the fate of the “Question Mark.” A vision implies a capability construct. This capability construct is built around three prime components: technology, capital, and managerial competence. Artificial intelligence technology-related dimension represents the prime component of this construct. This will touch some of the basic foundations of industries from healthcare, marketing, finance, and security to logistics and communication. It is more likely than not that AI-induced technology will take the lead with novel arenas emerging as a consequence and the capability construct acting as a dependent variable.

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Capability constructs will constitute a fundamental element of future visions. And a “Question Mark” that changes texture with every change in the underlying premises of vision.

1.3 The BCG Matrix Substitute Analysis conducted above could lead to a number of hypotheses. The first is that a “Question Mark” seen along the conventional BCG lines is becoming a function of dynamic forces of technology, industry, disruption, and business visions. It is not as “loose” as it used to be. Dynamics of these new forces could induce a path toward the Star segment of the matrix, the Dog segment of the matrix, or an end game altogether. The second is that a new BCG could be born. The parameters there should reflect contemporary and prospective business and technology forces, that is, data and artificial intelligence. Data could be analyzed along predictive or descriptive lines. Both the predictive and descriptive genres could induce shifts in industry and vision. Artificial intelligence would deliver technologies that reflect the forces of disruption and matrix dynamics if not framework altogether. SBU strategic clusters could reflect data and technology states and lead to different blend of these strategic clusters (Fig. 23.2).

Fig. 23.2  AI and data BCG matrix substitute

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The following figure provides an attempt in this direction. • A “Survival” strategy cluster combines predictive data analytics with existing technology. • A “Dog” strategy space would combine an existing technology with descriptive data analytics. • A “Star” would combine descriptive data analysis with AI-induced technologies. • A “Question Mark” would combine AI-induced technologies with prescriptive data analysis. It is a remarkably novel approach and outcome that accommodates the tenants of emerging technologies. It accommodates the premises of these technologies and points to a new direction in strategic thinking.

2 Illustrative Case Two: Porter’s Five Forces and the Chip Industry Porter’s five force analysis (See Fig. 23.3), the market leader in industry structure analysis, is the second case in point. It provided, for decades, a prime tool for industry analysis including the strategic dimension of the process. It, however, has been showing signs of age and, with that, conceptual and operational irrelevance. The building stones as well as the interactive flow are undermined by reconfigured global capital markets, disruptive technologies, and redefined parameters of globalization.

2.1 The Five Forces of Yesteryear One of the most serious flaws in Porter’s analysis, in the author’s view, is the marginal attention paid to capital markets and emerging technologies and the profound impact those would have on the scope, reach and impact of the five forces. Capital markets have undergone radical change (El Namaki 2014) since Porter’s early writing and there was, it goes without saying, no way for him to anticipate that. Let us recall, however, that

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Fig. 23.3  Porter’s five force analysis

Porter’s work and publication of Competitive Advantage (Porter 1985) ran parallel to key economic events as Reagan’s economic policy initiatives including deregulation (Boskin 1987) and the process of financial product, process and institution innovation that ensued. Powerful structured finance instruments and equally powerful investment institutions to match emerged. Monetary policies stimulated leveraged acquisition. Emboldened investment institutions as private equity and sovereign wealth funds enhanced merger and acquisition. The results of all of that were a new genre of strategies and a different pattern of strategic behavior. A behavior that embodies, among other things, capital markets as the underlying trigger of strategic moves, ruthless restructuring as the road to survival, concentration as the medium to strategic competitive advantage, and accelerated exit as ultimate remedy. This has altered the very premises of the five forces. Five forces gradual irrelevance could be attributed to the following factors.

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Entry Entry decisions into an industry are driven, according to Porter, by attractive returns. One may add, also, capital access. This access has assumed great significance since the continuous instability of capital markets including the credit crisis events of 2008 and beyond and the far-­reaching restructuring of the American, and European, investment banking industries that ensued. Investment banking institutions have restructured and, at times, ceased to exist. Global capital supply conditions changed too, with wealth and financial assets cumulating in emerging market economies and aging population, alternative investments, and attempts at financial regulation changing capital supply conditions. Entry is more likely than not to have a close association if not dependence on capital market volatilities more than anything else.

Substitutions Substitution is a function of several variables one of which is the size and scope of the R and D function as well as the speed of emergence of new technologies. The fast pace of emergence of new technologies had and continues to have a tangible impact on the substitution dimension of Porter’s five force analysis. The impact is not only on the product and process substitution but on the continuity of whole branches of industry altogether. Contemporary AI-driving forces, as we have outlined earlier, are leading to the disappearance of whole industries from products to processes, and the substitution issue is assuming new dimension. It is not only substitution; it is migration and reconfiguration.

2.2 Buyers, Suppliers, and Rivals The structure and the relationship between buyers, suppliers, and rivals have undergone considerable change since the days Porter declared his premise. Capital markets have had, ever since, a profound impact on the composition of the players, their attributes, and their core competencies. This was largely the result of waves of mergers and acquisitions

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consummated, primarily, by the private equity industry. Companies were acquired because of their patents, licenses, market share, supply chain, human resources, management, or even culture. The period between 2003 and 2009 is typical as it witnessed the latest of these waves (Kummer and Steger 2008) whose thrust was dampened by the events of the financial crisis. But the wave continued afterwards and levels of merger and acquisition in the technology sector led, in 2022, the M and A deal activity.

 orter’s Five Force Substitute: New Parameters of Industry P Structure Analysis Developments referred to above are leading to a new set of parameters for industries. Those touch a wide number of areas. Those areas are illustrated in the following diagram. There is market structure and boundaries and there is also the wide variety and patterns of concentration (core vs. periphery, permanent vs. temporary, and segment vs. total). And there are capital issues as access, returns, and replacement. And, last, but certainly not least, there are technology premises, speed, and economics (Fig. 23.4). The chart does not only illustrate the unravelling driving forces of industries but reveal, in addition, an opportunity and a decline zone. The opportunity zone reveals industry growth inducers and the decline zone refers to industry forces of eclipse.

2.3 Application to the Chip Industry The chip industry provides an illustration here. The industry is technology premised, intensive capital market investment prone, has strong entry barriers, and limited span of substitution. Bargaining powers of buyers and suppliers are, moreover, almost nonexistent. Porter’s five forces would draw a flawed picture here (Fig. 23.5).

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Markets Hi Opportunity zone

Concentration

Hi

Hi Technology

Decline zone

Hi Capital Fig. 23.4  Porter’s five force substitute

A high measure of what the author contends as capital market-driven consolidation in the industry has taken place with, as a result, a market share concentration in the hands of a few operators. The world’s top five semiconductor suppliers accounted for roughly 43% of total chip sales in 2017. Recent substantial increase in demand for chips has also contributed to the trend of more market share being concentrated in fewer hands. Samsung, Hynix, and Micron, for example, also saw substantial sales increase of both DRAM and NAND flash memories (The Concentration of Semiconductor Market Share, Dylan McGrath 04.12.2018 EE Times).

3 The Road Ahead Similar adjustment and at times substitutions of current strategic thinking concepts are overdue and these cases provide an illustration. The effort requires, however, serious drive by top management, consulting

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Top 5 Companies 43% 57%

Top 10 Companies Top 25 Companies Top 50 Companies

77% 88%

2007 Share of $278,18 Market: Top 5 Companies: 33% Top 10 Companies: 46% Top 25 Companies: 67% Top 50 Companies: 76%

Fig. 23.5  Semiconductor industry concentration 2017 (Not including foundry sales). (Source: IC Insights)

industry, and even educational institutions. All are, in the author’s view, slow in adopting the shift, each with its own rationale. Top management will have to go through a re-education process that could be demanding and painful. Consulting industry will have to distance from conventions and competencies that they have pursued for generations. Educational institutions will have to change almost each and every aspect of their operations from the competencies of faculty to the contents, the approach, the assessment, and the ultimate deliverables.

4 Summary and Conclusions The emergence of artificial intelligence, data sciences, internet of things (IoT), and other related and unrelated technologies have shaken the roots of many a phenomenon. AI paradigm, whether system, capability, or competency-related, has rendered many management processes, products, institutions, and even concepts obsolete. BCG matrix is a case in point.

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Those radical shifts will change the essence of the top management function with issues such as awareness, predictivity, vision, and plasticity assuming key role. So also is the structure and the flows within organization with traditional hierarchy and authority and responsibility premises may all but disappear and be replaced by more technology-compatible concepts and operations. All of that will require a process of “re-education” of all those involved in a business process. Top management as well as other layers of organizations involved in the fulfillment of a business function will have to learn to look at things through different eyes. They will have to learn to live with a technology that is changing the very parameters of the business process. Also with competencies that change scope, depth, and reach almost every business day. This will take time and consume resources but no organization will be able to afford to ignore those developments and if it does it may be at a heavy price. A price that will go beyond simple market share and reach the very existence of the organization. There was a time when BCG’s Growth Share Matrix broke all records of strategic management instrument popularity. Populist labels became industry standards. There were stars, cash cows, “Question Marks,” and not to forget dogs. All came to represent sets of business conditions, company positions, competitive forces, and possible venues for strategic behavior. Those days are gone and superior substitute instruments took over. Yet, a rather less remarkable segment of BCG’s Growth Share Matrix, the “Question Mark,” could, in the author’s view, reflect a novel role within the emerging disruptive technologies of Artificial Intelligence and data sciences. If seen along the conventional BCG lines, the “Question Mark” is becoming a function of dynamic forces of technology, industry, disruption, and business visions. It is not as loose as it used to be. Dynamics of the four forces could induce a shift towards the Star segment of the matrix or a decline and a drop to the Dog segment or an end game scenario. The second is that a “substitute” BCG matrix could be born where data and AI technologies are the driving parameters. Future research would sharpen this picture. The chapter introduces a novel approach to the application of AI and data sciences to strategic thinking.

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Porter’s five force analysis is another case in point. The premises were undermined by emerging forces of technology and changing structure of capital markets. And the outcome is a new paradigm relating industry structure to technology flows and capital market forces, among others. Similar adjustment and at times substitutions of current strategic thinking concepts are overdue and this case provides an illustration. The effort requires, however, serious drive by top management, consulting industry, and even educational institutions. All are, in the author’s view, slow in adopting the shift, each with its own rationale. Top management will have to go through a re-education process that could be demanding and painful. Consulting industry will have to take distance from conventions and competencies that they have pursued for generations. Educational institutions will have to change almost each and every aspect of their operations from the competencies of faculty to the contents, the approach, the assessment, and the ultimate deliverables.

References Boskin Michael J. Reagan and the Economy: The Successes, Failures, and Unfinished Agenda. Hardcover – January 1, 1987. El Namaki M, How damaged are investment capital markets today? Competitiveness Review: An International Business Journal Incorporating Journal of Global Competitiveness, Volume 24 (1): 8—Jan 14, 2014 El Namaki M, “Disruption in Business Environments: A Framework and Case Evidence” International Journal of Management and Applied Research, Issues (2018) Kummer C and Steger U “Merger & Acquisition Waves” Strategic Management Review, 2(1), 2008 McGrath D “The Concentration of Semiconductor Market Share”, 04.12.2018 EE Times Porter, M.E. (1985) Competitive Advantage. Creating and Sustaining Superior Performance. Free Press, New York.

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Ransbotham S, Kiron D, Gerbert P, Reeves M, “Reshaping Business with Artificial Intelligence, MIT Sloan Management Review, September 06, 2017 Reeves M, Sandy Moose S, Venema T, “BCG Classics Revisited: The Growth Share Matrix”, June 04, 2014, BCG Henderson B, “The Product Portfolio,” January 01, 1970, BCG McGrath R, “Think competitive arenas, not industries” September 2019— Duke Corporate Executive

Index

A

Achievement motivation, 158, 160, 161, 163, 164 Advanced top management competences, 208–209 Algorithm derived control, 209–210 Alternative enterprise traits, 171 Artificial Intelligence (AI), 15–28, 32, 33, 35–40, 130–131, 133–135 capabilities, 15, 16, 18, 26, 29 derived vision, 90–92 driving forces, 46 influence on enterprise, 169–172 influence on human intelligence, 133 influence on premises of enterprise, 169–172 insights, 19, 25 life cycle, 26, 29, 40

system, 15, 16, 18–27, 32, 33 time functions, 16–18 tomorrow’s outlook, 26–28 Artificial neurons, 21–23 Attention, 105, 107, 109, 112, 113 Awareness as top management competency, 84 Awareness of self, 80, 81, 83–85 B

The BCG matrix substitute, 234–235 Behavior, 56, 59 Behavioral competencies, 137–138, 141–143 Biological neurons, 19, 21–22 Brain and nervous system, 97–98 Brands crossing industry, 201 Building of vision, 94

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. S. S. El Namaki, Neo Strategic Management, https://doi.org/10.1007/978-3-031-37208-7

245

246 Index

Business organization developing implicit learning competencies, 199 Business reinventing core competencies, 201–202 C

Challenge to today’s managerial functions, 63–66 Cognition, 105–106, 108–114, 116 Cognitive computing, 187–193 Cognitive functions of brain, 105–110, 114, 116 Cognitive predictions, 105–117 Cognitive psychology, 108 Cognitive stimuli, 110–111 Communication behavior, 138, 140 Competitive advantage, 49–50, 56, 59 Competitive cognition, 213 Competitive synergy, 70 Conceived vision, 158 Concept of awareness, 79 Control, 57 Core competency text, 153, 154 Cross-cultural intelligence, 132–133

Data specific industry analysis, 219–220 Data strategies, 211–212 Demands on the organization, 175 Determinants, 157–162 Diagnostics, 69 Disruptions, 43–45, 56 Dynamic visioning, 210–211 E

Effectiveness of vision, 89–90 Emotional intelligence, 132 Enhanced dark side, 172 Enhancing propensity to enterprise, 216 Enhancing propensity to lead, 215 Essence of enterprise, 165–167 Essence of strategic control, 151–152 F

Five force substitute, 238, 239 From product to function, 47 Function specific arenas, 68 Future of corporation, 9, 13 Future of players, 13 Future of technology, 5, 13

D

G

Data base design, 220 Data based predictive analysis, 179 Data-driven decision-­ making, 214–215 Data foundations of strategic thinking, 180–184 Data-rooted strategic thinking, 178–179, 184, 185

Game theory behavior, 140–141 H

Human and machine cognition, 187–190 Human intelligence, 129–135 Hybrid intelligence, 131

 Index 

247

I

N

Illustrative case 1 the return of the BCG question mark, 229–234 Illustrative case 2 Porters five forces and the chip industry, 235–239 Implicit and explicit memory, 196–198 Implicit learning, 70–71, 195–204 Implicit learning and innovation, 195–204 Implicit learning as top management competency, 203 Induced enterprise innovation, 171 Industry fitness test, 152–154 Intelligence, 129–135 Intelligence and artificial intelligence top management competency, 133–134 Intent, 57, 58, 145–149

Neural plasticity, 119–125 Neurons, 97–101, 103 Neurons and thinking, 99–101, 103 New CEO profile, 51 New industry structure analysis, 53 The new paradigm: organization wide operational competencies, 216–220 New strategic thinking, 67 New strategy conceptual model, 51–53 Novel visionary competencies, 92–93 O

Organization-wide data systems, 177–179 Organization wide strategic thinking, implications for, 190–192

L

Leading with technology, 212–213 Leading with vision, 91 Learning, 195–204 Learning curve, 123–125 Locus of cognitive behavior, 138 Locus of control, 159–161, 163, 164 M

Management related behavior cognitive competencies, 138–139 Managing business arena shift, 214, 222–226 Managing self, 81–82, 85, 159–160 Managing self awareness, 81, 85 Memory, 105, 106, 109, 113–115

P

Perception, 105–107, 111, 112, 114 Perspective, 157–158, 160, 164 Plasticity and management and strategic thinking, 123 Plasticity system, 120, 122 Potential fitness test, 152, 154 Product and services innovation, 191, 193 Propensity to lead, 157–164 R

Reasoning, 107, 109, 115 Reformulating human resource policies and practices, 219

248 Index

Re-inventing organization structure, 217–218 Re-shaping of corporate culture, 218–219 The road ahead, 239–240 S

Scenario building, 66–67 Statement of intent, 146–147 Strategic behavior, 138–141 Strategic context of intent, 146–147 Strategic control, 123–126 Strategic control and strategic thinking, 154 Strategic control competency, 155 Strategic fulfillment, 71–72 Strategic intent as top management competency, 147–148 Strategy formulation, 67, 68 Synapses, 98–100

T

Technology crossing industries, 202 Technology-rooted behavioral competencies, 142 Thinking management competencies, 102, 104 Thinking to reasoning and learning, 101–103 Tomorrow’s executive, 12 Tomorrow’s perspective, 66–73 Top management context, 77–78 V

Vision, 45, 54–57, 59, 60, 87–94 Vision conception, 123, 124 W

What kills vision, 89 Widened opportunity behavior, 170