Digital Strategies and Organizational Transformation

The following sections are included:

• Dedication
• About the Editor
• About the Contributors
• Acknowledgments
• Contents

Over the past several years, digital strategy has become a common term within all industries and academia. Moreover, its impact on organizational transformation has become a high priority for executives and academic researchers. In other words, the implementation of digital strategy is transforming our organizations and industries. It will also make significant contributions to improve organizations’ value creation processes and national economies…

This chapter discusses the roles of normative factors that minimize the resistance to the successful implementation of digital strategies and organizational changes. A particular emphasis is made to discuss the roles of institutionalized normative and cultural factors in successful implementations of digital strategies and organizational changes in developing economies. Specifically, the chapter will discuss the systemic barriers in developing economies, as well as the roles of informal institutions such as normative and cultural factors and how they can impede their efforts to the benefits gained from digital technologies. The chapter will focus on discussing the weaknesses in developing economies’ regulative, normative, and cultural factors that create resistance to changes needed for the successful implementation of digital strategies and organizational transformations.

Finally, this chapter will provide policy recommendations on how to improve the institutional weaknesses within developing economies and what policies they may need to formulate and implement in order to benefit from their efforts in digital strategies.

Due to the proliferation of digital technologies in recent decades, digital transformation has emerged as an important phenomenon in both academia and industry (Tabrizi et al., 2019). However, despite much hype about digital transformation in the media and managerial articles, there is a great deal of fuzziness on what the term means, the role of technology in digital transformation, how it unfolds, and how incumbents and entrants should manage digital transformations. Our goal in this chapter is to review some of the recent work in this domain and provide our viewpoint on this topic, building on our prior and related work (Mithas, 2016).

Risk management entails various measures for lowering risks to a level deemed acceptable by an organization and enabling residual risk to be transferred to insurance providers. The effectiveness of these measures is heavily reliant on people’s perceptions of the threats to which they are or may be exposed. However, little is known regarding the connection between risk perception and cyber insurance. Therefore, the purpose of the research in this chapter is to examine the role of risk perception in determining people’s cyber risk-taking behaviors and the related implications for cyber insurance policies and dynamics. This study provides a high-level analysis of the existing risk perception literature through a detailed examination of social, demographic, economic, cultural, psychological, and performance factors. The findings suggest that the nature of the relationship between risk perceptions and cyber insurance benefits may be influenced primarily by an organization’s ability to keep its risk profile stable over time. More specifically, the cultural dimension of risk perception has been identified as one of the areas where risk reduction strategies have the most significant potential for lowering cyber insurance costs. The findings of this study can assist businesses and insurance companies in directing training efforts and forecasting organizational reactions to cyber threats as they navigate digital transformation.

Digital transformation comes in all shapes and sizes. Most digital transformation projects, however, are not really “transformative.” They do not disrupt business processes or whole business models. Instead, they’re “incremental,” or just as often part of planned technology “modernization” initiatives. These kinds of projects are safer, less risky, less expensive, and “politically” protective of executive reputations, which is perhaps why a vast majority of digital transformation projects focus on incremental changes or modernization. But digital transformation projects that are truly transformative are targeted at replacing or automating business processes, or replacing or automating whole business models — which are riskier, more expensive, and more politically dangerous projects than incremental and modernization ones. Impact follows risk: incremental and modernization projects are easily less impactful than disruptive ones. The “perils” and “promise” of digital transformation are defined around a simple risk/impact ratio. Companies should decide how and where they want to “transform,” acknowledging the likely return on their digital transformation investments. But eventually, because of the trajectories of technology and business, all companies will have to pursue more disruptive transformation.

The adoption of artificial intelligence (AI) is booming and was accelerated during the pandemic of 2020, directly impacting the pace of digital transformation across industry verticals. It is now critical to explore current and emerging challenges as well as opportunities offered by AI solutions to better understand how organizations and society could be affected. First, this chapter examines the evolution of computerization and its influence on AI. Second, we scrutinize how AI performance has been harnessed through digital solutions that are transforming for the better and the worse numerous industries. Third, we address fundamental ethical and moral challenges that derive from the application of AI in real life and its implications for human beings. Finally, we discuss the challenges of securing and protecting AI solutions against cyberattacks. The chapter concludes on the future of AI as it relates to technology convergence with cloud computing, Internet of Things, and eventually quantum computing.

Digital transformation drives sustained business value through the use of digital technology. However, there is often confusion regarding what steps organizations should follow to achieve this. In this chapter, we outline seven steps in a conceptual process model, the digital transformation loop, which organizations can adopt for what we call “agile digital transformation.” Our aim is to offer a compelling and comprehensive conceptualization which illustrates challenges faced by organizations and poses questions that they need to reflect upon to realize value from the agile use of digital technology. We argue that agile digital transformation must be part of achieving a clear strategic vision and should drive sustained and demonstrable business value.

As part of a digital strategy, machine learning (ML) has become a common toolset and capability across many businesses. However, the operational aspects of machine learning (MLOps) are often overlooked for ML projects until they are already installed and being executed in the business environment. This chapter provides a review of MLOps products and vendors to give data scientists the ability to set up the appropriate ML infrastructure in a proactive manner.

Advancements in technology allow humans to add many interesting features to everyday devices. As humans, we can tackle many problems in complex societies and manage the complexity of networked social systems. Sociability is a vital human capability that improves our social lives and manages social interactions. Adding this feature to smart devices can facilitate the management of complex and networked Internet of Things (IoT) settings. We can improve the relationship between social and smart devices in different ways. Nowadays, researchers are investigating the future generations of IoT, namely, social IoT. This study aims to develop a new method for updating and leveraging the friendships between IoT devices by using social IoT and social networking principles. We utilize a design science approach to propose a novel friendship management method for social IoT that is able to effectively evaluate and manage the relationships between devices. The new friendship development and updating models use the basic concepts of Knapp’s relational stage theory. In this chapter, a new simulation tool for evaluating the new friendship mechanism is proposed. The evaluation results show that the proposed method performs effectively and can enhance network navigability, service composition, and service discovery.

This work proffers that the biggest ongoing challenge currently facing cybersecurity professionals — namely ransomware — can be potentially mitigated by the expected benefits of quantum computing, a technology still in the nascent stages of demonstrating reliable and repeatable network defense support to broader commercial industries, particularly industries based in the United States. The author demonstrates that, counter to estimates of any public applications only appearing in the distant future, the commercial losses from ransomware — both incurred and expected — by US entities may be enough to justify the need for a concerted near-term “sprint” by US stakeholders to develop a quantum-based mitigation capability with all deliberate speed. The work highlights the competition among leaders in the quantum computing development space, the consequences to US national defense and business network’s health of not having mitigatory quantum computing capabilities, and the global shifts in political and commercial influence which would likely result depending on which nation-state eventually establishes dominance in the quantum computing space. Scientific and economic challenges to the development of quantum computing capabilities in the current environment are also explored alongside benefits of investing at certain points along the innovation cycle of quantum computing technology.

Digital disruption has been underway for more than a decade. Traditional methods no longer seem effective and are detrimental to survival. How well a business becomes digital and adapts to the rapidly changing market forces will to a great extent determine how much market share it will gain in comparison to its competitors.

While becoming digital is not an easy or an inexpensive journey, it is an essential one. It requires a firm commitment and dedication of resources to be truly successful. However, the rewards for undertaking such a journey are well worth the efforts and will determine the new set of market leaders.

This chapter proposes a car damage detection model using Convolutional Neural Networks. The automobile industry can utilize cameras to catch the defects in vehicles and provide better-quality inspection than manual methods. AI-based machines using computer vision will help make the entire supply chain process more efficient and reliable. The proposed model uses the VGG-16 Convolutional Neural Network that is optimized to provide the best accuracy and minimal loss. The model is trained and validated with a dataset containing images of vehicles that have no defects and ones that are damaged. Results show the potential of such a model being widely used in the industry since it can accurately detect damages for a diverse set of vehicles.

Starting with cloud computing, the foundation of the Internet of Things (IoT), combined it with artificial intelligence (AI), adds an essential capability to connected systems — action. In industrial scenes, AI-IoT is mainly used to achieve specific functions related to machine prediction and data processing, in addition to intelligent industrial robots, industrial vision detection, perception identification, and positioning.

We use Bosch’s AIoT transformation example to illustrate how AIoT technologies can help a company in manufacturing and supply chain management in the real world and analyze four forces that drive the AIoT transformation. Finally, we conduct SWOT analysis to provide a comprehensive view of how AI-IoT can renovate smart manufacturing and where we should pay attention as we integrate it into manufacturing.

This chapter presents a model for digitally transforming a business by first performing a digital maturity assessment of current capabilities, describing the maturity levels of the digital environment to define a starting point to base future modifications on, and then applying a Digital Business Strategy Model (DBSM) to ensure that required inputs and outputs for developing a Digital Business Strategy (DBS) will create and capture value for the firm from emerging digital technologies. A solution and cultural intelligence-driven approach is enabled through strategic alignment of a firm’s organizational structure, infrastructure architecture, and the innovation strategy being pursued. The recommended digital transformation (DX) process will overcome sponsor hesitancy to make the investment required to modernize corporate infrastructures. Organizational learning must be part of a comprehensive DX effort. An enhanced organizational learning model is presented with recommended practical implementation steps to ensure smart organizational transformation.

When it comes to digital transformation, the healthcare industry remains a laggard. The lack of adoption of digital products, services, and business models highlights the case. Despite the evidence that the rate of change is accelerating, such as in the hospital sector, true transformation will happen once digital skill sets are fully incorporated in the training and development of the healthcare workforce and its leadership. In the meantime, healthcare leaders should take pragmatic steps to enable the digital transformation of their respective organizations.

The following section is included:

• Index