21

The Age of Digitalization, Automation, Big Data, and Artificial Intelligence

Introduction

The relentless advances in technology have a dizzying effect on individuals and organizations. It is fair to say that the human resource development (HRD) profession is characterized as high-touch versus high-tech. This can be extended to HRD professionals using high-touch strategies for assisting workers to deal with the high-tech advances invading their work. Advances in technology have been astounding and the predictions from Elon Musk are aimed at saving the world.

The idea that HRD professionals would give up any of their professional tools and decision making to technology (e.g. artificial intelligence) is not a common stance. For the present, artificial intelligence is still unable to grasp cause and effect beyond what is humanly programmed. “Machine-learning systems can be duped or confounded by situations they have not seen before” (Bergstein, 2020). A human reprieve.

Human Resource Development in the Age of Digitalization, Automation, Big Data, and Artificial Intelligence

Contributed by Mesut Akdere, Purdue University–West Lafayette

A common theme in most science fiction novels and Hollywood movies is the pervasiveness of technology and how it makes things happen that have ordinarily belonged to a fantasized world. The acceleration of digitalization and automation in the workplace intensified by artificial intelligence seems to be bringing us closer to that world of fantasy. Today’s landscape, both at home and at work, shows a rapid integration of technology that might have seemed a far distant future just a decade ago. At home, we now have mobile technologies that connect many home utility functions called smart home, based on the concept of the Internet of Things (IoT), which refers to “the ability to connect physical objects (‘things’) to the Internet and thus equip them with the unprecedented functionality of autonomous context-adequate behaviour” (Strohmeier, 2018, 528). We can connect to our smartfridge from the comfort of our smartphone to help us make a grocery list without having to open the door of the fridge. We can now hover a drone above our home to constantly provide surveillance for safety. We can use our smartphones to check to see if a dining table sold online would fit in our dining room using augmented reality. At school, we can use mixed reality to dissect an artificial frog in a biology class. Or we can use virtual reality to conduct chemical experiments in a chemistry class without risking student safety. We can even take a stroll on the surface of Mars in a physics class without leaving Earth. And, with the implementation of 5G wireless technology, delivering “higher multi-Gbps peak data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to more users” (Qualcomm, n.d.), more technology integration will arrive. In sum, we in HRD are in for major disruptions and shifts in the workplace, including digitalization, automation, big data, and artificial intelligence. In this chapter, we will review these technological phenomena and discuss their implications for HRD functions and processes within the perspectives of performance and learning paradigms.

The technology-human connectedness now reveals itself in all HRD domains. In training and development, simulated learning environments, such as virtual reality, provide effective, personalized, safe, and convenient learning opportunities to trainees (Akdere, 2019). With a mounted headset, companies can virtually teleport their employees to another environment in which trainees can immerse, even environments that may be harmful or dangerous in the real world. In organization development, change champions can utilize people analytics to better manage and orchestrate the change process. Incorporating multiple data points instantaneously can accurately help them arrive at data-driven decision making. In career development, conversational agents known as chatbots can help coach individual employees to manage their own development goals and efforts without much monitoring or interference from an HRD professional. The software can provide employees with analytical feedback on a progressive basis in which employees can identify their strengths and areas of needed improvement. Such systems take away the human subjectivity of a supervisor or manager and allow individual employees to be more open about their development efforts and enable them to make modifications as needed. Considering the human + technology interface, it is critical for HRD to accurately identify work domains. In the quest for human + machine interaction, Daugherty and Wilson (2018) identified three work domains that include (1) machine-only activities such as transactions, iterations, predictions, and adaptation; (2) human-only activities such leading, empathizing, creating, and judging; and (3) the missing middle, as they articulated, is the work domain that includes training, explaining, sustaining, amplifying, interacting, and embodying. In this work domain, human and machine hybrid activities coexist where humans complement machines, and artificial intelligence gives humans superpowers. It is that missing middle HRD should shift its focus to in order to remain relevant and advance the organization.

DIGITALIZATION IN HUMAN RESOURCE DEVELOPMENT

Digitalization is an integral part of all aspects of contemporary life. Digitalization is defined as “the increased use of and restructuring of life domains” (Nöhammer & Stichlberger, 2019, 1192) of information technology. In this chapter, digitalization in HRD is considered within the subcomponents of simulated training, including video-based training simulations, virtual reality–based training simulations, augmented reality–based training simulations, and cloud-based platforms like chatbots, employee self-service portals, HR analytics suites, and learning experience platforms.

What started as a mere transformation of business texts, pictures, and sounds from traditional platforms (paper- and audio cassette–based) into computerized platforms toward the end of the twentieth century, digitalization grew fast, engulfing many traditional HRD functions and processes. Such rapid change can be observed in the approach to technology in HRD, as is evident in the previous editions of this book. In the first edition, technology was viewed as a tool and a process for democratization and information technology. In the second edition, technology in HRD was viewed within the lenses of tools and platforms such as slides, audio recordings, projectors, and video tapes as training tools. It was also considered through virtual organizations and virtual teams for organization development, performance improvement, and e-learning. Just a decade later, we find ourselves surrounded with new innovative digital technologies advancing every aspect of HRD functions in the organization.

Simulated Training Simulated training, where learners are placed in a training environment that most resembles the learning environment and context associated with the training, is not a new concept. Trainers traditionally made every effort to mimic learning environments that would best resemble the actual environment in which trainees would utilize the specific knowledge, skills, and abilities they were learning. While this was mostly limited to face-to-face training sessions, computer-based digital simulation platforms began to emerge with more technological advances. Computerized simulated training “consists of learning and developing different skills by using computerized models that can emulate a variety of real phenomena and processes” (Marcano et al., 2019, 414). Simulated training is typically characterized by real people operating simulated systems to perform tasks, such as learning or exercising motor skills, decision making, or communicating (Straus et al., 2019). “Trainer–trainee interactions, task factors and simulator technology may influence perceived level of fidelity and training quality” (Wahl, 2020).

The notion itself primarily relies on experiential learning, which is rooted in modern learning theories, such as constructivist conceptualizations of learning (Kolb, 1984; Lave and Wagner, 1991; Kolb, Boyatzis, and Mainemelis, 2001; Driscoll, 2005). The experiential learning theory is “the process whereby knowledge is created through the transformation of experience” (Kolb, 1984, 41). The theory considers two related modes of grasping experience—concrete experience and abstract conceptualization—and two related modes of transforming experience—reflective observation and active experimentation. Each of these modes directly relates to simulated training, as learners engage in their own experience-making. Dalgarno and Lee (2010) specifically highlighted experiential learning as one of the core affordances of simulated training, and experiential learning has been a common framework used in designing learning games and simulations (Gredler, 1996; Kiili, 2005).

Video-Based Training Simulations. Video-based simulated training, through increased digitalization, gave way to more elaborate and sophisticated simulations of learning environments that historically relied on objects to resemble the target environment. The popularity of video-based learning platforms such as YouTube, Khan Academy, Lynda, and edX has resulted in millions of learning experiences in recent years (Brooks et al., 2011). As widespread adoption of technology has multiplied, so has research into video-based learning, learner engagement with video-based learning platforms, and integration of video-based learning into existing structures and courses (Giannakos, 2013). Yousef and colleagues (2014) conducted a meta-analysis of research related to video-based simulations from 2003–2013. Despite conflicting results, they concluded several pertinent points including more positive aspects, such as the potential to improve learning outcomes and learner engagement and achieving higher levels of learner satisfaction compared to a traditional in-class training environment. Less positive attributes were also acknowledged, such as being most often connected with a more passive teacher-centered approach as opposed to utilizing student-centered learning and being less favorably looked upon by many instructors/trainers who deem it passive and not engaging (Kolås, 2015). Although widely used by most companies around the world for generic and mandatory employee training purposes, video-based simulated environments are being challenged with more immersive simulated training programs, such as virtual reality (VR) and augmented reality (AR).

Virtual Reality (VR)–Based Training Simulations. The idea of using VR for learning and training purposes is not new. Beginning in the 1960s and reaching maturity in the 1990s, researchers investigated various issues relating to VR for learning and training—for example, cognitive aspects of immersion (Psotka, 1995), learning transfer from VR to the real world (Kozak et al., 1993), and performance on visual-spatial tasks (Regian, Shebilske, and Monk, 1992). Early enthusiasm about the potential of VR eventually waned, however, largely due to technological limitations at the time. Hardware innovations in recent years have led to a renewed interest in VR for learning and training across a vast variety of sectors (Wang, 2018). Considerable research and development efforts are being put into training for jobs that require highly skilled performance such as operating room procedures and various types of surgery (Kugler, 2017; Seymour et al., 2002) or tasks that take place in high-risk environments such as mining (Grabowski and Jankowski, 2015), power line maintenance (García et al., 2016), construction (Zhao and Lucas, 2015), and agriculture (Dickey et al., 2013; Nakayama and Jin, 2015). General benefits of VR for learning platforms include heightened presence, sensory immersion, dynamic simulations, and real-time feedback (Pagano and C. T., 2017)—all of which are relevant for technical and non-technical skills development.

VR-based training simulations use technology that connects trainees with a simulated environment through an electronic eyewear that immerses the learner into a computer-simulated environment. This emerging technology provides trainees with immersive, experiential, and interactive learning. Initially considered an expensive piece of technology, VR headsets have advanced their optical resolution capacity and significantly reduced in cost. In fact, digital technologies like VR will be increasingly used to drive digital business transformation through focusing on people centricity, location independence, and resilient delivery (Burke, 2020). About two decades ago, VR was only played through a desk computer or custom-built proprietary mounted headsets. Today, commercially available VR headsets are not only much more economically affordable but also highly effective in providing a fully immersive experience in a virtual world (see figure 21.1).

Figure 21.1: Samples from Virtual Reality–Based Simulations

Source: Akdere et al., 2020.

Despite the emerging impact of the digital age, training and development has not fundamentally changed in many decades; we still use the training systems designed for an industrial society. Furthermore, training to reskill employees has traditionally been designed around the behavioral and cognitive components associated with job tasks and the standards of acceptable performance (Jusko, 2012). In addition to lectures and discussions, videos and in-class demonstrations provide real-life examples for technical content training. While these training approaches address gaps around critical technical skills development, they lack the capacity and flexibility to provide highly personalized, customized, safe, and scalable learning experiences that have become essential in today’s digital age (Wijma et al., 2018). Beyond traditional platforms, training has recently begun to employ immersive technologies such as VR to better simulate learning environments that are usually difficult to create in traditional training settings and to provide more interactive and experiential learning opportunities (Miller, 2016). With the advent of AI technology, VR capacity has been increased to include optimization algorithms to develop adaptive VR-based learning experiences that are customized, safe, and scalable. VR has advantages over more traditional training platforms due to its ability to cognitively decouple users from the physical environment and immerse them in the virtual one, which may potentially improve learner engagement and retention.

Figure 21.2: Samples from AR-Based Simulations

Source: Microsoft HoloLens, 2020.

Augmented Reality (AR)–Based Training Simulations. AR-based training simulations draw its origins from the mid-twentieth century in cinematography (Carmigniani, et al., 2011). With the development of computer technologies more generally, it has seen steady rises in usage and implementation (Krevelen and Poelman, 2010). In 2007, AR was identified as one of the top ten emerging technologies (Jonietz, 2007). According to some researchers, it is set to become one of the fundamental user interface paradigms of humankind (Kroeker, 2010). AR is usually defined following the virtuality continuum (Milgram and Kishino, 1994), where it resides closer to real life than pure virtual reality. As such, it combines aspects and objects of both the virtual and real world (see figure 21.2) (Azuma, 1997).

AR can be understood as a real-world view that is augmented and enhanced by virtual computer-generated information (Carmigniani et al., 2011; Klopfer and Squire, 2008), allowing users to have a real-time interaction with virtual creations in 3D space (Wu et al., 2013; Krevelen and Poelman, 2010). In other words, AR does not replace the real world as VR is purported to do, but in a way it supplements it through building a “mixed-reality” model (Bower et al., 2014; Chang, Morreale, and Medicherla, 2010). In terms of devices and user interaction, AR is mainly facilitated through two main display types: monitor-based and see-through. Presently, these largely include “head mounted displays” and “handheld display devices” (Kesim and Ozarslan, 2012; Klopfer and Squire, 2008). AR uses different technology that can range from basic hardware, such as video cameras for capturing live images, storage space for virtual objects, a processor powerful enough to build a 3D simulation in real time, and an interface for interactions with objects, both virtual and real, as well as more complex hardware that includes GPS, Wi-Fi connection, image recognition, and more (Bower et al., 2014).

Given that this technology allows users to engage with and experience phenomena otherwise not possible (Klopfer and Squire, 2008), AR has had many applications over the last decades, from manufacturing (Caudell and Mizell, 1992) and medicine (Bajura, Fuchs, and Ohbuchi, 1992) to sports and military (Azuma et al., 2001). Recently, special interest has developed around leveraging AR in learning contexts (Kesim and Ozarslan, 2012) because it can benefit learners along a number of dimensions, including learning spatial structure and function, memory retention, language learning, improved collaboration, increased motivation, and physical task performance (Radu, 2014). Dede (2009), for example, points out that the immersive side of AR can be leveraged to enhance learning through both multiple perspectives and situated experience, both of which are important opportunities in the learning process. More specifically, AR has been explored as a tool in technical training due to its ability to create 3D images, which is especially important in design technology and engineering (Thornton, Ernst, and Clark, 2012). Furthermore, AR positively affects learner motivation (Restivo et. al, 2014; Yoon et al., 2012). While most of these studies analyze the learners’ perspectives, there have also been applications of AR technology that aid in instructors’ formative assessment of learners (Holstein et al., 2017; Holstein et al., 2018), that facilitate interactions between learners and instructors (Peña-Ríos et al., 2012), and also teach industrial maintenance and assembly tasks in workplace training (Gavish et al., 2015). AR-based platforms overlay computer-generated images onto the real world and have many potential applications in technical and collaborative team training.

Cloud-Based Platforms Software-based computing and streaming led to advances in the internet and resulted in the creation of cloud-based platforms. These platforms host software platforms and services from remote locations that can be accessed via the internet anywhere around the globe. Cloud-based platforms enable storing of software programs and large databases through a single application instead of utilizing multiple computers. Most on-demand entertainment and training programs are housed in cloud-based platforms.

Chatbots. Chatbots are software applications of conversational agents that impersonate written and spoken language to simulate conversations or interactions with humans. Due to advances in machine learning and natural language processing, chatbots have become increasingly popular in the customer service realm. However, some companies have also started using chatbots for employee training and coaching purposes. In such contexts, chatbots assist employees in remaining focused on their learning goals and also serve as a personal coach through connecting employees via SMS text messaging platforms, providing the employee with guidance and reminders to achieve their development goals and targets. Furthermore, as more employees utilize this platform, larger data sets can be generated that can help shed light on the employee’s experience with keeping up on learning and development targets and achieving related goals.

Employee Self-Service (ESS) Portals. Providing a one-stop shop such as ESS portals to all employees enables an organization to shift its transactional activities to a digital platform and allocate available resources to more important unit functions, thus reducing costs and increasing efficiency. ESS portals offer “relative quick gains with low associated risks that can be achieved through the business-to-employee model” (Stein and Hawking, 2005, 101). For HRD, ESS portals can help employees manage their training and development needs and career development goals. From an OD perspective, ESS portals can be critical in orchestrating change where all OD process-related data and information can be stored and shared with employees.

HR Analytics Suites. HR analytics suites provide the organization with the analytical tools for human capital management. The rise of big data and data analytics paved the way to people analytics in which both prescriptive and predictive analytical approaches to HR are used to measure, characterize, and organize sophisticated employee data. Computational advances have made it possible for the organization to accurately collect the right data. Various cloud-based HR analytics suites are commercially available to help companies create their own databases and utilize existing industry-based databases for benchmarking purposes. For HRD, HR analytics suites can help determine various employee-related needs, such as organizational gap analysis for developing new training programs, employee learning and development needs, calculating training costs, and the return on investment in training activities.

Learning Experience Platforms (LXP). Rapid digitalization of the workplace through advances in cloud-based platforms led to the development of the Learning Experience Platform (LXP), which is a consumer-centered software designed to provide employees with more customized learning experiences and support them in identifying other learning and development opportunities. Based on the individual employee’s needs, the LXP uses artificial intelligence to identify different materials and venues and presents employees with a roadmap to completion of the recommended learning efforts.

AUTOMATION IN HUMAN RESOURCE DEVELOPMENT

Automation is the process in which operations that are traditionally conducted manually, based on human labor, are carried out by machines, computer-automated systems, and robots. In other words, it is “the execution by machine, usually a computer, of a function previously carried out by a human” (Parasuraman, 2000, 931). In this chapter, automation in HRD includes the robotics sub-component.

Automation is the reality in any given field or job. Some aspects of jobs and sectors have been impacted by automation at varying degrees that resulted in significant changes in the way and time traditional work tasks are completed and the emergence of new job tasks. For example, typewriters were replaced by personal computers in the beginning of workplace automation. However, dramatic advances in technology in general and artificial intelligence in particular have replaced humans using those personal computers with digital office assistants who further rendered the traditional office assistant concept irrelevant. Thus, “automation does not supplant human activity; rather, it changes the nature of the work that humans do, often in ways unintended and unanticipated by the designers of automation” (Parasuraman and Riley, 1997, 231). In HRD, automation is critical for the seamless integration of different digital processes into the workflow and strategic assistance to employees to help them successfully manage and utilize this type of platform to do their work more efficiently. Automation in the HRD context impacts the way HRD units operate and how HRD functions are reported to the rest of the organization. As a result, HRD strategy, especially around employee onboarding and performance improvement, should now take automation into account.

Robotics Primarily associated with robots, robotics is an interdisciplinary field based on engineering and computer science. Robotics aims to design smart machines to enhance human capabilities, especially in areas where human capacity is limited and tasks are generally repetitive, mundane, or hazardous. Although it started with the building of rudimentary crude robots used in industrial settings, the field of robotics has evolved from developing physically powerful robots to companions that humans come to depend on emotionally.

There is no doubt that robots and other automated systems will replace humans in most of the potentially automatable jobs involving routine activities, pattern recognition, data/information collection, or manual dexterity. Such workplace transformation will have lasting implications for traditional HRD functions, such as needs for employee reskilling and up-skilling, the redesigning of organizational structure, and formation of new organizational cultures more conducive to human + machine interaction. However, such rampant transformation of the organization to one in which humans interact directly and more frequently with robots and automated systems brings new opportunities for HRD. From a training perspective, the incorporation of automated systems as part of training design will help employees become familiar and comfortable in interacting with such systems. OD change champions, on the other hand, can orchestrate such organizational transformations where automation enables the organization to “modularize and control routine work” (Tschang and Mezquita, 2020), augmenting human capabilities with automation capacities for strategic collaboration on nonroutine work for superior performance.

BIG DATA IN HUMAN RESOURCE DEVELOPMENT

First used in the context of managing large data sets through visualization (Cox and Ellsworth, 1997), big data refers to “the data resulting from datafication” (Ylijoki and Porras, 2016, p. 69). Big data has evolved to focus on the analytical side of data, which involves operationalizing available large data sets to draw or deduce conclusions and stories that were otherwise previously unavailable.

Advances in technology enabled new data analytics affordances to many fields. Although relatively new, big data entered the HR realm when Jac Fitz-enz (1995) proposed the Return on Investment (ROI) model, involving the measurement of HR activities and outcomes in financial terms to demonstrate its impact on the organization’s bottom line. Big data in HR is considered a methodological analysis of data collected from various sources for extrapolating new information derived from the available data (Fitz-enz, 2009). Bassi (2011), on the other hand, described big data in HR as “the application of a methodology and integrated process for improving the quality of people-related decisions for the purpose of improving individual and/or organizational performance” (16). In HR, big data has been referred as “HR big data” (Azeem and Yasmin, 2016; Martin-Rios et al., 2017), “HR analytics” (Fitz-enz and Mattox, 2014; Angrave et al., 2016; Boudreau and Cascio, 2017), and “people analytics” (Isson and Harriott, 2016; Bhardwaj and Patnaik, 2019).

The concept of big data has already begun to appear in the HRD realm (Yoon, 2017; Jiang and Akdere, 2020), and more applications of big data in the HRD context will likely emerge as it provides descriptive, predictive, and prescriptive analytics (Fitz-enz and Mattox, 2014). One such stipulation from the existing HRD literature is that big data enables employees to “develop their own learning plans and determine their own pace for learning” (20). In turn, through collecting such data, HRD can “track employee learning process, and make more accurate evaluations of them” (Jiang and Akdere, 2020, 20). There are two critical considerations for the HRD field with respect to big data. First, innovative learning opportunities for current and future HRD professionals to become knowledgeable and competent in big data should be provided. Second, HRD researchers should explore theoretical underpinnings of big data, develop theories that can be applied to HRD practice, and further empirically test big data utility and applications in HRD. Furthermore, HRD professionals can also serve as guardians for ensuring ethical and bias-free big data practices for organizational practices in general and HRD functions in particular.

ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE DEVELOPMENT

Since its beginning in research on neural networks in the early 1950s for making machines that can think, artificial intelligence (AI) has evolved through machine learning (ML) in the 1980s to deep learning (DL) in the present day. Using large data sets, AI uses repetitive learning and discovery to “perform frequent, high-volume, computerized tasks reliably and without fatigue” (SAS, 2020). AI is defined “as systems that extend human capability by sensing, comprehending, acting, and learning” (Daugherty and Wilson, 2018, 3).

AI technology has dramatically progressed with the latest advances in ML techniques, which build computational models based on computer algorithms to identify or predict situations through the training of the data. Additionally, advances in ML are now leading new frontiers in DL, which uses large artificial neural networks with feature learning techniques to automatically identify and predict the representations needed for detection of features or classification of raw data. ML and DL advances in AI also began to change the existing prevailing notion that “the routine operational tasks that machines could handle were separated from the complex managerial tasks reserved for humans” (Raisch and Krakowski, 2020, 3). Furthermore, advances in quantum computing aim to develop high-performance computers that will significantly accelerate innovations in AI.

AI technologies have automated numerous processes in our daily lives and workplace and further enhanced them to be much more efficient through allowing more human and technology collaborations (Wijma et al., 2018). As computing powers increase, AI’s capacities also increase, presenting even more opportunities and emerging capabilities for human-machine collaboration. In a way, AI is the epiphany and amalgamation of digitalization, automation, and big data. For the first time in the history of human race, we can amplify human performance, productivity, and effectiveness, thanks to AI.

In HRD, AI in general and ML and DL in particular present new opportunities to the traditional HRD functions. Through ML, organizational knowledge-sharing and management can be revolutionized, moving from being knowledge repositories to actively utilizing and seamlessly integrating organizational knowledge into employee workflow and task completion. Similarly, AI can provide personalized and customized employee development opportunities through intelligent agent techniques, autonomously serving as the agent helping employees direct their development activities towards achieving their goals. Solution techniques of AI can help HRD in career development and succession planning through quantifiable optimization tasks. Text processing techniques of AI offer trainers the ability to conduct training gap analysis through probing, searching, classifying, mining, associating, ranking, and summarizing text-based documents used in such analysis. This can further be expanded to other AI systems in which employees can assess and determine their future training and development needs based on professional, sector, and economic changes and advances. There is no doubt that the prevalence of AI in HRD will continue to grow. As HRD utilizes more AI and data-driven learning as a result, its value-proposition within the organization will also increase (Gregory et al., 2020). However, it is vital to recognize potential challenges AI and all other technology-based advances would present, especially around ethics, data privacy and security, bias, and discrimination. As a field, HRD has the capability to address these challenges by serving as a champion of employee well-being and universal values.

Conclusion

This chapter highlights major technological advances impacting HRD, namely digitalization, automation, big data, and artificial intelligence—new frontiers for the profession. Just a decade ago, our notion of technology in HRD was limited to virtual organizations, virtual teams, e-learning, video-based training, and information technology. At work, employees now are asked to work with digital and automated systems frequently using data and feedback, which aim to accelerate productivity and minimize human errors. New examples of the pervasive nature of technology entering into our everyday lives from home to school to work can be given. In the face of such dramatic and rapid change can HRD survive with a business-as-usual mindset? What threats and opportunities does the future of work hold for HRD? What will the employee and the workplace of the future look like? We do not know what the future will hold. But, one thing we know for sure is that we can be proactive in identifying ways in which the human capacity and human mind and intelligence can still determine that direction while utilizing all affordances that technology presents for the betterment and advancement of humanity. “Rapid and accelerating digitization is likely to bring economic other than environmental disruption, stemming from the fact that as computers get more powerful, companies have less need for some kinds of workers” (Brynjolfsson and McAfee, 2014, 10–11). These technological advances have already begun to bring disruptive changes to industries at an economic scale.

Technological progress will undoubtedly benefit those employees with skills in education and competencies much aligned with the new workplace, and disadvantage those employees with ordinary skills. Considering this departure from classical organization to new frontiers, we need to consider (a) how new business models and organizational structures will affect work; (b) innovative ways to transform our educational and learning systems to adequately prepare our future workforce to effectively use new technologies to promote sustainable forms of work and livelihoods (Tschang and Mezquita, 2020); (c) the need to remain agile in the face of disruptive changes resulting from new technologies to continue to develop and increase human expertise for new discoveries and advances; and (d) the vitality of ethical HRD practices while maximizing all that technology offers. “Maximizing technology affordances does not only require the right organizational strategy but also collective economic and societal effort for a successful transformation to a new dimension” (Akdere, 2020, n.d.). HRD has the potential to contribute to efforts in revolutionizing the future work and learning.

Reflection Questions

1. Identify one concept of the simulated training in this chapter that is most interesting to you for training and development and explain why it is appealing.

2. Explain something about virtual reality–based simulated training that is new to you.

3. Explain how your own view of career development fits with advances in big data in general and HRD in particular.

4. Explain how AI will continue to revolutionize traditional HRD functions of training and development, organization development, and career development.

5. Explain potential implications of technological advances to economic, educational, psychological, and sociological foundations of HRD.

Internet Resources. Instructional support materials for this chapter can be found on this website: www.texbookresources.net