Keywords

3.1 Introduction

Western society for the past 300 years has been caught up in a fire storm of change. This storm, far from abating, now appears to be gathering force. (Toffler, 1970, p. 9)

The book Future Shock, first published in 1970, contemplates the massive change experienced by society in the previous 300 years, its consequences, and how it might be dealt with. The book was a hit, selling millions of copies, and made many notable accurate predictions, including the rise of artificial intelligence, virtual environments, increasing human-machine interactions, and a consistently increasing rate of technological development. These changes would have dire psychological, relational, and societal consequences unless humankind could learn to behave differently.

Decades later, society has indeed experienced massive technological development, as exemplified in Industry 4.0 (I4.0) and now Industry 5.0 (I5.0). While the terms are debated, I4.0 centers on technology-empowered cyber-physical systems that could enable higher levels of productivity and competitiveness. At its heart, while it may consider social and environmental well-being, it is ultimately profit focused—societal and environmental outcomes are nice-to-haves, and are ultimately at the service of a profit-driven model of exploitation. I5.0, with its insistence on human-centeredness, makes social sustainability a necessary component of production at micro, meso, and macro levels (Ghobakhloo et al., 2023). In other words, I5.0 holds that we, as individuals and as society, have to be well. This is evident in its interest in worker’s rights, mental and physical well-being, equality, greater responsibility, and meaningful work envisioned in the human centric approach (Breque et al., 2021).

Are we currently well? Not particularly. Decades after the publication of Future Shock, we note with some increased sense of urgency that humankind’s relationship with technology and technological change falls far from the ideal. Individually, worry and sadness appear to be steadily increasing and currently at record highs (Gallup, 2023). Organizations fair no better, with new technologies fueling a new era of “cyber-capitalist villainy” (Scott, 2023). As a society, rising global inequality and stagnating income growth (Stanley, 2022) mars any argument that technological advances of I4.0 have been to society’s overall benefit so far.

The argument from proponents of I4.0 and I5.0 alike has been, much like Toffler’s argument in Future Shock, that living well with new technology will require fundamentally new and better ways of behaving that go beyond understanding how to use any one technology. Up to this point, hard skills, how to use particular technologies well, have been the key determinants of winners and losers in the production system. This is because economic performance acts as the ultimate indicator of fitness by which organizations survive or perish. Under I4.0, the well-being of the individuals making up organizations might be of some interest to the degree that it serves economic profitability. A passable level of well-being is often enough to accomplish this aim. Sure, one needs to comply with legislation, and offering perks like free Yoga classes might help attract and retain talent, but a genuine interest in supporting employees is not vital.

If we take the human-centeredness of I5.0 seriously, this approach is no longer sufficient to achieve the human-centered, social sustainability espoused by I5.0. Technology may play some role in supporting the aims of I5.0, but increased attention has fallen away from just technology skills in favor of “human-centered” behaviors that make up how we related to others and to ourselves, or soft skills (Whitmore et al., 1974; Piwowar-Sulej, 2021). The scope of soft skills is large and includes cognitive skills such as general problem-solving, creativity, goal setting, learning, meta-cognition, and concentration; emotion regulation skills like uncertainty tolerance, emotional intelligence, empathy, and self-esteem; and interpersonal skills such as boundary setting, communication, leadership, and networking (Chiarello et al., 2021; Fareri et al., 2021). The need for more soft skills is widely acknowledged. In fact, 7 out of 10 of the World Manufacturing Report’s “Top 10 Skills for the Future of Manufacturing” (2019) do not relate directly to technology, and could be considered soft skills, while the World Economic Forum’s Future of Jobs (2020) report suggests that soft skills will be in higher demand than technical ones (p. 22), a suggestion that reflects current employer trends (Succi & Wieandt, 2019).

Why the increased interest? And does this interest reflect a genuine need or another management fad? This chapter explores the state of the art of soft skills as it relates to I5.0 and we consider the drivers behind an increased interest in soft skills, and the challenges and opportunities offered by I5.0.

We ask first:

Question 1. How do I5.0 trends drive a need for soft skills?

To summarize the argument to follow, I5.0 places increased and sometimes new demands on human beings. We suggest that soft skills play a moderating role in the relationship between production with new technologies and the sustainable, human-centered outcomes sought by I5.0, and review some recent trends that support this suggestion. But the relationship is not a simple one. While it seems plausible, perhaps even obvious that more soft skills are needed, this conclusion raises a number of questions that require answers if soft skills are to realize their potential for supporting environmental, social, and economic sustainability in productive systems. Thus, the next questions this chapter addresses are:

Question 2. What challenges exist for soft skills to support the goals of I5.0?

Question 3. What is the future of soft skills in Industry 5.0?

The chapter proceeds as follows: First, we consider how I5.0 trends could be driving an increased need for soft skills, reviewing psychological, business, and structural challenges brought on by current developments. Next, we explore challenges to supporting soft skills, considering first the widespread abuse of the term, and next the difficulties that a more scientific approach would imply. We offer an illustration of these challenges through a case involving our own soft skills training. Finally, we consider possible future directions for soft skills in research and in practice.

3.2 Industry 5.0 and the Need for Soft Skills

The need for soft skills is not new. Indeed, as a species, it has been argued that we homo sapiens derive our competitive advantage in large part from our ability to think creatively and to collaborate (Mcbrearty & Brooks, 2000), two quintessential soft skills. Interest in soft skills in modern systems of production is exemplified in the writings of Mary Parker Follett, who challenged notions of command and control in favor of a more human-centered approach to management grounded in psychology. Decades later, the emergence of corporate social responsibility and the triple bottom line extended the scope of interest as companies sought to do well by doing good. Decades of research explored whether and why corporate social performance would support financial performance, with results largely suggestive of a positive—if somewhat complex—connection (Wood, 2010). Whatever their merit, efforts at social and environmental performance in this connection remains subordinate to financial performance.

Now, there is little doubt that widespread interest in soft skills is increasing. Why? While we do not know with certainty, the issues of relevance and cost appear to be plausible. First, the value and relevance of soft skills are increasingly recognized. Not only are companies more aware of the value of employees with soft skills, potential employees also increasingly seek out companies they perceive as supporting soft skills (Succi & Wieandt, 2019). Second, the costs of falling behind are increasingly recognized. When the knowledge half-life for hard skills is estimated at less than 5-years (Tamayo et al., 2023) the ability to learn, a frequently cited desired soft skill, becomes more valuable, as do the relatively enduring and transferable soft skills.

Fig. 3.1
The center of the 2 concentric circles titled characteristics contains ecological change, technological change, and social heterogeneity. The outer circle titled threats contains macro-structural changes, meso or business and managerial changes, and micro or psychological strain.

I5.0 characteristics as driving micro, meso, and macro challenges

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New technologies have brought about both opportunities and genuine stressors that affect the need for soft skills, such as increased connectivity and distractions. How individuals respond to these stressors has significant implications for their well-being and can also impact the problem-solving capacity of groups (Ashforth & Lee, 1990). These challenges owe broadly to technological change, increased complexity due to interrelated elements of the system of production, rapidly shifting societal values, and increasingly drastic ecological change. As it concerns our system of production, these challenges represent psychological, business, and societal stresses that have been described in the workplace (Fig. 3.1). These are the elements that suggest a need for new skills. The following sections briefly review these challenges.

3.2.1 Psychological Challenges

That technological change impacts individual well-being has long been recognized. But new technologies present new challenges such as technostress (Tarafdar et al., 2007), overload (Maier et al., 2015), and increased connectivity (Ayyagari et al., 2011). Moreover, the omnipresence of smartphones and digital devices introduces digital distractions that disrupt focus and productivity, can lead to stress and frustration. The fear of missing out (FOMO) on vital information or online social events further contributes to anxiety and compulsive technology use (Tarafdar et al., 2010). Additionally, rapid technological changes create a sense of techno-uncertainty—uncertainty around the rate and direction of technological development—necessitating continuous adaptation to new tools and platforms, which can amplify feelings of insecurity and stress (Ragu-Nathan et al., 2008).

I5.0 technologies have the potential to both help and hinder individual well-being. For example, technology that allows people to connect remotely has enabled a transition to remote-working and flexible work arrangements for many, a rarity even a decade ago. Broadly, it appears that some remote work can have positive effects such as increased work-life balance, increased autonomy, decreased pollution and commuting times, and increased inclusivity. But that same connectivity comes with risks. Because it offers the possibility for connection and increased autonomy, remote workers often work longer hours, experience more stress and sleeping problems, and blur their work-life boundaries (Eurofound, 2018). Technology can facilitate already abusive work practices, making bad bosses worse, so much so that many countries have passed or are considering “Right to Disconnect” legislation.

Here, soft skills play a clear and significant role in moderating the impacts of new technology on psychological outcomes. Individuals can cope with psychological challenges to a better or worse degree (Pirkkalainen et al., 2019). The capacity to set boundaries well, to communicate, to delegate, and to regulate emotions are just a few of the soft skills that can be needed to manage connectivity. But increased connectivity is not the only challenge offered by I5.0, there are many. For example, Breque et al.’s (2021) report suggests that technology may be used to help us learn, and indeed, it appears it can be (Tamayo et al., 2023). But AI can also drive us to be less vigilant and less critical, and actually learn less (Cymek et al., 2023).

These are just two anecdotal examples of soft skills playing a moderating role between outcomes.

3.2.2 Business and Managerial Challenges

Businesses in the changing technological landscape grapple with a myriad of challenges that necessitate the cultivation of specific soft skills and strategic approaches in order to meet a changing market. As noted by Kannan and Garad (2021), an increasing need for enhanced flexibility in working times exists, a demand that necessitates soft skills like adaptability and time management. Moreover, technological advances and increased competition drive a need for decentralized operations, as highlighted by Chauhan et al. (2021), and have suggested communication skills can help bridge gaps across units or departments.

Adapting to the dynamic environment presents its own set of challenges (Won & Park, 2020). Both management and employees often exhibit resistance to change, and the benefits of such changes are not always immediately clear, even to managers charged with implementing them. Furthermore, the lack of existing infrastructure, knowledge, and systems compounds the challenges of transformation, creating challenges for strategic planning and resource management. The digitalization of processes, for all its promises of increased efficiency, comes with its own challenges, such as introducing bias into decision-making (Malik et al., 2022). A combination of skills related to learning, complex problem-solving, digital literacy, and management of change and communication has been suggested to address these and other challenges related to business and management, and enjoys some evidentiary support.

AI can help detect errors in production, but there is a risk that workers come to rely on AI-facilitated error-detections in risky ways, which presents not only a learning challenge for the individual but also a business challenge (Cymek et al., 2023). This suggests a need for developing the skill of remaining vigilant and the ability to respond to situational cues, even when the need to do so may not be immediately obvious (Milosevic et al., 2018).

3.2.3 Structural Challenges

Rapid technological development creates institutional gaps which businesses and other actors in the system of production must often confront. Issues such as a limited understanding of ethics and safety, the absence of standardized reference architecture, and the presence of ever-evolving government policies concerns surrounding cyber security, privacy, have been discussed (Chauhan et al., 2021; Arcidiacono et al., 2022). In some contexts, the unavailability of adequate broadband infrastructure, coupled with legal and contractual ambiguities and trade restrictions, further accentuates the intricacies of this evolving landscape (Chauhan et al., 2021). Soft skills again can play a role in supporting sustainable outcomes even in ambiguous environments. Indeed, the ability to tolerate ambiguity, which can be developed and practiced, as well as to solve complex problems, have both been cited as increasingly needed soft skills (Chauhan et al., 2021; Chari et al., 2022).

At a systemic level, key questions remain. For example, who should provide the training to keep up with the new skill demands? Higher education has struggled to keep up with the demands of the private sector’s evolving needs (Doherty & Stephens, 2023), with the private sector often favoring in-house development of hard skills. On the other hand, evidence suggests that companies rarely invest in on-the-job training for soft skills (Piwowar-Sulej, 2021).

This complexity threatens increased inequality. As disparities exist in data literacy and opportunities among countries, less developed ones at risk of falling further behind in terms of technological development (Gupta et al., 2022). Smaller businesses face a similar struggle, due to their limited resources and high cost of implementing many new technologies (Lepore et al., 2023). The extent to which soft skills could correct such disparities is unclear, though it has long been theorized that certain behaviors (e.g. non-defensive behavior, openness), can facilitate learning. Specialized competence centers have also formed in several EU countries whose role is to support small businesses in implementing I4.0 technology (Ietto et al., 2022).

3.2.4 Do Soft Skills Address These Challenges?

The previous discussion provides some anecdotal evidence of soft skills acting as a moderator between production and the sustainable outcomes sought by I5.0. Figure 3.2 captures this relationship for a non-exhaustive list of soft skills. For example, we have seen that effective boundary setting can support the avoidance of hyperconnectivity in some cases. Likewise, having a support network is linked with positive psychological outcomes, its absence with negative ones.

Fig. 3.2
A flow diagram runs as follows. 15.0 production, self-management, transversal technological, process, and other management skills. Environmental sustainability, adaptability and responsiveness, and human-centeredness.

Soft skills as moderating I5.0 outcomes

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This section has explored some of the ways that I5.0 developments are affecting the need for soft skills. Given that I5.0 is characterized in part by increased change, interconnectedness, and competition, the central question is not whether we need more soft skills—it is abundantly clear that we do—but rather in identifying which soft skills are needed in which contexts, and the effects of these, so that businesses and society can effectively deliver them. It is evident that the demand for soft skills is not a one-size-fits-all proposition. Some soft skills may be more urgently needed in specific situations and for particular individuals or groups. However, the challenge lies in pinpointing which soft skills are most critical, for whom, and how they can be effectively cultivated.

3.3 Challenges in Supporting Soft Skills

Unfortunately, as it stands, the discussion on soft skills typically falls woefully short of providing this sort of guidance.

3.3.1 Challenges in Defining the Term

Both in practice and in research, there is a tendency to describe skills in conflicting or ambiguous terms such that “whatever employers say is a skill has become regarded as a skill” (James et al., 2013, p. 957), which hinders our ability to understand actual skill use trends. To be sure, employers are an important part of the I5.0 vision, but their capacity to assess soft skills is currently limited, and their promotion of soft skills appears sometimes rhetorical, a marketing device to attract talent (Succi & Canovi, 2020). As will be seen, even well-meaning employers lack the means of identifying soft skills of their current and future employees. EU supported definitions contain ambiguities and indeed, rely on employer-led description of skill categories.Footnote 1

At the same time, EU tools for monitoring soft skills have not kept up with technology, and have been noted to be so high-level as to lack meaning (Colombo et al., 2019). As it stands, current use is more suggestive of a buzzword, a word with normative appeal but devoid of any real substance (Cairns & Krzywoszynska, 2016). The lack of clarity obstructs an evidence-based discourse on soft skills, creating a significant gap in knowledge. Unclear definitions prevent any serious comparison of phenomena, as serious comparison requires an acknowledgment of similarities and differences. Without a clear definition, researchers can easily duplicate efforts, and practitioners make decisions based on faulty conceptions (Rousseau, 2006).

It is therefore helpful to consider the development of the term in the first place. The earliest definition appears to exist in the context of US Military training, where it emerged alongside an interest in developing competent leaders (Whitmore et al., 1974). Presenters at the CONARC Soft Skills Training Conference 1972 emphasized adherence to behaviorist principles, included rigorous definition of behaviors in terms of their effectiveness relative to the outcome they were meant to bring about, and the importance of context. Soft skills as originally conceived were concerned with “people operators” rather than “machine operators”.

The early discussion on soft skills emphasizes a view grounded in behaviorism from which many popular definitions deviate substantially. While behaviorists themselves hold a wide range of views, generally, we can consider skills to refer to behaviors, meaning theoretically observable events, whether overt or covert. Walking to the supermarket is a behavior. Thinking about tax season positively can also be considered a behavior.Footnote 2 Being positive is not. Already a useful distinction can be made from some popular uses of the term found in Table 3.1: Skills refer to effective behaviors or abilities, the capacity to perform an effective behavior.

Table 3.1 Soft skill definitions through the years
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Thinking in terms of behaviorism illuminates the nature of the current skill shortage. Just like playing the violin, skills can be learned, require practice for mastery, and can become rusty or in some cases even forgotten (Ericsson et al., 1993). A second point in the original definition of skills is that it highlights the importance of defining skills in context. Skills are skills because they are considered effective relative to some goal, and thus they are inherently social. Taking these two points together, if we are to change the current way of behaving, we will need to learn it, and be able to apply it in the proper context.

These early discussions also reveal a known challenge about soft skills, namely, that both the desired outcome as well as the behavior needed to bring it about are more difficult to define or completely unknown compared to hard ones. For example, one may recognize a desire to maintain a good working relationship with one’s colleagues. But what does “good” mean, and what steps would bring it about? Even if we adopt evidence-supported theories of effective communication, meeting commitments, etc. both the definition of “good working relationship” as well as the best way to support it can vary widely from person-to-person, company to company, and culture to culture (Meyer, 2014; Schein & Schein, 2017). As a result, remaining skillful typically requires us to develop our own theories about how people operate, and to overcome what Whitmore referred to as “the common myths about the operation of people which are promulgated by our culture” (p. 14).

Deviations from this use of the word skill as anything other than “effective behavior” are common and may or may not be warranted, but they should be done with caution. For example, hiring someone with the ability to remain calm under stress may indeed be required, but selecting personnel on traits (e.g. personality) quickly becomes ethically charged, and likely has little to do with actual effectiveness. Traits may help to some degree in skill acquisition, but they are not skills, despite often being packaged as such by employers (Succi & Wieandt, 2019).

The definition of skill may be abused in practice, but it is fairly consistent in theory. The same cannot be said of the first term, “soft”, and if the distinction is to be valuable, this needs to be clear. In fact, from the beginning, the term “soft” was problematic. Conference attendees present when soft skills were first introduced were given a task to classify their course material as “hard” or “soft” complained that the two were not mutually exclusive, were irrelevant, created unnecessary misunderstandings, and should be eliminated as a distinction (Whitmore et al., 1974, p. II53). Subsequent authors have termed similar behaviors as non-cognitive, twenty-first Century, competencies, pervasive, professional, non-technical, transferable, core, power, personal, and employability, and meta-skills, amongst other terms (Claxton et al., 2016; Terblanche & De Clercq, 2021). These are not an exhaustive list of terms used, but are representative of the uses we have encountered in our research.

Figure 3.1 contains commonly used skill categories we have encountered. We will not review all of these, but it is important to note that these include both cognitive abilities such as reasoning and meta-cognition—roughly, the ability to monitor and understand one’s own thought processes—emotion regulation abilities, such as the ability to accurately identify emotional states, and interpersonal skills, such as the ability to negotiate. No universally accepted delineation exists, and some researchers omit certain categories altogether. Additionally, because the same behavior can support more than one outcome, significant overlap between these categories is unavoidable. The ability to regulate one’s emotions, for example, is associated with the performance of a wide variety of tasks—one could include it in a course on negotiation, surely, but it is equally important in complex problem solving.

If the term “soft” is so problematic, why bother with it at all? To be sure, any abstraction involves, by definition, some loss of nuance, but this can be justified and may be necessary for understanding abstract or relatively macro events. However, while it may sacrifice nuance, it must do so in a systematic way, so that some real aspect of reality can be better understood—abstraction does not imply vagueness (Sayer, 1992). Therefore, if the term is to be useful, it would need to provide some useful means of understanding current trends in a way that would not be possible without the abstraction.

In this sense, while the behaviors unified under the banner of soft skill are indeed numerous, potentially infinite, they do appear to share commonalities that could allow for some beneficial comparison. While their characteristics are not agreed upon, common aspects are transferability and habituation. Concerning transferability, soft skills refer to behaviors that are effective across contexts. For our purposes, we distinguish between process, technological, self, and other management (Piwowar-Sulej, 2021). Transversal technological are those that relate to effective use of technology that do not correspond to a particular software or hardware. Process skills refer to behaviors that facilitate organization and provide a framework for effective work. Self-management includes behaviors related to emotion regulation and successful problem solving, without necessarily involving a group, while other management refers to the capacity to work as a member of a group. Second, soft skills are often habituated (Gardner et al., 2016), meaning they work well enough that we normally perform them without deliberate reflection.

If we understand soft skills as behavior, we can move to a consideration of some known challenges in behavioral assessment that might impact their usefulness in relation to I5.0. These challenges are neither new with the Industry 5.0 transition nor a complete representation of challenges to behavioral science, or any science, for that matter. However, the issues of complexity, needed for context-specificity and evaluation, will present difficulties for both research and practice around soft skills and the I5.0 transition, and so we highlight these here.

3.3.2 Complexity

The first barrier to supporting an evidenced-based approach to soft skills is that behavioral science is complex, which makes understanding which behavior is needed for a particular situation difficult, if not impossible.

For one, the same behavior may produce a large number of outcomes, a one-to-many relationship. Take the case of a superior providing critical feedback to a group of subordinates. The same feedback may be interpreted in different ways, be motivating for some and demotivating for others. It may motivate and result in beneficial learning and at the same time create resentment, or any number of outcomes.

On the other hand, more than one behavior may produce the same outcome, a many-to-one relationship. When introducing oneself at a meeting, does it matter that one person says “Good morning” and another “Hello”? These behaviors appear functionally equivalent, meaning they serve the same purpose and have similar effects. But given our limits in predicting the outcome of a particular behavior, it is difficult to know which functionally equivalent behaviors will produce the “best” outcome.

Indeed, understanding the outcomes of a particular behavior depends on the interaction between multiple, interrelated systems such as physiological ones (systems of basic emotions), cognitive, and social ones. A comprehensive understanding of all of these is not possible. Finally, the previously discussed elements of complexity are given considering individual behavior. But production, certainly of the type in a transition to Industry 5.0, involves many actors who interact to achieve their goals. Is it enough to have one very skilled person in a leadership position, or do all employees need to be skilled?

These challenges do not provide easy solutions. It requires a need to emphasize behavioral flexibility in addition to just specific behaviors (Cheng, 2001). It is not just possessing the skill, but the ability to cycle through potentially effective behavioral strategies. Indeed, some evidence from the context of leadership suggests that a wider range of behaviors leads to more favorable outcomes (Hooijberg, 1996).

3.3.3 Context-Specificity

Soft skills are soft because of their transferability. Transferability is what makes them appealing, as training in them has high returns. Society gains a more effective workforce, the individual gains job security, companies gain performance and productivity (Leopold et al., 2016). But the outcomes of particular behaviors are context specific, and more so when dealing with the meaning-laden, intersubjective realm of soft skills. Whether a particular behavior, say providing a firm handshake when meeting a new client, can be considered effective depends on an infinite number of situational variables.

For example, effective leadership is known to take different forms depending on organizational structure and purpose. Situations of high urgency require quick responses in Western cultures might work best with direct, task-oriented styles—elsewhere this approach might be less effective (Yukl et al., 2003).

Context-dependency presents a challenge for soft skills because it directly contraposes the transferability which makes them so appealing. Context-dependence makes predicting which particular behavior will be effective difficult, if not impossible. Some knowledge of potential transferability is desirable because it would facilitate the identification of skills and research. The second is to be aware of situations in which transferability is likely to be more of an issue, i.e. which soft skills are more context dependent than others. The more people involved, and the more complex the behavior, the more context-specific it will be.

3.3.4 Evaluation

If companies invest in soft skills, how will they know they are working?

Evaluating behavioral change brings forth a constellation of challenges. Evaluating effectiveness involves inherent subjectivity, as what constitutes an effective change may vary from one observer to another. Discrepancies in perceptions and interpretations of behavioral outcomes can emerge (Cameron, 1986). Time also complicates evaluating the effects of behavior change. For example, skills display a “sleeper effects,” a delay between the implementation of interventions and the manifestation of their effects that complicates assessment. Furthermore, short-term outcomes (e.g. productivity gains) can reinforce behaviors which, on the long-term, would be harmful to the individual or group (Andriopoulos & Lewis, 2010). Adding to the complexity, the same behavior can yield both positive and negative effects (Cheng, 2001). This complexity in evaluation presents challenges for research and practice alike, as it suggests significant care and potentially resources to be able to observe and evaluate soft skill effects.

3.4 An Illustration: Training Researchers on Soft Skills

Appreciating the challenges of soft skills can help in their instruction. For many years, the first author has trained groups in a variety of soft skills in a variety of guises and contexts. This section briefly recounts this experience teaching emotion regulation to two groups in transition: lab scientists and economists. This sample may not represent the entire nature of I5.0, but they are two groups that feel many of its aspects. For one, both have experienced massive changes in their work due to technological changes. Both are also at the forefront of technological developments, being required to produce novel research, and both are also affected by shifts in societal values, depending to a large extent to public funding to continue their work.

The training was carried out during the 2022–2023 academic year. It was not meant to address a transition to I5.0 per se, but rather dealt with these elements because they were a part of the nature of the work of the participants. The training included a different set of soft skills, but both included emotion regulation, a set of behaviors aimed at identifying and understanding one’s emotions, decrease the frequency of unpleasant emotions, decrease vulnerability to emotions, and decreasing the effects of negative emotions. We focused on emotion regulation because, of the categories of soft skills, it is far reaching, potentially applicable to all situations, and in no way technologically dependent. Around 50 people participated in the training, which lasted from two h to several weeks.

Early career economists and lab scientists pursuing careers in research share roughly similar professional aims that make them appropriate for comparison. Both are employed to conduct and disseminate research, both tend to operate within a hierarchical university or non-profit research center, and both reported broadly similar sources of stress, including pressure to publish, uncertain career prospects, role and process ambiguity (Hargreaves et al., 2014), and, unfortunately, both groups reported high levels of mental health problems relative to their highly educated, non-researcher peers (Levecque et al., 2017). In terms of looking at soft skills, it is a boon because, not only is it a field defined by newness, it is also a time of significant transition for the researchers. In the case of PhD training, upon completion they are expected to be capable of independent research, while at the beginning they are not. Postdoctoral and other early career researchers, on the other hand, may have even more role expectations, including administration, grant seeking, and forming collaborations. In short, this is a group that is expected to learn a wide variety of skills in a highly ambiguous and shifting environment, reflective of the change and ambiguity we can expect from an Industry 5.0 transition.

While our experiences in training lab scientists and economists in emotion regulation provide valuable insights into the applicability of soft skills within specific contexts, we should acknowledge several limitations. Firstly, these case studies represent isolated instances and should not be regarded as comprehensive representations of the broader research community or I5.0 transitions. Both groups were selected due to their unique characteristics and experiences, and as such, the findings are not meant to generalize to other contexts. Furthermore, the training sessions were not explicitly designed to address the complete I5.0 transition. Instead, they were shaped by the participants’ immediate work-related challenges and dynamics, which may not encompass the full spectrum of Industry 5.0’s complexity and implications. Therefore, the applicability of our observations to broader Industry 5.0 contexts should be considered within this specific context. The projects were presented in a summarized form, providing only a high-level overview of the training experiences. Detailed nuances and individual variations within the training process were omitted for brevity. Finally, our approach for addressing the challenges is in no means meant to be prescriptive, but rather illustrative.

3.4.1 Complexity and Context-Dependency

Emotion regulation is arguably one of the most universally applicable soft skills. Everyone has emotions all the time—even the feeling that one has no emotions would be to “feel numb”—and therefore emotions can be said to influence all cognition and behavior to some degree (see Tyng et al., 2017). Emotion regulation revolves around the impartial observation of one’s emotions, fostering the ability to differentiate emotions from objective reality and enabling behavior not solely driven by mood. Consequently, our training sessions commenced with a fundamental practice: the non-judgmental observation of emotional states, an evidence-supported skill that has a range of positive individual outcomes (Grossman et al., 2004).

However, despite the common foundation, the nature of the training sessions diverged significantly for the lab scientists and economists. Lab scientists found themselves grappling with immediate, visceral challenges demanding frequent, deliberate, and challenging emotion regulation. Their daily experiences were punctuated by acute stresses, often entailing clashes with supervisors and colleagues over limited resources in the lab. This high-pressure environment not only provided ample motivation but also offered frequent opportunities for practice, often within emotionally charged situations where mastering skillful behavior proved especially challenging.

In contrast, economists engaged in less frequent interpersonal interactions within their work. They acknowledged the relevance of emotion regulation but experienced fewer daily stressors. Their research did not hinge on physical access to specific locations or equipment, and their interactions with supervisors typically involved infrequent, high-stakes meetings. Adapting the training to their unique context required tailoring the exercises to mirror their work patterns. As a result, the training included some role play around the need for occasional but pivotal interactions as well as much more time spent on the uncertain, ambiguous time in between.

3.4.2 Evaluating Results

Were the skills working? We included mechanisms in the training so that we could appreciate the participants’ experience as well as collect some “hard data” that would allow us to connect the result of the skills training on performance. This proved unfeasible, so in the end we relied entirely on self-reports. Our training included applying the skill in practice and recording the results on a diary card. This provided some indication of their effectiveness, but with limitations. For one, it required the participant to accurately recall and record the outcome, and only included their perspective. For economists the issue was compounded because feedback was so infrequent that self-perception was the only source of information.

Even so, even something as beneficial as emotion regulation for the individual had sometimes surprising links to the organization. One researcher was so fed up with her supervisor in another lab that she left. Had she been able to better regulate her emotions, would she have stayed or left sooner? Which would be the positive outcome? For the research center that lost an employee and the corresponding investment in training, the answer may be clearer.

Because we had limited opportunity to connect with participants and evaluate the impact of the use of the skills, so we dedicated as much time as possible to critically examine the outcomes of skill use together in the relatively safe setting of the training. To appreciate context-dependence and challenges of evaluation, we strived to create a space during the training where we could critically evaluate both the execution of the behavior (did you perform the behavior as expected) and its effect (did it work)?

For the individuals participating in the training, based on surveys, follow-up interviews, and in-training feedback, we were fairly confident that the soft skills training had a positive impact. Not every participant used every technique, and in larger groups the skill use and impact was more difficult to assess. The impact on the organization was less clear. While many reported positive impacts on their working relationships, several reported increased awareness of their own negative feelings about their workplace, and some even took steps to distancing themselves from their work as a result. Of course, this could be an outcome of the poor design of the training, but it illustrated a case where a positive outcome for the individual, appeared to result in a negative outcome for the organization.

3.5 Conclusions

This chapter address the question of how Industry 5.0 (I5.0) trends are driving the need for soft skills, and the challenges that exist to impeding the potential impact of soft skills on achieving these new aims. I5.0 holds opportunity, but also creates challenges for individuals, businesses, and society. We considered some anecdotal evidence that suggests that soft skills play a moderating role in this relationship, supporting the achievement of well-being when they are present and hindering it when they are not. However, that is not to say they should be taken for granted—to say soft skills are required is not to identify which soft skills, in what form, or in what sets might be needed.

The second question this chapter addresses is on the barrier to supporting the capacity of soft skills to support I5.0 outcomes. Here, we identify ambiguity in its definition as central, and attempt clarify the meaning of the term soft skill and to consider its place in the transition to I5.0. For our part, a focus on skills is a hopeful one, and, as others have noted, it represents a shift from thinking that abilities were innate and fixed to one that better appreciates real human potential (Ackerman, 1987). When we use the term soft skills, it signals that we mean behaviors that can be learned and practiced; we are not saying that I5.0 would need people with certain inherited attributes. Deviations from the behavioral foundations of the term’s development may well have value—Ray’s (1989) critical evaluation of the term from a sociological perspective is one example—but these should be carried out with care. To confuse behaviors with personality or characteristics adds unnecessary complexity and ambiguity to an already challenging endeavor.

While research is broadly supportive of the idea that increased soft skills would indeed facilitate a transition to I5.0, this chapter has highlighted some challenges related to achieving this vision. The challenges of any behavioral science must be addressed by researchers and practitioners if soft skills are to facilitate a transition to I5.0. Practitioners seeking to facilitate soft skills in companies could acknowledge complexity, context-dependence, and the difficulties of evaluating initiatives aimed at supporting soft skills. Because soft skills are context-dependent to some degree or another, we should be cautious of one-size-fits-all solutions, and this could suggest that some level of in-house development of soft-skills would be beneficial. Recent trends suggest that individuals increasingly seek ways of increasing their soft skills, often in ways that are not supported by the evidence (Travers, 2022) or in ways that negatively impact the organization—the recent trend of quiet quitting could be seen as a manifestation of this. Here, Piwowar-Sulej’s (2021) observation that companies are not investing in their own training initiatives means that companies are forgoing a need to adapt soft skills to their local environment. Formal education systems, despite some improvements, still do not provide these (Carayannis & Morawska-Jancelewicz, 2022).

We do not wish to understate the implications of calls for more soft skills. Unlike hard skills, soft skills imply a fundamental shift of how we behave across a range of situations. Such a shift, then, implies that in the future we will communicate with ourselves and others in largely different ways than we do now, which would allow us to adapt well to technological, societal, and environmental conditions and change. Given a current lack of training, the potential for AI to support skill acquisition is appealing, but largely untested.

We would hope to see organizations foster environments where a diversity of behavioral repertoires is embraced and in which learning can take place in-situ. But the nature of soft skills, dealing with our thinking and doing at a fundamental level, means they go to our core identities. These are the most difficult to confront and change and naturally result in defensive reactions (Ashforth & Lee, 1990). However, as soft skills typically include behaviors directed at reducing defensiveness and increasing the capacity for critical reflection, organizations who manage this change can expect supporting soft skills to become less challenging over time.

We do not mean to suggest that behaviorism provides a definitive solution to the issue of soft skills for supporting I5.0. Indeed, the key issue highlighted in common discussions around soft skills is not the lack of a particular but rather of any scientific approach to the issue. Indeed, our treatment of behavioral concepts is limited and meant only to highlight what we see as key deficiencies in current discussions. We hold the view that, just as a diversity of behaviors seems to support the sustainable outcomes sought by I5.0, so too are a diversity of perspectives needed to understand the role of soft skills in the transition.