Beyond productivity and efficiency: design tools, methods and frameworks for psychological well-being, and (un)exploited potentials to assist employees at work

1. Introduction

The world of work is expected to undergo a major transformation in the future. On the one hand, work environments are being increasingly technologized, spurred by technical developments such as artificial intelligence, increasing computational power, efficient algorithms, new sensors, and ubiquitous connectivity. On the other hand, in several countries, sociodemographic challenges are mounting.

For one, an aging workforce will have to be motivated and retained, and also possibly tasked with non-routine, higher-productivity tasks that require a high degree of flexibility, creativity, and problem-solving skills (Holford, 2019; Smit et al., 2020). For instance, in the next ten years, a quarter of the Japanese workforce will be over 60 (Van Katwyk, 2012), and, confronted with an aging workforce, Germany also faces a shortage of skilled labor in the foreseeable future (Thun et al., 2007). Two, triggered by sociodemographic changes, the preferences of prospective jobseekers are also changing. When it comes to the field of work, several sectors may no longer be attractive (Cedefop, 2015), and to compensate for a decline in apprenticeships in these sectors, industries will have to substitute a skilled, retiring workforce with young, unskilled workers. There is also evidence that the psychological expectations of younger generation of workers (e.g. millennials or gen. Z) from the employers differ from those of other generations (Schroth, 2019), for instance, they are more likely to prioritize having an interesting job than older workers (Drabe et al., 2015), or place a higher importance on the societal and organizational impact of their work (Gabrielova and Buchko, 2021). Further, it seems that the notion of work itself is undergoing a major transformation, evidenced by “the Great Resignation or Reshuffle” (Sull et al., 2022). The effects of the COVID-19 pandemic have triggered many employees to reassess whether their work fulfills their needs, which has led to a renewed academic interest in employee mental or psychological well-being (hereafter referred to as PWB), and has pressured employers to prioritize the same. Taken together, employers face an uphill struggle on several fronts. If technology is to play a role here, future efforts will have to aim at creating a man-machine symbiosis that best serves the needs of the employees and helps fulfill the updated psychological contract. This entails developing interactive solutions that meet these requirements:

R1: improve employability of older workers: help them keep up with technological change, compensate for their loss in physical/cognitive abilities, and/or

R2: facilitate intergenerational knowledge transfer: so that younger, low-skilled workers can upgrade their skills and knowledge, both from formal and informal sources, and,

R3: foster workers' mental well-being.

In recent years, one of the ways in which requirements R1 and R2 have been tackled technologically is by developing assistance systems, digital assistants, or digital assistance applications that combine conventional interaction techniques with augmented and mixed reality (AR/MR) technologies to guide and train workers at various levels of expertise. However, research shows that it is R3, or worker PWB, which moderates productivity (Isham et al., 2021) and engagement (Aiello and Tesi, 2017) and may drive the success of R1 and R2. At its core, PWB is understood as both “feeling good (hedonic) and functioning well (eudaimonic)” (Ryan and Deci, 2001), and workplace PWB is conceptualized as both enjoyment of the job and how it contributes to building a coherent sense of the self (e.g. in terms of growth, meaning, purpose, etc.) (Rothausen et al., 2012). Scholars also note that mental well-being is to be distinguished from a mere absence of negative symptoms (such as stress, anxiety and depression), or ill-being (Chen, 2014), and ignoring it can have significant economic consequences (Pinheiro et al., 2017).

People spend a significant proportion of their lives at their workplace, and it is not surprising that workplace well-being has also garnered significant research attention in the last decades. While both physical and mental well-being are connected and important (Kropman et al., 2022), the approaches that are necessary to foster employee physical well-being may be different from those that support PWB. The former has been studied under various rubrics, more traditionally under physical ergonomics, and more recently, as reviews indicate, in conjunction with digital applications incorporating persuasive techniques to encourage users to maintain physical fitness and physical health at work (Orji and Moffatt, 2018; Huang et al., 2019; Damen et al., 2020). However, PWB has largely been neglected as a potential outcome of technological assistance in workplace contexts. We argue that the reason for these developments is simply that industrial technology, and, in particular, assistance applications are primarily seen as a method to solve issues of productivity and efficiency in industrial tasks by re-representing (and, in turn, redesigning) them with the help of interactive technology to reduce cognitive load. While this may be an important prerequisite, the current approach falls short in that it may not facilitate PWB and motivation to tackle these problems (Klippert et al., 2018).

For this reason, in this article is we focus specifically on the foundations and components of PWB. As recent movements such as positive computing (Calvo and Peters, 2014) illustrate, researchers already recognize the importance of incorporating PWB in design, and over the years, there have been several efforts rooted in diverse theoretical backgrounds to create tools, methods and frameworks intended to help designers to comprehend and integrate the various manifestations and facets of PWB into the design of interactive tools and products. However, when it comes to designing workplace assistance, we find ourselves in the initial stages of mapping the problem domain and identifying solutions that could be used to support workers' PWB in the future.

The aim of this article is to advance the state of design of technological assistance at work by (1) summarizing and assembling the various facets of PWB at work and how they can be seen as potential design outcomes of human-technology interaction, and (2) reviewing and organizing existing design frameworks, methods and tools for PWB according to its various facets to provide a repertoire of strategies and guiding questions for prospective designers. To this end, we first provide an overview of assistance in industrial environments and their technological capabilities. Second, we extract the core mechanisms behind workplace PWB from extant literature in the form of a morphology and determine the research gap with respect to existing efforts to support worker PWB in industrial contexts. Third, we conduct a literature review where we identify 21 articles that propose possible design strategies, methods, tools and frameworks and explicate their varied origins. We categorize these different candidates using the morphology and integrate them by assigning their role as a possible design approach to explore and support the relationship between the core categories of PWB (orientation, behavior and experiences) and the core actors involved (self and others). Finally, we propose some guiding questions that could be used to initiate design efforts in creating technological assistance for work and discuss additional avenues for exploration.

2. Related work

2.1. Assistance in industrial work

According to the dictionary definition, the word assistance comes from the Latin word assistere which means to stand by or take a stand near, and can mean both the act of assisting someone and that which is supplied during this act. Assistance is driven by a need, consists of aid that is provided by a provider to the recipient, with the aim of achieving a goal, and can be viewed as assistance generally when it brings about a positive change. Digital assistance is modeled after the work of human assistants, who are subordinate to their principals or experts and work closely with them, with a capacity for situational awareness and readiness that is necessary to preempt and support the needs of the principal. This is achieved via communication (i.e., delegation), observation (self-initiative) and action (executing tasks) in a joint work environment with formalized processes and conventions. In working together, assistants and principals use tools and resources that are jointly accessible and contribute to a shared understanding of a situation (Dhiman et al., 2022).

Following this model, in the computing domain an assistant typically consists of an application or an agent which provides interactive support to the user in a given activity context, either based on user delegation or agent initiative. The support can be cognitive or physical in nature, the former is the classic case for Human-Computer Interaction (HCI) [e.g., conversational agents or productivity assistants (Tur et al., 2010)], whereas the latter comprises cases of human-robot interaction (Guerin et al., 2015). The application is accessible to the user via an interface, observes the user and the state of the activity (in terms of the tools and the state of the object of the activity), and supports the user by (a) acting on the tool or the object, and/or (b) by furnishing information in particular modalities at the interface (via speech or visual modality), on the tool (haptic feedback or augmented tools), on the object (in case of augmented reality), or anchored to the work environment (in case of mixed reality). Prototypical use cases include supporting workers in manufacturing and assembly (Mark et al., 2021), logistics (Schwerdtfeger and Klinker, 2008) and maintenance (Dhiman and Rocker, 2019). Figure 1 showcases some of these use cases. As such, the design space of assistance is large, and applications are increasingly incorporating machine intelligence to enhance their abilities to observe the work environment, make decisions and communicate with the user, rooted in fields of research such as context recognition, image processing, natural language interaction, mixed initiative, planning and support (Dhiman et al., 2022). While the word “assistance system” is widely used to describe such applications that assist workers in the industrial domain, in this article we use the terms “assistant” or “assistance application” synonymously to aim for parity with the terms ‘virtual assistant' or ‘intelligent assistant' that are becoming increasingly ubiquitous.

FIGURE 1

Figure 1. Two examples of assistance applications from the shop floor. (A) An augmented reality (AR) assistant for guiding through manual assembly. (B) A mixed reality assistant for machine maintenance.

But what constitutes good assistance? The question has conventionally been answered by focusing on three main aspects: the user's attitude (and acceptance), usability and efficiency, and characteristics of the assistant (such as its anthropomorphic nature, its look and feel, personalization, predictability, maintainability, sociability etc.) (Dhiman et al., 2022). Following this line of research, in the industrial domain, the primary goal of assistant development has been the enhancement of employee productivity and efficiency. Hence, measures such as completion time, error rate, and instruction recall are most commonly evaluated, compared against different mediums of instruction (e.g. paper-based instruction) or interaction (e.g. with gamification vs. without) (Mark et al., 2021).

However, in recent years, the research focus of HCI has shifted from usability to user experience, and even further onto user PWB, as advanced by recent movements in human computer interaction such as positive technology (Riva et al., 2012) and positive computing (Calvo and Peters, 2014). According to these movements, although concepts such as usability remain important, a sole focus on productivity and efficiency may be too narrow to comprehend how user experience influences users' PWB. In the industrial context, studies from the field show that, although assistance applications exhibit potential in increasing productivity, especially by reducing error rates, their “human-centricity” is questionable, especially as these systems in their current form have brought about no change in the assignment, organization, diversity or creativity of work, or engendered any opportunities for employee growth and exchange of knowledge (Klippert et al., 2018). Similarly, the authors in Aringer-Walch et al. (2018) report on an “immanent loss of competence and autonomy”, and Warnhoff and De Paiva Lareiro (2019) provide an account of how interacting with such systems can result in a devaluation of experiential and process knowledge by prompting operators to follow concrete work instructions. Further, Apt et al. (2018) warns that assistants which control the execution of tasks create work situations in which they transform human workers into function executing agents, and the resulting cognitive under use negatively impacts motivation, mental health, and long-term workability. Technological advancement has brought about positive changes, but at the same time, put employees in a precarious situation by increasing job demands and regressing workplace motivation and well-being (Parker and Grote, 2022). Hence, it is crucial to comprehend the driving factors behind employee PWB and incorporate them into design.

2.2. Conceptualizations of psychological well-being

The achievement and maintenance of human PWB can be considered to be one of the central themes of human existence. It was a topic of extensive philosophical inquiry and debate in ancient times, and the discussion around what constitutes PWB, and how to achieve it, still continues to attract scientific interest and scrutiny.

According to Ryan and Deci (2001), our understanding of PWB can be traced back to two philosophical strands–the hedonic view, and the eudaimonic view. The word hedonia is related to the hedonist view of human happiness propagated by ancient Greek philosophers such as Aristippus, who argued that happiness be seen in terms of a maximization of pleasure. In well-being psychology, the hedonic side of PWB is captured by the concept of subjective well-being (SWB) (Diener et al., 1999) which measures three components - life satisfaction, the presence of positive mood, and the absence of negative mood. In contrast, the eudaimonic view of PWB is often attributed to Aristotelian ethics (Aristoteles, 1962), where eudaimonia is used to characterize a life lived in accordance with the “highest human good”, that is, a life that is purposeful and conforms to high moral standards. Well-being psychology borrows this term and uses it in a divergent manner (Fowers, 2016). In it, eudaimonia refers to a meaningful life characterized by personal growth, autonomy and a sense of vitality and thriving (Ryan et al., 2008; Ryan and Martela, 2016). According to Huta and Waterman (2014), eudaimonic well-being (EWB) converges around four key processes: excellence (striving for high standards and quality), growth (gaining knowledge, insight, and skill), meaning (experiencing significance and value), and authenticity (clarifying one's values).

Research shows that hedonic and eudaimonic well-being are somewhat related, but are still distinct components of PWB. Whereas the former refers to the affective experience, the other refers to cognitive evaluations of one's activities associated with what is worth doing (Waterman et al., 2008). Hence, not all pleasure inducing activities generate a sense of attaining valued objectives in life. For instance, when pleasure is pursued directly by engaging in activities that are instantly gratifying (e.g., consuming delicious food, watching a movie or going to the spa), the resulting change in positive affect only lasts for a short term, whereas pursuing activities that are aimed at achieving eudaimonic goals (e.g., helping someone, putting in extra effort in tasks, having meaningful discussions, taking time to introspect about one's values, organizing and cleaning one's surroundings) result in long term changes in positive affect (Huta and Ryan, 2010). Accordingly, several well-being researchers have argued for the superiority of the eudaimonic view. According to them, it is only when one is self-determined to pursue the ideals of meaning, growth, and excellence, that one achieves long-lasting PWB (Huta and Waterman, 2014).

Consequently, eudaimonia has been studied in psychology as different stages of human behavior, namely, orientations (priorities, motives or goals that are behind human activity), behaviors (specific activities that are pursued to fulfill these motives), experiences (state-level subjective feelings, emotions, appraisals that are indicative of how well an activity supports eudaimonia), and functioning (long-term trait abilities, achievements, habits, etc. that contribute to a general sense of PWB) (Huta and Waterman, 2014). Further, eudaimonic and hedonic orientations can also be differentiated on the basis of their focus of concern: me vs. we—eudaimonic goals reflect changes one wants to bring about in the self as well as beyond (self), whereas hedonic orientations are primarily focused on the self; now vs. future—eudaimonic orientations are about the future, whereas hedonic orientations relate to the present time perspective; tangible vs. broad implications—eudaimonic orientation focuses on how the concrete and tangible relates to the big picture, whereas hedonic orientation is only about the concrete and tangible (Pearce et al., 2021). Altogether, these dimensions of eudamonic well-being can be grouped morphologically (Figure 2).

FIGURE 2

Figure 2. Morphology of concepts related to eudaimonic well-being, as proposed by Huta and Waterman (2014) and Pearce et al. (2021).

How are the components of and stages in Figure 2 linked? According to Ryan and Martela (2016), the process of EWB be explained via SDT: when one pursues intrinsic individual and social goals rather than extrinsic goals (orientation), regulates one's behavior autonomously (activity or behavior) and leads a reflective life (evaluating one's experiences and how they relate to the other two antecedent components), one can be expected to satisfy the needs for competence, autonomy and relatedness, which results in positive experiences at the state level, and over time, stable states of “vitality and thriving” at the trait level (functioning). This process is captured by the “Eudaimonic Activity Model” (EAM) proposed by Martela and Sheldon (2019). The EAM emphasizes that eudaimonia is both “doing-well” and “feeling-well” (Figure 3).

FIGURE 3

Figure 3. Eudaimonic Activity Model (EAM) proposed by Martela and Sheldon (2019).

2.3. Role of workplace psychological well-being

The two conceptualizations of hedonic and eudaimonic well-being can also be found in studies of employee PWB, where the concept of job satisfaction has been termed a hedonic measure since it captures employees' pleasure or enjoyment derived from having or doing a job, i.e. subjective reports of positive affect (related to hedonia, i.e., feeling good, happy, and satisfied) (Rothausen et al., 2012; der Kinderen and Khapova, 2021). The eudaimonic perspective, on the other hand, examines the presence of factors that support outcomes related to one's skill development, growth, sense of contribution, personal relationships and the construction of life in a general sense (Rothausen et al., 2012; Budd and Spencer, 2015).

A number of such factors have been identified and are supported by extensive research. For example, the job characteristics model originally proposed five characteristics of satisfying work: autonomy, skill variety, task identity, task significance and task feedback (Hackman and Oldham, 1976). This was extended by Humphrey et al. (2007) further to also include social (e.g., social support, feedback from others) and work context characteristics. Research in the past few decades that highlights the role of the fulfillment of the psychological needs of competence, autonomy and relatedness, as proposed by self-determination theory (SDT) in supporting employee PWB (Broeck et al., 2010). More recently, der Kinderen and Khapova (2021) carried out a systematic literature review and identified the following drivers of EWB at the workplace: task/job significance, skill enhancement, learning new things, crafting feedback, opportunities to give/receive social support, utilization of strengths and skills, perceived control and autonomy and psychological safety. From an outcome perspective, they noted that fostering eudaimonia in work contexts positively affects several job and career-related outcomes, amongst them work-engagement and personal initiative (Hahn et al., 2012; Aiello and Tesi, 2017), willingness to invest time and energies beyond formal tasks (Turban and Yan, 2016; Sattar et al., 2017), contributing to organizational efficiency and performance (Turban and Yan, 2016), job satisfaction (Jones et al., 2015), and reduction of work stress and anxiety (Merrick et al., 2017).

2.4. Workplace psychological well-being and HCI

Technological efforts to enhance employee well-being at the workplace mostly view the term “well-being” in terms of physical well-being, with efforts directed at developing digital applications to persuade users to maintain physical fitness and physical health in sedentary or industrial work environments (Orji and Moffatt, 2018; Huang et al., 2019; Damen et al., 2020; Heikkilä et al., 2021). The problem of supporting workplace PWB via technology, however, remains largely unexplored. Although the hedonic and eudaimonic conceptualizations of PWB at work have been studied extensively in the domains of organizational psychology, they are only beginning to be explored by the HCI community. Recent studies have shown that, contrary to the hedonic aspect, technology-induced eudaimonic experiences are associated with long-term importance, need-fulfillment, positive affect, and meaningfulness (Mekler and Hornbæk, 2016; Laschke et al., 2020). Research shows that eudaimonia can be fostered by modifying non-technical aspects of work through interventions that consist of eudaimonic components, e.g., strengths based interventions (Oades et al., 2017) or job-crafting (Wrzesniewski and Dutton, 2001; Demerouti, 2014). As the ongoing discussion in well-being psychology further clarifies the notion of eudaimonia and its distinction from hedonia, it also opens up room for ideation on how to operationalize HCI techniques to practically apply the lessons of EWB in assistance applications at work. To this end, we carried out a literature review to synthesize design streams, strategies, methods and frameworks, with the aim of identifying techniques that could aid designers in the future.

3. Designing for eudaimonia: where we are now

We undertook a systematic literature review that analyzes and synthesizes all available research relevant to a research focus. While prior reviews dealt with design for total well-being (Kafaee et al., 2021) and physical well-being (health and wellness) (Orji and Moffatt, 2018), our review collects and organizes the frameworks, methods, and tools that have been developed for improving EWB. Because of its qualitative nature, this review employed qualitative analysis and synthesis of extant studies unlike meta-analysis. Informed by several recognized guidelines (Tranfield et al., 2003; Moher et al., 2009; Page et al., 2021), we followed three generic steps: (1) identification of candidate articles; (2) screening the candidate articles for relevance and eligibility; (3) qualitatively analyzing and synthesizing the final sample of eligible articles. Figure 4 illustrates a flow diagram (Page et al., 2021) for our review. The following sections describe the details of each step.

FIGURE 4

Figure 4. Flow of systematic literature review process.

3.1. Step 1: identification

Our strategy for literature identification was threefold. First, we chose Web of Science (WoS) as our primary database because of its broad coverage of qualified articles. In addition to this, we also selected databases from major publishers: Elsevier (ScienceDirect), Tayler & Francis, and Sage. These three were added to increase coverage. Second, the search string was designed by combining two sets of terms related to design framework, method, and tool (Set A in Table 1) and eudaimonic well-being (Set B in Table 1). Design scholars use disparate terms to describe something developed to help designers. Two of the authors, one from the design research and another from the HCI domain, collected typical terms for Set A from each research domain. For Set B, the synonyms of eudaimonic well-being were extracted from the previous review (Vittersø, 2016). Other often-used synonyms of well-being such as “physical health”, “quality of life”, and “welfare” were excluded to avoid papers that regard well-being as a physical health condition and socio-economic indicator. Third, types of articles were limited in original research papers written in English. According to the search strategy, literature was collected between 1st May and 10th May 2023. Our primary search using WoS resulted in identifying 1,214 records. The search string was restricted to Title, Keywords, and Abstract to find more relevant articles. Additionally, our complementary searches using the other databases allowed us to add 56 records. The total number of candidates became 1,262 after the removal of duplicates.

TABLE 1

Table 1. Search terms.

3.2. Step 2: screening

The screening process was separated to three steps. At first, the title and abstract of each record were checked to judge its relevance to our review. This step sifted out 1,198 records considered irrelevant. Then, the remaining 64 articles were retrieved and assessed for eligibility. We conducted a full-text review for each record and applied the following inclusion criteria:

1. proposing or customizing a specific design framework, method, and/or tool;

2. concerned with psychological well-being and not physical well-being;

3. useful in a broad context or applicable to designing assistance applications.

As a result, 13 eligible articles were left. Finally, further articles were searched through cross-reference snowballing and Google Scholar to be more comprehensive. We decided to include 8 of 11 additional candidates. In total, we found 21 eligible articles for analysis and synthesis.

3.3. Step 3: analysis and synthesis

The qualitative analysis consisted of three steps. First, one of the authors read and initially coded the 21 articles. The coding scheme had two categories according to our research questions: what facets of well-being each article postulates and, how each design method supports designers. In the “what” category, we used the morphological classification of eudaimonia (Figure 2) as an analytical lens. To be more precise, three dimensions in Figure 2 gave sub-categories and codes to the “what” category: design objects (which stage it takes into account), outcomes (which element it is intended to achieve or increase), orientations (which orientation it guides users and/or designers to). The “how” category included two sub-categories, the domain and type of method, but detailed codes were incrementally built through the analysis. The latest coding scheme was determined by two of the authors through discussions, and finally the articles were fully coded.

In the second step, a chronological analysis of the literature was conducted to identify the research streams that have different concerns and aims of design. The result was visually mapped, incorporating the “milestone” articles that were not in our literature base but additionally identified as the theoretical sources of each research stream. Through this analysis, we revealed four research streams that had a different tendency in the “what” category.

The third step aimed to organize design methods proposed in the literature. To do so, we created a matrix referring to the four research streams and the types of method in the “how” category. Each design method proposed in the literature was mapped in the matrix. It provides an overview of the available toolkits for eudaimonic assistant design and potential areas for the further development of design methods. We discuss the results in the next section.

3.4. Result 1: four research streams and eudaimonic well-being

Figure 5 shows the chronological map of the literature, and Table 2 reports the result of coding for each article. This map provides us an overview of the research streams. The design approaches considering eudaimonic well-being were presented in early 2010s. There are two different theoretical roots that influenced the articles: self-determination theory in psychology (Ryan and Deci, 2000, 2017; Ryan et al., 2008) and the capability approach in economics (Sen, 1999; Nussbaum, 2003). In the downstream, recent advancements could be synthesized into four streams with different aims. In the following sections, we explain each research stream, distinguishing its different points of view and treatment of eudaimonic well-being in design.

FIGURE 5

Figure 5. Chronological map of the various streams of psychological well-being and their milestone contributions upon which design tools/methods/frameworks are based.

TABLE 2

Table 2. The list of 21 articles with the result of coding.

3.4.1. Design for positive experiences and emotion

The pioneering work that addressed PWB in design research was presented by Hassenzahl et al. (2010). They incorporated psychological needs (Ryan and Deci, 2000; Sheldon et al., 2001) in user experience (UX) design to broaden understanding of pleasurable experience. Based on the list proposed by Sheldon (Sheldon et al., 2001), they used six psychological needs, i.e., relatedness, stimulation, competence, popularity, security, meaning, to represent facets of UX. Subsequently, Desmet and Pohlmeyer (2013) offered a more holistic approach, named positive design, bundling three key components of subjective well-being: pleasure, personal significance, and virtue. Positive design has been applied not only to product design, but also to the work domain for meaningful experiences at work (Lu and Roto, 2015).

After the initial conceptualization, positive design takes an outcome-orientation, focusing on the emotional aspect of PWB. Yoon et al. (2021) promoted design for positive emotions based on the typology of 25 positive emotions. Similarly, Dirin et al. (2022) consolidated 11 existential feelings and related emotions (e.g., feeling of engagement and emotions of boldness and confidence). They associated the existential feelings with phases of UX informed by the temporality of experience model (Karapanos et al., 2009). Perrino and Burmester (2020) expanded the temporal dimension of UX design by integrating time perspective theory (Zimbardo and Boyd, 1999). Their design framework aimed to realize temporal harmony that promotes users' PWB through evoking and balancing the past, present, and future positive experiences. Wu et al. (2022) devoted their attention to the positive emotional arousal when users are in negative moods. They integrated strategies for emotion regulation into five categories.

In summary, this research stream primarily aims at the “experience” stage of eudaimonia, i.e., positive experiences and emotions, but most studies did not provide certain design objects. Some authors viewed “meaning” and “excellence” as important components to make experiences positive (Desmet and Pohlmeyer, 2013; Lu and Roto, 2015). Others have been keen to encourage positive emotions, which would be indistinguishable from a hedonistic approach. Whereas most of them let design participants to think of emotional changes in the “self” (focal users) at “present” (or near future), one paper has attempted to extend their view to a long-term perspective “beyond present” (Perrino and Burmester, 2020).

3.4.2. Design for autonomous motivation and engagement

The second research stream has also, but more strongly, inherited self-determination theory (SDT) (Ryan and Deci, 2000, 2017). SDT as a meta-theory consists of six interrelated theories, including basic psychological needs satisfaction theory and other motivational theories (Ryan and Deci, 2017). The former theory elaborates on three basic psychological needs, namely competence, autonomy, and relatedness, universally essential for human thriving. The satisfaction of these needs leads to intrinsic motivation for the activity, distinguished from extrinsic motivation controlled by rewards, pressures, shame, etc. In our literature base, Szalma (2014) first explicitly employed SDT in proposing principles and procedural guidelines for motivational design in the domain of ergonomics. The guideline emphasizes the importance of task/environment analysis and interface design to prevent negative influences on competence, autonomy, and relatedness. At the same time, Calvo and Peters coined the term “positive computing” (Calvo and Peters, 2014) that systematizes well-being-supportive design for all technology. They offered a multidisciplinary review on the determinant factors of PWB in their book (Calvo and Peters, 2014), but recently have concentrated on SDT as the foundation of their detailed design methodology (Peters et al., 2018, 2020; Peters, 2022). Their core proposition, named METUX model, features the three basic psychological needs as reliable mediators of motivation, engagement, and PWB (Peters et al., 2018). In subsequent publications, they have investigated more practical design tools and heuristics based on the METUX model (Peters et al., 2020; Peters, 2022). Similarly, some other authors have identified design strategies and patterns based on SDT's conceptualizations of intrinsic motivation (Deterding, 2015) and basic psychological needs (Villalobos-Zúñiga and Cherubini, 2020).

The studies mentioned in this section are targeting autonomous motivation and engagement in technology use. To this end, SDT supplies a useful set of basic psychological needs to be satisfied. If those needs are satisfied, user “behaviors” in technology use become more intrinsically motivated. The need for autonomy and intrinsic motivation emphasizes “authenticity” in interactions with technology, whereas the need for competence refers to “excellence” and “growth” in activities using technology. Although the need of relatedness implies the presence of others, their design activities are directed toward improving behaviors of the focal user.

3.4.3. Design for positive activities and behavior change

The third research stream is a collection of four studies concerning behavior that have branched off from several sources. Ozkaramanli et al. (2017) built a bridge from positive design to behavioral design, spotlighting on dilemmas between an immediate pleasure and a long-term achievement. They found three design strategies to change user behaviors that threaten long-term goals. Bhattacharya et al. (2018) zoomed on to a pivotal moment in behavior change, defined as “the time when a person decides to adopt new behavior(s) for their personal well-being and/or becomes ready to make progress toward positive change” (Bhattacharya et al., 2018, p. 130). From literature and interviews, they structured a design space that forms interventions for people who have not yet been motivated to change their behavior. Klapperich et al. (2019) focused not on behavior change, but on enhancing daily practice of the users. Informed by social practice theory (Reckwitz, 2002), they proposed a design process that centers on gathering and analyzing positive everyday practices of the focal user, e.g., brewing coffee. The gathered practices “will serve as further inspirations to (re)design practices” (Reckwitz, 2002, p. 159) and supportive technologies. In addition, to foster sustained PWB by designing technology used in daily activities, Wiese et al. (2020) brought the knowledge of positive psychology interventions (Lyubomirsky, 2008), that is, intentional happiness-enhancing activities (e.g., expressing gratitude, savoring, etc.). In contrast to Klapperich et al. (2019), they took a theory-driven approach that incorporated 14 happiness-enhancing activities (theories) in their design process. Bentvelzen et al. (2022) concentrated on reflection, one of the important positive activities that nurture psychological well-being (Lyubomirsky, 2008; Ryan and Deci, 2017), and provided four design resource categories for supporting users' reflection.

As remarked above, this collection especially focuses on the “behavior” stage of eudaimonia because a positive experience comes from positive activities. This is similar to design for autonomous motivation and engagement, but this research stream is willing to proactively manipulate their behavior for the purpose of more “functioning” life. In terms of the four key elements, all could be covered depending on the behavior (change) actually targeted in the design, but the possible connotation would be “growth” as a change in behavior. It can be said that this research stream tends to see “beyond present” because changes in behavior require a long-term perspective and bring about sustained PWB.

3.4.4. Design for shared values and capabilities

The fourth research stream is more normative and broader than the others. It mainly stems from the capability approach and value sensitive design (VSD). First, VSD was originally proposed in Friedman (1996) with ethical concerns on the design of technology and has been developed further by many researchers. Values refer to desirable goals often shared by members of the society that guide their activities and serve as motivational components (Kheirandish et al., 2020). VSD is an approach to the design of technology that accounts for human values throughout the design process. Kheirandish et al. (2020) structured nine value clusters and developed educational design tools for students. The value clusters contain meaningfulness, personal development, and respect for oneself, which closely relate to eudaimonic well-being, and others such as status, justice, and ecology. It therefore could partially correspond to design for eudaimonia.

The capability approach was coined by the economist and philosopher Amartya Sen in the context of social justice (Sen, 1999). He argued that poverty and equity should be assessed by the extent of capabilities, not income and owned resources. The notion of capability has been described as the freedom that people have to be whom they want to be and do the activities that they wish to engage in Oosterlaken (2009), which stems from Aristotelian ethics. Subsequently, Nussbaum (2003) presented a more normative approach listing 10 basic capabilities, e.g., having good health, having emotions and emotional attachments, being able to play, etc. In design research, the capability approach was imported by Oosterlaken (2009). Steen (2016) advanced it by converting the Nussbaum's list of basic capabilities into a tangible design tool. With a specific concern on the design of complex legal systems (e.g., law, finance, and government services), Hagan and Özenç (2020) explored effective design patterns at the interface level that help people navigate the systems. The identified five design patterns can enhance people's legal capabilities, which in turn increase long-term well-being. Finally, the two streams of the capability approach and VSD have joined in their downstream. Jacobs (2020) criticized that VSD claims moral authority without founded ethical theories that provide grounds for its justification. To overcome this challenge, the author combined the capability approach as a substantive ethical theory and the methods of VSD, named capability sensitive design.

The studies in this research stream have highlighted both “orientation” and “behavior” stages of eudaimonic well-being, since they have paid much attention to which orientations one ought to develop. “Meaning” and “growth” have been explicitly intended, as shown in the value clusters (Kheirandish et al., 2020) and human capabilities (Hagan and Özenç, 2020; Jacobs, 2020). In addition, the target of design is no longer limited to technologies but sometimes zoomed out depicting a big picture of sociotechnical systems that enable and restrain people to achieve valuable beings and doings, i.e., “beyond tangible”.

3.5. Result 2: organizing design approaches

The design approaches identified in the literature were mapped as Table 3. Through the analysis, we identified five types of approaches: methods/techniques, design spaces, design tools, frameworks and heuristics. The following sections summarize each type of existing design methods.

TABLE 3

Table 3. Design approaches found in our review.

3.5.1. Methods and techniques

The first type of design approach offers practical instructions in a step by step manner to achieve particular design goals that can be applied in any design domain. Design procedures have been developed by scholars according to their concerns: seven steps of motivational design (Szalma, 2014), 5 steps in gameful design (Deterding, 2015), six steps for designing for well-being using the positive practices approach (Klapperich et al., 2019), three steps to design for temporal harmony (Perrino and Burmester, 2020), and a 3-step process to structure the logical design requirements from an abstract capability to concrete design requirements (Jacobs, 2020).

3.5.2. Design tools

Design tools are tangible objects for designers to ideate and record user experiences. Some of them suggest designers on how to conduct interviews and what questions to ask when gathering information from potential users (or focal actors in general). For example, the positive-practice canvas (Klapperich et al., 2019) provides a visual guide for a semi-structured interview and several key questions to find a positive daily practice and draw out contextual information about the practice. The time perspective persona (Perrino and Burmester, 2020) provides a framework for representing users with the information of demographics, personality, scenario, past positive experiences, present hedonistic experiences, and preference for the future. To help designers stay sensitive to human values during the design process, Kheirandish et al. (2020) introduced the value wheel and value word cards. Similarly, well-being design cards are aimed at designers to incorporate psychological needs (Peters et al., 2020) and user emotions in design (Yoon et al., 2021).

3.5.3. Design spaces

Design spaces map the solution space for a particular problem domain in terms of the design decisions to be made along with alternatives (Shaw, 2012). Such pre-defined design patterns can give designers inspirations and partially change design actions from creating to choosing. In most cases, the authors use a process of literature review and workshops/qualitative research to create specific design patterns [e.g., patterns to enhance user capabilities (Hagan and Özenç, 2020), strategies to navigate self-control dilemmas (Ozkaramanli et al., 2017) or 16 mechanisms to stimulate behavior (Wiese et al., 2020)], specific activities [e.g., 14 happiness-enhancing activities (Wiese et al., 2020) and 207 picture cards including activity and product/service (Kheirandish et al., 2020)] and design spaces [e.g., design space to catalyze pivotal moments (Bhattacharya et al., 2018)].

3.5.4. Frameworks

Frameworks present an organization of concepts to introduce a topic or explicate the problem domain, and suggest broad themes for design. For instance, the positive design framework (Desmet and Pohlmeyer, 2013) offers a bundle of three directions: design for pleasure, personal significance, and virtue. It is rather abstract, but Lu and Roto (2015) made it slightly specific by associating experiential goals in the work domain with the three directions. Another example is the METUX model (Peters et al., 2018) that differentiates between the different spheres-of-experience that can be affected by interactions with the designed technology and argues for a holistic approach to design. Wiese et al. (2020) framework for sustained well-being aims to fill the gap between positive experiences and sustained well-being by linking product features to specific behavioral support, which can be leveraged to engage users in positive activities.

3.5.5. Heuristics

Heuristics are easy to use checklists, underpinned by empirical research, which can be used to quickly and inexpensively evaluate prototypes. While heuristics for usability are well known, our literature review revealed two guidelines that have been curated to evaluate if design fulfills psychological needs. The first is the list of questions proposed by Szalma (2014), and a similar list of heuristics has more recently been proposed by Peters (2022). Deterding (2015) developed the lens of “intrinsic skill atoms” that suggests seven questions to quickly check for completeness of design considerations.

4. Discussion

Given that there are various approaches to design for PWB, which approach should one take, and when? In this section, we identify the commonalities and points of divergence between these tools, methods and frameworks, and based on an understanding of the process and factors behind PWB, we synthesize the various design approaches into a simple heuristic to aid designers in choosing the appropriate design tool.

4.1. On the state of design methods and frameworks for eudaimonia

From a theoretical perspective, the field of design is rich with suggestions and frameworks for designing for PWB. As Tables 2, 3 show, design for PWB has been understood through various conceptual lenses, and among those frameworks adhering to the eudaimonic view, only certain aspects of eudaimonic categories and core elements are considered. Our classification of the various methods, tools and frameworks aims to complete the picture and while doing so, also highlights some inconsistencies. First, there is a considerable divergence in the level of abstraction of the design activity these tools and methods intend to support. In some cases, they intend to inspire (e.g., Desmet and Pohlmeyer, 2013; Dirin et al., 2022) while in others they offer procedural guidance (e.g. Szalma, 2014; Perrino and Burmester, 2020); whereas some offer methods and patterns to arrive at concrete design prototypes [e.g. (Bhattacharya et al., 2018; Klapperich et al., 2019; Hagan and Özenç, 2020; Villalobos-Zúñiga and Cherubini, 2020; Bentvelzen et al., 2022)], others present heuristics to evaluate the design effort (Peters, 2022). Second, we also see a difference in the sources from which these frameworks and methods draw their content from. While (Szalma, 2014) and (Peters et al., 2018) are based on theory (e.g., self-determination theory) and attempt to operationalize it, others propose mining situated human practice and experience to finalize design ideas for prospective users (e.g., Klapperich et al., 2019), and some reviews summarize concrete design features (e.g., Villalobos-Zúñiga and Cherubini, 2020; Bentvelzen et al., 2022). Finally, there is also differentiating focus on the temporal aspect of human behavior and experience. Some frameworks focus on envisioning a user's future-self (e.g., Lu and Roto, 2015), others focus more on shaping the interaction in the present (e.g., Peters et al., 2018) and some rely on comprehending and reflecting past user experiences and pivotal moments [e.g., (Bhattacharya et al., 2018; Klapperich et al., 2019; Perrino and Burmester, 2020; Bentvelzen et al., 2022)].

We presume that these distinctions are not completely arbitrary, but are contingent on the existence and significance of existing user experiences/activities, as well as the design goals. A theory-based perspective is helpful because it is based on justified, objective past knowledge, and can serve as a first recourse in novel situations or when behavior change is the aim. As Figure 3 suggests, eudaimonic well-being can be viewed as a process, beginning from specific motivations, goals to performing specific behaviors that lead to particular experiences and feelings. This can be clarified using a goal hierarchy (Höchli et al., 2018). Values are superordinate goals, they are identity based, entail an extended temporal perspective, and are linked to a broad scope of context. These superordinate goals are realized through intermediate goals that correspond to “a general course of action bound to a certain behavioral context” (Höchli et al., 2018). At the lowest level are subordinate goals that precisely define what and how to do something.

Analogously, one can initiate the design activity in a future oriented, top-down manner, (e.g., Hagan and Özenç, 2020; Kheirandish et al., 2020) and bridge the gap between superordinate and intermediate goals by suggesting how the structure of an activity (or behavior) corresponds to a superordinate goal (or value) in a manner that facilitates internalization and endorsement by the self. Moreover, design patterns and strategies (e.g., Hagan and Özenç, 2020) can here serve as concrete suggestions that could help users meet subordinate goals, prompt pivotal moments (Bhattacharya et al., 2018) or help resolve dilemmas resulting from conflicting intermediate and superordinate goals (Ozkaramanli et al., 2017). The second step in Figure 3 refers to mechanisms whose presence during the behavior is necessary to enable PWB. Here, it is implied that intermediate goals that are endorsed by the participant, and, in the pursuit of which (via subordinate goals) the participant experiences events that are supportive of autonomy, competence and relatedness, contribute to well-being. Viewed in the top-down manner, a designer can rely on suggested patterns (Villalobos-Zúñiga and Cherubini, 2020), and in a prospective sense, check that the design provides adequate support for autonomy, competence and relatedness in the process, the material aspects of interaction (e.g., Peters, 2022) as well as opportunities for reflection (Bentvelzen et al., 2022).

The second case relates to (re)creating experiences that assume an existing activity context (or behavior) that may already be a well-established source of well-being within which users can express their imagined and lived experience, that is, their envisaged, perceived, and reconstructed experience (future-present-past) (Doherty and Doherty, 2018; Perrino and Burmester, 2020). Here, in a bottom-up manner, one can mine for the presence of particular behaviors (intermediate goals) and materials supporting both need satisfaction and superordinate goals that could form the basis of future products and applications (e.g., Klapperich et al., 2019), or ask users to articulate their imagined, future experience (Perrino and Burmester, 2020). At the same time, users' remembrances of the past are often colored by cognitive biases and, based on the design goal, may or may not be applicable as predictors of future choices in a given situation (Doherty and Doherty, 2018).

4.2. Designing assistance to support eudaimonia

In the context of our research focus, there are no specific design efforts or principles dictating the eudaimonic design of assistants or assistance applications. Rather, we think that a systematic mapping of existing approaches could provide various pathways to design.

As we mentioned previously, to enhance employee PWB, assistants ought to positively enrich an employee's possibilities of internalizing and achieving eudaimonic goals, and incorporate mechanisms that support them, among others, task/job significance (meaning), skill enhancement (excellence), learning new things (growth), crafting feedback (authenticity), opportunities to give/receive social support (meaning), utilization of strengths and skills (excellence), perceived control and autonomy (authenticity) and psychological safety. The choice of a particular design method, or framework, corresponds to the temporal perspective and level of goal hierarchy that the design process targets. To aid the choice of design methods, we have synthesized these facets in Figure 6. The nodes of the diagram represent the core elements of eudaimonia (orientation, behavior, and experience), and the actors (self and others). The edges denote the relationships between entities that can be supported via assistance. The role of the assistant can begin with helping the employee understand how a particular eudaimonic orientation (excellence, growth, meaning and authenticity) involves the self, how it contributes to the future-oriented self, whether it also involves others, and if yes, whom (Edges 1 and 2). For example, when it comes to work, excellence can be considered in terms of meeting standards of quality via one's skill, which involves the employee, but could also include it support from other, more experienced colleagues. Next, the assistance focuses on Edge 3 to support the employee to understand how a particular eudaimonic orientation can be achieved in an activity by setting intermediate goals (e.g. how is quality defined in terms of concrete standards of work tasks and how should the employee's skills be supported). Finally, the concrete activity of doing the task, the entities involved in a particular activity and the support one receives from the assistant (how), its structure and the events that originate in the form of interaction with the assistant and others are taken into consideration (Edges 4 & 5) in the form of measurable, subordinate goals and design of feedback. Proceeding further, the emphasis lies on supporting the employee to reflect and remember the experiences (Edge 6, here excellence, that is, the actual quality of work achieved) and provide ways for others (Edge 7, e.g. colleagues) to help the employee reflect and make connections between the felt experience and components of the activity and one's behavior (Edge 8) to prompt changes to be initiated, e.g. re-defining goals, clarifying problems or customizing assistant features.

FIGURE 6

Figure 6. Facets of eudaimonic design synthesized from our literature review: vertices represent the core elements and actors, whereas edges highlight the relationships between them as units of design and analysis. The edges are color coded to reflect their emphasis on the self, others, or the activity.

Table 4 consolidates the different themes each edge represents, and proposes design tools, patterns and heuristics that could be of use. Some methods or approaches, such as psychological needs, are arguably applicable in all situations, others could be used in a particular phase.

TABLE 4

Table 4. Various facets of supporting eudaimonic well–being through assistant design.

5. Conclusion

In this paper we have argued that the design of assistants or assistance applications could benefit both employees and organizations by moving beyond the classical outcomes of productivity and efficiency, and by adopting a more holistic, eudaimonic view of well-being which includes eudaimonic orientations (excellence, growth, meaning and authenticity) and psychological need fulfillment. In order to leverage existing design knowledge, we have systematically reviewed and organized different design models, frameworks, heuristics and tools. Finally, we have striven to complete the designers' tool set by connecting the various design methods to particular relationships between actors and core PWB entities and by suggesting guiding questions corresponding to each facet in order to facilitate future design activities.

5.1. Theoretical and practical contributions

The present article makes several contributions to the research communities of design and HCI. Although scholars have pointed out the importance of fostering workplace psychological well-being, no reviews until now have considered bringing together design methods, tools, patterns or frameworks to do so. Our first contribution is highlighting the implications of advancing the design of workplace assistance toward PWB and offering a consolidated set of options to aid developers and designers. Researchers could use the guiding questions and list of applicable design tools to inform their research efforts. The methods proposed here are not specific to designing assistance or to be used only in the workplace context, but could be extended to other design domains such as mobile apps, games, and human-robot interaction.

Second, it is the first paper that has gathered the diverse definitions and viewpoints through which design for PWB (and, EWB) has been proposed and/or achieved, revealing four research streams: design for positive experiences and emotions, design for autonomous motivation and engagement, design for positive activities and behavior change, and design for shared values and capabilities. This classification contributes to design research by identifying different design objects, outcomes, and orientations within the same overarching field of design. It would be helpful to theorize design for PWB, and useful for scholars to clarify their position within the research streams.

5.2. Limitations

While we have striven to carry out an extensive review with utmost care, we recognize that our review has some limitations. First, we took a deliberate but informed decision to focus on psychological well-being. While this provided us the opportunity to deeply study the design approaches in this area, we had to exclude reviews on physical wellness such as those by Orji and Moffatt (2018), Huang et al. (2019) and (Damen et al., 2020). Second, our review primarily focuses on promoting psychological well-being through the design of interactive artifacts. There are other reviews that summarize well established therapies, trainings and interventions that may be delivered digitally for enhancing workplace mental well-being (e.g Bartlett et al., 2019; Daniels et al., 2021), but which do not take up design as their object. A different review could address the possibility of identifying and organizing such methods to draw potential candidates for design. Finally, although activities form the core of mental well-being, our review does not engage with other theoretical frameworks such as activity theory (Kaptelinin and Nardi, 2009) or how domains and activities in a workplace context could be analyzed to find opportunities for enhancing psychological well-being. This could be the focus of future work, and we discuss some prospects next.

5.3. Other pieces of the puzzle

Along with the proposed design methods and approaches, there are several other areas which could be investigated further.

Other sources of design inspiration: An additional source of design inspiration, and one, which in our view, harmonizes well with the design process, especially in the eudaimonic sense, is that of psychological interventions, or activities that are deliberately designed to foster PWB (Lyubomirsky and Layous, 2013). We see these interventions as a part of a continuum of PWB related concepts, starting from the four core, but rather abstract themes of eudaimonia, followed by workplace drivers that have been demonstrated to enhance employee well-being, to concrete prescriptive interventions that could be implemented. The intervention approach offers actionable suggestions, since technology adoption at work occurs in the backdrop of existing work practices, which are already sources of workplace joy or frustration. Interventions are designed to bring about change in people's behaviors or emotional states in everyday life, and since assistants are intimately tied to work practices, they can serve as the technological mechanism to introduce them in the workplace. Furthermore, interventions offer us concrete, prescriptive methods rather than abstract concepts such as psychological need fulfillment. The framework proposed by Wiese et al. (2020) already includes positive activities and behavior change interventions, but there are also sources of interventions especially tailored for the work domain, for instance the job demands and resources (JDR) based job-crafting interventions (Demerouti, 2014) and positive-psychology interventions for resilience (Seligman et al., 2005). These could be mined for potential design patterns and strategies.

Existing domain knowledge and organizational constraints: In industrial contexts, it is the system and process characteristics that delineate the problem space within which workers act. Furthermore, depending on the level of manual involvement, there may be differentiated levels of worker experience and know-how necessary to complete the tasks. How do we extract existing know-how that workers already posses and input it into the design process? And how can expert knowledge be used to design assistants to observe, evaluate and provide feedback to novice workers? Here, we propose that tools for activity analysis (Kaptelinin and Nardi, 2009) and in the organizational sector, participatory domain modeling and collaborative learning workshops such as Event Storming (Brandolini, 2017) and machine learning techniques are crucial to better understand and capture the context of work and recognize opportunities to provide assistance. Still, the room for improving worker well-being through technological interaction may be constrained by the overall structure of the worker's activities and their relationship to broader organizational contexts, a view which is echoed in the analysis of eudaimonia in work contexts (der Kinderen and Khapova, 2021). Work design as an area of research is already well established, and may be used as an investigative lens to distinguish those aspects of work that can be enhanced with, or risk regressing, via digital assistance and digitalization (Parker and Grote, 2022).

Evaluating eudaimonic experiences using digital applications at work: Efficiency and productivity is relatively straightforward to measure—as completion time, error rate, or recall rate in an experimental setting. Cooper (Fisher, 2014) provides an overview of instruments for measuring hedonic and eudaimonic well-being at work. Other measures include flow and interest (Bakker, 2008), work motivation (Gagné et al., 2015), scales for meaning of and in work (Both-Nwabuwe et al., 2017), psychological need satisfaction (Reis et al., 2000; Peters et al., 2018), meaning experience and elevation (Huta and Ryan, 2010), authentic pride (Tracy and Robins, 2007) and goal commitment (Seijts and Latham, 2000). At present, most of the discussion about user experience centers around technologies designed for leisure, and the work domain has largely been neglected (Bargas-Avila and Hornbæk, 2012). Research shows that positive experiences brought about by need fulfillment in work contexts differ from those in leisure contexts (Tuch et al., 2016). Investigating the hedonic and eudaimonic aspects of user experience at work would advance our knowledge in this domain.

Our hope is that the overview of design approaches and the synthesized toolkit presented here enables designers and developers to design assistants that not are only targeted at improving employee productivity, but also their psychological well-being.

Author contributions

HD and YN contributed to the writing of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the SAIL (SustAInable Life-cycle of Intelligent Socio-Technical Systems) project funded by the Ministry of Culture and Science of the State of North Rhine Westphalia, Germany under Grant number NW21-059C. This work was also supported by JSPS KAKENHI Grant number 22K12705.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Aiello, A., and Tesi, A. (2017). Psychological well-being and work engagement among italian social workers: examining the mediational role of job resources. Soc. Work Res. 41, 73–84. doi: 10.1093/swr/svx005

CrossRef Full Text | Google Scholar

Apt, W., Schubert, M., and Wischmann, S. (2018). Digitale assistenzsysteme-perspektiven und herausforderungen für den einsatz in industrie und dienstleistungen. Berlin: Institut für Innovation und Technik (iit) in der VDI/VDE Innovation+ Technik GmbH.

Google Scholar

Aringer-Walch, C., Besserer, S., and Pokorni, B. (2018). “Nutzerbedürfnisse an ein digitales Assistenzsystem im Kontext der Industrie 4.0,” in Weidner, R., and Karafillidis, A. (eds). Konferenzband zur 3. Transdisziplinären Konferenz qTechnische Unterstützungssysteme, die die Menschen wirklich wollenq 2018 (Hamburg: Helmut-Schmidt University).

Google Scholar

Aristoteles (1962). The Nicomachean Ethics. Englewood Cliffs, NJ: Prentice Hall.

Google Scholar

Bakker, A. B. (2008). The work-related flow inventory: construction and initial validation of the WOLF. J. Vocat. Behav. 72, 400–414. doi: 10.1016/j.jvb.2007.11.007

CrossRef Full Text | Google Scholar

Bargas-Avila, J., and Hornbæk, K. (2012). Foci and blind spots in user experience research. Interactions 19, 24–27. doi: 10.1145/2377783.2377790

CrossRef Full Text | Google Scholar

Bartlett, L., Martin, A., Neil, A. L., Memish, K., Otahal, P., Kilpatrick, M., et al. (2019). A systematic review and meta-analysis of workplace mindfulness training randomized controlled trials. J. Occup. Health Psychol. 24, 108–126. doi: 10.1037/ocp0000146

PubMed Abstract | CrossRef Full Text | Google Scholar

Bentvelzen, M., Woaniak, P. W., Herbes, P. S., Stefanidi, E., and Niess, J. (2022). “Revisiting reflection in HCI: four design resources for technologies that support reflection,” in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (New York, NY: ACM), 6. doi: 10.1145/3517233

CrossRef Full Text | Google Scholar

Bhattacharya, A., Kolovson, S., Sung, Y. C., Eacker, M., Chen, M., Munson, S. A., et al. (2018). Understanding pivotal experiences in behavior change for the design of technologies for personal well-being. J. Biomed. Inform. 79, 129–142. doi: 10.1016/j.jbi.2018.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Both-Nwabuwe, J. M. C., Dijkstra, M. T. M., and Beersma, B. (2017). Sweeping the floor or putting a man on the moon: how to define and measure meaningful work. Front. Psychol. 8, 1658. doi: 10.3389/fpsyg.2017.01658

PubMed Abstract | CrossRef Full Text | Google Scholar

Brandolini, A. (2017). Introducing Event Storming: An Act of Deliberate Collective Learning. Victoria: LeanPub.

Google Scholar

Broeck, A., Vansteenkiste, M., Witte, H., Soenens, B., and Lens, W. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction scale. J. Occup. Organ. Psychol. 83, 981–1002. doi: 10.1348/096317909X481382

CrossRef Full Text | Google Scholar

Budd, J. W., and Spencer, D. A. (2015). Worker well-being and the importance of work: Bridging the gap. Eur. J. Industrial Relat. 21, 181–196. doi: 10.1177/0959680114535312

CrossRef Full Text | Google Scholar

Calvo, R. A., and Peters, D. (2014). Positive Computing: Technology for Well-being and Human Potential. Cambridge: MIT press. doi: 10.7551/mitpress/9764.001.0001

CrossRef Full Text | Google Scholar

Cedefop–European Centre for the Development of VocationalCedefop (2015). “European Centre for the development of vocational training,” in Skill Shortages and gaps in European Enterprises: Striking a Balance Between Vocational Education and Training and the Labour Market. Publications Office, LU. Available online at: https://data.europa.eu/doi/10.2801/042499

Google Scholar

Chen, P. Y. (2014). Work and Well-being. Number Volume III in Well-being: A Complete Reference Guide. John Wiley & Sons. Malden, MA: Chichester, West Sussex.

Google Scholar

Damen, I., Brombacher, H., Lallemand, C., Brankaert, R., Brombacher, A., van Wesemael, P., et al. (2020). A scoping review of digital tools to reduce sedentary behavior or increase physical activity in knowledge workers. Int. J. Environ. Res. Public Health 17, 499. doi: 10.3390/ijerph17020499

PubMed Abstract | CrossRef Full Text | Google Scholar

Daniels, K., Watson, D., Nayani, R., Tregaskis, O., Hogg, M., Etuknwa, A., et al. (2021). Implementing practices focused on workplace health and psychological well-being: a systematic review. Social Sci. Med. 277, 113888. doi: 10.1016/j.socscimed.2021.113888

PubMed Abstract | CrossRef Full Text | Google Scholar

Demerouti, E. (2014). Design your own job through job crafting. Eur. Psychol. 19, 237–247. doi: 10.1027/1016-9040/a000188

CrossRef Full Text | Google Scholar

der Kinderen, S., and Khapova, S. N. (2021). “Positive psychological well-being at work: the role of Eudaimonia,” in The Palgrave Handbook of Workplace Well-being, Dhiman, S. K. (ed). Cham: Springer International Publishing, 141–168. doi: 10.1007/978-3-030-30025-8_79

PubMed Abstract | CrossRef Full Text | Google Scholar

Desmet, P. M., and Pohlmeyer, A. E. (2013). Positive design: an introduction to design for subjective well-being. Int. J. Design. 7, 3.

Google Scholar

Deterding, S. (2015). The lens of intrinsic skill atoms: a method for gameful design. Human-Comp. Interact. 30, 294–335. doi: 10.1080/07370024.2014.993471

CrossRef Full Text | Google Scholar

Dhiman, H., and Rocker, C. (2019). “Worker assistance in smart production environments using pervasive technologies,” in 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Kyoto, Japan: IEEE, 95-100. doi: 10.1109/PERCOMW.2019.8730771

CrossRef Full Text | Google Scholar

Dhiman, H., Wächter, C., Fellmann, M., and Röcker, C. (2022). Intelligent assistants: conceptual dimensions, contextual model, and design trends. Bus. Inf. Syst. Eng. 64, 645–665. doi: 10.1007/s12599-022-00743-1

CrossRef Full Text | Google Scholar

Diener, E., Suh, E. M., Lucas, R. E., and Smith, H. L. (1999). Subjective well-being: three decades of progress. Psychol. Bull. 125, 276–302. doi: 10.1037/0033-2909.125.2.276

CrossRef Full Text | Google Scholar

Dirin, A., Nieminen, M., and Laine, T. H. (2022). Feelings of being for mobile user experience design. Int. J. Hum. Comput. Interact. 0, 1–21. doi: 10.1080/10447318.2022.2108964

CrossRef Full Text | Google Scholar

Doherty, K., and Doherty, G. (2018). The construal of experience in HCI: understanding self-reports. Int. J. Hum. Comput. Stud. 110, 63–74. doi: 10.1016/j.ijhcs.2017.10.006

CrossRef Full Text | Google Scholar

Drabe, D., Hauff, S., and Richter, N. F. (2015). Job satisfaction in aging workforces: an analysis of the USA, Japan and Germany. Int. J. Human Res. Manage. 26, 783–805. doi: 10.1080/09585192.2014.939101

CrossRef Full Text | Google Scholar

Fisher, C. D. (2014). “Conceptualizing and measuring well-being at work,” in Well-being, Cooper, C. L. (ed). Chichester, UK: John Wiley & Sons, Ltd. 1–25. doi: 10.1002/9781118539415.wbwell018

CrossRef Full Text | Google Scholar

Fowers, B. J. (2016). “Aristotle on eudaimonia: on the virtue of returning to the source,” in Handbook of Eudaimonic Well-being, Vittersø, J. (ed). Cham: Springer International Publishing, 67–83. doi: 10.1007/978-3-319-42445-3_4

CrossRef Full Text | Google Scholar

Friedman, B. (1996). Value-sensitive design. Interactions 3, 16–23. doi: 10.1145/242485.242493

CrossRef Full Text | Google Scholar

Gabrielova, K., and Buchko, A. A. (2021). Here comes generation z: millennials as managers. Bus. Horiz. 64, 489–499. doi: 10.1016/j.bushor.2021.02.013

CrossRef Full Text | Google Scholar

Gagné, M., Forest, J., Vansteenkiste, M., Crevier-Braud, L., van den Broeck, A., Aspeli, A. K., et al. (2015). The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries. Eur. J. Work Organizat. Psychol. 24, 178–196. doi: 10.1080/1359432X.2013.877892

CrossRef Full Text | Google Scholar

Guerin, K. R., Lea, C., Paxton, C., and Hager, G. D. (2015). “A framework for end-user instruction of a robot assistant for manufacturing,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA: IEEE, 6167–6174. doi: 10.1109/ICRA.2015.7140065

CrossRef Full Text | Google Scholar

Hackman, J. R., and Oldham, G. R. (1976). Motivation through the design of work: test of a theory. Organ. Behav. Hum. Perform. 16, 250–279. doi: 10.1016/0030-5073(76)90016-7

CrossRef Full Text | Google Scholar

Hagan, M., and Özenç, F. K. (2020). A design space for legal and systems capability: interfaces for self-help in complex systems. Design Issues 36, 61–81. doi: 10.1162/desi_a_00604

CrossRef Full Text | Google Scholar

Hahn, V. C., Frese, M., Binnewies, C., and Schmitt, A. (2012). Happy and proactive? The role of hedonic and eudaimonic well–being in business owners' personal initiative. Entrepreneurship Theory Pract. 36, 97–114. doi: 10.1111/j.1540-6520.2011.00490.x

CrossRef Full Text | Google Scholar

Hassenzahl, M., Diefenbach, S., and Göritz, A. (2010). Needs, affect, and interactive products - Facets of user experience. Interact. Comput. 22, 353–362. doi: 10.1016/j.intcom.2010.04.002

CrossRef Full Text | Google Scholar

Heikkilä, P., Honka, A., Kaasinen, E., and Väänänen, K. (2021). Quantified factory worker: field study of a web application supporting work well-being and productivity. Cognition, Technol. & Work 23, 831–846. doi: 10.1007/s10111-021-00671-2

CrossRef Full Text | Google Scholar

Höchli, B., Brügger, A., and Messner, C. (2018). How focusing on superordinate goals motivates broad, long-term goal pursuit: a theoretical perspective. Front. Psychol. 9, 1879. doi: 10.3389/fpsyg.2018.01879

PubMed Abstract | CrossRef Full Text | Google Scholar

Holford, W. D. (2019). The future of human creative knowledge work within the digital economy. Futures 105, 143–154. doi: 10.1016/j.futures.2018.10.002

CrossRef Full Text | Google Scholar

Huang, Y., Benford, S., and Blake, H. (2019). Digital interventions to reduce sedentary behaviors of office workers: scoping review. J. Med. Internet Res. 21, e11079. doi: 10.2196/11079

PubMed Abstract | CrossRef Full Text | Google Scholar

Humphrey, S. E., Nahrgang, J. D., and Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: a meta-analytic summary and theoretical extension of the work design literature. J. Appl. Psychol. 92, 1332–1356. doi: 10.1037/0021-9010.92.5.1332

PubMed Abstract | CrossRef Full Text | Google Scholar

Huta, V., and Ryan, R. M. (2010). Pursuing pleasure or virtue: the differential and overlapping well-being benefits of hedonic and eudaimonic motives. J. Happiness Stud. 11, 735–762. doi: 10.1007/s10902-009-9171-4

CrossRef Full Text | Google Scholar

Huta, V., and Waterman, A. S. (2014). Eudaimonia and its distinction from hedonia: developing a classification and terminology for understanding conceptual and operational definitions. J. Happiness Stud. 15, 1425–1456. doi: 10.1007/s10902-013-9485-0

CrossRef Full Text | Google Scholar

Isham, A., Mair, S., and Jackson, T. (2021). Worker well-being and productivity in advanced economies: re-examining the link. Ecol. Econ. 184, 106989. doi: 10.1016/j.ecolecon.2021.106989

CrossRef Full Text | Google Scholar

Jacobs, N. (2020). Capability sensitive design for health and well-being technologies. Sci. Eng. Ethics 26, 3363–3391. doi: 10.1007/s11948-020-00275-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Jones, N., Hill, C., and Henn, C. (2015). Personality and job satisfaction: their role in work-related psychological well-being. J. Psychol. Africa. 25, 297–304. doi: 10.1080/14330237.2015.1078086

CrossRef Full Text | Google Scholar

Kafaee, M., Ansarian, Z., Taqavi, M., and Heidari, S. (2021). Design for well-being: the fourth generation of technology development. Technol. Soc. 67, 101775. doi: 10.1016/j.techsoc.2021.101775

CrossRef Full Text | Google Scholar

Kaptelinin, V., and Nardi, B. A. (2009). Acting with Technology: Activity Theory and Interaction Design. Cambridge: MIT Press.

Google Scholar

Karapanos, E., Zimmerman, J., Forlizzi, J., and Martens, J.-B. (2009). “User experience over time: an initial framework,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA: ACM), 729–738. doi: 10.1145/1518701.1518814

CrossRef Full Text | Google Scholar

Kheirandish, S., Funk, M., Wensveen, S., Verkerk, M., and Rauterberg, M. (2020). HuValue: a tool to support design students in considering human values in their design. Int. J. Technol. Design Educ. 30, 1015–1041. doi: 10.1007/s10798-019-09527-3

CrossRef Full Text | Google Scholar

Klapperich, H., Laschke, M., Hassenzahl, M., Becker, M., Cürlis, D., Frackenpohl, T., et al. (2019). “Mind the gap: a social practice approach to well-being-driven design,” in Design for Well-being. London: Routledge, 154–169. doi: 10.4324/9781315121383-11

CrossRef Full Text | Google Scholar

Klippert, J., Niehaus, M., and Gerst, D. (2018). Mit digitaler Technologie zu Guter Arbeit? Erfahrungen mit dem Einsatz digitaler Werker-Assistenzsysteme. WSI-Mitteilungen 71, 235–240. doi: 10.5771/0342-300X-2018-3-235

CrossRef Full Text | Google Scholar

Kropman, D., Appel-Meulenbroek, R., Bergefurt, L., and LeBlanc, P. (2022). The business case for a healthy office; a holistic overview of relations between office workspace design and mental health. Ergonomics 66, 1–18. doi: 10.1080/00140139.2022.2108905

PubMed Abstract | CrossRef Full Text | Google Scholar

Laschke, M., Braun, C., Neuhaus, R., and Hassenzahl, M. (2020). “Meaningful technology at work - a reflective design case of improving radiologists' well-being through medical technology,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu HI USA: ACM, 1–12. doi: 10.1145/3313831.3376710

CrossRef Full Text | Google Scholar

Lu, Y., and Roto, V. (2015). Evoking meaningful experiences at work—a positive design framework for work tools. J. Engineering Des. 26, 99–120. doi: 10.1080/09544828.2015.1041461

CrossRef Full Text | Google Scholar

Lyubomirsky, S. (2008). The How of Happiness: a Scientific Approach To Getting The Life You Want. New York: Penguin.

Google Scholar

Lyubomirsky, S., and Layous, K. (2013). How do simple positive activities increase well-being? Curr. Dir. Psychol. Sci. 22, 57–62. doi: 10.1177/0963721412469809

CrossRef Full Text | Google Scholar

Mark, B. G., Rauch, E., and Matt, D. T. (2021). Worker assistance systems in manufacturing: a review of the state of the art and future directions. J. Manufact. Syst. 59, 228–250. doi: 10.1016/j.jmsy.2021.02.017

CrossRef Full Text | Google Scholar

Martela, F., and Sheldon, K. M. (2019). Clarifying the concept of well-being: psychological need satisfaction as the common core connecting eudaimonic and subjective well-being. Rev. General Psychol. 23, 458–474. doi: 10.1177/1089268019880886

CrossRef Full Text | Google Scholar

Mekler, E. D., and Hornbæk, K. (2016). “Momentary pleasure or lasting meaning?: distinguishing eudaimonic and hedonic user experiences,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. San Jose California USA: ACM, 4509–4520. doi: 10.1145/2858036.2858225

CrossRef Full Text | Google Scholar

Merrick, A. D., Grieve, A., and Cogan, N. (2017). Psychological impacts of challenging behaviour and motivational orientation in staff supporting individuals with autistic spectrum conditions. Autism 21, 872–880. doi: 10.1177/1362361316654857

PubMed Abstract | CrossRef Full Text | Google Scholar

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group, T. P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. PLoS Med. 6, 1–6. doi: 10.1371/journal.pmed.1000097

PubMed Abstract | CrossRef Full Text | Google Scholar

Nussbaum, M. C. (2003). Capabilities as fundamental entitlements: sen and social justice. Fem. Econ. 9, 33–59. doi: 10.1080/1354570022000077926

CrossRef Full Text | Google Scholar

Oades, L. G., Steger, M. F., and Fave, A. D. (2017). The Wiley Blackwell Handbook of the Psychology of Positivity and Strengths-Based Approaches at Work. Hoboken: Wiley/ Blackwell. doi: 10.1002/9781118977620

CrossRef Full Text | Google Scholar

Oosterlaken, I. (2009). Design for development: a capability approach. Design Issues. 25, 91–102. doi: 10.1162/desi.2009.25.4.91

CrossRef Full Text | Google Scholar

Orji, R., and Moffatt, K. (2018). Persuasive technology for health and wellness: state-of-the-art and emerging trends. Health Informat. J. 24, 66–91. doi: 10.1177/1460458216650979

PubMed Abstract | CrossRef Full Text | Google Scholar

Ozkaramanli, D., Özcan, E., and Desmet, P. (2017). Long-term goals or immediate desires? Introducing a toolset for designing with self-control dilemmas. Design J. 20, 219–238. doi: 10.1080/14606925.2017.1272831

CrossRef Full Text | Google Scholar

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. Int. J. Surg. 88, 105906. doi: 10.1016/j.ijsu.2021.105906

PubMed Abstract | CrossRef Full Text | Google Scholar

Parker, S. K., and Grote, G. (2022). Automation, algorithms, and beyond: why work design matters more than ever in a digital world. Appl. Psychol. 71, 1171–1204. doi: 10.1111/apps.12241

CrossRef Full Text | Google Scholar

Pearce, K., Huta, V., and Voloaca, M. (2021). How eudaimonic and hedonic orientations map onto seeing beyond the “me, now, and tangible”. J. Posit. Psychol. 16, 610–621. doi: 10.1080/17439760.2020.1791943

CrossRef Full Text | Google Scholar

Perrino, C. H., and Burmester, M. (2020). Designing for temporal harmony: exploring the well-being concept for designing the temporal dimension of user experience. Multimodal Technol. Interact. 4, 1–28. doi: 10.3390/mti4030066

CrossRef Full Text | Google Scholar

Peters, D. (2022). Well-being supportive design–research-based guidelines for supporting psychological well-being in user experience. Int. J. Human-Comp. Interact. doi: 10.1080/10447318.2022.2089812

CrossRef Full Text | Google Scholar

Peters, D., Ahmadpour, N., and Calvo, R. A. (2020). Tools for well-being-supportive design: features, characteristics, and prototypes. Multimodal Technol. Interact. 4, 1–19. doi: 10.3390/mti4030040

CrossRef Full Text | Google Scholar

Peters, D., Calvo, R. A., and Ryan, R. M. (2018). Designing for motivation, engagement and well-being in digital experience. Front. Psychol. 9, 797. doi: 10.3389/fpsyg.2018.00797

PubMed Abstract | CrossRef Full Text | Google Scholar

Pinheiro, M., Ivandic, I., and Razzouk, D. (2017). “The economic impact of mental disorders and mental health problems in the workplace,” in Mental Health Economics, Razzouk, D. (ed). Cham: Springer International Publishing, 415–430. doi: 10.1007/978-3-319-55266-8_28

CrossRef Full Text | Google Scholar

Reckwitz, A. (2002). Toward a theory of social practices: a development in culturalist theorizing. Eur. J. Social Theory 5, 243–263. doi: 10.1177/13684310222225432

CrossRef Full Text | Google Scholar

Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., and Ryan, R. M. (2000). Daily well-being: the role of autonomy, competence, and relatedness. Pers. Soc. Psychol. Bull. 26, 419–435. doi: 10.1177/0146167200266002

CrossRef Full Text | Google Scholar

Riva, G., Ba nos, R. M., Botella, C., Wiederhold, B. K., and Gaggioli, A. (2012). Positive technology: using interactive technologies to promote positive functioning. Cyberpsychol. Behav. Social Network. 15, 69–77. doi: 10.1089/cyber.2011.0139

PubMed Abstract | CrossRef Full Text | Google Scholar

Rothausen, T. J., Larson, A. M., and Christenson, S. M. (2012). Expanding desirability of turnover: hedonic and eudaimonic job satisfactions and well-being. Acad. Manage.Proc. 2012:11481. doi: 10.5465/AMBPP.2012.11481abstract

CrossRef Full Text | Google Scholar

Ryan, R. M., and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78. doi: 10.1037/0003-066X.55.1.68

PubMed Abstract | CrossRef Full Text | Google Scholar

Ryan, R. M., and Deci, E. L. (2001). On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Annu. Rev. Psychol. 52, 141–166. doi: 10.1146/annurev.psych.52.1.141

PubMed Abstract | CrossRef Full Text | Google Scholar

Ryan, R. M., and Deci, E. L. (2017). Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. New York: Guilford Publications. doi: 10.1521/978.14625/28806

CrossRef Full Text | Google Scholar

Ryan, R. M., Huta, V., and Deci, E. L. (2008). Living well: a self-determination theory perspective on eudaimonia. J. Happiness Stud. 9, 139–170. doi: 10.1007/s10902-006-9023-4

CrossRef Full Text | Google Scholar

Ryan, R. M., and Martela, F. (2016). “Eudaimonia as a way of living: connecting Aristotle with self-determination theory,” in Handbook of Eudaimonic Well-being, Vittersø, J. (ed). Cham: Springer International Publishing, 109—122. doi: 10.1007/978-3-319-42445-3_7

CrossRef Full Text | Google Scholar

Sattar, M. A., Rasheed, M. I., Khan, I. U., Tariq, H., and Iqbal, J. (2017). Why adaptable individuals perform better: the role of orientation to happiness. Aust. J. Career Dev. 26, 134–141. doi: 10.1177/1038416217724516

CrossRef Full Text | Google Scholar

Schroth, H. (2019). Are you ready for gen z in the workplace? Calif. Manage. Rev. 61, 5–18. doi: 10.1177/0008125619841006

CrossRef Full Text | Google Scholar

Schwerdtfeger, B., and Klinker, G. (2008). “Supporting order picking with Augmented Reality,” in 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. Cambridge, UK: IEEE, 91–94. doi: 10.1109/ISMAR.2008.4637331

PubMed Abstract | CrossRef Full Text | Google Scholar

Seijts, G. H., and Latham, G. P. (2000). “The construct of goal commitment: measurement and relationships with task performance,” in Problems and Solutions in Human Assessment, Goffin, R. D. and Helmes, E. (eds.). Boston, MA: Springer US, 315–332. doi: 10.1007/978-1-4615-4397-8_14

CrossRef Full Text | Google Scholar

Seligman, M. E. P., Steen, T. A., Park, N., and Peterson, C. (2005). Positive psychology progress: empirical validation of interventions. Am. Psychol. 60, 410–421. doi: 10.1037/0003-066X.60.5.410

PubMed Abstract | CrossRef Full Text | Google Scholar

Sen, A. (1999). Development as Freedom. Oxford: Oxford University Press.

Google Scholar

Shaw, M. (2012). The role of design spaces. IEEE Software 29, 46–50. doi: 10.1109/MS.2011.121

CrossRef Full Text | Google Scholar

Sheldon, K. M., Elliot, A. J., Kim, Y., and Kasser, T. (2001). What is satisfying about satisfying events? Testing 10 candidate psychological needs. J. Personal. Social Psychol. 80, 325–339. doi: 10.1037/0022-3514.80.2.325

PubMed Abstract | CrossRef Full Text | Google Scholar

Smit, S., Tacke, T., Lund, S., Manyika, J., and Thiel, L. (2020). The Future of Work in Europe. Brisbane: McKinsey Global Institute.

Google Scholar

Steen, M. (2016). Organizing design-for-well-being projects: using the capability approach. Design Issues 32, 4–15. doi: 10.1162/DESI_a_00412

CrossRef Full Text | Google Scholar

Sull, D., Sull, C., and Zweig, B. (2022). Toxic culture is driving the great resignation. MIT Sloan Manage. Rev. 63, 1–9.

Google Scholar

Szalma, J. L. (2014). On the application of motivation theory to human factors/ergonomics: motivational design principles for human-technology interaction. Human Fact. 56, 1453–1471. doi: 10.1177/0018720814553471

PubMed Abstract | CrossRef Full Text | Google Scholar

Thun, J., Größler, A., and Miczka, S. (2007). The impact of the demographic transition on manufacturing: Effects of an ageing workforce in German industrial firms. Journal of Manufacturing Technology Management 18:985–999. doi: 10.1108/17410380710828299

CrossRef Full Text | Google Scholar

Tracy, J. L., and Robins, R. W. (2007). The psychological structure of pride: a tale of two facets. J. Pers. Soc. Psychol. 92, 506–525. doi: 10.1037/0022-3514.92.3.506

PubMed Abstract | CrossRef Full Text | Google Scholar

Tranfield, D., Denyer, D., and Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manage. 14, 207–222. doi: 10.1111/1467-8551.00375

CrossRef Full Text | Google Scholar

Tuch, A. N., Schaik, P. V., and Hornbæk, K. (2016). Leisure and Work, Good and Bad: The Role of Activity Domain and Valence in Modeling User Experience. ACM Trans. Comput. Hum. Interact. 23:1–32. doi: 10.1145/2994147

CrossRef Full Text | Google Scholar

Tur, G., Stolcke, A., Voss, L., Peters, S., Hakkani-Tur, D., Dowding, J., et al. (2010). The CALO meeting assistant system. IEEE Trans. Audio Speech Lang. Processing 18, 1601–1611. doi: 10.1109/TASL.2009.2038810

CrossRef Full Text | Google Scholar

Turban, D. B., and Yan, W. (2016). Relationship of eudaimonia and hedonia with work outcomes. J. Manag. Psychol. 31, 1006–1020. doi: 10.1108/JMP-07-2015-0271

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Katwyk, P. T. (2012). The Changing Workforce Demographics in Asia Pacific. Oxford: Oxford University Press. doi: 10.1093/oxfordhb/9780195385052.013.0058

CrossRef Full Text | Google Scholar

Villalobos-Zú niga, G., and Cherubini, M. (2020). Apps that motivate: a taxonomy of app features based on self-determination theory. Int. J. Human Computer Stud. 140, 102449. doi: 10.1016/j.ijhcs.2020.102449

CrossRef Full Text | Google Scholar

Vittersø, J. (2016). “The most imporant idea in the world: An introduction,” in Handbook of Eudaimonic Well-being,Vittersø, J.(ed.). Cham: Springer International Publishing, 1–24. doi: 10.1007/978-3-319-42445-3_1

CrossRef Full Text | Google Scholar

Warnhoff, K., and De Paiva Lareiro, P. (2019). “Skill development on the shop floor - heading to a digital divide?,” in Weizenbaum Conference. Berlin: Weizenbaum Institute for the Networked Society Version Number: 1.

Google Scholar

Waterman, A. S., Schwartz, S. J., and Conti, R. (2008). The Implications of two conceptions of happiness (hedonic enjoyment and eudaimonia) for the understanding of intrinsic motivation. J. Happiness Stud. 9:41–79. doi: 10.1007/s10902-006-9020-7

CrossRef Full Text | Google Scholar

Wiese, L., Pohlmeyer, A. E., and Hekkert, P. (2020). Design for sustained well-being through positive activities—a multi-stage framework. Multimodal Technol. Interact. 4, 1–25. doi: 10.3390/mti4040071

CrossRef Full Text | Google Scholar

Wrzesniewski, A., and Dutton, J. E. (2001). Crafting a job: revisioning employees as active crafters of their work. Acad. Manage. Rev. 26, 179. doi: 10.2307/259118

CrossRef Full Text | Google Scholar

Wu, C., Li, X., and Dong, C. (2022). The internet design framework for improvement of users' positive emotions. KSII Transact. Internet Informat. Syst. 16, 8. doi: 10.3837/tiis.2022.08.014

CrossRef Full Text | Google Scholar

Yoon, J. K., Pohlmeyer, A. E., Desmet, P. M., and Kim, C. (2021). Designing for positive emotions: issues and emerging research directions. Design Journal 24, 167–187. doi: 10.1080/14606925.2020.1845434

CrossRef Full Text | Google Scholar

Zimbardo, P. G., and Boyd, J. N. (1999). Putting time in perspective: a valid, reliable individual-differences metric. J. Pers. Soc. Psychol. 77, 1271–1288. doi: 10.1037/0022-3514.77.6.1271

CrossRef Full Text | Google Scholar