Human future traits and cultural equity in AI-augmented work: a multilevel psychological framework for human value creation

Abstract

Generative artificial intelligence is shifting the boundary between human and machine work deep into knowledge-intensive domains that were previously considered “non-automatable,” calling into question existing future-skills frameworks that are largely expert-based, conceptually heterogeneous, and only weakly validated. In this article, we develop a psychologically grounded framework that specifies which enduring human capacities remain central in AI-augmented workplaces and how they scale up to organizational outcomes via cultural mechanisms. Rather than relying on broad and often unsystematic skills lists, we propose to focus on a bundle of cross-cutting human trait dimensions—future time perspective, complex problem solving in ill-defined problem spaces, cognitive flexibility, volitional implementation strength, and social outcome capability—for which robust empirical links to performance, adaptability, and health have been documented across contexts. We integrate these “Human Future Traits” into the concept of Cultural Equity, understood as an intangible but performance-relevant cultural asset that is reflected in stable patterns of learning, cooperation, error robustness, and technology integration. Building on this, we outline a multilevel model in which individual traits give rise to emergent cultural properties that, in turn, shape innovation, transformation speed, and performance in AI-augmented organizations. Finally, we sketch a research agenda using multilevel, longitudinal, and quasi-experimental designs around AI implementation to empirically test the proposed pathway from Human Future Traits to Cultural Equity and organizational outcomes.

1 Introduction

The rapid diffusion of generative artificial intelligence is shifting the boundary between human and machine work deep into knowledge-intensive domains that were previously considered “non-automatable” (World Economic Forum, 2020, 2025). In many organizations, AI systems now perform not only routine information processing, but also increasingly sophisticated forms of analysis, pattern recognition, and content generation (Brynjolfsson and McAfee, 2014; Haenlein et al., 2019). This raises a deceptively simple but practically urgent question: which specifically human psychological capacities remain central for value creation when much of what knowledge workers used to do can be automated or AI-augmented?

Over the past decade, policy reports and consultancy frameworks have sought to answer this question by assembling lists of “future skills,” often highlighting complex problem solving, critical thinking, creativity, and social competencies as key to employability in the digital age (World Economic Forum, 2020, 2025). Prominent examples include the World Economic Forum's Future of Jobs reports, which have strongly shaped public and organizational discourse about the future of work. However, these frameworks are largely based on expert surveys, exhibit considerable conceptual heterogeneity, and have only rarely been subjected to prospective empirical validation (Vista, 2020; Teyssier-Roberge et al., 2025; Kain et al., 2024). As a result, they provide limited guidance on which psychological characteristics actually predict individual and organizational success in AI-rich environments.

A parallel response to the same disruption has emphasized AI-specific competencies, most prominently AI literacy—the knowledge and skills required to understand, evaluate, and use AI systems. While clearly relevant for participation in AI-augmented work, this construct remains psychometrically immature and heterogeneous, with a recent systematic review documenting substantial variation in conceptualization and measurement quality across AI-literacy scales (Lintner, 2024). Its relationship to effective use is also not monotonic: across multiple studies and countries, lower AI literacy predicts greater receptivity to AI, because less knowledgeable users are more likely to experience AI as “magical” (Tully et al., 2025). At the same time, a growing human-AI interaction literature shows that the value humans add is conditional rather than guaranteed—combinations of humans and AI do not automatically outperform the better of the two and yield synergy mainly under specific conditions (Vaccaro et al., 2024)—and that effective use imposes substantial metacognitive demands of goal formulation, output evaluation, and self-assessment (Tankelevitch et al., 2024). Taken together, these findings suggest that tool-specific skills, while necessary enabling conditions, are insufficient to explain who reliably realizes valuable results in AI-augmented work, motivating a shift from rapidly obsolescing skills toward a small set of enduring, cross-domain human dispositions.

In this article, we argue that advancing the psychology of work in the age of AI requires moving beyond broad and partly ad hoc skills lists toward a more sharply defined, empirically anchored set of cross-cutting human trait dimensions. Specifically, we propose the Human Impact Factor (HIF) as a formative bundle of “Human Future Traits” that support result realization under uncertainty in AI-augmented work systems. Building on existing research on future time perspective, complex problem solving, cognitive flexibility, volitional implementation strength, and social outcome capability (Andre et al., 2018; Kooij et al., 2018; Greiff and Funke, 2017; Duckworth and Gross, 2014; Woolley et al., 2010; Riedl et al., 2021), we develop a multilevel framework that links individual traits to an emergent cultural asset we term Cultural Equity and, ultimately, to organizational innovation, transformation speed, and performance.

Our contribution is both theory-building and theory-extending in nature. Following (Colquitt and Zapata-Phelan 2007) distinction between theory building and theory testing, we introduce HIF as a novel higher-order construct that has the potential to reconceptualize established pre-AI frameworks of human capital, employability, and work performance. At the same time, our framework remains firmly anchored in existing theoretical traditions by integrating and reinterpreting established constructs within the context of AI-augmented work. Rather than replacing prior theory, we seek to elaborate and extend it, demonstrating how foundational insights from the psychology of work can be reassembled into a coherent explanatory framework for understanding human value creation in the age of AI.

This Hypothesis and Theory article pursues three main aims. First, we synthesize conceptual and empirical limitations of current future-skills frameworks and articulate result orientation under uncertainty as a functional criterion for selecting human trait dimensions (Vista, 2020; Teyssier-Roberge et al., 2025). Second, we introduce HIF as a psychologically grounded, formative construct and differentiate it from related concepts such as psychological capital, adaptability, and learning agility (Avey et al., 2011; DeRue et al., 2012; Pulakos et al., 2000). Third, we integrate HIF into a multilevel model of Cultural Equity and derive testable propositions and a research agenda outlining how future work can empirically assess human value creation in AI-augmented organizations.

2 From future-skills lists to result orientation under uncertainty

Over the last years, numerous “future skills” frameworks have been proposed by international organizations, consultancies, and educational bodies to guide workforce development and curriculum design in the face of technological change (World Economic Forum, 2020, 2025; European Commission, 2018; Taguma and Barrera, 2019). These frameworks typically converge on a familiar set of broad competences such as complex problem solving, critical thinking, creativity, collaboration, and digital literacy as key to employability in a rapidly changing world (World Economic Forum, 2020; Ehlers, 2020). At the same time, their conceptual scope, level of abstraction, and operationalization differ markedly, resulting in a heterogeneous landscape of overlapping but not fully compatible skill taxonomies (Kain et al., 2024; Kotsiou et al., 2022; Teyssier-Roberge et al., 2025).

Recent reviews and data-driven analyses highlight at least three limitations of this landscape. First, many frameworks are primarily based on expert judgement and stakeholder consultations rather than systematically derived from psychological theory or longitudinal evidence on career and organizational outcomes (Vista, 2020; Kain et al., 2024; Kotsiou et al., 2022). Second, they often blur distinctions between relatively stable dispositions, trainable skills, and domain-specific knowledge, which complicates both measurement and intervention design (Kain et al., 2024; Teyssier-Roberge et al., 2025). Third, their predictive validity under conditions of widespread AI adoption remains largely unknown: it is unclear which of the proposed skills will retain a comparative human advantage when AI systems can already perform many forms of analytic and even creative work (World Economic Forum, 2025; Vista, 2020).

An important step forward has been the shift from purely normative skill lists toward empirical assessments of how different skills support transitions across occupations and changing labor market conditions. For example, Vista (2020) proposes a transferability-value metric that quantifies the extent to which specific skills facilitate job transitions within and across occupational fields, showing that certain cross-cutting competences—such as complex problem solving, judgment and decision making, and social perceptiveness—serve as hubs in occupational networks. Similarly, scoping reviews of future-skills frameworks identify meta-categories of broadly transferable competences that appear across diverse models and contexts (Kain et al., 2024; Kotsiou et al., 2022). These contributions underscore that not all skills are equally valuable for navigating uncertain career trajectories and that cross-cutting, transferable capabilities play a disproportionate role.

Building on this work, we argue that the core challenge in AI-augmented work environments is not merely possessing a particular list of competences, but reliably realizing valuable results under conditions of uncertainty. By result orientation under uncertainty we refer to the capacity of individuals and collectives to move from intentions to jointly endorsed outcomes in dynamic, partly opaque, and technologically mediated contexts. This functional perspective foregrounds a specific bundle of psychological mechanisms—anticipating and prioritizing future consequences, framing and structuring ill-defined problems, flexibly adapting mental models, sustaining goal-directed effort, and coordinating social interaction—which together determine whether AI-augmented work systems produce robust and responsible results (Andre et al., 2018; Duckworth and Gross, 2014; Greiff and Funke, 2017; Woolley et al., 2010; Riedl et al., 2021).

In the remainder of the paper, we therefore shift from enumerating “skills” to specifying a formative set of Human Future Traits that directly contribute to result realization under uncertainty and that are likely to retain a comparative human advantage vis-à-vis advancing AI capabilities. We conceptualize this bundle as the Human Impact Factor (HIF) and derive explicit selection criteria that distinguish HIF from broader, less functionally focused future-skills lists.

3 The human impact factor: concept and selection criteria

In line with this functional perspective, we propose the Human Impact Factor (HIF) as a formative bundle of Human Future Traits that jointly underpin result orientation under uncertainty in AI-augmented work systems. Rather than positing a single latent factor that explains all variance in performance across contexts, HIF captures a set of distinct but functionally aligned trait dimensions that together enable individuals and collectives to move from intentions to robust outcomes in dynamic, technologically mediated environments. Conceptually, HIF specifies the psychological “backbone” of human value creation in AI-rich work: it delineates which enduring capacities remain uniquely human, where they complement AI systems, and how they can be systematically developed and measured in organizations (Andre et al., 2018; Kooij et al., 2018; Duckworth and Gross, 2014).

3.1 Approach to theory development

This is a Hypothesis and Theory article; it develops and integrates existing evidence rather than reporting new empirical data. We follow an integrative, theory-building approach: we synthesize established trait literatures with the emerging literature on AI-augmented and human-AI work to derive a formative construct and a set of testable propositions. Candidate dimensions were drawn from psychological literatures with cumulative, preferably meta-analytic or large-scale assessment evidence—future time perspective (Kooij et al., 2018; Andre et al., 2018), complex problem solving (Greiff and Funke, 2017), self-control and grit (Duckworth and Gross, 2014), collective intelligence and social processes (Woolley et al., 2010; Riedl et al., 2021)—and were then filtered through three explicit inclusion criteria: (C1) a direct, theoretically and empirically supported contribution to result realization under uncertainty in AI-augmented contexts; (C2) a comparative human advantage relative to advancing AI; and (C3) conceptualization as a relatively stable, cross-domain disposition assessable with established or emerging psychometric instruments. Frameworks such as psychological capital, learning agility, and broad 21st-century skills taxonomies were not adopted as the organizing structure because they fail one or more of these criteria—through conceptual heterogeneity, conflation of predictors with outcomes, or the absence of a single functional focus (see Section 5 and Table 1). All propositions are theoretically derived and are offered for empirical test in the research agenda; we make no claim that they are already established.

We define HIF as the formative combination of five Human Future Traits: future time perspective, complex problem solving in ill-defined problem spaces, cognitive flexibility, volitional implementation strength, and social outcome capability. Each of these dimensions has been shown to predict meaningful indicators of performance, adaptability, and wellbeing across educational and work settings, often above and beyond general cognitive ability and broad personality traits (Greiff and Funke, 2017; Woolley et al., 2010; Riedl et al., 2021; Avey et al., 2011). At the same time, they differ in their proximal functions: future time perspective provides a temporal and normative frame for goal setting; complex problem solving structures ill-defined situations into tractable decision spaces; cognitive flexibility enables adaptive reframing and integration of conflicting information; volitional implementation strength sustains goal-directed effort under distraction and setback; and social outcome capability coordinates joint action and accountability in interdependent systems. HIF thus does not assume a single underlying psychological essence, but a configuration of complementary dispositions that jointly support result realization under uncertainty.

To justify the selection of these five traits and distinguish HIF from broader skills catalogs, we articulate three explicit selection criteria. First, each dimension must make a direct, theoretically and empirically supported contribution to result realization under uncertainty in AI-augmented contexts—that is, it must help actors translate intentions into outcomes when goals, constraints, and consequences are partially unknown or shifting (Andre et al., 2018; Greiff and Funke, 2017; Woolley et al., 2010). Second, each dimension must retain a comparative human advantage relative to advancing AI capabilities, either because it relies on embodied, normative, or relational judgement that is difficult to automate, or because it governs the coordination between humans and AI systems (Funke et al., 2017; Chen et al., 2025). Third, each dimension must be conceptualizable as a relatively stable, cross-domain disposition or capability cluster that generalizes across tasks and roles and can be assessed with established or emerging psychometric instruments (Kooij et al., 2018; Avey et al., 2011; Duckworth and Gross, 2014).

Taken together, these criteria differentiate HIF from existing future-skills frameworks in two respects. Substantively, HIF narrows the focus to a small set of theoretically grounded traits that are directly tied to the functional problem of result realization under uncertainty, rather than aggregating a wide array of desirable skills. Methodologically, it treats HIF as a formative construct whose components are selected based on their joint coverage of key psychological functions, not as interchangeable indicators of a single latent factor.

3.2 Inclusion and exclusion of candidate dimensions

Applying the aforementioned criteria yields a deliberately parsimonious set of five Human Future Traits and, equally importantly, excludes several competencies that feature prominently in future-skills discourse. Because HIF is a formative construct, its components are not selected to provide exhaustive coverage of all desirable human attributes, but to jointly cover the psychological functions required for result realization under uncertainty. A candidate is therefore excluded when it is (a) better understood as an outcome or process produced by the five traits rather than an upstream disposition, (b) a trainable, tool-bound skill that lacks cross-domain trait stability and mature measurement, or (c) functionally already covered by one or more retained dimensions. We illustrate this logic for four frequently proposed candidates—creativity, critical thinking, AI literacy, and ethical judgment (Table 1).

Creativity is not retained as a separate pillar for two reasons. Conceptually, it is heterogeneous—spanning divergent thinking, creative self-efficacy, and creative achievement—and is better treated as an outcome of the interplay between cognitive flexibility and complex problem solving than as a distinct upstream disposition (Wu and Koutstaal, 2020; Kenett et al., 2018). Functionally, the comparative human advantage in raw idea generation is also eroding: generative AI raises individual creative output while reducing the collective diversity of ideas (Doshi and Hauser, 2024), so the durable human contribution lies less in producing novelty than in selecting, framing, and evaluating it—functions already captured by the retained dimensions.

Critical thinking is not retained as a separate dimension because it is conceptually contested as a stable, separately measurable trait and overlaps substantially with general cognitive ability, cognitive flexibility, and complex problem solving. It is most usefully understood as a process enacted within problem framing and the flexible evaluation of information—including the critical appraisal of AI outputs and the avoidance of automation bias. Recent evidence reinforces this process character: in GenAI-assisted work, the enactment and effort of critical thinking are situationally moderated by users' confidence in themselves and in the system (Lee et al., 2025), and habitual AI use is associated with greater cognitive offloading and weaker critical thinking (Gerlich, 2025). HIF therefore captures the dispositional substrate of critical evaluation through cognitive flexibility and complex problem solving rather than positing critical thinking as an additional trait.

AI literacy is excluded because it fails the stability-and-measurement criterion. It is a trainable, tool-bound competency that tracks specific, rapidly changing systems and therefore lacks the cross-domain temporal stability expected of a Human Future Trait, and its measurement remains heterogeneous and psychometrically immature (Lintner, 2024). Its relationship to effective AI use is also non-monotonic, with lower literacy predicting greater receptivity to AI (Tully et al., 2025). We therefore treat AI literacy not as a constituent trait but as an enabling boundary condition and a candidate moderator of the relationship between HIF and human-AI collaboration quality (see H3).

Ethical judgment is not added as a separate dimension because the exercise of normative and ethical judgment in AI-augmented decisions is already operationalised within two retained traits. Complex problem solving in ill-defined spaces explicitly encompasses framing and reconciling conflicting values in normatively charged, contested decisions, and social outcome capability encompasses negotiating ethical boundaries, distributing accountability, and involving affected stakeholders in AI-related choices. Treating ethical judgment as a distinct trait would also conflict with the formative logic of HIF, as it is highly context- and culture-dependent and lacks an established, cross-domain trait measure suitable for the work setting.

Treating ethical judgment as a distinct trait would conflict with the formative logic of HIF. Research in moral psychology suggests that ethical judgment is strongly shaped by contextual and cultural factors rather than representing a stable, cross-situational disposition (Bentahila et al., 2021; Graham et al., 2016). Moreover, existing instruments typically assess moral reasoning or judgment within specific dilemma contexts rather than providing a universally accepted cross-domain trait measure applicable to workplace settings (Lind, 2006).

More broadly, this exclusion logic clarifies the scope of HIF's coverage claim. HIF does not assert that the five traits exhaust the human capacities relevant to work, but that they are functionally sufficient for the specific problem of result realization under uncertainty in AI-augmented systems—a claim that is, by design, empirically testable (see H1–H3 and the research agenda).

3.3 The comparative human advantage as a dynamic bounded claim

Because a central premise of HIF is that the five traits retain a comparative human advantage relative to advancing AI, this claim warrants careful qualification. We do not assert an absolute or permanent advantage. Consistent with evidence that human-AI combinations do not automatically outperform the stronger of the two and yield synergistic gains mainly under specific conditions (Vaccaro et al., 2024), and that human performance with AI support depends on whether a task lies inside or outside AI's effective capability frontier (Dell'Acqua et al., 2023), we conceptualize the human advantage as task-located and dynamic. It is strongest where goals must be negotiated, constraints are uncertain, evaluation criteria are contested, and outcomes must be socially endorsed—conditions under which framing, normative judgment, calibration, and coordination, rather than computation, are decisive. Where tasks are well defined and data-rich, the human advantage narrows and is appropriately ceded to AI.

This framing also identifies the specific locus of human value as AI capability advances. As routine computation, pattern recognition, and even substantial parts of idea generation are automated (Doshi and Hauser, 2024; Brynjolfsson et al., 2025), the residual human contribution shifts upward toward meta-level regulation: deciding which problems to pose, calibrating trust in AI outputs against evidence rather than surface cues, and assuming accountability for consequences. The trust literature shows that humans systematically mis-calibrate—displaying algorithm aversion after observing errors (Dietvorst et al., 2015) yet algorithm appreciation under other conditions (Logg et al., 2019), with trust often driven by factors unrelated to actual AI performance (Kaplan et al., 2023; Glikson and Woolley, 2020; Qin et al., 2025). HIF isolates precisely the dispositions that support this regulatory layer. We therefore treat the comparative human advantage as a testable, boundary-conditioned proposition rather than a fixed premise, and revisit its temporal stability in the Limitations.

4 Human future traits: theoretical foundations

Building on the selection criteria outlined above, we identify five Human Future Traits that jointly constitute the Human Impact Factor: future time perspective, complex problem solving in ill-defined problem spaces, cognitive flexibility, volitional implementation strength, and social outcome capability. Each of these dimensions has a distinct theoretical tradition and established measurement approaches, and meta-analytic and large-scale assessment research links them to important outcomes such as achievement, performance, adaptability, and wellbeing across educational and work contexts (Kooij et al., 2018; Valiente, 2014; Taguma and Barrera, 2019; Martin and Rubin, 1995; Duckworth and Gross, 2014; Woolley et al., 2010; Riedl et al., 2021). In the following subsections, we briefly review the conceptual foundations and key empirical findings for each trait and specify its unique contribution to the functional problem of realizing valuable results under uncertainty in AI-augmented work systems.

4.1 Future time perspective

Future time perspective (FTP) refers to the tendency to organize present action in light of longer-term goals and anticipated future consequences (Strathman et al., 1994). Instruments such as the Consideration of Future Consequences scale and occupational time perspective measures capture individual differences in how strongly people let future outcomes guide their current decisions. Meta-analytic and longitudinal studies show that FTP is positively related to self-regulation, learning behavior, and performance in educational and work contexts (Andre et al., 2018; Kooij et al., 2018). In occupational settings, a broader and more positive future time perspective has been linked to stronger work commitment, greater performance orientation, and lower turnover intentions (Rudolph et al., 2018).

Emerging research further suggests that FTP is specifically relevant for innovation-related behaviors and proactive engagement with new technologies. Employees with a pronounced future focus more often initiate change-oriented behaviors, assume leadership roles, and adopt new technologies proactively (Mrisho, 2023). At the team level, shared future orientation is associated with higher creative performance because teams are more willing to explore alternative scenarios, consider long-term consequences, and accept short-term costs for long-term gains (Hoppe et al., 2023). Studies on strategic decision making indicate that organizations whose leaders systematically scan for future signals, develop scenarios, and anticipate technological trends are more likely to identify and exploit digital and AI-related opportunities early (Pahlavanyali, 2024).

As the temporal and normative anchor of the framework, future time perspective provides the frame for result realization under uncertainty. It shapes which futures are considered relevant, how intertemporal trade-offs are resolved, and how AI-enabled options are evaluated in light of desired long-term outcomes. By foregrounding human, value-laden anticipation rather than purely data-driven forecasting, FTP addresses precisely those aspects of goal setting and prioritization that cannot easily be delegated to AI systems, thereby fulfilling HIF's selection criteria of causal contribution, comparative human advantage, and cross-domain relevance.

4.2 Complex problem solving in ill-defined problem spaces

Complex problem solving in ill-defined problem spaces denotes the ability to develop effective solutions for novel, multifaceted situations in which many variables interact, information is incomplete, and goals may be ambiguous or partly conflicting. Classic work in the tradition of complex and dynamic problem solving characterizes such problems by high complexity, interconnectedness, dynamics, intransparency, and polytely and shows that they pose qualitatively different demands than well-structured tasks (Dörner, 1996; Funke, 2010, 2012). Research using computer-simulated microworlds and large-scale assessments indicates that complex problem solving, while correlated with general intelligence, captures unique variance in learning and performance outcomes and is therefore regarded as a distinct competence domain (Wüstenberg et al., 2012; Greiff et al., 2013; Greiff and Funke, 2017). In international assessments such as PISA and related OECD programs, problem solving in technology-rich environments predicts employment prospects, income, and occupational mobility above basic literacy and numeracy skills (Taguma and Barrera, 2019).

In AI-augmented contexts, the role of complex problem solving changes qualitatively. AI systems are particularly powerful when problems are well defined, goals and constraints are explicitly specified, and large datasets are available—for example in forecasting, pattern recognition, or optimization tasks. Humans, by contrast, retain a comparative advantage in ill-defined situations where goals must be negotiated, constraints are uncertain, and evaluation criteria are contested, such as in strategic trade-offs, ethical dilemmas, or politically sensitive decisions (Funke, 2010; Funke et al., 2017). In these settings, the core challenge is not merely to compute an optimal solution, but to frame the problem, surface and reconcile different stakeholder perspectives, and decide which trade-offs are acceptable (Palmer, 2024). Research on teams and collective intelligence suggests that collaborative problem solving, particularly the ability to integrate distributed knowledge and iteratively refine problem frames, is a key predictor of group performance in complex tasks (Woolley et al., 2010; Riedl et al., 2021). Empirically, the human contribution in AI-assisted problem solving is conditional: gains concentrate on ill-defined tasks and turn negative once tasks exceed AI's effective frontier (Vaccaro et al., 2024; Dell'Acqua et al., 2023).

Functionally, complex problem solving in ill-defined spaces transforms uncertainty into workable decision spaces. It connects the anticipatory impulses of future time perspective with cognitive flexibility and social outcome capability by operationalizing goals, generating and structuring options, and enabling collective decisions in ambiguous, often AI-mediated environments. Unlike narrow forms of problem solving that can be delegated to algorithms, this dimension focuses on framing, evaluation, and accountability in conflict-laden, normatively charged problem situations—areas in which human judgement remains indispensable. In doing so, it directly satisfies HIF's selection criteria of causal contribution to result realization under uncertainty, comparative human advantage, and cross-domain relevance.

4.3 Cognitive flexibility

Cognitive flexibility refers to the capacity to shift perspectives, adapt thinking and behavior to changing situational demands, and reorganize mental representations in response to new information. It is commonly assessed with self-report measures such as the Cognitive Flexibility Scale, which captures awareness of alternatives, willingness to adapt, and perceived efficacy in flexible responding, as well as with performance-based tasks targeting set-shifting and task-switching (Martin and Rubin, 1995; Martin and Anderson, 1998). Research across developmental and educational contexts identifies cognitive flexibility as a core component of executive functioning that supports planning, problem solving, and learning and contributes to academic achievement (Diamond, 2013; Taguma and Barrera, 2019).

A growing body of work links cognitive flexibility to creativity, innovation, and adaptive performance in work settings. Flexible individuals are better able to switch between categories and strategies, to combine disparate ideas, and to revise their approaches when feedback indicates that a chosen path is ineffective, which in turn facilitates creative idea generation and evaluation (Wu and Koutstaal, 2020; Kenett et al., 2018). Studies in applied contexts show that employees high in cognitive flexibility adapt more quickly to new tasks, roles, and technologies and exhibit higher adaptive performance in dynamic environments (Oprins et al., 2018; Rincón-Campos et al., 2019). At the team level, flexible members enhance collective intelligence by helping groups shift between exploration and exploitation, integrate diverse perspectives, and adjust strategies in response to unexpected events (Woolley et al., 2010; Ouellette et al., 2024). Research on managerial cognition suggests that cognitive flexibility in decision-makers is associated with higher-quality strategic decisions and a better balance between habitual and deliberative decision modes (Hodgkinson and Healey, 2011).

In AI-augmented work, cognitive flexibility plays a specific role at the human-AI interface. While AI systems can simulate multiple options or strategies, they lack the ability to decide when and how to switch strategies in a context-sensitive, normatively informed, and socially embedded manner. Studies on human-AI interaction indicate that flexible individuals are more capable of critically evaluating AI outputs, selectively adopting or rejecting recommendations, and recombining AI-generated suggestions with their own knowledge in creative ways (Siqi-Liu and Egner, 2020; Woodcock, 2023; Chen and Xiao, 2025). Cognitive flexibility thus helps prevent unreflective automation bias and supports the construction of shared mental models in human-AI teams. Calibrated reliance is non-trivial because humans mis-calibrate trust in both directions—aversion after observing errors and over-appreciation elsewhere—often on grounds unrelated to actual AI performance (Dietvorst et al., 2015; Logg et al., 2019; Kaplan et al., 2023; Qin et al., 2025).

Cognitive flexibility, in turn, operates as a switchboard between stability and change. It enables actors to move between established routines and novel strategies, to integrate conflicting information, and to reframe AI-generated proposals into contextually appropriate and ethically acceptable decisions. In this way, it provides a distinctly human form of context sensitivity and meaning-making that current AI systems cannot replicate and, as a relatively stable, cross-domain disposition, fulfills HIF's selection criteria of causal contribution, comparative human advantage, and generality across roles and tasks.

4.4 Volitional implementation strength

Volitional implementation strength denotes the capacity to pursue intended goals over extended periods despite internal and external obstacles. It builds on constructs such as action orientation, self-control, and grit, which capture individuals' ability to shield goal pursuit from distractions, regulate impulses, and maintain effort in the face of setbacks (Kuhl, 1992; Forstmeier and Rüddel, 2008; Duckworth and Gross, 2014). Longitudinal studies show that self-control predicts academic achievement, occupational status, income, health, and reduced problem behavior, sometimes as strongly as or more strongly than intelligence or socioeconomic status (Moffitt et al., 2011; Duckworth and Gross, 2014; De Ridder et al., 2012). Grit—defined as perseverance and passion for long-term goals—adds incremental predictive value for career success and engagement beyond cognitive ability and demographic variables (Duckworth et al., 2007; Lechner et al., 2019).

In organizational contexts, volitional resources have been linked to performance, engagement, and successful adaptation to change. Meta-analytic evidence on strengths and psychological capital interventions indicates that fostering perseverance, self-regulation, and related resources increases work engagement and job performance (Avey et al., 2011; Luthans et al., 2015; Vásquez-Pailaqueo et al., 2025). At the same time, research on technology-related distraction shows that constant digital interruptions undermine self-regulation and sustained effort, contributing to lower engagement and higher strain (Orhan et al., 2021; Van der Merwe et al., 2023). Studies on skill obsolescence and career adaptability suggest that individuals high in grit and volitional persistence are more likely to proactively acquire new skills, reinterpret technological disruption as a learning opportunity, and navigate occupational transitions successfully (Aljofan et al., 2024).

In AI-augmented work, volitional implementation strength becomes particularly salient because AI systems can greatly expand the space of conceivable options and scenarios. While AI can generate analyses, recommendations, and forecasts, it cannot decide which options should be pursued, assume responsibility for those decisions, or sustain the effort required to implement them over time. This creates a risk of “paralysis by analysis,” in which organizations generate ever more information without translating it into consistent action. Volitional implementation strength addresses this implementation gap by ensuring that decisions are not only made but also carried through in the face of uncertainty, competing demands, and setbacks. This is sharpened by AI's tendency to expand options and by the metacognitive load of supervising AI, which raises the premium on disciplined follow-through (Tankelevitch et al., 2024).

Volitional implementation strength supplies the framework's execution engine that turns the options generated by future time perspective, complex problem solving, and cognitive flexibility into coherent action sequences. It maintains goal-directed effort under distraction, supports persistence during change processes, and anchors AI-supported planning in sustained human agency. As a relatively stable, cross-domain disposition with documented links to a wide range of performance indicators, it meets HIF's selection criteria of causal contribution to result realization under uncertainty, comparative human advantage, and generality across tasks and roles.

4.5 Social competence oriented toward results

Social outcome capability encompasses those social and communicative skills that are specifically oriented toward achieving joint results in interdependent settings, including perspective taking, constructive communication, cooperation, and conflict management (Kanning, 2009). Unlike broader notions of sociability or likability, it focuses on behaviors that enable individuals and teams to align goals, coordinate actions, and resolve disagreements in ways that support shared task accomplishment. Instruments such as inventories of social competence and empathy scales show that these capabilities contribute to success and wellbeing in both private and professional domains and are rated by employers as at least as important as technical expertise (Kanning, 2009; Clausen et al., 2019).

Research on team performance and collective intelligence highlights the central role of social processes for group effectiveness. Studies by Woolley et al. (2010) and Riedl et al. (2021) demonstrate that team performance is better predicted by social sensitivity, balanced conversational turn taking, and the ability to integrate diverse perspectives than by average individual intelligence. Work on psychological safety and social capital further shows that relational climates characterized by trust, reciprocity, and shared norms foster learning, innovation, and adaptation (Edmondson, 1999, 2023; Clausen et al., 2019). Prosocial motivation—wanting to benefit others—has been linked to higher team performance in highly interdependent tasks, as it supports helping behaviors, coordination, and persistence in the face of difficulties (Grant, 2007; Grant and Berg, 2011).

In AI-augmented organizations, social outcome capability becomes a key interface between human actors and technical systems. It underpins the negotiation of AI use, roles, responsibilities, and ethical boundaries—for example, decisions about which tasks to automate, who remains accountable for AI-supported decisions, and how affected stakeholders are involved in these choices. Social outcome capability is also critical for establishing and maintaining psychological safety around AI, enabling employees to speak up about AI errors, discuss model limitations, and suggest improvements without fear of negative consequences (Edmondson, 1999, 2023). Recent work on human-AI teams indicates that misunderstandings and opaque AI outputs can erode trust and communication efficiency, whereas robust social processes and high-quality team relationships help buffer these effects and support sustained collaboration (Bedué and Fritzsche, 2022; Chen and Zhang, 2025). Trust calibration and psychological safety are themselves socially constructed, making relational competence a precondition for sound human-AI oversight (Glikson and Woolley, 2020; Edmondson, 1999).

Finally, social outcome capability forms the social backbone of result realization under uncertainty. It coordinates the contributions of individuals and AI systems, distributes accountability, and translates individual-level traits into collective patterns of learning, cooperation, and responsible AI use. By enabling human actors to negotiate meaning, responsibility, and ethics in socio-technical systems—capabilities that current AI cannot reproduce—it clearly fulfills HIF's selection criteria of causal contribution, comparative human advantage, and relevance across a wide range of organizational roles and contexts.

5 Differentiation from related constructs and frameworks

5.1 Relations to psychological capital, adaptability, and proactivity

HIF is most readily confused with Psychological Capital (PsyCap), which integrates hope, efficacy, resilience, and optimism (HERO) as a higher-order, state-like positive resource (Luthans et al., 2007; Avey et al., 2011). The constructs differ first in measurement architecture. PsyCap is modeled reflectively: the four facets are interchangeable manifestations of one underlying resource, are expected to covary strongly, and their unique variance is treated as error. HIF, by contrast, is formative: its five dimensions are constituents rather than indicators, are not assumed to be highly intercorrelated, and each carries criterion-relevant variance that a reflective composite would discard (on formative vs. reflective specification, see Jarvis et al., 2003; Diamantopoulos and Winklhofer, 2001). Consequently, HIF preserves information about distinct psychological functions—temporal, problem-structuring, adaptive, volitional, and social—that a single positive-resource factor cannot represent.

The constructs also differ in functional scope. The HERO facets are predominantly motivational and affective: hope, efficacy, and optimism are expectancy and confidence appraisals, and resilience concerns recovery from adversity. In terms of the classic motivation-volition distinction, PsyCap is located on the pre-decisional, motivational side of the action sequence (Heckhausen and Gollwitzer, 1987; Kuhl, 1992). HIF additionally spans the post-decisional, volitional region through volitional implementation strength, which governs the shielding and enactment of intentions against distraction and setback (Gollwitzer, 1999)—a function PsyCap does not contain. HIF further incorporates cognitive-ability content absent from PsyCap: complex problem solving carries variance beyond general intelligence (Wüstenberg et al., 2012) and cognitive flexibility indexes executive set-shifting. Two individuals with identical PsyCap may therefore differ substantially in their capacity to frame an ill-defined problem, reframe under conflicting information, sustain implementation, and coordinate a joint result—precisely the capacities that determine result realization in AI-augmented work.

These differences imply a specific, testable relationship rather than redundancy. We position HIF as a more dispositional, upstream substrate from which state-like resources such as PsyCap can be built, rather than as an alternative label for them; HIF dimensions plausibly support the development of hope, efficacy, and optimism but are not reducible to them. We therefore predict that HIF explains incremental variance in adaptive performance and human-AI collaboration quality over and above PsyCap, because its complex-problem-solving, cognitive-flexibility, volitional, and social-outcome content lies outside the HERO space. We note one boundary of the distinction: future time perspective shares a temporal element with hope and optimism, but it denotes the cognitive structuring and consideration of future consequences (Strathman et al., 1994; Zimbardo and Boyd, 1999) rather than positive outcome expectancy, and is therefore conceptually separable.

5.2 Relations to learning agility and 21st-century skills

Learning agility describes the ability and willingness to learn from experience and apply it in novel situations (DeRue et al., 2012). It overlaps with HIF in emphasizing cross-cutting adaptation, but it has been criticized for conflating predictors (e.g., curiosity, cognitive ability), processes (e.g., feedback seeking), and outcomes (e.g., performance in new roles) under a single label (DeRue et al., 2012). HIF avoids this conflation by construction: it specifies enduring dispositions with established measures, separates them from the outcomes they predict, and binds inclusion to one functional criterion. Where learning agility names a desirable adaptive profile, HIF identifies the specific dispositional inputs that are expected to produce it.

HIF shares with learning agility and 21st-century skills an emphasis on cross-cutting capabilities that facilitate adaptation and performance in dynamic environments, but it differs in two important ways. First, HIF is explicitly grounded in established psychological trait literatures with validated measures and robust evidence linking each dimension to performance, adaptability, and wellbeing across contexts (Kooij et al., 2018; Duckworth and Gross, 2014; Greiff and Funke, 2017; Woolley et al., 2010). Second, HIF is defined by a clear functional criterion—result realization under uncertainty in AI-augmented work systems—and a small number of explicit selection criteria, whereas many 21st-century skills frameworks aggregate a wide range of desirable attributes without specifying why particular competences are included or how they jointly address a specific functional challenge.

5.3 Adaptive performance

Adaptive performance is a behavioral taxonomy of how effectively individuals adjust to changing task, social, and technological demands (Pulakos et al., 2000, 2002). It is not a trait bundle but a criterion—an outcome. HIF and adaptive performance therefore occupy different positions in the nomological network: HIF specifies the dispositional antecedents—problem structuring, flexible reframing, volitional follow-through, and social coordination—whose configuration is expected to produce adaptive performance, which we model as a dependent variable (H1a) rather than as a competing construct.

5.4 Social capital

Social capital refers to resources embedded in relationships—trust, reciprocity, and network ties. It overlaps with HIF only through social outcome capability, but the two differ in kind and level. Social outcome capability is an individual disposition oriented toward joint result realization—perspective taking, conflict regulation, and coordination—whereas social capital is a relational resource residing in network structure rather than in the person. In our framework, social-capital-like properties belong to the collective level and are more closely related to Cultural Equity than to HIF.

5.5 Dynamic capabilities

Dynamic capabilities denote an organization's capacity to sense, seize, and reconfigure resources under change and are defined at the firm level. HIF is not a competitor at this level; it is the micro-foundational psychological substrate from which such capabilities can emerge, consistent with work locating the psychological foundations of dynamic capabilities in managerial cognition (Hodgkinson and Healey, 2011). The constructs are linked across levels rather than substitutable within one.

5.6 Psychological safety

Psychological safety is a shared belief that interpersonal risk-taking is safe, defined primarily as a team-level climate (Edmondson, 1999; Edmondson and Bransby, 2023). In our model it is not a HIF trait but an emergent meso-level property within Cultural Equity, for which social outcome capability acts as an individual-level antecedent—by enabling employees to surface AI errors and discuss model limitations. Treating psychological safety as a HIF dimension would conflate an emergent collective state with the dispositional inputs that generate it. To position HIF and Cultural Equity within the broader literature, Table 2 compares Cultural Equity with several related organizational and relational constructs. The comparison highlights areas of conceptual overlap while clarifying the distinct functional scope and contribution of Cultural Equity in AI-augmented work systems.

5.7 Why HIF is not just another skills list

Given the proliferation of competence and skills models, it is essential to clarify why the Human Impact Factor is not merely another addition to an already crowded landscape. Conceptually, HIF narrows the focus to five Human Future Traits that (a) make a demonstrable contribution to result realization under uncertainty, (b) retain a comparative human advantage over advancing AI capabilities, and (c) can be conceptualized as relatively stable, cross-domain dispositions with established or emerging psychometric tools. Methodologically, HIF is specified as a formative construct: its components are selected because they cover complementary psychological functions—anticipating futures, structuring ill-defined problems, flexibly updating mental models, sustaining goal pursuit, and coordinating social interaction—rather than as interchangeable indicators of a single underlying factor.

This functional and formative framing distinguishes HIF from broad skills catalogs that primarily enumerate desirable attributes. HIF provides a theoretically grounded, empirically anchored lens for identifying which human capacities remain central for value creation in AI-augmented work systems and for designing measurement and intervention strategies that target these capacities directly. In the next section, we extend this logic to the collective level by introducing the concept of Cultural Equity and specifying how individual HIF profiles can give rise to emergent cultural patterns and organizational outcomes.

6 From human future traits to cultural equity: a multilevel framework

While the Human Future Traits specified in the previous section describe individual-level capacities, organizations compete and adapt primarily through collective properties such as culture, climate, and shared capabilities. Building on multilevel theories of emergence in organizations, we argue that the Human Impact Factor scales up to the collective level through recurrent interaction patterns and shared practices that constitute what we term Cultural Equity. In this section, we conceptualize Cultural Equity as an intangible but performance-relevant cultural asset, outline the micro-to-macro pathways through which HIF profiles give rise to emergent cultural patterns, and specify the HIF → Cultural Equity → organizational outcomes pathway in AI-augmented work systems.

Figure 1 provides an overview of the proposed multilevel framework.

Figure 1

6.1 Conceptualizing cultural equity as an intangible but performance-relevant cultural asset

Organizational culture research has long emphasized that shared norms, values, and behavioral patterns constitute a form of intangible asset that shapes learning, coordination, and performance (Denison, 1990, 1996; Yilmaz and Ergun, 2008). Empirical studies link specific cultural traits—such as adaptability, involvement, and mission clarity—to objective indicators like growth, profitability, and quality, demonstrating that culture is both measurable and performance-relevant (Denison and Mishra, 1995; Hartnell et al., 2011). Building on this tradition, we conceptualize Cultural Equity as an intangible but performance-relevant cultural asset that resides in an organization's stable patterns of learning, cooperation, error handling, and technology integration.

We define Cultural Equity as an emergent, intangible organizational capability: a performance-relevant configuration of shared practices through which an organization learns, cooperates, handles error, and integrates AI into its core work. Conceptually, it belongs to the resource-based tradition of organizational culture research, in which shared norms and behavioral patterns constitute an intangible asset that shapes coordination and performance (Denison, 1990, 1996; Denison and Mishra, 1995; Hartnell et al., 2011). It is deliberately not defined as a climate construct in the narrow sense of momentary shared perceptions, nor as a relational network resource; rather, it is a relatively durable, culturally embedded capability that is partly observable through climate-like perceptions but resides in stable patterns of practice. As a joint property of social and technical arrangements, it is best understood as a sociotechnical-cultural asset (Trist and Bamforth, 1951; Cartelli, 2007).

This conception treats Cultural Equity as an accumulated stock of cultural resources rather than a momentary state: it builds up—or erodes—over time as an organization repeatedly learns, cooperates, handles error, and integrates technology. The logic parallels the established treatment of other intangibles: brand equity, for example, is recognized as a separately identifiable intangible asset for which disciplined, auditable assessment procedures exist, illustrating that value-relevant intangible assets can be conceptualized and measured rigorously. We make an analogous claim for Cultural Equity as a performance-relevant intangible asset, while locating our contribution at the level of psychological and cultural theory.

Cultural Equity is distinguished from organizational culture in general by the same functional criterion that defines the Human Impact Factor at the individual level: result realization under uncertainty in AI-augmented work systems. Whereas organizational culture encompasses the full set of an organization's shared values and assumptions, Cultural Equity denotes only those cultural features that specifically enable an organization to realize valuable results when work is AI-augmented—reflected in four facets: learning, cooperation, error robustness, and technology integration. In this sense, Cultural Equity is to organizational culture what HIF is to broad future-skills lists: a functionally selected, theoretically anchored subset rather than an exhaustive description.

This functional definition separates Cultural Equity from several adjacent constructs. It is broader than a learning culture, which centers on knowledge acquisition and sharing, because it additionally specifies cooperation, error robustness, and—distinctively—the integration of AI into core processes. It overlaps with an adaptive culture (Denison and Mishra, 1995) in emphasizing responsiveness to change, but is differentiated by its explicit technology-integration facet and by being theorized as the emergent aggregate of specific Human Future Traits. Psychological safety—a team-level belief that interpersonal risk-taking is safe (Edmondson, 1999; Edmondson and Bransby, 2023)—is positioned as a team-level mechanism upstream of Cultural Equity (see Section 6.2), not as Cultural Equity itself, which additionally comprises learning, cooperation, and technology-integration practices at the organizational level.

We propose to operationalise Cultural Equity as a four-facet construct—learning, cooperation, error robustness, and technology integration—assessed primarily through a referent-shift consensus model, with items worded at the collective level and aggregated to the team or unit level with justification from interrater agreement and reliability indices (rwg, ICC[1], ICC[2]). Because Cultural Equity is conceptualized as a partly configural, accumulated capability, perceptual measures should be complemented by behavioral and archival indicators (for example, the frequency and quality of post-incident reviews, cross-boundary collaboration, and indicators of responsible AI adoption). Construct validation should proceed via confirmatory factor analysis of the four facets, discriminant validity against established culture and psychological-safety measures using the heterotrait-monotrait criterion (Henseler et al., 2015; Fornell and Larcker, 1981), reliability estimation for congeneric measures (McDonald's omega; McDonald, 1999), and model-fit assessment against conventional criteria (Hu and Bentler, 1999). The choice between composition and compilation aggregation should follow the final conceptualization and be tested empirically. This sociotechnical framing follows the long tradition of jointly optimizing the social and technical subsystems of work (Trist and Bamforth, 1951; Cherns, 1976).

6.2 Micro-to-macro pathways: from individual HIF profiles to emergent cultural patterns

We specify the micro-to-macro pathway as a multi-stage chain rather than a direct aggregation. At the proximal stage, higher collective HIF fosters team-level mechanisms—in particular intensive team learning and high psychological safety—that raise the quality of coordination in interdependent, AI-mediated work (Edmondson, 1999; Edmondson and Bransby, 2023). These mechanisms shape transient cultural states, such as a learning and adaptation climate and execution discipline. Over time, the repetition and reinforcement of these states accumulate—or, where the mechanisms are weak, erode—into Cultural Equity as a durable cultural asset. This staged formulation distinguishes fluctuating climate states (proximal, perception-based) from Cultural Equity (the accumulated, capability-like stock), and it makes explicit the mediating mechanisms through which individual traits become an organizational asset. The intensity of AI augmentation acts as a moderator across the chain, amplifying both the demands on Human Future Traits and the value of the resulting cultural asset.

For example, high average future time perspective and volitional implementation strength within a unit are likely to foster longer-term orientation and persistence in change efforts, whereas high levels of complex problem solving and cognitive flexibility support sophisticated sensemaking and adaptive reframing of AI-related challenges. Social outcome capability influences how disagreements about AI use are surfaced and resolved and how psychological safety around experimentation and error reporting develops (Edmondson and Bransby, 2023). Over time, such trait-driven behavioral regularities can crystallize into shared norms and routines—for instance, a “speak-up” climate around AI errors, a habit of cross-functional problem solving, or a pattern of proactive technology experimentation—that constitute Cultural Equity as a meso-level property. The way AI is embedded in work coordination further conditions these dynamics: highly prescriptive algorithmic management can constrain autonomy and suppress the voice and cooperation on which Cultural Equity depends (Keegan and Meijerink, 2025; Röttgen et al., 2025). Embedding these practices links Cultural Equity to current debates on responsible AI, which emphasize accountability, transparency, and human oversight in AI-supported work (Mikalef et al., 2023).

6.3 The HIF → cultural equity → organizational outcomes pathway

The proposed multilevel pathway links individual Human Future Traits to organizational outcomes via Cultural Equity. At the individual level, HIF profiles influence how employees set goals, structure ill-defined problems, adapt to changing demands, persist in implementation, and coordinate with others in AI-augmented work processes. At the collective level, the aggregation and interaction of these behaviors shape Cultural Equity, reflected in shared practices of learning, cooperation, error robustness, and technology integration (Denison and Mishra, 1995; Kozlowski and Klein, 2000). In turn, Cultural Equity is expected to predict organizational outcomes such as innovation rate, transformation speed, and performance, especially in environments with high levels of AI augmentation (Woolley et al., 2010; Riedl et al., 2021).

This pathway implies several testable propositions. First, higher average HIF levels within teams and units should be associated with stronger Cultural Equity indicators over time, beyond structural factors such as industry, size, or baseline performance. Second, Cultural Equity should mediate the relationship between collective HIF and organizational outcomes, such that units with high HIF profiles and strong Cultural Equity outperform those with similar HIF levels but weaker cultural patterns. Third, the strength of these relationships should increase with the degree of AI integration in core processes, as AI-augmented work amplifies both the demands on Human Future Traits and the importance of Cultural Equity as an intangible asset for responsible and effective value creation.

7 Hypotheses and research agenda

Based on the framework outlined above, we derive a set of testable propositions that link the Human Impact Factor, Cultural Equity, and organizational outcomes in AI-augmented work systems. Consistent with the formative specification of HIF, propositions are stated at the composite level where the argument concerns result realization as a whole, and at the dimension level where theory predicts differential effects (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003). Tables 3, 4 summarize the causal structure of the individual-level and collective-level hypotheses, respectively.

7.1 Individual-level propositions

7.1.1 H1a (direct effect)

Adaptive performance denotes the capacity to adjust behavior to changing, uncertain, and novel work demands, and has been established as a distinct performance dimension with its own antecedent structure (Pulakos et al., 2000, 2002). AI augmentation intensifies precisely the conditions that define it: task boundaries shift, routine components are automated, and the residual human contribution moves toward judgment and regulation. Because the five Human Future Traits jointly describe the regulatory capacity to convert intention into valued outcomes under such conditions, we expect them to predict adaptive performance as a composite.

Individuals with higher HIF show higher adaptive performance in AI-augmented work contexts than individuals with lower HIF.

7.1.2 H1b (mediated effect)

We do not expect a direct effect on wellbeing. Motivational and volitional theories locate wellbeing downstream of successful goal pursuit rather than of capacity as such: the transition from intention to action, and the experience of realizing what one set out to do, is what sustains engagement (Heckhausen and Gollwitzer, 1987; Kuhl, 1992; Gollwitzer, 1999). Relatedly, the perception of making a valued difference mediates the effect of work characteristics on motivation (Grant, 2007). We therefore model experienced result realization as the mediating mechanism rather than positing a direct path.

HIF relates to work engagement and wellbeing indirectly, mediated by experienced result realization—the perceived capacity to translate intentions into valued outcomes—rather than through a direct effect on wellbeing.

7.1.3 H1c (moderation)

Trait effects require situational latitude to become behaviorally consequential. Where AI augmentation is high, discretion over how to frame problems, evaluate outputs, and allocate effort increases, and human judgment becomes more decisive. Where work is instead coordinated through prescriptive algorithmic control, autonomy is constrained and trait variance has less room to express itself (Keegan and Meijerink, 2025; Röttgen et al., 2025). We therefore expect AI-augmentation intensity and job autonomy to strengthen the relationship.

The HIF-adaptive performance relationship is stronger under higher AI-augmentation intensity and higher job autonomy.

7.1.4 H1d (incremental validity)

HIF is specified formatively and spans motivational, volitional, and cognitive content, whereas psychological capital is a reflective higher-order construct restricted to motivational and affective resources (Luthans et al., 2007; Avey et al., 2011), and learning agility centers on learning from experience. Because a formative index retains the criterion-relevant variance of each dimension rather than only their common core (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003), HIF should explain variance beyond these constructs. Incremental validity is the decisive test of whether HIF constitutes a distinct contribution rather than a relabeling of existing constructs, and we therefore state it as a hypothesis rather than assert it.

HIF explains incremental variance in adaptive performance and human-AI collaboration quality beyond Psychological Capital, learning agility, and adaptive-performance baselines.

7.1.5 H2 (dimension-specific effects with boundary condition)

The five dimensions are not interchangeable. Future time perspective concerns the temporal horizon over which goals are represented (Zimbardo and Boyd, 1999), and volitional implementation strength concerns their maintenance and enactment over time (Kuhl, 1992; Gollwitzer, 1999); both should therefore matter most for outcomes that accrue over long horizons. Complex problem solving, cognitive flexibility, and social outcome capability instead operate on the immediate structuring of ill-defined tasks and on their social coordination. The boundary condition follows from evidence that AI support improves performance on tasks lying inside its effective capability frontier but degrades performance outside it, where human framing and contextual judgment are decisive (Dell'Acqua et al., 2023).

Future time perspective and volitional implementation strength relate more strongly to long-term, transformation-related outcomes (e.g., sustained upskilling, career adaptability), whereas complex problem solving, cognitive flexibility, and social outcome capability relate more strongly to innovation- and collaboration-related outcomes. The effects of complex problem solving and cognitive flexibility on outcome quality are stronger for ill-defined than for well-defined tasks.

7.1.6 H3a (mechanism)

The principal failure mode of human-AI collaboration is mis-calibrated reliance. People abandon algorithms after observing them err, even when the algorithm demonstrably outperforms them (Dietvorst et al., 2015), yet defer to algorithmic advice under other conditions (Logg et al., 2019), and trust is frequently driven by features unrelated to actual system performance (Kaplan et al., 2023; Glikson and Woolley, 2020; Qin et al., 2025). Calibrating reliance therefore requires reinterpreting outputs, shifting strategies, and resisting the pull of the default answer—the functional definition of cognitive flexibility. We accordingly model reduced automation bias and better-calibrated reliance as the mediating mechanism.

Cognitive flexibility is associated with higher-quality human-AI collaboration, mediated by reduced automation bias and better-calibrated reliance on AI outputs.

7.1.7 H3b (mechanism)

Collaboration quality is also socially constituted. Reporting an AI error, questioning an automated recommendation, or admitting uncertainty are interpersonally risky acts, and psychological safety is the established condition under which team members perform them (Edmondson, 1999; Edmondson and Bransby, 2023). Social outcome capability—interpersonal sensitivity, conflict regulation, and trust coordination—is the individual disposition that produces this condition. We therefore model team psychological safety around AI use as the mediating mechanism.

Social outcome capability is associated with higher-quality human-AI collaboration, mediated by team psychological safety around AI use.

7.1.8 H3c (moderation)

Finally, these dispositions presuppose a minimal enabling competency. AI literacy is trainable and tool-bound rather than trait-like, and the available scales remain methodologically heterogeneous (Lintner, 2024); its relationship to effective use is not monotonic, as lower AI literacy predicts greater receptivity to AI (Tully et al., 2025). Effective use additionally imposes metacognitive demands that presuppose some understanding of the system (Tankelevitch et al., 2024). We therefore treat AI literacy as an enabling moderator rather than as a constituent dimension of HIF.

AI literacy moderates these relationships as an enabling condition: HIF translates into collaboration quality more strongly when baseline AI literacy is adequate.

7.2 Cross-level and organizational-level propositions

Individual traits do not act upon organizations directly. Multilevel theory requires that the emergence process by which individual attributes become collective properties be specified rather than assumed, and distinguishes composition from compilation forms of emergence (Kozlowski and Klein, 2000; Klein and Kozlowski, 2000). We therefore specify this process as a staged chain rather than as simple aggregation.

7.2.1 H4a (HIF → team mechanisms)

Team learning and psychological safety are the proximal collective mechanisms through which individual dispositions become shared practice, and psychological safety is itself an established antecedent of team learning behavior (Edmondson, 1999; Edmondson and Bransby, 2023).

Teams with higher collective HIF exhibit stronger team-level mechanisms—intensive team learning and higher psychological safety—and, through these, higher coordination quality.

7.2.2 H4b (mechanisms → cultural states)

Repeated enactment of these mechanisms produces the transient cultural states—a learning and adaptation climate, and execution discipline—that constitute the perceptual layer of culture and are analytically distinguishable from its more durable substrate (Denison, 1996).

Stronger team mechanisms foster cultural states such as a learning and adaptation climate and execution.

7.2.3 H4c (accumulation into cultural equity)

Cultural Equity is theorized as an accumulated stock rather than a momentary state, consistent with treatments of culture as a performance-relevant intangible asset that is built up over time (Denison and Mishra, 1995; Hartnell et al., 2011). Because emergence depends on the configuration of individual attributes and not only on their mean (Kozlowski and Klein, 2000), we specify a minimum level of social outcome capability and a moderating role of within-unit dispersion. Because sustained AI use can erode the underlying human capacities (Budzyń et al., 2025; Gerlich, 2025), we further specify erosion as the counterfactual path that obtains where these mechanisms are weak.

Sustained cultural states accumulate over time into Cultural Equity as a durable asset, whereas weak mechanisms lead to its erosion; emergence additionally requires a minimum level of social outcome capability and is moderated by within-unit dispersion of HIF.

7.2.4 H5a (proximal outcomes)

Organizational culture shows an established, if moderate, association with objective effectiveness criteria, and adaptive, learning-oriented cultures are the types most consistently linked to innovation and adaptability (Denison and Mishra, 1995; Hartnell et al., 2011). We distinguish proximal outcomes, which culture should affect relatively directly, from distal financial and retention outcomes, which lie further along the causal chain, are more strongly confounded by structural factors, and are therefore expected to show smaller and temporally lagged effects.

Higher Cultural Equity is associated with a higher innovation rate, faster transformation and decision speed, and higher decision quality.

7.2.5 H5b (distal outcomes, lagged)

Higher Cultural Equity is associated with better organizational performance, lower error costs, higher retention, and higher return on AI adoption, with effects that are smaller and temporally lagged relative to H5a, controlling for structural confounds.

7.2.6 H6 (partial mediation)

We specify partial rather than full mediation because aggregated individual adaptive performance (H1a) provides a second, direct pathway to organizational outcomes that does not pass through culture, consistent with multilevel accounts in which lower-level effects may operate both directly and through emergent collective properties (Kozlowski and Klein, 2000).

The relationship between collective HIF and organizational outcomes is partially mediated by Cultural Equity.

7.2.7 H7 (moderated mediation)

The rationale is that augmentation simultaneously widens the decision latitude within which Human Future Traits operate and raises the organizational cost of poorly calibrated AI use, amplifying both ends of the chain. Human-AI combinations yield synergy only under specific conditions (Vaccaro et al., 2024), performance depends on the location of tasks relative to AI's capability frontier (Dell'Acqua et al., 2023), and exposure differences already translate into measurable labor-market effects (Brynjolfsson et al., 2025). AI-augmentation intensity moderates the chain: both the collective HIF → Cultural Equity relationship (H4) and the Cultural Equity → organizational outcomes relationship (H5) are stronger under higher AI augmentation, so that the indirect effect specified in H6 is conditional on AI-augmentation intensity.

7.3 Research agenda

Table 5 summarizes a research agenda that links the hypothesis clusters to suitable designs, measures, and analytic approaches.

These hypotheses call for future research that:

• uses multilevel designs to model individuals nested within teams and organizations;

• employs longitudinal data to capture the emergence of Cultural Equity and changes in outcomes over time;

• exploits AI implementation phases as quasi-experimental opportunities to examine how differences in HIF and Cultural Equity shape the impact of technological change.

Table 5 summarizes a research agenda that links the hypothesis clusters to suitable designs, measures, and analytic approaches. Two requirements follow directly from the framework: aggregation of individual perceptions to collective constructs must be justified empirically rather than assumed (Kozlowski and Klein, 2000), and Cultural Equity must be shown to be discriminable from established culture and psychological-safety measures (Fornell and Larcker, 1981; Henseler et al., 2015).

8 Theoretical and practical implications

8.1 Implications for theories of organizational culture and human–AI collaboration

Theoretically, our framework reframes organizational culture in AI-augmented work environments not merely as a set of shared values and assumptions (Denison, 1996), but as Cultural Equity: a specific configuration of learning, cooperation, error-handling, and technology-integration patterns that emerges from the distribution of Human Future Traits. This reframing builds on the resource-based tradition in which culture is treated as a performance-relevant intangible asset (Denison and Mishra, 1995; Hartnell et al., 2011) and on sociotechnical systems theory, which holds that performance depends on the joint optimization of the social and technical subsystems (Trist and Bamforth, 1951; Cherns, 1976). It provides an explicit bridge between micro-level psychological traits and meso-level culture theories by specifying how future time perspective, complex problem solving, cognitive flexibility, volitional implementation strength, and social outcome capability shape culture-relevant practices, thereby responding to long-standing calls to specify the emergence processes that link individual attributes to collective properties (Kozlowski and Klein, 2000; Klein and Kozlowski, 2000). In doing so, it also supplies the micro-foundational substrate that dynamic-capabilities accounts presuppose but rarely specify psychologically (Hodgkinson and Healey, 2011).

The framework also extends theories of human-AI collaboration. Empirical syntheses show that human-AI combinations do not automatically outperform the better-performing party, and that synergy is conditional rather than guaranteed (Vaccaro et al., 2024); performance further depends on whether a task falls inside or outside AI's current capability frontier (Dell'Acqua et al., 2023). Effective use additionally imposes substantial metacognitive demands on the user (Tankelevitch et al., 2024). By specifying which human capacities—problem framing, normative judgment, and social negotiation—remain non-substitutable, HIF identifies the dispositions that plausibly determine when these conditional synergies are realized, and thus the quality and resilience of human-AI teams.

8.2 Implications for HR, leadership, and change management

Practically, the Human Impact Factor shifts attention from broad, loosely specified competence lists toward a small, clearly defined bundle of traits that are particularly critical for value creation in AI-augmented work. This matters because AI-specific competency constructs remain psychometrically heterogeneous (Lintner, 2024) and because greater AI literacy does not translate straightforwardly into greater receptivity or better use (Tully et al., 2025). For HR, talent assessment, development, and placement can therefore be oriented toward the five traits rather than toward generic future-skills inventories, for example through targeted selection procedures, development programmes, and intervention-linked measurement.

Evidence from adjacent resource-development literatures suggests that such an orientation is feasible but should be calibrated. Psychological capital can be raised through controlled interventions, with small-to-moderate effects (Avey et al., 2011; Lupsa et al., 2020), and volitional strategies such as implementation intentions reliably improve goal realization (Gollwitzer, 1999). We return to the limits of trainability in Section 9.

For leadership and change management, the framework offers a compass for designing and developing teams such that individual HIF profiles give rise to strong Cultural Equity: by fostering psychological safety, a well-established antecedent of learning behavior and error reporting in teams (Edmondson, 1999; Edmondson and Bransby, 2023); by cultivating error cultures in which failure is treated as information (Edmondson, 2023); and by creating structured reflection formats around AI use that shift part of the metacognitive burden documented in generative-AI work onto team routines (Tankelevitch et al., 2024). Conversely, the manner in which AI is embedded into coordination matters: highly prescriptive algorithmic management can constrain autonomy and suppress the voice and cooperation on which Cultural Equity depends (Keegan and Meijerink, 2025; Röttgen et al., 2025).

Conceptualizing Cultural Equity as a measurable asset would allow organizations to monitor and develop it deliberately, for example by characterizing units along a Cultural Equity gradient and integrating this information into people-analytics practice, so that AI implementation is framed as deliberate culture-building rather than a purely technological project. We advance this as a design proposal that follows from the framework rather than as an empirically validated tool; its value depends on the measurement programme outlined in Section 6 and remains to be tested.

9 Limitations and future directions

The framework proposed in this article is conceptual and requires systematic empirical validation. Although each Human Future Trait is grounded in established literatures, their combination as a formative Human Impact Factor is a specification choice with consequences: in formative measurement the index is defined by its indicators, so omitting a relevant dimension changes the construct itself, and indicator weights must be estimated rather than assumed (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003). The relative importance of specific traits, the exact functional form of their combination, and potential omitted dimensions therefore remain open empirical questions.

A second limitation concerns scope and generalizability. Our argument primarily targets knowledge-intensive, AI-augmented work in organizational contexts that offer substantial autonomy and interdependence. The extent to which the Human Impact Factor and Cultural Equity operate similarly in other settings—such as low-autonomy jobs, public-sector organizations, or non-Western cultural contexts—remains unclear and should be examined in comparative and cross-cultural research. This caveat is not merely conventional: where AI is embedded through prescriptive algorithmic control rather than augmentation, the very autonomy that the framework presupposes may be reduced (Keegan and Meijerink, 2025; Röttgen et al., 2025).

A third limitation concerns the malleability of the five traits, which we treat as relatively stable yet developable. The evidence here is heterogeneous rather than uniformly encouraging, and it differs by dimension. For volitional implementation strength the evidence is strong: simple if-then plans reliably improve goal attainment (Gollwitzer, 1999). For resource-like constructs adjacent to our dimensions, controlled interventions produce reliable but modest gains (Avey et al., 2011; Lupsa et al., 2020; Robertson et al., 2015; Vanhove et al., 2016). For the cognitive dimensions, however, the picture is considerably less favorable: meta-analytic evidence indicates that working-memory and executive-function training produces near but little far transfer, and that cognitive training does not enhance general cognition (Melby-Lervåg et al., 2016; Kassai et al., 2019; Sala and Gobet, 2019). Because cognitive flexibility is closely related to executive functioning, claims about its trainability should be advanced cautiously and interventions targeting it evaluated against domain-specific rather than general-transfer criteria. Which aspects of HIF are most malleable, over what time frames, and with which organizational supports is therefore an open question rather than a settled premise.

A fourth limitation is that our framework treats AI augmentation primarily as a context that raises the value of Human Future Traits, whereas emerging evidence points to a reciprocal pathway in which AI use may erode the very capacities the framework relies upon. Prolonged exposure to AI assistance has been associated with skill degradation in expert performance (Budzyń et al., 2025), and frequent AI use correlates with greater cognitive offloading and weaker critical thinking, although this evidence is cross-sectional and cannot establish causality (Gerlich, 2025). Generative AI has also been shown to raise individual creative output while reducing the collective diversity of that output (Doshi and Hauser, 2024). If such erosion effects are substantiated, HIF would be simultaneously a predictor of effective AI use and a potential casualty of it—an endogeneity that longitudinal designs must address.

A fifth limitation concerns the temporal stability of the comparative human advantage we attribute to the five traits, which is dynamic rather than fixed. As AI systems advance, the boundary between tasks on which humans retain an advantage and those better delegated to AI will shift (Dell'Acqua et al., 2023), and labor-market evidence already indicates measurable employment effects in strongly AI-exposed, automating occupations (Brynjolfsson et al., 2025). Our framework treats this boundary as an explicit object of study: the propositions specify where human advantage should be strongest—ill-defined, normatively charged, and socially embedded tasks—and imply that these relationships must be re-estimated as capabilities evolve rather than assumed to hold.

Finally, our propositions deliberately abstract from measurement challenges. Instruments for HIF must satisfy the requirements of formative specification (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003), while Cultural Equity requires defensible aggregation from individual perceptions to a collective property (Kozlowski and Klein, 2000), evidence of discriminant validity against established culture and psychological-safety measures (Fornell and Larcker, 1981; Henseler et al., 2015), and reliability estimates appropriate for congeneric measures (McDonald, 2013; Hu and Bentler, 1999). The current immaturity of AI-related measurement scales underscores that this is a non-trivial undertaking (Lintner, 2024). Despite these limitations, we believe that articulating a psychologically grounded model of Human Future Traits and Cultural Equity offers a useful starting point for a cumulative research programme on human value creation in AI-augmented organizations.

10 Conclusion

This article has proposed a psychologically grounded framework for understanding human value creation in AI-augmented work by introducing the Human Impact Factor and linking it to the concept of Cultural Equity. By shifting attention from heterogeneous future-skills lists to a small set of theoretically anchored Human Future Traits, the framework specifies which enduring human capacities remain central for realizing valuable results under uncertainty, and how these capacities can scale up to collective, culture-level assets through the emergence processes that multilevel theory requires to be made explicit (Kozlowski and Klein, 2000).

We have argued that future time perspective, complex problem solving in ill-defined problem spaces, cognitive flexibility, volitional implementation strength, and social outcome capability together form a formative bundle that underpins result realization in AI-rich environments and, via Cultural Equity, influences innovation, transformation speed, and organizational performance. This claim—that the human contribution is increasingly regulatory rather than computational—is consistent with evidence that human-AI combinations deliver synergy only under specific conditions (Vaccaro et al., 2024) and that culture operates as a performance-relevant organizational asset (Hartnell et al., 2011). The resulting multilevel model yields a set of testable propositions and points to concrete research priorities, including multilevel, longitudinal, and quasi-experimental studies around AI implementation.

Taken together, the Human Impact Factor and Cultural Equity offer an integrative lens for theorizing and empirically examining how humans and AI jointly contribute to organizational outcomes. We hope that this framework stimulates further theoretical refinement and empirical work and supports practitioners in designing AI-augmented workplaces that deliberately invest in the human traits and cultural conditions that make responsible and robust value creation possible.

References

• 1

  AljofanA.FalehR.AlrofiaiF. (2024). Skill obsolescence in the digital age: grit, strength based-leadership approach, and employee's career adaptability in multinational corporations. Open J. Bus. Manage.12, 3395–3416. doi: 10.4236/ojbm.2024.125170

  • CrossRef
  • Google Scholar

• 2

  AndreL.van VianenA. E. M.PeetsmaT. T. D.OortF. J. (2018). Motivational power of future time perspective: meta-analyses in education, work, and health. PLoS ONE13:e0190492. doi: 10.1371/journal.pone.0190492

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  AveyJ. B.ReichardR. J.LuthansF.MhatreK. H. (2011). Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance. Hum. Res. Dev. Q.22, 127–152. doi: 10.1002/hrdq.20070

  • CrossRef
  • Google Scholar

• 4

  BeduéP.FritzscheA. (2022). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. J. Enterp. Inf. Manage.35, 530–549. doi: 10.1108/JEIM-06-2020-0233

  • CrossRef
  • Google Scholar

• 5

  BentahilaL.FontaineR.PennequinV. (2021). Universality and cultural diversity in moral reasoning and judgment: a review of the literature. Front. Psychol.12:764360. doi: 10.3389/fpsyg.2021.764360

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  BrynjolfssonE.ChandarB.ChenR. (2025). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford, CA: Stanford Digital Economy Lab.

  • Google Scholar

• 7

  BrynjolfssonE.McAfeeA. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton.

  • Google Scholar

• 8

  BudzyńK.RomańczykM.KitalaD.et al. (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy. Lancet Gastroenterol. Hepatol.10, 896–903. doi: 10.1016/S2468-1253(25)00133-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  CartelliA. (2007). Socio-technical theory and knowledge construction: towards new pedagogical paradigms?Issues Inform. Sci. Inform. Technol.4, 1–14. doi: 10.28945/928

  • CrossRef
  • Google Scholar

• 10

  ChenD.YoonH. J.WanZ.AlluruN.LeeS. W.HeR.et al. (2025). Advancing human-machine teaming: concepts, challenges, and applications. arXiv. arXiv:2503.16518.

  • Google Scholar

• 11

  ChenN.ZhangX. (2025). When misunderstanding meets artificial intelligence: the critical role of trust in human-AI and human-human team communication and performance. Front. Psychol.16:1637339. doi: 10.3389/fpsyg.2025.1637339

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  ChenX.XiaoL. (2025). Serendipitous sparks: AI information encounter, cognitive flexibility, AI literacy, and university student creativity. Front. Psychol.16:1623730. doi: 10.3389/fpsyg.2025.1623730

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  ChernsA. (1976). The principles of sociotechnical design. Hum. Relat.29, 783–792. doi: 10.1177/001872677602900806

  • CrossRef
  • Google Scholar

• 14

  ClausenT.MengA.BorgV. (2019). Does social capital in the workplace predict job performance, work engagement, and psychological well-being? A prospective analysis. J. Occup. Environ. Med.61, 800–805. doi: 10.1097/JOM.0000000000001672

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  ColquittJ. A.Zapata-PhelanC. P. (2007). Trends in theory building and theory testing: a five-decade study of the academy of management journal. Acad. Manage. J.50, 1281–1303. doi: 10.5465/amj.2007.28165855

  • CrossRef
  • Google Scholar

• 16

  De RidderD. T.Lensvelt-MuldersG.FinkenauerC.StokF. M.BaumeisterR. F. (2012). Taking stock of self-control: a meta-analysis of how trait self-control relates to a wide range of behaviors. Pers. Soc. Psychol. Rev.16, 76–99. doi: 10.1177/1088868311418749

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  Dell'AcquaF.McFowlandE. I. I. I.MollickE. R.Lifshitz-AssafH.KelloggK.RajendranS.et al. (2023). Navigating the Jagged Technological Frontier (HBS Working Paper No. 24-013). Boston, MA: Harvard Business School.

  • Google Scholar

• 18

  DenisonD. (1990). Corporate Culture and Organizational Effectiveness. New York, NY: Wiley.

  • Google Scholar

• 19

  DenisonD. R. (1996). What is the difference between organizational culture and organizational climate? A native's point of view on a decade of paradigm wars. Acad. Manage. Rev.21, 619–654. doi: 10.2307/258997

  • CrossRef
  • Google Scholar

• 20

  DenisonD. R.MishraA. K. (1995). Toward a theory of organizational culture and effectiveness. Organ. Sci.6, 204–223. doi: 10.1287/orsc.6.2.204

  • CrossRef
  • Google Scholar

• 21

  DeRueD. S.AshfordS. J.MyersC. G. (2012). Learning agility: in search of conceptual clarity and theoretical grounding. Ind. Organ. Psychol.5, 258–279. doi: 10.1111/j.1754-9434.2012.01444.x

  • CrossRef
  • Google Scholar

• 22

  DiamantopoulosA.WinklhoferH. M. (2001). Index construction with formative indicators: an alternative to scale development. J. Market. Res.38, 269–277. doi: 10.1509/jmkr.38.2.269.18845

  • CrossRef
  • Google Scholar

• 23

  DiamondA. (2013). “Want to optimize executive functions and academic outcomes? Simple, just nourish the human spirit,” in Minnesota Symposia on Child Psychology: Developing Cognitive Control Processes: Mechanisms, Implications, and Interventions (Vol. 37), (Hoboken, NJ: John Wiley and Sons), 203–230. doi: 10.1002/9781118732373.ch7

  • CrossRef
  • Google Scholar

• 24

  DietvorstB. J.SimmonsJ. P.MasseyC. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol.144, 114–126. doi: 10.1037/xge0000033

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  DörnerD. (1996). The Logic of Failure: Why Things Go Wrong and What We Can Do To Make Them Right. New York, NY: Metropolitan Books.

  • Google Scholar

• 26

  DoshiA. R.HauserO. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Sci. Adv.10:eadn5290. doi: 10.1126/sciadv.adn5290

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  DuckworthA. L.GrossJ. J. (2014). Self-control and grit: related but separable determinants of success. Curr. Dir. Psychol. Sci.23, 319–325. doi: 10.1177/0963721414541462

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  DuckworthA. L.PetersonC.MatthewsM. D.KellyD. R. (2007). Grit: perseverance and passion for long-term goals. J. Pers. Soc. Psychol.92, 1087–1101. doi: 10.1037/0022-3514.92.6.1087

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  EdmondsonA. (1999). Psychological safety and learning behavior in work teams. Adm. Sci. Q.44, 350–383. doi: 10.2307/2666999

  • CrossRef
  • Google Scholar

• 30

  EdmondsonA. C. (2023). Right Kind of Wrong: The Science of Failing Well. New York, NY: Simon and Schuster.

  • Google Scholar

• 31

  EdmondsonA. C.BransbyD. P. (2023). Psychological safety comes of age: observed themes in an established literature. Annu. Rev. Organ. Psychol. Organ. Behav.10, 55–78. doi: 10.1146/annurev-orgpsych-120920-055217

  • CrossRef
  • Google Scholar

• 32

  EhlersU. D. (2020). Future Skills: The Future of Learning and Higher Education. Norderstedt: BoD – Books on Demand. doi: 10.1007/978-3-658-29297-3

  • CrossRef
  • Google Scholar

• 33

  European Commission (2018). European Skills Index. CEDEFOP. Available online at: https://www.cedefop.europa.eu/en/tools/european-skills-index (Accessed August 14, 2026).

  • Google Scholar

• 34

  FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res.18, 39–50. doi: 10.1177/002224378101800104

  • CrossRef
  • Google Scholar

• 35

  ForstmeierS.RüddelH. (2008). Measuring volitional competences: psychometric properties of a short form of the volitional components questionnaire (VCQ) in a clinical sample. Psychol. Sci. Q.50, 135–147. doi: 10.2174/1874350100801010066

  • CrossRef
  • Google Scholar

• 36

  FunkeJ. (2010). Complex problem solving: a case for complex cognition?Cogn. Process.11, 133–142. doi: 10.1007/s10339-009-0345-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  FunkeJ. (2012). “Complex problem solving,” in Encyclopedia of the Sciences of Learning (New York, NY: Springer), 682–685. doi: 10.1007/978-1-4419-1428-6_685

  • CrossRef
  • Google Scholar

• 38

  FunkeJ.FischerA.HoltD. V. (2017). “Competencies for complexity: Problem solving in the twenty-first century,” in Assessment and Teaching of 21st Century Skills: Research and Applications, eds. E. Care, P. Griffin, and M. Wilson (Cham: Springer International Publishing), 41-53.

  • Google Scholar

• 39

  GerlichM. (2025). AI tools in society: impacts on cognitive offloading and the future of critical thinking. Societies15:6. doi: 10.3390/soc15010006

  • CrossRef
  • Google Scholar

• 40

  GliksonE.WoolleyA. W. (2020). Human trust in artificial intelligence: review of empirical research. Acad. Manage. Ann.14, 627–660. doi: 10.5465/annals.2018.0057

  • CrossRef
  • Google Scholar

• 41

  GollwitzerP. M. (1999). Implementation intentions: strong effects of simple plans. Am. Psychol.54, 493–503. doi: 10.1037/0003-066X.54.7.493

  • CrossRef
  • Google Scholar

• 42

  GrahamJ.MeindlP.BeallE.JohnsonK. M.ZhangL. (2016). Cultural differences in moral judgment and behavior, across and within societies. Curr. Opin. Psychol.8, 125–130. doi: 10.1016/j.copsyc.2015.09.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  GrantA. M. (2007). Relational job design and the motivation to make a prosocial difference. Acad. Manage. Rev.32, 393–417. doi: 10.5465/amr.2007.24351328

  • CrossRef
  • Google Scholar

• 44

  GrantA. M.BergJ. M. (2011). “Prosocial motivation at work: when, why, and how making a difference makes a difference,” in The Oxford Handbook of Positive Organizational Scholarship, eds. K. S. Cameron and G. M. Spreitzer (New York, NY: Oxford University Press), 28–44.

  • Google Scholar

• 45

  GreiffS.FischerA.WüstenbergS.SonnleitnerP.BrunnerM.MartinR. (2013). A multitrait–multimethod study of assessment instruments for complex problem solving. Intelligence41, 579–596. doi: 10.1016/j.intell.2013.07.012

  • CrossRef
  • Google Scholar

• 46

  GreiffS.FunkeJ. (2017). “Interactive problem solving: exploring the potential of minimal complex systems,” in The Nature of Problem Solving: Using Research to Inspire 21st Century Learning, eds. B. Csapó and J. Funke (Paris: OECD Publishing).

  • Google Scholar

• 47

  HaenleinM.KaplanA.TanC. W.ZhangP. (2019). Artificial intelligence (AI) and management analytics. J. Manage. Anal.6, 341–343. doi: 10.1080/23270012.2019.1699876

  • CrossRef
  • Google Scholar

• 48

  HartnellC. A.OuA. Y.KinickiA. (2011). Organizational culture and organizational effectiveness: a meta-analytic investigation of the competing values framework's theoretical suppositions. J. Appl. Psychol.96, 677–694. doi: 10.1037/a0021987

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  HeckhausenH.GollwitzerP. M. (1987). Thought contents and cognitive functioning in motivational versus volitional states of mind. Motiv. Emot.11, 101–120. doi: 10.1007/BF00992338

  • CrossRef
  • Google Scholar

• 50

  HenselerJ.RingleC. M.SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Market. Sci.43, 115–135. doi: 10.1007/s11747-014-0403-8

  • CrossRef
  • Google Scholar

• 51

  HodgkinsonG. P.HealeyM. P. (2011). Psychological foundations of dynamic capabilities: reflexion and reflection in strategic management. Strateg. Manage. J.32, 1500–1516. doi: 10.1002/smj.964

  • CrossRef
  • Google Scholar

• 52

  HoppeK.AuerT.ThommesK. (2023). Future Orientation and Team Performance in Creative Tasks. Paderborn: Universität Paderborn. Advance online publication.

  • Google Scholar

• 53

  HuL. T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model.6, 1–55. doi: 10.1080/10705519909540118

  • CrossRef
  • Google Scholar

• 54

  JarvisC. B.MacKenzieS. B.PodsakoffP. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res.30, 199–218. doi: 10.1086/376806

  • CrossRef
  • Google Scholar

• 55

  KainC.KoschmiederC.Matischek-JaukM.BergnerS. (2024). Mapping the landscape: a scoping review of 21st century skills literature in secondary education. Teach. Teach. Educ.151:104739. doi: 10.1016/j.tate.2024.104739

  • CrossRef
  • Google Scholar

• 56

  KanningU. P. (2009). Inventar Sozialer Kompetenzen (ISK).Göttingen: Hogrefe. doi: 10.1007/978-3-476-05226-1_19

  • CrossRef
  • Google Scholar

• 57

  KaplanA. D.KesslerT. T.BrillJ. C.HancockP. A. (2023). Trust in artificial intelligence: Meta-analytic findings. Hum. Factors65, 337–359. doi: 10.1177/00187208211013988

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  KassaiR.FutoJ.DemetrovicsZ.TakacsZ. K. (2019). A meta-analysis of the experimental evidence on the near- and far-transfer effects among children's executive function skills. Psychol. Bull.145, 165–188 doi: 10.1037/bul0000180

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  KeeganA.MeijerinkJ. (2025). Algorithmic management in organizations? From edge case to center stage. Annu. Rev. Organ. Psychol. Organ. Behav.12, 395–422. doi: 10.1146/annurev-orgpsych-110622-070928

  • CrossRef
  • Google Scholar

• 60

  KenettY. N.LevyO.KenettD. Y.StanleyH. E.FaustM.HavlinS. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proc. Natl. Acad. Sci.115, 867–872. doi: 10.1073/pnas.1717362115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  KleinK. J.KozlowskiS. W. J. (2000). From micro to meso: critical steps in conceptualizing and conducting multilevel research. Organ. Res. Methods3, 211–236. doi: 10.1177/109442810033001

  • CrossRef
  • Google Scholar

• 62

  KooijD. T. A. M.KanferR.BettsM.RudolphC. W. (2018). Future time perspective: a systematic review and meta-analysis. J. Appl. Psychol.103, 867–893. doi: 10.1037/apl0000306

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  KotsiouA.Fajardo-TovarD. D.CowhittT.MajorL.WegerifR. (2022). A scoping review of future skills frameworks. Ir. Educ. Stud.41, 171–186. doi: 10.1080/03323315.2021.2022522

  • CrossRef
  • Google Scholar

• 64

  KozlowskiS. W. J.KleinK. J. (2000). “A multilevel approach to theory and research in organizations: contextual, temporal, and emergent processes,” in Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions, eds. K. J. Klein and S. W. J. Kozlowski (San Francisco, CA: Jossey-Bass), 3–90.

  • Google Scholar

• 65

  KuhlJ. (1992). A theory of self-regulation: action versus state orientation, self-discrimination, and some applications. Appl. Psychol.41, 97–129. doi: 10.1111/j.1464-0597.1992.tb00688.x

  • CrossRef
  • Google Scholar

• 66

  LechnerC. M.DannerD.RammstedtB. (2019). Grit (effortful persistence) can be measured with a short scale, shows little variation across socio-demographic subgroups, and is associated with career success and career engagement. PLoS ONE14:e0224814. doi: 10.1371/journal.pone.0224814

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  LeeH. P.SarkarA.TankelevitchL.DrososI.RintelS.BanksR.et al. (2025). “The impact of generative AI on critical thinking: self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers,” in Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, (New York, NY: Association for Computing Machinery (ACM)), 1–22. doi: 10.1145/3706598.3713778

  • CrossRef
  • Google Scholar

• 68

  LindG. (2006). The Cross-Cultural Validity of the Moral Judgment Test (MJT). Ethics in Progress / Related Validation Work. Münster: LIT Verlag.

  • Google Scholar

• 69

  LintnerT. (2024). A systematic review of AI literacy scales. NPJ Sci. Learn.9:50. doi: 10.1038/s41539-024-00264-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  LoggJ. M.MinsonJ. A.MooreD. A. (2019). Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process.151, 90–103. doi: 10.1016/j.obhdp.2018.12.005

  • CrossRef
  • Google Scholar

• 71

  LupsaD.VîrgăD.MaricutoiuL. P.RusuA. (2020). Increasing psychological capital: a pre-registered meta-analysis of controlled interventions. Appl. Psychol.69, 1506–1556. doi: 10.1111/apps.12219

  • CrossRef
  • Google Scholar

• 72

  LuthansF.YoussefC. M.AvolioB. J. (2007). Psychological capital: investing and developing positive organizational behavior. Posit. Organ. Behav.1, 9–24. doi: 10.4135/9781446212752.n2

  • CrossRef
  • Google Scholar

• 73

  LuthansF.Youssef-MorganC. M.AvolioB. J. (2015). Psychological Capital and Beyond. Hoboken, NJ: Oxford University Press. doi: 10.1002/smi.2623

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  MartinM. M.AndersonC. M. (1998). The cognitive flexibility scale: three validity studies. Commun. Rep.11, 1–9. doi: 10.1080/08934219809367680

  • CrossRef
  • Google Scholar

• 75

  MartinM. M.RubinR. B. (1995). A new measure of cognitive flexibility. Psychol. Rep.76, 623–626. doi: 10.2466/pr0.1995.76.2.623

  • CrossRef
  • Google Scholar

• 76

  McDonaldR. P. (1999). Test Theory: A Unified Treatment.Mahwah, NJ, United States: Lawrence Erlbaum Associates.

  • Google Scholar

• 77

  McDonaldR. P. (2013). Test Theory: A Unified Treatment. Psychology Press. doi: 10.4324/9781410601087

  • CrossRef
  • Google Scholar

• 78

  Melby-LervågM.RedickT. S.HulmeC. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”. Perspect. Psychol. Sci.11, 512–534. doi: 10.1177/1745691616635612

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  MikalefP.IslamN.ParidaV.SinghH.AltwaijryN. (2023). Artificial intelligence (AI) competencies for organizational performance: a B2B marketing capabilities perspective. J. Bus. Res.164:113998. doi: 10.1016/j.jbusres.2023.113998

  • CrossRef
  • Google Scholar

• 80

  MoffittT. E.ArseneaultL.BelskyD.DicksonN.HancoxR. J.HarringtonH.et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl. Acad. Sci.108, 2693–2698. doi: 10.1073/pnas.1010076108

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  MrishoD. H. (2023). The effect of future time perspective on innovative work behaviors of employees in Tanzania's banking sector. Int. Res. J. Econom. Manage. Stud (IRJEMS) 2, 520–526. doi: 10.56472/25835238/IRJEMS-V2I3P166

  • CrossRef
  • Google Scholar

• 82

  OprinsE. A.van den BoschK.VenrooijW. (2018). Measuring adaptability demands of jobs and the adaptability of military and civilians. Mil. Psychol.30, 576–589. doi: 10.1080/08995605.2018.1521689

  • CrossRef
  • Google Scholar

• 83

  OrhanM. A.CastellanoS.KhelladiI.MarinelliL.MongeF. (2021). Technology distraction at work: impacts on self-regulation and work engagement. J. Bus. Res.126, 341–349. doi: 10.1016/j.jbusres.2020.12.048

  • CrossRef
  • Google Scholar

• 84

  OuelletteS.LongatoS.MayerW.BouchardF.BoudriasJ. S. (2024). Cognitive flexibility and adaptive performance among workers: testing the moderating effect of situational strength on this relationship. Can. J. Adm. Sci.41, 549–557. doi: 10.1002/cjas.1744

  • CrossRef
  • Google Scholar

• 85

  PahlavanyaliN. (2024). Future-oriented skills in strategic decision-making: a case study of a leading technology firm. Prosperitas 11. doi: 10.31570/prosp_2024_0113

  • CrossRef
  • Google Scholar

• 86

  PalmerI. (2024). Accelerating Decision-Making through Human-Machine Teaming. Maxwell AFB, AL: Wild Blue Yonder.

  • Google Scholar

• 87

  PulakosE. D.AradS.DonovanM. A.PlamondonK. E. (2000). Adaptability in the workplace: development of a taxonomy of adaptive performance. J. Appl. Psychol.85, 612–624. doi: 10.1037/0021-9010.85.4.612

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  PulakosE. D.SchmittN.DorseyD. W.AradS.BormanW. C.HedgeJ. W. (2002). Predicting adaptive performance: further tests of a model of adaptability. Hum. Perform.15, 299–323. doi: 10.1207/S15327043HUP1504_01

  • CrossRef
  • Google Scholar

• 89

  QinX.ZhouX.ChenC.WuD.ZhouH.DongX.et al. (2025). AI aversion or appreciation? A capability–personalization framework and a meta-analytic review. Psychol. Bull.151, 580–599. doi: 10.1037/bul0000477

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  RiedlC.KimY. J.GuptaP.MaloneT. W.WoolleyA. W. (2021). Quantifying collective intelligence in human groups. Proc. Natl. Acad. Sci.118:e2005737118. doi: 10.1073/pnas.2005737118

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  Rincón-CamposY. E.Sanchez-LopezJ.López-WalleJ. M.Ortiz-JiménezX. (2019). Dynamics of executive functions, basic psychological needs, impulsivity, and depressive symptoms in American football players. Front. Psychol.10:2409. doi: 10.3389/fpsyg.2019.02409

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  RobertsonI. T.CooperC. L.SarkarM.CurranT. (2015). Resilience training in the workplace from 2003 to 2014: a systematic review. J. Occup. Organ. Psychol.88, 533–562. doi: 10.1111/joop.12120

  • CrossRef
  • Google Scholar

• 93

  RöttgenC.DzaackH.HerbigB.WeinmannT.MüllerA. (2025). Algorithmic management: psychological measurement and associations with work design and mental strain. BMC Psychol.13:1327. doi: 10.1186/s40359-025-03680-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  RudolphC. W.KooijD. T. A. M.RauvolaR. S.ZacherH. (2018). Occupational future time perspective: a meta-analysis of antecedents and outcomes. J. Organ. Behav.39, 229–248. doi: 10.1002/job.2264

  • CrossRef
  • Google Scholar

• 95

  SalaG.GobetF. (2019). Cognitive training does not enhance general cognition. Trends Cogn. Sci.23, 9–20. doi: 10.1016/j.tics.2018.10.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  Siqi-LiuA.EgnerT. (2020). Contextual adaptation of cognitive flexibility is driven by task- and item-level learning. Cogn. Affect. Behav. Neurosci.20, 757–782. doi: 10.3758/s13415-020-00801-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  StrathmanA.GleicherF.BoningerD. S.EdwardsC. S. (1994). The consideration of future consequences: weighing immediate and distant outcomes of behavior. J. Personal. Soc. Psychol.66, 742–752. doi: 10.1037/0022-3514.66.4.742

  • CrossRef
  • Google Scholar

• 98

  TagumaM.BarreraM. (2019). OECD Future of Education and Skills 2030: Curriculum Analysis.Paris, France: OECD.

  • Google Scholar

• 99

  TankelevitchL.KewenigV.SimkuteA.ScottA. E.SarkarA.SellenA.et al. (2024). “The metacognitive demands and opportunities of generative AI,” in Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–24. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3613904.3642902

  • CrossRef
  • Google Scholar

• 100

  Teyssier-RobergeG.GagnonJ.TremblayS.HodgettsH. (2025). A quantitative analysis of 21st century skills: a case of semantic and psychometric overlap. Int. J. Sel. Assess.33:e70030. doi: 10.1111/ijsa.70030

  • CrossRef
  • Google Scholar

• 101

  TristE. L.BamforthK. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: an examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Hum. Relat.4, 3–38. doi: 10.1177/001872675100400101

  • CrossRef
  • Google Scholar

• 102

  TullyS. M.LongoniC.AppelG. (2025). Lower artificial intelligence literacy predicts greater AI receptivity. J. Market.89, 1–20. doi: 10.1177/00222429251314491

  • CrossRef
  • Google Scholar

• 103

  VaccaroM.AlmaatouqA.MaloneT. (2024). When combinations of humans and AI are useful: a systematic review and meta-analysis. Nat. Hum. Behav.8, 2293–2303. doi: 10.1038/s41562-024-02024-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  ValienteO. (2014). The OECD skills strategy and the education agenda for development. Int. J. Educ. Dev.39, 40–48. doi: 10.1016/j.ijedudev.2014.08.008

  • CrossRef
  • Google Scholar

• 105

  Van der MerweT. M. D.SeroteM.MalomaM. (2023). A systematic review of the challenges of e-learning implementation in sub-Saharan African countries: 2016–2022. Electron. J. e-Learn.21, 413–429. doi: 10.34190/ejel.21.5.3075

  • CrossRef
  • Google Scholar

• 106

  VanhoveA. J.HerianM. N.PerezA. L. U.HarmsP. D.LesterP. B. (2016). Can resilience be developed at work? A meta-analytic review of resilience-building programme effectiveness. J. Occup. Organ. Psychol.89, 278–307. doi: 10.1111/joop.12123

  • CrossRef
  • Google Scholar

• 107

  Vásquez-PailaqueoM. P.Acosta-AntognoniH.Leiva-BianchiM. (2025). Character strengths-based interventions and their efficacy related to work engagement and job performance: a meta-analysis. Int. J. Appl. Posit. Psychol.10, 21–40. doi: 10.1007/s41042-025-00215-3

  • CrossRef
  • Google Scholar

• 108

  VistaA. (2020). Data-driven identification of skills for the future: 21st-century skills for the 21st-century workforce. SAGE Open10:2158244020915904. doi: 10.1177/2158244020915904

  • CrossRef
  • Google Scholar

• 109

  WoodcockT. (2023). “Decision-support systems and human-machine interaction,”Talk presented at the REAIM Summit, The Hague, Netherlands.

  • Google Scholar

• 110

  WoolleyA. W.ChabrisC. F.PentlandA.HashmiN.MaloneT. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science330, 686–688. doi: 10.1126/science.1193147

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  World Economic Forum (2020). The Future of Jobs Report 2020.Geneva: World Economic Forum.

  • Google Scholar

• 112

  World Economic Forum (2025). The Future of Jobs Report 2025.Geneva: World Economic Forum.

  • Google Scholar

• 113

  WuY.KoutstaalW. (2020). Charting the contributions of cognitive flexibility to creativity: self-guided transitions as a process-based index of creativity-related adaptivity. PLoS ONE15:e0234473. doi: 10.1371/journal.pone.0234473

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114

  WüstenbergS.GreiffS.FunkeJ. (2012). Complex problem solving—more than reasoning?Intelligence40, 1–14. doi: 10.1016/j.intell.2011.11.003

  • CrossRef
  • Google Scholar

• 115

  YilmazC.ErgunE. (2008). Organizational culture and firm effectiveness: an examination of relative effects of culture traits and the balanced culture hypothesis in an emerging economy. J. World Bus.43, 290–306. doi: 10.1016/j.jwb.2008.03.019

  • CrossRef
  • Google Scholar

• 116

  ZimbardoP. G.BoydJ. 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
  • Google Scholar