Building Psychological Capital With Appreciative Inquiry

Abstract

Appreciative Inquiry (AI) is growing in popularity as a strength-based approach to organization and whole system development. Despite numerous accounts on AI’s outcomes positively impacting on organizations and persons, a dearth of quantitative studies exists measuring AI’s impact on individual-level outcomes. This quantitative study investigates how participating in AI impacts on individuals’ psychological capital (PsyCap) through fulfilling their basic psychological needs (BPN) for competence, autonomy, and relatedness. Results, based on data from 213 participants who worked in social profit organizations and either belonged to a group with AI experience or without AI experience, indicate that satisfying the need for competence mediates the relationship between participating in AI and the PsyCap dimensions self-efficacy, optimism, resilience, and hope. Furthermore, results show that participating in AI satisfies the three BPN. Next to theoretical implications, the article provides insights into how leaders of change can build organizations in which people thrive.

Keywords

Appreciative Inquiry psychological capital basic psychological need satisfaction development of human potential learning

Appreciative Inquiry (AI) is growing in popularity and usage as a strength-based approach to organization development and transformative whole system change. AI invites people, often in a large-group summit format (Cooperrider, 2012; Powley, Fry, Barrett, & Bright, 2004), to appreciate and inquire into each other’s stories about what gives life or energy in their shared experience in order to tap “into the [often hidden] natural capacity for cooperation and change that is in every social system” (Barrett & Fry, 2005, p. 25). These energy-giving themes and practices then form the foundation from which to collectively envision, design, and move towards a shared wished-for future, without being told or asked to do so (Bushe, 2012a, 2012b, 2013; Bushe & Kassam, 2005; Cooperrider, 2012; Cooperrider & Srivastva, 1987; Fitzgerald, Oliver, & Hoxsey, 2010).

As a global phenomenon, AI has been argued to revolutionize the field of organization development (Quinn, 2000) and provide a sound paradigm for transformational change (Bushe & Kassam, 2005; Faure, 2006) grounded in social constructionism (Gergen, 1982, 1994). Many published accounts report impressive outcomes of AI positively impacting on persons and collectives (groups, organizations, multi-stakeholder systems)—although there is an emphasis on describing outcomes at the collective level (e.g., Barrett & Fry, 2005; Bushe & Kassam, 2005; Cooperrider, 2012; Fry, Barrett, Seiling, & Whitney, 2002). However, much of the writing on AI process and outcomes continues to be largely descriptive and anecdotal (Bushe, 2012a; Bushe & Kassam, 2005). Moreover, based on a review of 50 published studies on AI, Yaeger, Sorensen, and Bengtsson (2005) concluded that a great deal of the reported work exhibits qualitative methodologies. Although these studies are valuable and point to the merits of AI, more quantitative empirical articles are needed that explore contingencies, mediators and moderators interfering with results of AI interventions (Bushe, 2012a; Bushe & Coetzer, 1995; Jones, 1998).

Furthermore, the impact of successful AI practices on persons and organizations is mostly reported in abstract terms. Among others, typical examples are a shift in attitude and language (Finegold, Holland, & Lingham, 2002), conversational convergence (Mantel & Ludema, 2004), a generative metaphor compelling new actions (Bushe & Kassam, 2005), energizing individuals and empowering them to access new possibilities (Egan & Lancaster, 2005), and the establishment of the new and eclipsing of the old (Cooperrider & Godwin, 2012). Tapping into and screening all these abstractions can lead to a theoretical breakthrough in understanding AI as a successful change approach (Bushe, 1995). However, this requires a clear operational definition of AI and a theoretical framework from within which its effects can be studied.

This quantitative study focuses on how AI practices impact on the experience and development of psychological capital (PsyCap; Luthans & Youssef, 2007) of participants through satisfying their basic psychological needs (BPN; Deci & Ryan, 2000; Ryan & Deci, 2000) as a mediating variable. PsyCap is a core construct of the Positive Organizational Behavior (POB) approach (Luthans, 2002; Luthans & Youssef, 2007)—a subset of Positive Organizational Scholarship (POS; Dutton, Glynn, & Spreitzer, 2006). PsyCap refers to an individual’s positive psychological state of development composed of “the state-like psychological resource capacities of self-efficacy, hope, optimism, and resilience” (Luthans & Youssef, 2007, p. 328). POS focuses on life-giving, capacity-creating dynamics in organizations (e.g., AI practices) that “contribute to human strengths and virtues, resilience and healing, vitality and thriving, and the cultivation of extraordinary states in individuals, groups, and organizations” (Dutton et al., 2006, p. 641). As such, POS has been argued to offer a rich theoretical grounding for AI (Cooperrider & Sekerka, 2006; Dutton et al., 2006), suggesting there exists a positive link between participating in AI practices and the individual-level states comprising PsyCap (i.e., self-efficacy, hope, optimism, and resilience) and its development. However, we know of no prior empirical work that actually models and empirically tests this idea.

Moreover, we further develop this theoretical reasoning by identifying BPN satisfaction as an intervening or mediating mechanism between AI practices and PsyCap. Self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2000) argues that there are three innate BPN, which, when satisfied, are the nutriments of psychological growth, well-being, and the most volitional forms of motivation. These needs are autonomy, competence, and relatedness. Social conditions can either support or hinder the satisfaction of these needs. The activity of doing AI has been described as energy-giving and generative—evoking new possibilities, ideas, and actions (Bushe, 2007, 2010, 2012a, 2012b; Bushe & Kassam, 2005; Cooperrider, 2012; Fry et al., 2002). Drawing on evidence from self-determination theory, Quinn and Dutton (2005, p. 44) proposed a positive relationship between energy—“the feeling that one is eager to act and capable of acting” (p. 36)—and satisfying the three BPN:

Changes in autonomy, competence, and relatedness change a person’s energy because they imply the meeting of basic human needs. Presumably, it is adaptive for someone to have reinforcing experiences like increased energy when basic needs are met and “punishing” experiences like decreased energy when basic needs are not met.

The remainder of the article is structured as follows. We start with a discussion of AI, the principles behind the approach, and the 4-D intervention model of AI. We then explore how AI practices impact on PsyCap through the satisfaction of BPN of employees as a mediating variable. We derive hypotheses and test them. We conclude with a discussion of the implications of our study.

Appreciative Inquiry: Inquiring Into What Gives Life, Co-Creating the New

Originally, Cooperrider developed AI as a new form of action research (Cooperrider, 1986) capable of building more generative social theory (Gergen, 1982), that is, social knowledge that evokes new ways of thinking and action possibilities among coauthors of a new shared social reality (Bushe, 2007, 2012a, 2012b; Gergen, 1994; Shotter, 1993). During his PhD project at the Cleveland Clinic, Cooperrider discovered that inviting organization members to inquire into the life-giving properties of their system energized them and opened up new possibilities that evoked activities in support of their highest aspirations (Barrett & Fry, 2005; Cooperrider, 1986; Cooperrider & Sekerka, 2006; Cooperrider & Srivastva, 1987). What started as a study into how to develop generative theory evolved into a life-centric approach for understanding and enhancing organizational innovation (for an in-depth discussion of the emergence and evolution of AI, see Bushe, 2012a, 2012b; Cooperrider & Godwin, 2012).

AI is anchored in five theoretical principles (Cooperrider & Whitney, 2001). The social constructionist principle is based on the premise that all organizational realities are local social constructions that people co-create through their relational processes (e.g., exchanges, dialogues, inquiries, storytelling) (Gergen, 1994; Hosking, 2011; Lambrechts, Grieten, Bouwen, & Corthouts, 2009; Shotter, 1993). Language is seen as the most important carrier of meaning-making and organization-in-the-making: Words create worlds (Srivastva & Cooperrider, 1990). Language is not “a mere representation of an outside reality, but . . . an activity of mutual creation and influence” (Bouwen, 1998, p. 305). The simultaneity principle states that inquiry is intervention and vice versa. The moment we begin to inquire into a situation, we are already changing that situation in the direction of what we ask about. Questions are not neutral but fateful: “We live in worlds our inquiries create” (Cooperrider, 2012, p. 108). The poetic principle argues that we can choose what we inquire into. We can decide to study what goes wrong, or we can study what gives life. A basic premise is that what we inquire into will grow (Barrett & Fry, 2005). The three first principles all strongly underline the importance of taking time to formulate questions that open up possibilities. The anticipatory principle states that as we anticipate the future, we are already changing our (inter)actions in the direction of that future image, thus co-creating a new reality (Cooperrider, 1990). Hence, positive anticipatory images of the future evoke positive (inter)actions. Closely related, the positive principle is based on the idea that human systems are heliotropic (Cooperrider, 1990): “They tend to grow in the direction of the helio, or life source” (Barrett & Fry, 2005, p. 48). AI builds on and amplifies this natural tendency (Cooperrider, 1990) by explicitly inviting people to collectively inquire into their system’s life-giving properties and positive anticipatory images of their common future. AI theorists argue that while engaging in these joint activities people are building new generative connections that bring a feeling of joy, aliveness, hope, caring, and potential, leading people to create more and new things without being told or asked to do so (Bushe, 2007; Fry & Bushe, 2012).

Although there is not one best way to do AI, many AI practitioners use the 4-D cycle—published in the late 1990s (Cooperrider & Whitney, 1999)—to guide their change interventions (Bushe, 2012a, 2012b). The 4-D cycle is a concretization of the five principles and visualizes the AI process as a dynamic learning cycle consisting of four sets of task-oriented, collaborative activities, centered on an affirmative inquiry topic (Barrett & Fry, 2005) or design task (Cooperrider, 2012) (i.e., a clear articulation of a new powerful task/purpose with high transformational potential expressing what one really wants to create as a human system).

The four sets are discovery, dream, design, and destiny. In the discovery phase, people engage in a rigorous exploration of experiences regarding the best of the past in order to identify and understand common energy-giving factors of success. As a first step, people are invited to interview each other in pairs with a focus on discovering and valuing what exactly gives life in stories of high-point experiences (“mine for the gold”). These discoveries are then shared in small groups of pairs in order to surface the most common life-giving factors (or “positive core assets”; Cooperrider, 2012, p. 107). The stage is then set for the dream phase (“what might be?”) where people envision together new possibilities about the most common preferred future (e.g., “imagine your organization in 2018, your dreams have become a reality, what is going on, how does the organization look like?”). In the design phase (“what should be?”), people codesign in small groups their common wished-for future. In doing so, they begin to shift their focus from energy-giving factors and dreams to concrete actions and activities. This phase usually results in actionable “provocative propositions” (i.e., challenging and stretching statements provoking the imagination to consider the organization’s positive core—“all past, present, and future (potential) capacity”) or tangible prototypes about what should be in the future (Barrett & Fry, 2005; Cooperrider, 2012, p. 112). In the destiny phase, the groups self-organize and begin to set up activities and projects in order to realize the preferred future. Through improvisation and learning they create value and aim to build an appreciative learning system (Barrett, 1995; Barrett & Fry, 2005; Bushe, 2012a; Cooperrider, 2012).

In the following section, we develop our hypotheses. We start with discussing the relationship between participating in AI practices and the development of PsyCap. We then argue the case for taking BPN satisfaction as an important mediating mechanism.

Hypothesis Development

AI Practices and Psychological Capital

According to Bushe (2012a), the seminal article on AI by Cooperrider and Srivastva (1987) was a precursor to POS, which, in turn, has been argued to provide a rich theoretical grounding of AI (Cooperrider & Sekerka, 2006; Dutton et al., 2006). POS is “the study of that which is positive, flourishing, and life-giving in organizations” (Cameron & Caza, 2004, p. 731) as related to improving performance outcomes in organizations. POS emerged in parallel (Dutton et al., 2006) with the positive psychology movement christened in 1998 by former APA president Seligman. In reaction to the dominant focus in psychology and clinical applications on mental illness and negative affect, Seligman made a strong call to focus more research attention on human thriving and positive affect, and the conditions, virtues and strengths nurturing these valued outcomes (Seligman & Csikszentmihalyi, 2000).

POB is an aspect of POS (Dutton et al., 2006); however, whereas POS has a more organization-level orientation, POB focuses more on positive individual, micro-level states and their development as related to impact on employee performance outcomes (Luthans & Avolio, 2009; Luthans & Youssef, 2007). Indeed, POB is “the study and application of positively oriented human resource strengths and psychological capacities that can be measured, developed, and effectively managed for performance improvement in today’s workplace” (Luthans, 2002, p. 59). These state-like (versus dispositional, fixed, and trait-like) psychological capacities open to development comprise self-efficacy, optimism, resilience, and hope. In combination, these concepts constitute the higher-order, multidimensional construct of PsyCap. Building on evidence from POS and positive psychology, particularly the work of Fredrickson (2001, 2009), Cooperrider (2012) suggests, but does not elaborate upon, how experiencing AI impacts on important PsyCap aspects:

Human systems might well become more resilient and capable of realizing their potentials the more we engage . . . positive emotions—for example, hope, inspiration, and joy. . . . As people come together through the elevation of inquiry, the emotions they experience are often amplified positive emotions, which tend to broaden-and- build. . . . Positivity tends to open thought-action repertoires whereby we are able to see the best in the world. . . . Positive emotions help create a storehouse or build-up of resources over time. These resources might be higher quality relationships or an “accumulation” of such things as positive anticipation, confidence and sense of efficacy, and the build-up of new knowledge. (p. 115)

In the following paragraphs we theoretically argue a positive link between participating in AI practices and the various PsyCap aspects.

Self-efficacy refers to “one’s confidence in his or her ability to mobilize the motivation, cognitive resources, and courses of action necessary to execute a specific course of action within a given context” (Luthans & Youssef, 2004, p. 153). According to POB scholars, self-efficacy can be developed through actual mastery experiences of success, vicarious learning or modelling from others’ successes or success stories, and social persuasion through positive feedback and support leading to psychological arousal (Luthans, Avey, Avolio, Norman, & Combs, 2006; Luthans & Youssef, 2004, 2007). This arousal, stemming from positive social influences, fosters cognitive and emotional positivity (Luthans & Youssef, 2007), which has a broaden-and-build effect (Fredrickson, 2001). Experiencing positive emotions opens and broadens minds—people become more open to others and see more action possibilities—and builds intellectual, relational, and psychological resources (Cooperrider, 2012; Fredrickson, 2001; Luthans & Youssef, 2007).

We propose that participating in AI practices is likely to impact self-efficacy in a number of ways. During the discovery phase, people take the time to inquire into the best in relation to an affirmative transformational inquiry topic (Barrett & Fry, 2005) or design task (Cooperrider, 2012). Through sharing stories of peak experiences and digging into underlying life-giving forces in the spirit of discovery (e.g., “what made that experience so powerful, can you tell me a little bit more?”)—suspending own judgments—participants engage in a kind of relating (characterized by active listening and genuine empathic inquiry into that what gives life) that energizes and leads to positive cognitions and emotions. People feel “eager to act and capable of acting” (Quinn & Dutton, 2005, p. 36, italics added) and build on this energy in the dream phase to collectively envision their ideal shared future where strengths are connected to strengths. Indeed, POB scholars such as Luthans and Youssef (2004, p. 155) underline that “imaginal experiences” of success and being successful in the future enhance self-efficacy. In the words of Cooperrider (1990, p. 118), positive images of the future provoke “confident and energized action.” In the design phase, people can have mastery experiences of building together a prototype of their wished-for organization, affirming their strengths and relationships (for concrete company examples, see Cooperrider, 2012). Actually, doing AI together—and experiencing its generative dynamics and outcomes—might be by itself a powerful mastery experience. For these reasons, we propose that participating in AI is likely to enhance self-efficacy.

Hope refers to “a positive motivational state that is based on an interactively derivedsense of successful (1) agency (goal-directed energy) [willpower] and (2) pathways (planning to meet goals) [waypower]” (Luthans & Youssef, 2007, p. 330). POB scholars underscore the importance of challenging and clear goal setting with manageable subgoals allowing people to direct their will- and waypower effectively. Moreover, in order to develop a sense of successful agency (internalized control), people need to be “in control of their own and their organization’s present and future” (Luthans & Youssef, 2004, p. 155). Organizational cultures and initiatives that loosen managerial control and give people the space to develop both themselves and the organization, nurture the agency or willpower aspect of hope (Lewis, Passmore, & Cantore, 2008; Ludema, Wilmot, & Srivastva, 1997; Luthans & Youssef, 2004, 2007). In addition, the waypower part of hope (i.e., one’s ability to find multiple pathways towards goals; see Luthans & Jensen, 2002), can be stimulated by inviting people to engage in future-oriented anticipatory thinking (e.g., scenario planning), creativity and visualizing future events.

Participating in AI practices likely impacts hope in several ways. By inviting the whole system to collectively work on an affirmative inquiry topic or design task (Barrett & Fry, 2005; Cooperrider, 2012; Lewis et al., 2008), AI allows people to focus their agency and pathways. Engaging the whole system in a collaborative effort to create the best in human enterprise leads to a sense of collective control of moving towards that purpose. Participants are invited, particularly in the dream phase, to collectively envision new possibilities and create images of a shared ideal future state—an activity which Cooperrider (2012, p. 113) calls “big picture scenario development.” Unlike POB scholars (Luthans & Youssef, 2004), who frame this envisioning activity as mental rehearsal to be better prepared for the future, AI scholars—building on the anticipatory and positive principle—rather stress the social reality-building function of co-creating positive images and design pathways of the future (Barrett & Fry, 2005; Cooperrider, 2012). Generating positive images of the future “catalyzes an affirmative emotional climate . . . of heightened optimism, hope, care, joy, altruism, and passion” (Cooperrider, 1990, p. 118). Given these elements, we suggest that participating in AI practices is likely to heighten one’s hope.

Optimism refers to “a positive explanatory style that attributes positive events to internal, permanent, and pervasive causes, and negative events to external, temporary, and situation-specific ones” (Luthans & Youssef, 2004, p. 153). Optimism builds positive expectancies about the future emerging from both the self and others. Without optimism, it is unlikely that people build self-efficacy and hope: Indeed, people, who enact a more pessimistic style, attribute favourable events to external causes and negative events to internal causes hindering learning from and through successes, and successful agency (Luthans & Youssef, 2004, 2007). However, according to POB scholars, it is possible to develop optimism towards the future through both appreciation of current positive aspects of life and seeking new opportunities for development with a positive, curious regard (Luthans & Youssef, 2004). Moreover, Luthans et al. (2006) suggest that activities and experiences conducive to self-efficacy and hope development can also have a positive effect on the enhancement of optimism.

Doing AI likely fosters optimism through the same broaden-and-build mechanism (Fredrickson, 2001) that enhances self-efficacy and hope. AI taps into the life-giving (connections of) strengths that a whole system of people has, building on and amplifying these (combinations of) strengths in the process of imagining new possibilities, and designing and enacting provocative propositions/prototypes of a desired future. As people experience that they themselves—as a collective—can cause their own wished-for positive future through a mutually energizing and rewarding joint activity, and a meaning making language of affirmation and potential, they feel stronger (Cooperrider, 2012; Barrett & Fry, 2005; Whitney, 2010) and more optimistic (Faure, 2006). AI triggers a positive energy spiral (Bouwen, 2002) building optimism through the experience of positive emotions. Research evidence shows that, over time, positive emotions indeed build resources such as optimism, resilience, and elevated relationships (Fredrickson, 2001; Fredrickson, Tugade, Waugh, & Larkin, 2003). Taking these elements together, we propose that engaging in AI is likely to enhance one’s optimism.

Resilience is “the capacity to . . . bounce back from adversity, conflict, failure, or even positive events, progress, and increased responsibility” (Luthans & Youssef, 2007, p. 332). Both asset-focused and process-focused strategies can build resilience beyond reducing risk factors (or stressors) associated with an increased probability of undesired outcomes. Asset-focused strategies call attention to developing resilience through broadening and nurturing one’s assets (e.g., competencies, expertise, knowledge, interpersonal relationships), heightening the probability of better performance. Process-focused strategies focus on creating the system conditions (for example, through fostering strategic planning, creativity and organizational learning) that facilitate people to adaptively employ their assets both reactively (in response to adversary) and proactively (future-oriented) (Luthans & Youssef, 2004, 2007). Resilience can be enhanced through “the practice of caring relationships” (Wilson & Ferch, 2005, p. 45; see also Gittell, 2008), that is, a way of interpersonal relating through which people empower and help each other to grow towards their potential. Likewise, Benard (1993, in Luthans, 2002) identifies social competence (i.e., the ability to build positive relationships through eliciting positive responses from others, flexibility, empathy, caring, good communication skills, and humor) as an important attribute of resilient individuals—next to problem solving skills (i.e., the ability to think abstractly and reflectively, and to plan and attempt alternative possibilities/solutions seeking help from others as needed), autonomy (i.e., having a sense of self and control over what happens), and a sense of purpose (i.e., having positive aspirations and the ability to imagine a positive future).

Participating in AI practices is likely to enhance resilience as follows. AI scholars propose that appreciatively inquiring into what is best and life-giving in each other—as a stepping-stone to envision and design new shared ideas and possibilities—energizes and enhances “the quality of the relational space” fostering cooperative capacity building (Cooperrider in Barrett & Fry, 2005, p. 11; Fry & Hovelynck, 2010). Cooperative capacity building, which can be seen as a combination of asset-focused and process-focused strategies building resilience (Luthans & Youssef, 2004, 2007), is “the process of elaborating and expanding on a system’s strengths . . . in order to move that system from good to great” (Barrett & Fry, 2005, p. 19) becoming a learning organization “continually expanding its capacity to create its own future” (Senge, 1990, p. 14).

Indeed, several authors (e.g., Bouwen, 2002; Bushe, 2007, 2010, 2013; Cooperrider & Sekerka, 2006; Fry & Bushe, 2012; Lambrechts et al., 2009) argue that AI is a relationship-enhancing activity that opens up more and new possibilities to co-create a bright future in a world of possibilities (Cooperrider & Godwin, 2012), fostering the production of self-directed change. The act of genuinely exploring, appreciating, and enhancing the value of others’ contributions (Fry & Bushe, 2012; Fry & Hovelynck, 2010) might be more important than the content of what is being shared (Lambrechts et al., 2009). Similarly, research by Dutton and Quinn (2005) points to the intimate link between experiencing positive energy (through for example AI) and building high-quality connections characterized by mutual appreciation, trust, responsiveness, and authenticity. It is in these high-quality relationships that people feel more engaged, open, and competent (Dutton & Quinn, 2005; Lambrechts et al., 2009; Quinn, Spreitzer, & Lam, 2012). Moreover, according to Fredrickson (2001) and Fredrickson et al. (2003), positivity builds resilience creating a reservoir of strengths that can be employed and developed in order to create the future one really wants. For these reasons, we suggest that participating in AI practices is likely to heighten one’s resilience.

Luthans and Youssef (2007)—who point to several empirical studies supporting the discriminant validity of self-efficacy, hope, optimism, and resilience—argue that particularly in episodes of change and development, PsyCap is addressed in individuals (Luthans & Youssef, 2004). Given the proposed relationships between AI and the four components of PsyCap, we hypothesize that

Hypothesis 1: Participating in AI practices is positively and significantly related to the four components of an individual’s psychological capital: (a) self-efficacy, (b) hope, (c) optimism, and (d) resilience.

AI and Psychological Capital: Basic Psychological Need Satisfaction as Mediator

In this section we take our logic one step further in order to deepen our understanding about why participating in AI practices is likely to nurture PsyCap. Drawing on self-determination theory (SDT), we propose that participating in AI practices likely enhances PsyCap through supporting the satisfaction of people’s basic psychological needs (BPN).

SDT assumes that humans are inherently proactive and oriented towards vitality, engaging interesting activities, optimal psychological development, integrated functioning, and well-being. However, these natural tendencies do not operate automatically—they require the satisfaction of the BPN for competence, autonomy, and relatedness (Deci & Ryan, 2000; Ryan & Deci, 2000).

The need for competence refers to the inherent striving to be able to do something well, or to be effective in mastering the environment—learning new skills in the process—and feeling the pleasure of being effective (Deci & Vansteenkiste, 2004; Sheldon, Turban, Brown, Barrick, & Judge, 2003). In their striving to fulfil the need for competence, people seek challenging environments to stretch their capabilities (Van den Broeck, Vansteenkiste, De Witte, Soenens, & Lens, 2010). Furthermore, when satisfied, the need for competence energizes self-efficacy (Deci & Ryan, 2000).

The need for autonomy refers to “volition—the organismic desire to self-organize experience and behavior and to have activity be concordant with one’s integrated sense of self” (Deci & Ryan, 2000, p. 231). Autonomy thus concerns the innate propensity of self-choice and self-control while (inter)acting (versus being forced to do something): People aspire to create and control their own reality without pressure and experience a sense of freedom and integration in doing so (Deci & Ryan, 2000; Van den Broeck et al., 2010). This does not mean that every action has to be self-initiated. It is also possible to be autonomous in response to others (Deci & Vansteenkiste, 2004). Moreover, rather than the feeling of independence, the autonomy concept in SDT refers to the feeling “that one’s behaviour emanates from and is endorsed by oneself” (Kasser & Ryan, 1999, p. 937).

The need for relatedness refers to “the desire to feel connected to others—to love and care, and to be loved and cared for” (Deci & Ryan, 2000, p. 231). It is the inherent need to feel belongingness and connectedness, and to reciprocally contribute to each other’s development (Deci & Ryan, 2000). Moreover, SDT scholars stress that deep relatedness requires meaningful and in-depth social contacts (Reis, Sheldon, Gable, Roscoe, & Ryan, 2000; Van den Broeck et al., 2010).

Within SDT, needs are defined as universal necessities specifying “innate psychological nutriments that are essential for ongoing psychological growth, integrity, and well-being” (Deci & Ryan, 2000, p. 229, italics added). In this paper, PsyCap is seen as an operationalization of “ongoing psychological growth” as it refers to an individual’s positive psychological state of development. Needs are not learned or open to development—as is the case for the components of PsyCap—but are inherent to human nature. As such, needs operate to promote positive psychological development and full realization of human potentials across the lifespan (Deci & Ryan, 2000; Ryan & Deci, 2000). According to SDT, the three needs are also essential, that is, all of them have to be satisfied for people to thrive. SDT scholars stress that social-contextual conditions can either support or thwart BPN satisfaction, respectively facilitating or forestalling optimal functioning, healthy development, and vitality. As Deci and Ryan (2000) put it,

This framework [SDT], which is built upon the dialectical relation between people, as innately active organisms, and the social environment in which they attempt to satisfy their basic needs, suggests that the degree of basic psychological need satisfaction influences development, performance, and well-being. In short, needs specify the conditions under which people can most fully realize their human potentials. (p. 263)

There is ample research evidence guided by SDT that social environments conducive to the satisfaction of the needs for autonomy, competence, and relatedness foster the natural processes of (a) intrinsic motivation (i.e., doing an activity because people find it inherently interesting, with a full sense of volition and choice), (b) fuller internalization of formerly external regulations of extrinsically motivated behavior to the self in order that people can experience autonomy while enacting them, and (c) pursuing intrinsic aspirations (i.e., life goals such as personal growth, meaningful relationships, and community contribution because they are directly linked to BPN satisfaction), resulting in well-being and positive psychological growth (for reviews detailing supportive empirical studies, see Deci & Ryan, 2000; Deci & Vansteenkiste, 2004; Ryan & Deci, 2000). As such, SDT can both contribute to our knowledge base about the causes of people’s behavior (i.e., people behave like they do because they want to satisfy their BPN in a social environment that either supports or thwarts this striving) and the understanding and design of social practices and conditions (e.g., AI practices) that foster optimal functioning, development, and performance (Ryan & Deci, 2000).

Although there have been calls to use SDT as a theoretical base for deepening understanding of positive experiences and positive human development in positive psychology (e.g., Deci & Vansteenkiste, 2004; Seligman & Csikszentmihalyi, 2000; Sheldon et al., 2003), the explicit link between the mechanism of BPN satisfaction and nurturing PsyCap is missing. Indeed, SDT and POB scholars have not yet sufficiently explored the possibility of synergy between their respective knowledge bases. We, however, build on SDT and argue that AI practices are likely to positively impact on PsyCap because AI enacts a social context that is supportive of the satisfaction of the three BPN.

When people go into conversation about the best of the past in order to co-create their ideal collective future, they often produce energy-in-conversation (Quinn & Dutton, 2005) and new possibilities and actions (“generativity”) (Bushe, 2007, 2010, 2012a, 2012b, 2013; Bushe & Kassam, 2005; Cooperrider, 2012; Cooperrider & Srivastva, 1987; Fry et al., 2002). Energy-in-conversation is defined by Quinn and Dutton (2005) as

(1) a person’s energy level, which that person interprets automatically as a reflection of how desirable a situation is, (2) a person’s interpretation of a conversational partner’s energy from his or her expressive gestures, and (3) a feeling of being eager to act and capable of acting, which affects how much effort a person will invest into the conversation and into subsequent, related activities (Marks, 1977). (p. 43)

Drawing on self-determination theory, Quinn and Dutton (2005) proposed that people feel more energy when they interpret speech acts to satisfy their needs for competence, autonomy, and relatedness, and energy decreases when people interpret speech acts to thwart the fulfillment of those needs. Building on this idea, we suggest that AI allows people to experience increased energy because their BPN are met, which in turn enhances individual positive psychological growth or PsyCap.

Given the discussion above on AI and its potential impact on PsyCap, we argue that AI is an activity that supports BPN satisfaction. To start with, AI is likely to fulfill the need for competence. From the beginning, people are invited to share and inquire into experiences about when they were at their best (“peak experiences where they were in their strength or felt most competent”) examining the life-giving factors that helped make these experiences happen. The discovery of these shared strengths energizes people to envision, design, and enact their ideal image of the future together while building cooperative capacity (Barrett & Fry, 2005; Bushe, 2010, 2013; Cooperrider & Srivastva, 1987; Fitzgerald et al., 2010). AI invites people to stretch in new directions while experiencing a sense of progress (Barrett, 1995). Moreover, during the whole AI process, people are invited to build and experience mastery experiences that affirm and amplify their strengths (e.g., asset-focused and process-focused strategies).

AI is also likely to satisfy the need for autonomy. Indeed, a key aspect of AI’s transformational potential is its emphasis on supporting self-directed change processes emerging from self-generated new ideas rather than implementing a preordained plan created by management (Barrett & Fry, 2005; Bushe, 2007, 2010, 2012a, 2013; Bushe & Kassam, 2005). Loosening control is the very beginning of AI. This enables people to take the space to increasingly become self and organization developers, or architects of their own future. They themselves create what they feel is the best for the organization and activate self-supporting structures (Barrett & Fry, 2005; Bushe & Kassam, 2005), building an organizational culture that fosters the satisfaction of the need for autonomy.

As a relationship-enhancing activity (e.g., Bouwen, 2002; Bushe, 2007, 2010, 2013; Cooperrider & Sekerka, 2006; Fry & Bushe, 2012; Lambrechts et al., 2009), AI is also likely to fulfill the need for relatedness. As Cooperrider (in Barrett & Fry, 2005, p. 11) puts it: “Relationships . . . come alive where there is an appreciative question, when there is a deliberate search for the good and the best in one another; . . . AI [is] a way of creating a relational space for the cooperative construction of reality.” Moreover, AI centers people’s attention on an affirmative inquiry topic or design task (Barrett & Fry, 2005; Cooperrider, 2012), which often addresses life goals or highest intrinsic aspirations (e.g., personal development, meaningful relationships, community contribution), thereby supporting the satisfaction of the three BPN (Deci & Ryan, 2000; Ryan & Deci, 2000). Given the discussion thus far, we hypothesize that:

Hypothesis 2: Participating in AI practices is positively related to the satisfaction of the three basic psychological needs for (a) autonomy, (b) competence, and (c) relatedness.

Hypothesis 3: The relationship between participating in AI practices and individual psychological capital is fully mediated by the satisfaction of the three basic psychological needs for (a) autonomy, (b) competence, and (c) relatedness.

Method

Participants and Procedure

In late fall 2009, a total of 213 people fully completed an online questionnaire. All respondents worked in social profit organizations, i.e., organizations that use capital cost-efficiently in order to maximize the achievement of a social objective, thereby creating added social value in terms of socio-economic effects and benefits (Bouckaert & Vandenhove, 1998). Respondents belong to two groups: a group with AI experience (“AI organizations”) (n = 81) and a group without AI experience (“non-AI organizations”) (n = 132).

As for the group with AI experience, we invited the members of the Flemish AI learning network to fill out the survey. During a 15-month period (October 2008–December 2009), a total of 79 network members from 44 organizations learned how to apply the AI principles and approach. Experienced AI scholars and practitioners facilitated the network activities. Participation in the activities led up to the “Appreciative Inquiry Certificate in Positive Business and Society Change,” certified by the Weatherhead School of Management (Case Western Reserve University). Three Flemish organizations were prepared to participate in our research (response rate: 6.8%): a community development organization (response rate: 73% of employees), an employment and housing agency (response rate: 100% of employees), and an educational services provider (response rate: 56% of employees). At the time of the survey, these organizations were working with AI practices for at least one year and two to eight of their staff members had been participating in the learning network. The main raison for the other organizations not to participate was a lack of time.

Besides participation in the collective activities, members of the AI learning network were involved in action learning groups, carried out an AI pilot project in their respective organizations, and created a personal learning portfolio drawing on their own experiences. The collective network activities consisted of 10 days of AI training. In the kickoff meeting, participants engaged in AI interviews for a first time. Next, a 4-day workshop on “AI fundamentals” focused on the principles of AI, its theory base, the 4-D intervention method, practices, and applications. Particular attention went to framing the AI pilot projects, setting up the action learning groups (nine in total), formulating design topics, and crafting generative questions. During a 1-day “Design Factory” and a 2-day AI specialization training, participants practiced the design and destiny phase of the 4-D cycle in order to advance their respective projects. A 2-day workshop concluded the collective activities with network members’ presentations to an audience of 300 people (Bouwen, Hovelynck, & Verheijen, 2010).

Each action learning group convened 2 full days a month. Here, participants discussed—with the help of a facilitator—the progress of their pilot projects while experimenting with AI principles to develop constructive group cooperation in the here and now. They connected on a more personal level, reflected on experiences, and deepened knowledge of AI together. The purpose of the pilot projects was using AI to enact an “appreciative organization practice,” connecting strength to strength and passion to passion (topics include, but are not limited to, mission and identity, learning organizational culture, and leadership). For example, participants stimulated their employees and other stakeholders to conduct AI interviews with each other, and they organized summits using the 4-D cycle guiding their organization development efforts (Bouwen et al., 2010).

As for the comparison group without AI experience, we used a convenience sample employing a snowball technique (Fricker, 2008). The first author invited several persons working in social profit organizations via his personal network to fill out the online questionnaire that was distributed through several social media in Flanders and the Netherlands. At the same time, these invitees were asked to pass on the invitation to other relevant actors in their networks to expand the sample size. This process resulted in 132 respondents from social profit organizations who stated to have no prior experience with AI. The use of the snowball technique makes it impossible to calculate the response rate of the questionnaire (see also “Limitations and Future Research”). The demographic characteristics of the total sample are presented in Table 1.

Table 1.

Demographic Characteristics of the Sample.

	%	n	M	SD
Gender
Comparison group		132
Men	25.00
Women	75.00
AI organization		81
Men	23.50
Women	76.50
Age
Comparison group		132	41.98	10.26
AI organization		81	38.68	8.78
Total		213	40.74	9.84
Tenure
Comparison group		132	11.23	10.10
AI organization		81	6.29	7.25
Total		213	9.39	9.43

Measures

Independent variable

Experience with AI was measured as a dichotomous variable, taking on the value of 1 if AI experience was present (“AI organizations”) and taking on the value of 0 if AI experience was lacking (“non-AI organizations”).

Mediating variables

The variables measuring the satisfaction of the three basic psychological needs (for competence, autonomy, relatedness; “BPN satisfaction”) were the mediators in our study. Participants filled out the Basic Need Satisfaction at Work Scale (e.g., Deci et al., 2001). In previous research this scale has demonstrated satisfactory psychometric characteristics, and the scale has been frequently used (e.g., Deci et al., 2001; Ilardi, Leone, Kasser, & Ryan, 1993). The scale was translated to Dutch and subsequently cross-translated back to English by a linguist who had not seen the original English version. The scale contains 21 items related to the needs for competence, autonomy, and relatedness. Six items assess the satisfaction of the need for competence (e.g., “People at work tell me I am good at what I do”), seven items measure the satisfaction of the need for autonomy (e.g., “I feel like I can pretty much be myself at work”), and eight items assess the fulfilment of the need for relatedness (e.g., “I get along with people at work”). All items were rated on a 7-point Likert-type scale ranging from 1 (not at all true) to 7 (very true). After reverse scoring of the items formulated in a negative direction, for all items a higher score pointed to a higher degree of satisfying the needs for competence, autonomy, and relatedness.

Dependent variables

The dependent variables in our study comprise of the four dimensions of Psychological Capital. Participants responded to 13 items derived from the Psychological Capital Questionnaire (Luthans, Norman, Avolio, & Avey, 2008; Luthans, Youssef, & Avolio, 2007). These items were also translated to Dutch and then cross-translated back to English by the same linguist. The original 24-items questionnaire has demonstrated acceptable psychometric properties (e.g., Luthans, Avolio, Avey, & Norman, 2007). The most important reason to shorten the scale to 13 items was to reduce the length of time necessary to fill out the questionnaire. The 13 items were chosen based on the highest factor loadings from Luthans’ research. The subscale self-efficacy contains three items (e.g., “I feel confident analyzing difficult problems to find a solution”), the subscale hope four items (e.g., “I can think of many ways to get out of a jam”), the subscale optimism three items (e.g., “In uncertain times, I usually expect the best”), and the subscale resilience three items (e.g., “I usually manage difficulties one way or another”). Participants rated the items on a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). After recoding all items, a higher score on the measure referred to a higher level of PsyCap.

Control variables

Gender, age, and tenure were registered for each participant. We performed preliminary analyses to find potential confounding effects of these variables. As neither age, tenure nor gender were related to PsyCap, we decided to exclude these variables from further analyses.

Analyses and Results

As all data are self-reported and collected through a single questionnaire using a cross-sectional research design, we first performed Harman’s one-factor test to assess if common method variance (i.e., variance that is attributed to the measurement method rather than the constructs of interest) poses a problem to interpretation of observed correlations. The variables measuring BPN satisfaction and PsyCap were entered into an exploratory factor analysis, using unrotated principal axis analysis. This was done to determine the number of factors necessary to account for the variance in the variables. If a substantial amount of common method variance is present, either a single factor will emerge from the factor analysis, or one general factor will account for a large proportion of the covariance among the variables (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Podsakoff & Organ,1986). A single factor could only account for 26.28% of the variance, confirming that no general factor is apparent (Podsakoff et al., 2003; Podsakoff & Organ, 1986). Hence, common method bias is negligible in our data. As none of the predictors correlated more than .66 with each other (see Table 2), multicollinearity did not pose a threat to the validity of the analyses. Table 2 presents the means, standard deviations, Cronbach’s alpha (on the main diagonal) for each scale and correlations between the variables.

Table 2.

Descriptive Statistics for the Variables.

	M	SD	Self-efficacy	Hope	Optimism	Resilience	Autonomy	Competence	Relatedness
Self-efficacy	5.57	0.81	.76
Hope	5.31	0.77	.73	.80
Optimism	5.12	1.16	.42	.57	.82
Resilience	5.40	1.06	.51	.61	.68	.83
Autonomy	4.95	1.01	.28	.34	.31	.33	.77
Competence	5.32	0.97	.46	.54	.39	.42	.66	.73
Relatedness	5.19	0.79	.31	.18	.13	.14	.32	.50	.76

Note. All correlations of .14 and above are statistically significant at the .05 level; the main diagonal (in bold) reflects the Cronbach’s alphas for each scale.

To test our hypotheses, we performed a mediation analysis following the steps proposed by Kenny, Kashy, and Bolger (1998). First, we used t-tests to examine the direct relationships between AI, our independent variable, and the four dimensions of PsyCap, our dependent variables. In the second step we performed several t-tests to investigate the direct relationships between AI and the satisfaction of the three basic psychological needs, our mediators. Table 3 displays the results of both sets of analyses.

Table 3.

Direct Relationships Between AI and the Dependent Variables (Psychological Capital Dimensions) as Well as the Mediators (Basic Psychological Need Satisfaction).

Significance test^a					Effect size
Variable	M non AI	M AI	t	p	d
Psychological capital
Self-efficacy	5.50	5.69	−1.64	.10	0.24
Hope	5.24	5.41	−1.46	.14	0.21
Optimism	4.98	5.35	−2.27	.02	0.33
Resilience	5.24	5.65	−2.69	.01	0.39
Basic psychological need satisfaction
Autonomy	4.69	5.37	−5.05	.00	0.72
Competence	5.13	5.63	−3.75	.00	0.54
Relatedness	5.03	5.46	−3.92	.00	0.56

t-test with df = 207; Cohen’s d is reported as an effect size.

The results show that AI organizations differ significantly from non-AI organizations on two PsyCap scales: AI organizations score significantly higher on optimism (p = .02) and resilience (p = .01). The relationships between AI and the PsyCap elements self-efficacy and hope are positive but not significant. Based on Cohen’s d, the differences were rather small in size. These results partially support Hypothesis 1, which states that participating in AI practices is positively and significantly related to people’s self-efficacy, hope, optimism, and resilience. The results also demonstrate that all differences between AI organizations and non-AI organizations with respect to BPN satisfaction were significant (p< .001 for all three BPN) and of medium size. These results provide full support for Hypothesis 2, which suggests that participating in AI practices is positively and significantly related to the satisfaction of the three BPN.

The third step in the mediation analysis consisted of testing a direct effect between the mediators and the outcome variables. An additional analysis is then needed to assess whether the direct effect between AI and PsyCap decreases when the mediators are added to the model. A path analysis in AMOS 20 was performed with manifest variables (i.e., the summated scale scores) as this allowed us to test all conjectured relationships or hypotheses in a single model. As a fully saturated model was tested (i.e., all possible relationships between the variables in the model were estimated), no fit measures were calculated (as a saturated model evidently yields a perfect model fit; see Kline, 2010). Preliminary linear regression analyses were performed and standardized residuals were inspected in order to detect possible outliers that could falsely drive significant findings. Based on this analysis, data from one participant was removed from the data set.

Figure 1 depicts the direct relationships between the variables in the model. Nonsignificant relationships are omitted from the figure. Results show that satisfying the need for competence has direct relationships with all four PsyCap dimensions. Satisfying the needs for autonomy and relatedness do not have direct significant relationships with the components of PsyCap. As none of the direct effects between organization type (AI or non-AI organizations) and PsyCap were significant in this analysis step, we can conclude that the effect of AI on the four PsyCap dimensions is mediated by the satisfaction of the need for competence. In other words, the relationship between AI and the four PsyCap dimensions only becomes positive and significant through the fulfilment of the need for competence. Hence, the mediating mechanism of competence need satisfaction explains the relationship between AI and the PsyCap elements.

Figure 1.

Path analysis relating AI with Basic Psychological Need satisfaction (mediators) and Psychological Capital dimensions.

In the fourth and final step we used a bootstrap method to formally test the indirect effects of AI on the four PsyCap dimensions through the satisfaction of the need for competence. As autonomy and relatedness need satisfaction had no significant relationship with PsyCap, we omitted the test of the indirect effect for these variables. Bias corrected confidence intervals were calculated around the parameters of the indirect effects in order to test their significance. Indirect effects with 95% confidence intervals not containing zero were considered significant.

The results in Table 4 confirm that all indirect effects between AI and the four dimensions of PsyCap through fulfilling the need for competence are significant. Thus, the results of the third and fourth step of the analysis partially support Hypothesis 3, which states that basic psychological need satisfaction mediates between participating in AI practices and PsyCap.

Table 4.

Bias Corrected Bootstrapped Confidence Intervals for the Indirect Effects of AI on Psychological Capital Dimensions Through Satisfaction of the Need for Competence.

		Confidence interval
Outcome variable	B (β)	Lower bound	Upper bound
Self-efficacy	.207 (.124)	.097	.344
Hope	.183 (.115)	.061	.287
Optimism	.242 (.101)	.097	.375
Resilience	.238 (.109)	.102	.371

Discussion

Responding to calls for more quantitative studies of AI (e.g., Bushe, 2012a, 2012b; Bushe & Coetzer, 1995; Jones, 1998), we studied the impact of AI on PsyCap through the mediating mechanism of BPN satisfaction. Using data from 213 participants who worked in social profit organizations and either belonged to a group with AI experience or without AI experience, we found that the satisfaction of the need for competence mediated the relationship between participating in AI practices and the PsyCap dimensions self-efficacy, optimism, resilience, and hope. Furthermore, we discovered that participating in AI practices deeply satisfies the three BPN for competence, autonomy, and relatedness.

The present study makes several contributions. Drawing on self-determination theory describing BPN satisfaction as the active mechanism fostering full development of human potentials and the importance of the social context either supporting or thwarting BPN satisfaction (Deci & Ryan, 2000; Ryan & Deci, 2000), we suggested that people who participate in AI practices would likely co-create a social context supportive of a high degree of BPN satisfaction. Most people with firsthand experience of participating in AI practices describe AI as energizing and generative. In trying to explain this experience, we argued that while doing AI, people are often likely to produce more energy-in-conversation as an indication of increasing BPN satisfaction (Quinn & Dutton, 2005). Our results confirm this expectation. In comparison with people from the group without AI experience, people working in AI organizations reported significantly higher scores on the satisfaction of all three BPN. This finding suggests that people’s behaviour and experiences while doing AI might be better understood in terms of their striving to satisfy their BPN for competence, autonomy, and relatedness co-creating a new social context that is supportive of BPN satisfaction. Careful framing of life-centric generative questions that open up possibilities regarding an affirmative transformational inquiry topic or design task in the 4-D learning cycle is likely very important as to guiding this supportive reality creation effort (e.g., Barrett & Fry, 2005; Bushe, 2007, 2010; Cooperrider, 2012).

Building on the basic postulate of self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2000) that satisfying all three BPN is essential for ongoing psychological growth—operationalized here as an individual’s positive psychological state of development or PsyCap (Luthans & Youssef, 2007)—we proposed that satisfaction of the three BPN would likely mediate the relationship between participating in AI practices and PsyCap. As measured in this study, however, only the satisfaction of the need for competence showed significant direct relationships with the four PsyCap dimensions mediating the relationship between AI and PsyCap. In comparison with people from the group without AI experience, people working in AI organizations experienced an increased satisfaction of their need for competence, leading to higher levels of resilience, self-efficacy, hope, and optimism.

At first, we were surprised with this result because we expected that the satisfaction of all three BPN would likely mediate the relationship between AI and PsyCap. However, after deeper inquiry, we propose that the finding ties in with what Bushe (2012a, p. 94) calls “perhaps the most underexplored theory of change behind AI”: AI’s foundational idea that every organization has the natural capacity for cooperation and change embedded in (often implicit) life-giving practices and that explicitly inquiring into these practices, with the aim of amplifying them through an AI learning cycle, elevates the capacities of both organizations and individuals “to realize their enormous potentials” (Barrett & Fry, 2005, p. 12; Bushe, 2010, 2012a; Cooperrider, 2012; Cooperrider & Srivastva, 1987; Fitzgerald et al., 2010). AI scholars have always been stressing that AI is above all a powerful strength-based learning approach to building cooperative and individual capacity (Barrett & Fry, 2005; Cooperrider, 2012; Cooperrider & Srivastva, 1987). In this sense AI is primarily focused on building learning organizations, continually expanding social systems’ capacity to create their own futures (Senge, 1990) through building on, amplifying and connecting (combinations of) strengths and capacities. Concerning the impact of AI on individuals’ “state-like psychological resource capacities of self-efficacy, hope, optimism, and resilience” (Luthans & Youssef, 2007, p. 328) or PsyCap, our findings suggest that AI’s ability to strongly address people’s innate need for competence is very important. This finding connects well with the view of self-determination theory scholars who argue that “the adaptive consequences of . . . a need for competence are . . . the most straightforward [of the three needs], because an interested, open, learning organism can better adapt to new challenges in changing contexts” (Deci & Ryan, 2000, p. 252). People have the inherent need to “experience satisfaction from learning for its own sake” making it more likely for them “to develop new potentialities” (Deci & Ryan, 2000, p. 252) and to “develop an expansive competence, an ability to see the nascent potential and radical possibilities that can expand beyond the boundaries [of the present state]” (Barrett & Fry, 2005, p. 41). Our findings suggest that AI allows and promotes people to fulfil this need. This is not to say that AI’s ability to satisfy the needs for autonomy and relatedness is not important. However, as PsyCap is concerned, satisfying the need for competence seems more central.

This study has also shown the potential of using self-determination theory to explore underlying mechanisms that enrich understanding of why AI works and this particularly in relation to heightening individual’s psychological capital. Furthermore, our examination has demonstrated the synergistic value of linking several aspects of the knowledge bases of AI, SDT, and POB scholarship. However, future researchers might want to explore cross-fertilization further.

Limitations and Future Research

Although this study presents interesting findings, it is not without its limitations, which point to future research. First, the sample of the study may limit generalizability to other sectors as the study was based on data provided by organization members all working in social profit organizations. To overcome this potential problem, future research could focus on other sectors and types of organizations.

Second, although our comparison group was similar in many respects to the AI group and all participants worked in the social profit sector, the comparability with the AI group could be further improved. That is, a follow-up study could purposefully target a comparison group of organizations that are prescreened for similarity on a larger number of variables with the AI group in order to lessen concerns related to alternative explanations. Similarly, future research might also find it fruitful to inquire into and build theory on the uniqueness of AI in producing the outcomes observed in this study vis-à-vis other change methods that engage people in meaningful change.

Third, although measuring AI as a dichotomous independent variable (AI organizations or non-AI organizations) is straightforward, it does not allow theory building about (combinations of) multiple working ingredients or dimensions of AI practices and their possible differential impact on human and organization development. Future research could contribute by developing a psychometrically sound scale capable of measuring multiple dimensions of AI. A good starting point might be to devise items capable of capturing (the interplay between) appreciating and connecting strengths, recently identified by Cooperrider and Godwin (2012) and Cooperrider (2012) as two interacting core dimensions of AI and flourishing organizations.

In combination and complementary to this scale building effort, future research might benefit from longitudinal qualitative research (Bushe, 2012a) that inquires into the concrete relational practices of AI and their outcomes over time (e.g., Lambrechts et al., 2009; Lambrechts, Bouwen, Grieten, Huybrechts, & Schein, 2011). This type of research is likely to offer more insight into the intricacies of how and why AI works and impacts on the development of persons and organizations. Particularly, analyzing data from a combination of participant observation (detailing observable behaviour) and in-depth interviewing (detailing stories and sense making) is likely a fruitful approach. Indeed, a mixed method approach (Jick, 1979) using quantitative and qualitative approaches in a complementary way promises to contribute highly to our knowledge base on why AI works. It then becomes possible to inquire more comprehensively into the “inner workings” of AI, its potential impact on persons (e.g., in terms of heightening intrinsic motivation, or learning orientation) and organizations (e.g., in terms of building systemic health, cooperative capacity, or adaptive and generative learning systems), and potentially important intervening or underlying mechanisms.

Practical Implications for Leaders of Change

A principal agenda for most of today’s leaders is to help create organizing contexts in which people can thrive and are able to continually develop individual and cooperative capacity for exceptional performance. In light of this challenge, our results suggest that leaders of change would be well advised to help enact and sustain the principles and 4-D learning cycle of AI described in this article. There are several good reasons to do so. First, our results clearly indicate that engaging in AI practices greatly fulfils people’s three basic psychological needs for competence, autonomy, and relatedness, “lead[ing] a person to thrive [according to self-determination theory] in the same way that a plant thrives when it is given sun, soil, and water” (Sheldon et al., 2003, p. 366). Second, our results also suggest that participating in AI unlocks and develops the human potential of organization members in terms of heightening their confidence, hope, optimism, and resilience—or their psychological capital—particularly through satisfying the innate need to be competent and to be able to learn with and from others. In conclusion, AI is an effective way to increase psychological capital as well as basic needs satisfaction, both of which are conditions for co-creating new possibilities and effective systemic change.

Footnotes

Acknowledgements

We would like to thank Professor Emeritus René Bouwen (Leuven University), Professor Ronald Fry (Case Western Reserve University), and Professor Gervase Bushe (Simon Fraser University) for inspiring us to write this paper. We would also like to thank the organizers and participants of the AI learning network initiated by Stebo for their support. We thank the editor and the three anonymous reviewers for their comments and assistance in developing this manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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