THE RELATIONSHIP BETWEEN APPRECIATIVE INQUIRY AND WORK ENGAGEMENT

THE RELATIONSHIP BETWEEN APPRECIATIVE INQUIRY AND WORK ENGAGEMENT

Master thesis, Master of Science Business Administration, specialization Change Management Faculty of Management and Organization, University of Groningen

October 19, 2014 JELMER IJBEMA Student number: 1752847 Liebergerweg 734 1223 PZ Hilversum Tel.: +31 (0)6- 55724013 E-mail: j_ijbema@hotmail.com Supervisor University: H. Grutterink

Faculty of Management and Organization, University of Groningen

Supervisor field of study:

R. Masselink

Abstract

Despite the fact that Appreciative Inquiry (AI) is a worldwide accepted approach for conducting organizational change and although current literature often links AI with work engagement, little empirical evidence can be found. The present study expands AI literature by hypothesizing that AI relates positively to Work engagement. In addition, based on positive psychological theories, about psychological resources, I hypothesize that AI relates to work engagement through increases in (1) relatedness, (2) perceived expertise affirmation, and (3) self-efficacy. Data was collected among 123 employees working in 4 organizations. Although the mediating mechanisms could not be empirically supported, the results showed that AI was associated with work engagement. The practical implications of the research are discussed and I conclude that AI can be a practical tool in workplaces to enhance work engagement.

INTRODUCTION

Our world is changing and becoming increasingly complex. As the external environment is evolving and developing, organizations have to be prepared to adapt their organizational models to new models and trends (Cummings & Worley, 2004). When organizations are in a change process, employees are confronted with situations like job insecurity, role conflict, resource reductions and uncertainty about their own future (Burnes, 2004). These organizational uncertainties and changes often have a strong impact on employees’ psychological well-being, nurturing more uncertainty and more stress (Burnes, 2004). However, most organizations expect their employees to show initiative and show high quality performance standards (Sonnentag, Volmer, & Spychala, 2008). In recent years, work engagement has been receiving increased interest, as it can be seen as a competitive advantage (Avey, 2008).

Work engagement is crucial for the realization of change (Avey, Wensing, & Luthans 2008). Work engagement refers to a fulfilling, affective-motivational state of work- related well-being that is the contrast of a burnout (Bakker, Schaufeli, & Salanova, 2006). Engaged employees are important for several reasons. First, engaged employees are more loyal to their organizations than disengaged employees (Federman, 2009). Second, engaged employees lead to better business outcomes and is the best business predictor (Colan, 2009). Third, engaged employees have consistently shown to be more productive, safer, precise and less like to leave an organization (Federman, 2009). In contrast, disengaged employees cost organizations billions in years in losses due to high absenteeism and high turnover (Colan, 2009).

Although, it has almost become a truism that the way to maximize work engagement during change is to include employees in the change process, research has shown that many of the traditional change approaches do not deliver the desired results (Axelrod, 2002). The traditional change approaches traditionally isolate a problem, diagnose it, and find a solution (Burnes, 2004). A demerit of focusing on problems is that people create even more and bigger problems (Bushe, 2012). Organizations can get stuck in a vicious circle of problem solving, and in the end, problems will only grow in number and severity. This can leave organizational members disempowered, demoralized, and hopeless about their future (Cooperrider & Whitney, 1999). Consequently, people could become disengaged.

Only by acknowledging and addressing the needs of employees, organizations will meet their change aspirations (Head, 2000). These insights have led to a number of new approaches that are based on positive psychology (Bushe, 2012). This positive perspective focuses on understanding the characteristics, processes and factors that occur when organizations function at peak performance in both human and organizational terms (Buckingham & Clifton, 2001). The best-known approach is Appreciative Inquiry (AI) (Bushe, 2012).

(Cooperrider & Whitney, 1999). At the centre of AI is the Appreciate interview, in which people uncover what gives life to their organization when it is at its best (Peelle, 2006). According to AI proponents, AI could have several important effects: positive emotions, a more compelling image of the organizational future and eventually work engagement (Whitney & Trosten Bloom, 2003). Furthermore, case studies in health showed that AI socially constructs an environment of cohesiveness (Bushe, 1998). Cohesiveness is the degree to which members are attracted to a group or organization and motivated to remain part of it (Schermerhorn, Hunt, & Osborn, 2002). In sum, several literature streams from different fields, i.e. positive psychology (Fredrickson, 2009), performance management (Buckingham & Coffman, 1999), and health (Omish 1998) share a common belief in the increase of motivation to change that can be achieved through a focus on the positive. They suggest that successful change and motivation will arise when employees strive to reach an inspirational vision, as in an AI approach.

In spite of the fact that literature suggests that AI is promising, little empirical evidence can be found about the effectiveness of AI. This study aims to evaluate the expected positive relationship between AI and work engagement. Moreover, it aims to shed light on the psychological mechanisms (self-efficacy, perceived expertise affirmation and relatedness) driving this relationship.

Self-efficacy, is an individual’s belief about his or her ability and capacity to achieve a task (Bandura, 1997). The concept of self-efficacy is gaining popularity among researchers and practitioners, since it has been found to increase motivation (Schunk, 1995), innovative behaviours and creativity (Spreitzer, Kizilos, & Nason, 1997). Although the benefits of the concept self-efficacy are already shown for organizations, it appears to be hard to increase the level of self-efficacy in organizations due to a lack of knowledge about what self-efficacy really is about.

Since AI refers to active stepping into high quality relations, relatedness is included in this study. Relatedness is about building meaningful connections with other people (Deci & Ryan, 2000). Another reason for including relatedness in this study is the fact that researchers found a relation between relatedness and performance (Baard, Deci, & Ryan, 2004).

The third mediator, perceived expertise affirmation was chosen, since recent research showed promising results (Kenny, 1994; Grutterink, Molleman, & Van der Vegt, 2012). Kenny (1994) found that when there is a relation between perceived expertise affirmation and performance. Furthermore, work in the area of reciprocal expertise affirmation, found a relation with effort on the individual level (Grutterink et al., 2012).

THEORY

Engaged employees are desirable for every organization, because engagement is an important predictor for reducing turnover intention (Federman, 2009). Work engagement is especially crucial for organizations during change (Axelrod, 2002). Not only has research shown that engaged employees will undergo change more successfully (Avey Patera, & West, 2006), engaged employees are also more positive and able to create alternate pathways when there are obstacles (Avey et al, 2006). In short, because employees are responsible for adapting and behaving in ways managers intend, during organizational change, work engagement is vital for change success (Armenakis & Bedeian, 1999).

Work engagement

Bakker, Schaufeli and Salanova (2006) defined work engagement as the positive antipode of burnout that can be defined as a positive, fulfilling, work-related state of mind. According to them this concept consists of three dimensions: absorption, vigor, and dedication. Bakker et al. (2006) explain that absorption refers to total concentration on and happy immersion in work characterized by time passing quickly and finding it difficult to detach oneself from one’s work. The dimension of vigor refers to high levels of energy during work, an employee’s willingness to make appreciable efforts in his or her job even in difficult situations (Bakker et al., 2006). The third dimension of engagement is called dedication. Dedication is characterized as a strong psychological involvement at work, combined with a sense of enthusiasm, inspiration, significance, pride and challenge. According to Bakker et al (2006), work engagement is a combination of these three dimensions (Bakker et al., 2006).

Appreciative Inquiry

While there are many ways to describe AI as a philosophy and methodology for change, a practice-oriented definition of AI is: “The cooperative, co evolutionary search for the best in people,

their organizations, and the world around them. AI involves systematic discovery of what gives “life” to an organization or a community when it is most effective and most capable in economic, ecological, and human terms” (Cooperrider & Whitney, 2005: 8).

AI began life in the late 1980s as a reconfiguration of action research (Cooperrider & Srivastva, 1987). AI evolved out of a set of experiences of David Cooperrider at Case Western Reserve University beginning with the Cleveland clinic that developed into a systematic process of change focusing on the strengths and positive components of an organization and is a reaction on the traditional problem solving models to change. AI challenges the traditional paradigm with an affirmative approach, which can be seen in table 1. The traditional models are primarily deficit and problem based (Cummings & Worley, 2005). The proponents of AI stated that traditional problem solving approaches to change rarely result in new vision, are slow, conservative and are notorious for generating resistance to change (Barrett, 1995; Cooperrider & Whitney, 2004). AI in contrast, helps employees to understand and appreciate their organization when it function at its, furthermore, they use the best practices to achieve a better future. Therefore, it can be argued that an AI approach is a more healthier and effective way of approaching change (Cooperrider & Whitney, 2005).

Table 1

Conventional change methods versus Appreciative Inquiry

Traditional methods Appreciative Inquiry

Focus on deficit Focus on the positive

Focus on the current situation Focus on the past and future

Focus on problems Focus on solutions

Focus on requirements Focus on an ideal situation

Focus on objectives Focus on inspiration

Note: Adapted from Masselink (2008)

The practice of AI

ways to realize the ideal common vision that embody the hopes and vision of the employees (Cooperrider & Whitney, 2005). Now the cycle can start again.

FIGURE 1

Appreciative Inquiry 4-D cycle

Phase Description

Discovery Discovering the best of what is through appreciative interviews.

Dream _{Dreaming of what might be and sharing these dreams by presenting as dramatic} enactments.

Design Designing an ideal future by drafting possibility statements.

Destiny _{The sustaining of the changes undertaken through communication of intended ideas} and the utilization of groups in order to strategically plan and implement the required action.

Note. Adapted from Willoughby & Tosey, 2007

Theorists claim that AI is powerful because participants become engaged and inspired by focusing on their own peak performances. Most of the time, in a workshop setting participants remember and relate personal experiences of success, identify the common elements of these experiences, and devise statements and action plans for making those experiences occur more often in the organization (Faure, 2006).

TABLE 2 Appreciative interviews

Function Description

Setting a positive tone Sharing past successes magnifies these successes in the minds of the group and makes the current challenges feel more manageable. “We’ve done it before, why can’t we do it again?”

Valuing participants Everybody has a success story to tell, no matter how small. Retelling it in a supportive environment gives a sense of personal achievement that is highly motivational and helps ensure the active participation of all

Creating personal connections The interview questions are deep, important questions that invite intimacy, thus creating close personal connections. Such personal connections not only facilitate working together on the spot but also later into the day-to-day environment.

Reducing differences The interviews are usually conducted in “improbable pairs” of people that would not normally work together or speak to each during their normal work day. The distrust that often exists between such groups melts away as they discover their similarities to be far greater than their differences

Reducing anxiety Interviews are held in small groups, because many people feel anxious in large groups, and are unsure whether their voice will be heard. The intimacy of one-on-one connections provides the feeling of safety and comfort they need.

The five principles of appreciative inquiry

The theoretical underpinnings of AI consists of five principles, which can be seen in table 3. These principles are described by Cooperrider, Whitney and Stavros (2003). The first principle is the constructionist principle. The constructionist principle states that reality is socially constructed. Social knowledge and organizational destiny are interwoven. The questions asked become the material out of which the future is conceived and constructed (Cooperrider et al., 2003). That is why people need to “think of words as actions, as powerful tools to do things” (Barrett & Fry, 2005)

The second principle is the principle of simultaneity. This explains how inquiry and change occur simultaneous. The practice of AI requires a process where inquiry is used positively as a part of the change process. “The moment we ask a question, we begin to create a change” (Whitney & Trosten-Bloom, 2010: 54). The moment one starts to explore a topic, respondents recall such moments; questions can stimulate ideas and generate possibilities. Therefore, the principle of simultaneity argue that the seeds of change are the things people think and talk about, the things people discover and learn, and the things that inform dialogue and inspire images of the future. The moment one starts to explore an affirmative topic, other employees recall such moments, ask questions, which can stimulate innovative ideas and actions (Cooperrider et al., 2005).

The third principle is the poetic principle. Because reality is a human construction, an organization is like an open book in which its story is being co-authored continually by its members and those who interact with them (Cooperrider et al., 2008). The important implication is that one can study virtually any topic related to human experience in any human system or organization (Cooperrier et al., 2003).

The fourth principle, the anticipatory principle postulates that the image an organization has of its future guides organization’s current behaviour. An organization’s positive images of its future will anticipate, or lead to, positive actions (Cooperrider et al., 2003). Therefore, it can be concluded that AI foster a self-fulfilling prophecy of hope and optimism.

TABLE 3

The Five Appreciative Inquiry Principles

Principle Description

The constructionist principle The idea that a social system creates or determines its own reality

The principle of simultaneity The idea that inquiry and change occur simultaneously and that inquiry (the nature of questions asked) has a determining impact on the nature of change.

The poetic principle Teams and organizations, like open books, are endless sources of study and learning. What we focus on grows.

The anticipatory principle The idea that images of the future guide and inspire present day actions and achievements

The positive principle Positive questions lead to positive actions. The more positive the questions used guiding a change process, the more long-lasting and successful the change effort.

The relationship between AI and work engagement

This research proposes that there is a positive relationship between AI and work engagement. Different studies in health and positive psychology have shown that positive organizational phenomena can make a significant contribution in organizational outcomes (Fineman, 2006). Fredrickson and Losada (2005) found that positive emotions, positive communication and expressions of support resulted in higher team performance, ideas, initiatives and work engagement. Moreover, they found that positive emotions lead to more initiatives. In line with this, Bakker, Demerouti and Eeuwema (2005) found in their study among 1000 teachers that job resources buffered the impact of job demands on burnout. More precisely, they found that job demands such as stress, and emotional and physical demand did not result in high levels of burnout if employees experienced high levels of autonomy, feedback, social support or coaching. Furthermore, in another study it was found that appreciation by colleagues, and a positive organizational climate influenced work engagement (Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007). In conclusion it can be argued that engagement can be fostered by a positive climate, positive images of the future and a social climate.

the appreciative interview. During this interview, every employee has the opportunity to tell his or her story of past successes and peak performances (IJbema & Masselink, 2011). As a result, employees show strengths and experience positive feelings (Cooperrider et al., 2008). Consequently, the focus on strengths engages the curiosity and enthusiasm, which can result in positive emotions, such as hope, pride and satisfaction (Peelle, 2006). Several case studies (Bouwen, 2010; Verheijen, 2010) showed that an AI approach can result in positive experiences, as desired to generate an increased level of work engagement. Fredrickson & Losada (2005) empirically validated that positive expressions and communication of support among team members clearly distinguished engaged teams over disengaged teams. More detailed, in their research with 60 management teams, the authors showed that 15 teams clearly produced better results (as indicated by customer satisfaction, evaluations by superiors, peers, and subordinates and profitability) based upon their speech acts. Positive speech was coded for encouragement, support, and appreciation, while negative speech was coded for disapproval, cynicism, and sarcasm. Nineteen teams with negative verbal interactions showed inferior performance, while sixteen teams with mixed verbal interactions showed average performance.

Furthermore, the anticipatory principle, which refers to the idea that images of the future guide and inspire present day actions and achievements, can foster positive images of the future and a self-fulfilling prophecy of optimism and hope (Cooperrider & Whitney, 1999). In AI, participants’ images of what the future “might be” derive from existing practices. Therefore, AI reframes team members from problems to be overcome into sources of hope and inspiration. This is in line with positive psychology literature (Lockwood & Kunda, 1999). They found that a positive image increased scores of performance and effort. This in turn can lead to employee engagement.

In conclusion, it can be argued that AI leads to employee engagement for two reasons. First, because AI inquire into positive experiences which may lead to a positive climate and therefore engagement. Second, in AI, inquire into positive experiences and best practices foster positive images of the future, and a self-fulfilling prophecy of optimism and hope, which in turn can lead to employee engagement. Therefore the first hypothesis can be drawn:

The mediating role of Self-Efficacy

Self-efficacy have been researched by various researchers in the organizational field, drawing on social cognitive theory (Wood & Bandura, 1989). It can be defined as an individual’s belief about his or her ability and capacity to achieve a task (Bandura, 1997). Individuals’ self-efficacy beliefs have a significant effect on how people think, feel, motivate themselves and take actions (Bandura, 1997). For example, self-efficacious people show initiative in stressful situations and take a broader role responsibility in contrast to people with low self-efficacy (Parker, 1994). Bandura (1986) affirms that four elements can affect self-efficacy: (a) past achievements: positive experiences make people believe in themselves; (b) observation of others, that is the experience of success stories that can be followed as references; (c) verbal persuasion, verbal stimulation that motivate people to believe in their own abilities; (d) emotional state, which refers to the spirit or situation encountered by individuals.

AI can be expected to increase self-efficacy, and in turn work engagement. In AI, the constructionist principle refers to the theory that our actions are shaped by our view of the world and that this view is itself shaped by our past experiences and especially by our interpretation of those experiences through dialogue and discussion with other (Cooperrider & Whitney, 1999). Furthermore, the AI process naturally results in positive emotions such as optimism and hope. (Cooperrider et al., 2003). The discussions about peak performances during the appreciative interviews and dream phase foster positive images of the future and themselves, which can result in self-fulfilling prophecy as stated in the anticipatory principle. Therefore, AI could convince people that they have what it takes to achieve their tasks. This is in accordance with the transformational leadership research (Ilies, Nahrgang, & Morgesen, 2007). They found that transformational leadership research showed that self-efficacy can be positively influenced by communicating positive about past experiences (Ilies, et al., 2007). Taken that appreciative leaders may influence self-efficacy by communicating in a positive way, it can be assumed that AI enhances self-efficacy.

In turn, it can also be expected that self-efficacy increases work engagement. Prior positive psychology and empowerment research demonstrated that self-efficacy influences effort, persistence and work engagement (Bandura & Schunk, 1981; Fredrickson, 2009). Self-efficacious people have hope and optimism (Cameron, Dutton, & Quinn, 2003). Hope and optimism in turn leads to motivation, performance and work engagement (Peelle, 2006). In line with this, Bakker (2008) found that self-efficacious people have lower levels of perceived stress response, which is directly related to a higher level of work engagement (Bakker & Schaufeli, 2008). Therefore, I assume that the greater the level of self-efficacy is in relation to an activity, the greater will be his/her engagement

of AI work engagement through self-efficacy.

The mediating role of relatedness

AI can be expected to increase relatedness, and relatedness in turn is expected to create work engagement. Relatedness is about building meaningful connections with other people (Deci & Ryan, 2000). Trusting and supporting relationships are important drivers of work engagement (Kahn, 1990). Employees can experience relatedness when they are part of a close-knit team or group, and when they have the ability to support others and feel supported by others (Deci & Ryan, 2000). An AI approach, in particular, the constructionist, poetic, and anticipatory principle, strives toward co-sharing of strengths, beliefs, values, vision and commitment leading towards an interdependent community (Bouwen & Taillieu, 2004). Therefore, there are several reasons that AI can foster relatedness.

First, during appreciative interviews, to assess the optimal situation and peak performances, the interviews will be conducted in improbable pairs who would not work together on a daily basis (Peelle, 2006). Every single employee is involved and has the possibility to tell his or her peak performance and best practice. The interviews will be conducted in a safe environment. Therefore, intimate relations and close personal connections can be created on the spot, but also later in work situations. Furthermore, differences can be reduced, because people discover similarities with colleagues (Faure, 2006). This is in accordance with Reis et al. (2000). They found that feeling understood and appreciated were strongly related to relatedness

Second, during AI, and in particular the design and destiny phase, employees are encouraged to work together and create goals, a shared vision, and an action plan based on what gives strength and life in the organization. This is in accordance with the social exchange literature, that showed that an increased level of relatedness will apply if employees share common goals and a socially oriented workplace (Kirkpatrick, 1998). Furthermore, self-determination theory posits that for fulfilment of relatedness, a sense of connectedness to others is indispensable. As argued, AI meets these conditions, by strengthening the bonding between employees through the social climate. In sum, it can be expected that AI can increase relatedness.

On the other hand, it can also be expected that relatedness foster work engagement. The empowerment literature has consistently found that relationship oriented behaviour by the leader is positively associated with work engagement (Seligman & csikszentmihalyi, 2000). Furthermore, studies in health found that employees who are better integrated in social networks and who feel connected with others possess better physical and mental health and show higher performance and effort (Reis et al. 2000). In line with this, they found that high quality relations help people cope more effectively with stress and fosters bonding with co-workers (Ornish, 1998). In addition, Nohria, Hasselblad, & Stebbins et al. (2008) found that cohesiveness with colleagues and the organization boost engagement.

Hypothesis 3: The relationship between the perceived application of the AI principles and employees’ engagement is mediated by employees’ relatedness

The mediating role of perceived expertise affirmation

It can be expected that the relation between AI and work engagement is mediated by perceived expertise affirmation. Perceived expertise affirmation fosters the extent to which team members respect, value, and affirm each other’s expertise (MacPhail, Roloff, & Edmondson, 2009). Basically it is how team members think other team members see them (Grutterink, van der Vegt, & Molleman, 2012). Research have shown that individuals who think that other employees are aware of their expertise, show more effort, performance and are more motivated (Kenny, 1994).

It can be expected that an AI approach enhances the level of perceived expertise affirmation, in particular the appreciative interviews. Employees are telling their success stories in a supportive environment, and are creating close personal connection. Furthermore, when employees discuss about their qualities and peak performances, they discover each other’s qualities and show in a positive way which role they want to play (Faure, 2006). Moreover, they also see which role they see for their colleagues, based on their qualities (Cooperrider et al., 2003). Therefore, it is likely employees get the idea that other employees will know them and their expertise better, which in turn will increase perceived expertise affirmation.

It can also be argued that perceived expertise affirmation increases work engagement, for several reasons. First, individuals who think that others are aware of their expertise exert more effort (Grutterink, 2012). In line with this, when employees experience is a high level of perceived expertise affirmation, employees believe that their contribution to the task and performance is recognized, which motivates them to contribute to the team task (Mac Phail et al., 2009). Furthermore, Kenny (1994) found that individuals who think that others are aware of their expertise are more motivated, exert more effort and perform better. Performance and effort in turn, are directly related to work engagement (Kahn, 1990). Therefore it can be argued that perceived expertise affirmation would increase work engagement. In conclusion, based on the arguments it can be proposed that there is a positive indirect effect of AI on work engagement through perceived expertise affirmation.

Therefore the following hypothesis can be drawn:

METHOD Respondents and procedure

The heterogeneous group of employees and managers consisted of 8 departments from 4 different Dutch organizations, within the retail (2), IT, and public sector. The sample comprised 130 employees who volunteered to participate. Prospective organizations were targeted through the network of the author. All targeted organizations followed an AI instruction, with the author as facilitator. The participants were told that their participation would take up to 5 to 10 minutes. Paper questionnaires were sent to the managers of the departments who then distributed the questionnaires to their subordinates. Out of the 130 employees, 123 returned their completed survey, resulting in a response of 94%. It was explained that participation in the study was on a voluntary basis and that individual responses were kept confidential. Participants’ ages ranged from 19 to 63 with a mean of 38.5 (sd = 12.7) years. They had a mean of 8.7 (sd = 10.9 ) years of experience in the organization and a mean of 7.6 years (sd = 9.7 ) in their current position. Department sizes ranged from 4 to 60 (M = 15.1, sd = 3.0) employees. Most participants were men (69%) and 32 participants worked as manager. 61 Participants worked as social services employees (48.8%); 32 as retail professionals (26.4%); 13 as HR professionals (10.7%); 6 as consultants (5.0%); 6 as developers (5.0%) and 5 as sales and marketing professionals (3.3%).

Measures

Since, to my knowledge, there is no valid and reliable scale to measure AI, AI was measured with 5 new items that were developed based on the 5 principles of AI: (a) simultaneity; (b) poetic; (c) anticipatory; (d) positive; and (e) constructionism (Cooperrider, 2000). The questions were discussed with several AI-practitioners and deemed adequate. Each of the five items of the scale reflects a principle (e.g. To what extent is the positive principle applied in your department). All items were written in Dutch and rated on a 5-point Likert scale; 1 = ‘completely agree’ and 5 = ‘completely disagree’. Cronbach’s alpha of the scale was .95.

Work engagement was measured using the 17 items scale of UWES (Schaufeli, Bakker, &

Salanova, 2006). This scale consists of 3 dimensions; vigor (e.g., “At my work, I feel bursting with energy”), dedication (e.g., “I am enthusiastic about my job”) and absorption (“I feel happy when I am working intensely”).

All items were rated on a 5-point Likert scale; 1 = ‘completely agree’ and 5 = ‘completely disagree’. After factor analysis two were deleted (see Appendix for a list of all 15 items α = .96.

Self-efficacy was measured using the validated 3 items scale derived from Faber, Janssen, &

Relatedness was measured using the 6 items scale from the corresponding dimension from the 18

items of the self-determination theory scale from Deci & Ryan (2000). These items were translated to Dutch and all negative items were recoded. This scale consisted of the following items: “I don’t feel really connected with other people at my job,” “At work, I feel part of a group”; “I don’t really mix with other people”; “At work I can talk with people about things that really matters to me”; “I often feel alone when I am with my colleagues”; “People I work with, are really close friends”. All items were rated on a 5-point Likert scale; 1 = ‘completely agree’ and 5 = ‘completely disagree’. After factor analysis, the relatedness item “My colleagues are my friends” was removed from the scale. The α of the scale with the 5 remaining items was .91.

Perceived expertise affirmation was measured using the validated three items scale from Grutterink

et al. (2012). These items were translated to Dutch, e.g. ‘Other members are fully aware of my expertise’. The items were rated on a 5-point Likert scale; 1 = ‘completely agree’ and 5 = ‘completely disagree’. α for this scale was 90.

Control variables were also measured. Past research has shown that gender and age can influence

employee work attitudes slightly positive (Hui & Tan, 1996). They found that older people and men are likely to express higher job satisfaction than younger people and women. Therefore, I used these as control variables in my analysis.

Factor analysis

Before starting the initial factor analysis all negatively phrased items were recoded. These questions are marked with (r) in Appendix 2. Next, a principle component analysis was performed using Varimax rotation in order to explore the underlying structure of the variables (Cooper & Schindler, 2003). All variables were tested in 1 factor analysis.

Expected was that from the 34 items, 7 factors would be extracted. Only 5 factors were finally extracted, as can be seen in the initial factor analysis: appendix 2. The initial factor analysis showed 5 KMO-SMA for this analysis was .91 which is higher than the 0.6 limit (Cooper & Schindler, 2003). Bartlett’s test of Sphericity was significant (.00).

on the same factor as the AI items, therefore it also seems to measure the level of AI, because positivity is an important driver of AI. This item consisted of two parts. It also seems to measure the amount of positive principle of AI, instead of work engagement. The third deleted item was “At my work I feel bursting with energy”. This item had a moderate loading on its own factor (.57) but showed a cross loading with the AI factor, suggesting that it also measured the level of AI. It seems that this question also consisted of two parts, it also seems to measure the positive principle of AI rather than work engagement, because energy is also an important driver of AI. The five factors explain 75% of the variance, all factors have an Eigenvalue that is >1. The results of the final factor analysis can be found in Appendix 3.

Data analysis

SPSS 17.0 software for Windows (SPSS Inc, Chicago, IL) was used for all the analyses. All hypotheses were tested using the SPSS-macro for estimating indirect effects in multiple mediation (Preacher & Hayes, 2004). All hypotheses concern mediation, thus, where an independent variable (X) affects a dependent variable (Y) through one or more intervening variables, or mediators (M) (Baron, & Kenny, 1986; Preacher, & Hayes, 2004). Typically, simple mediation is assessed heuristically by testing three separate models. These criteria follow directly from the definition by Baron and Kenny (1986). Variable M is considered a mediator if: (1) X significantly predicts Y, (2) X significantly predicts M, and (3) M significantly predicts Y controlling for X.

The Sobel test statistically tests for an indirect effect through a mediator rather than just testing the criteria as described by Baron and Kenny (1986). Still, the Sobel test has several drawbacks, such as the assumption of normality within the sampling distribution when working with small samples.

Results

In this section the results of the analyses are presented. First, the correlation matrix is discussed, followed by the results from the tests for indirect effects as hypothesized using the bootstrapping method from Preacher & Hayes (2008).

Correlations and descriptive statistics

Means (M), Standard Deviations (SD), and Pearson Correlation of the Variables

Variable M SD 1 2 3 4 5 6

1. Appreciative Inquiry 3.63 1.09

2. Relatedness 3.73 .88 .68**

3. Perceived expertise affirmation 3.63 1.07 .71** .63**

4. Self-efficacy 3.87 .81 .39** .45** .27**

5. Work engagement 3.48 .86 .73** .56** .61** .22*

6. Gender 1.30 .46 .07 .08 .02 .09 .05

7. Age 38.56 1.2 .05 .07 -.03 .12 -0.19 -.02

Hypothesis testing

Figure 1. Illustration of a multiple mediation model tested in the study

Step 1 of our approach was to regress our 3 proposed mediators onto AI. This is showed in Table 5. Results of these analyses indicated that AI was significantly associated with all three proposed mediators and all were in the theoretically proposed direction. Specifically, a higher level of AI was associated with a higher level of relatedness (β = .57 t = 10.30, p = .00), perceived expertise affirmation (β = .73 t = 11.12, p = .00), and self-efficacy (β = .30 t = 11.80 p = .00).

As can be seen in table 5, step 2 examined the association of our 3 proposed mediators with work engagement. Results of this analysis indicated that relatedness (β = .11 t = 1.25, p = .22), perceived expertise affirmation (β = .12 t = −4.51, p = .10), and self-efficacy (β = -.11 t = -1.47 p = .14) were not significantly associated with increased work engagement.

Steps 3 and 4 examined the total and direct effects of AI on work engagement, respectively. Results of step 3 (total effect) indicated that higher levels of AI was associated with higher levels of work engagement (β = .61 t = 11.89 p = .00). Furthermore, in step 4, after controlling for our proposed mediators, the direct effect of AI was also significant (β = .49, t = 6.10, p = .00). In conclusion, according to the macro, there was an overall indirect effect. It showed that the combination of mediators had an effect in the relationship, however, no significant relation was be found between the specific mediators and work engagement, and therefore, the mediating relations were not significant.

The final analysis examined the multiple mediation effect. Results of this analysis are found in Table 6. In this table, significance of our mediators is determined when the 95% confidence interval

does not overlap ‘0’. In brief, none of our set of 3 proposed mediators significantly mediated the relationship between AI and work engagement. However, pairwise contrasts indicated that perceived expertise affirmation was of significantly greater magnitude than self-efficacy ( 95% CI of .035 to .214). This means, the mediating effect of perceived expertise affirmation was statistically stronger than that of self-efficacy. In conclusion:

Test of hypothesis 1

The results showed that the practice of AI was positively related to work engagement. Therefore,

Hypothesis 1: There is a positive relation between the perceived application of the AI principles and employees’ engagement was accepted.

.

Test of hypothesis 2

The results showed that the practice of AI and work engagement was not mediated by perceived expertise affirmation. Therefore, hypothesis 2: The relationship between the perceived application of

the AI principles and employees’ engagement is mediated by employees’ self-efficacy was rejected.

Test of hypothesis 3

The results showed that the practice of AI and work engagement was not mediated by relatedness. Therefore: Hypothesis 3: The relationship between the perceived application of the AI

principles and employees’ engagement is mediated by employees’ relatedness was rejected

Test of hypothesis 4

The results showed that the practice of AI and work engagement was not mediated by self-efficacy. Therefore: Hypothesis 4: The relationship between the perceived application of the AI

principles and employees’ engagement is mediated by employees’ perceived expertise affirmation was rejected

TABLE 5

Results of the direct effects of AI to mediators (a paths) and the direct effect of mediators on work engagement (b paths)

Relationship between AI and the mediators (a paths)

Mediators Coefficients SE t p

Relatedness .57 .05 10.30 .00

PEA .73 .06 11.12 .00

Self-efficacy .30 .06 4.65 .00

Relationship between the mediators and work engagement (b paths)

Mediators Coefficients SE t p

Relatedness .11 .08 1.24 .21

PEA .12 .07 1.64 .10

Self-efficacy -.10 .07 -1.47 .14

Total effect of AI on work engagement (c path)

Coefficients SE t P

AI .60 .05 11.89 .00

Direct relationship between AI and work engagement (c’ path)

Coefficients SE t P

TABLE 6

Mediation of the effect of appreciative inquiry on work engagement through relatedness,

perceived expertise affirmation and self-efficacy

Product of

Coefficients

Bootstrapping

Percentile 95%

CI

BC 95% CI

BCa 95% CI

Point

Estimate

SE

Z

Lower Upper Lower Upper Lower Upper

Indirect Effects

Relatedness

.06

.05

1.25

-.02

.16

-.01

.16

-.01

.17

PEA

.09

.05

1.65

-.01

.17

-.00

.18

-.00

.18

Self –

efficacy

-.03

.02

-1.42

-.08

.01

-.08

.01

-.09

.01

TOTAL

.11

.06

1.88

-.00

.23

.00

.23

.00

.23

Contrasts

Relatedness

vs. PEA

-.02

.08

-.28

-.16

.13

-.16

.13

-.16

.14

Relatedness

vs.

self-efficacy

.09

.06

1.57

-.01

.22

-.01

.23

-.01

.23

PEA vs.

self-efficacy

.11

.05

2.15

.03

.21

.03

.21

.04

.21

Discussion

The goal of this study was to examine the relation between AI and work engagement. The present study contributes to the knowledge about AI in different ways. More specifically, the results indicated that AI is related to work engagement through the mediators perceived expertise affirmation, relatedness and self-efficacy. In addition we found support that AI contributes significantly to the three mediators. I will discuss the contributions of this study in more detail below.

Findings

People spend a substantial part of their lives working, whether in a high-tech start-up, a financial institution or a garment factory. As a result, the quality of their workplace experience is inevitably reflected in their quality of lives. Business leaders worldwide must raise the bar on employee engagement. Increasing engagement is vital to achieving sustainable growth for companies and for putting the global economy back on track to a more prosperous and peaceful future. An AI approach assumes to create flourishing organizations through a positive and dialoguing approach (Cooperride et al, 2003). The main objective of this study was to investigate the impact of an AI approach on individual employees’ work engagement. Work engagement refers to involvement and energy, reinforced by personal and job resources (Bakker & Demerouti, 2008).

As for hypothesis 1, the results of this research suggest that employees’ positive emotions and attitudes as in AI may be important in creating work engagement, which can be seen as the opposite of a burnout (Bakker et al, 2006). Specifically, the higher the perceived practice of AI, the more negative emotions and reactions are combatted. This indicates that an AI approach can be interpreted as a higher level of absorption, dedication and vigor in the workplace. Therefore it can be argued that an AI approach fosters the job resources as needed for more work engagement.

Finally, we tested the mediating effect of perceived expertise affirmation, relatedness and self-efficacy in the relation between AI and engagement. Before making statements in general about the influence of the mediating effect on the relation between AI and work engagement, I will discuss the findings relating the mediation role of self-efficacy, relatedness and perceived expertise affirmation.

Although the hypotheses are rejected, the correlations were high with AI and work engagement and regression analysis confirmed the first part, from AI to relatedness, self-efficacy and perceived expertise affirmation. Because of the high correlation, the findings are encouraging for the relation between the mediators and work engagement. However, because of high correlations, it can be a blurry picture (Nunnally, 1978). A careful examination of results shows that there could be a mediating relation. Therefore, although if a clear relationship was not confirmed, there are assumptions that the relationship does exist and that the hypothesis should not be rejected. Although there was no significant mediation, the total mediation model was stronger than the direct effect (table 2). This implicates that the mediators did have a positive effect on the outcome. Furthermore, this implicates that there was a partial mediating effect for the total mediating model. This is interesting, given that theorists mainly considered direct outcomes (Bushe, 2012; Bushe & Kazam, 2005).

Mediating mechanisms could help understanding how AI may affect outcomes in the field. Employees who perceived high levels of the practice of the principles of AI, are likely to have more positive emotions, and subsequently show a higher level of work engagement and therefore have less job stress and are less cynical. However, surprisingly, there was a negative relation between self-efficacy and work engagement. This is in contrast with the positive psychology literature (Fredrickson, 2009). She found a positive and significant relation between positive emotions and performance and effort. This research results build more empirical support for the mediation theory of employee emotions in the workplace, future research could replicate and extend this study.

Limitations and future research

A second limitation is that no causal conclusions can be drawn. No manipulation was part of this research. For instance, it is possible that a third variable influences the relation between AI and work engagement. Following, future longitudinal and experimental studies can establish some causal impact of AI-practices on employees.

Third limitation is the small sample size. Only 123 employees participated, a small sample sized might lead to finding with low validity or reliability. Therefore, for future studies to find more reliable results, a larger sample size is needed.

Fourth limitation is that there is no validated questionnaire of AI. This can affect the answers. Perhaps, this limited deepening hypothesized relations. Future research could focus on a validated scale of AI. Moreover, an unanticipated problem was encountered during the factor analysis. The AI-scale we developed for the purpose of this study loaded on the same factor as perceived expertise affirmation, suggesting a low divergent validity. Therefore it seems that AI and perceived expertise affirmation seems 1 factor. However, based on Cooper & Schindler (2003) sufficient loadings depend on the sample size. Generally, it can be taken that sufficient loadings depend on the sample of the data set. Therefore, future research should test the same with a bigger sample size to confirm this.

Implications for research

This research aided in highlighting the potential of AI and positive change. In accordance with the expectations of AI practitioners (Masselink, 2006; IJbema & Masselink, 2011), the research makes strong empirical contributions to research on AI. More specifically, the results showed that the perceived practice of AI principles yields more effect on employees’ work engagement.

In order to further explore the effect of AI in general, several possibilities can be suggested. First, in order to generalize the results of this research, the study should be repeated with larger sample sizes. Second, the research provided the AI network interesting information through the integration of AI into the field of employee behaviour. Future research should test other mediators and variables to show the impact on organizational outcomes. Third, future research should focus on validating an AI scale, in order to understand AI better.

Practical implications

This research also provides some recommendations for organizations to stimulate Appreciative Inquiry. According to the effects of AI, it provides implications for developing AI in organizations. For example, Van Rhenen (2008) showed that work engagement does have significant implications at the individual level. They found that work engagement is positively related to health, change, well-being, social relationships and retention. Furthermore, Federman (2010) found that work engagement is the best business predictor. Therefore, the results of the current study indicate that AI may be effective in organizational change and results.

Furthermore, through AI change can be reached more rapidly. Engaged employees are more willing to support and participate in change processes, which increases the speed of change (Federman, 2009). Another related implication is the competitive advantage. Research found that an engaged workforce is a strong competitive advantage (Federman, 2009).

In conclusion, the results make an important contribution to research in understanding and demonstrating the effects of AI in practice. The research showed that AI is likely to be a valuable way of realizing positive long lasting change. It seems that the positive and dialoguing character of AI contributes to the development of a workforce that show more relatedness, perceived expertise affirmation and self-efficacy and work engagement. In addition, the results showed useful information on points of excellence, talent management and future possibilities, which can lead to discussions in organizations. Therefore, this study adds support to the claim that AI may be an effective way of approaching change management (Cooperrider & Whitney, 2005). Although this study only tested the relationship between AI and engagement, it seems crucial that organizations introduce AI.

Conclusion

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Appendix 1: Employee questionnaire

Appreciative Inquiry in teams

Rijksuniversiteit Groningen

Voor dit onderzoek naar het gebruik van Appreciative Inquiry in teams, vragen we u een vragenlijst in te vullen over uw werkbeleving met betrekking tot uw team binnen de gemeente Zwolle.

Het invullen van de vragenlijst duurt ongeveer 10 minuten. In eerste instantie lijken het misschien veel vragen, maar u zult merken dat de vragen gemakkelijk zijn en dat u de lijst snel in kunt vullen. Soms lijken vragen op elkaar. Toch willen we u vragen alle vragen te beantwoorden.

Uw gegevens zullen vanzelfsprekend strikt vertrouwelijk behandeld worden en niemand anders dan de onderzoekers zal uw individuele antwoorden zien. Het uiteindelijke rapport bevat alleen informatie over voldoende grote groepen, zodat individuele antwoorden niet te achterhalen zijn.