Wk 3, IOP 490: Creating a Change Strategy

AchievingSuccessatwork_DevelopmentandvalidationoftheMotivationalClimateatworkQuestionnaire.pdf

Home >Psychology homework help >Wk 3, IOP 490: Creating a Change Strategy

Achieving success at work: development and validation of the Motivational Climate at Work Questionnaire (MCWQ) Christina G. L. Nerstad1, Glyn C. Roberts2, Astrid M. Richardsen1

1Department of Leadership and Organizational Behaviour, BI Norwegian Business School 2Department of Coaching and Psychology, Norwegian University of Sport Sciences

Correspondence concerning this article should be addressed to Christina G. L. Nerstad, Department of Leadership and Organizational Behaviour, BI Norwegian Business School, N-0442 Oslo, Norway. E-mail: [email protected]

doi: 10.1111/jasp.12174

Abstract

Although work represents an important achievement setting, research on the per- ceived motivational climate, as defined by the achievement goal theory (AGT), remains limited. Calls have been made for research on the salience of such situ- ational influences based on traditional AGT. Therefore, the aim of this research was to develop a scale to measure the motivational climate at work. In a pilot study, participants completed a developed questionnaire and the findings sup- ported psychometric properties of the questionnaire. Two further studies were conducted and the findings provided evidence of content validity, criterion-related validity, construct validity, and internal consistency. The findings suggest the ques- tionnaire might be used to determine the perception of the extant motivational climate in the workplace.

Over the past 30 years, motivation theories based on social cognitive constructs have gained in recognition (e.g., Bandura, 1986; Nicholls, 1989). The achievement goal theory (AGT) has been particularly influential primarily because it incorporated both personal and environmental determinants of achievement behavior (e.g., Ames, 1992b; Nicholls, 1989) and triggered a wave of research into the dynamics of motiva- tion (e.g., Kaplan & Maehr, 2007; Maehr & Braskamp, 1986; Nicholls, 1989). The popularity of AGT and the proliferation of studies have often resulted in unspecified variability in the original conceptualizations (DeShon & Gillespie, 2005; Kaplan & Maehr, 2007). As a result, a paradigm shift from the original AGT (see Papaioannou, Zourbanos, Kromidas, & Ampatzoglou, 2012) to more distanced and complex theo- retical approaches has occurred (e.g., Elliot & McGregor, 2001; Vandewalle, 1997). Such “new and improved” versions of traditional AGT (Nicholls, 1984, 1989) have been accepted and supported in the industrial/organizational psychological literature, but these extensions are not without their critics (e.g., Maehr & Zusho, 2009; Papaioannou et al., 2012; Roberts, 2012). Unlike the new perspectives, the most power- ful aspect of AGT is that it incorporates not only individual difference variables (goal orientations), but also the situ- ational determinants (motivational climate) of achievement behavior within the same conceptual structure (Nicholls, 1989; Roberts, 2012). Thus, AGT becomes pertinent to

further clarify the relevance of contextual information for employee motivation in line with the theorizing of Nicholls’s traditional AGT perspective (Payne, Youngcourt, & Beaubien, 2007): How does the structure of the environ- ment make it more or less likely that an individual will strive to achieve success? The premise of this line of research—and of this present research—is that the individual interprets the existing criteria of success and failure in the environment, perceiving the behaviors necessary to achieve success and/or avoid failure (Roberts, 2012).

The construct of primary interest in this study is conceptu- alized as the motivational climate (Ames, 1992a). We assume that the perceived achievement criteria of success and failure in the workplace are highly relevant for predicting employee outcomes. Such a work climate can influence employees’ per- ceptions and understanding of what is valued and expected in a certain work setting (Kopleman, Brief, & Guzzo, 1990). Most previous research has focused on individual achieve- ment goal orientation (DeShon & Gillespie, 2005; Payne et al., 2007), while a few real work-setting studies have inves- tigated goal orientation at the team or group level of analysis (e.g., Bunderson & Boumgarden, 2010). However, there is no common conceptualization, and the existing measures are neither conceptually coherent with traditional AGT nor with recommended approaches to the assessment of climate (cf. Kuenzi & Schminke, 2009). Therefore, it is difficult to follow

bs_bs_banner

Journal of Applied Social Psychology 2013, 43, pp. 2231–2250

up on Payne et al.’s (2007) call for more research using Nicholls’ (1984, 1989) AGT approach. To begin addressing this issue, the main purpose of the three current studies is to present initial work in the development and validation of a new, reliable, and valid measure of the perceived motivational climate at work, termed the Motivational Climate at Work Questionnaire (MCWQ).

The MCWQ is theoretically based on traditional AGT. The intended contribution of the three studies presented here is fourfold. First, we present a measure that is theoretically con- sistent with traditional AGT that can be used to investigate how employees perceive the motivational climate at work. Second, we demonstrate how climate perceptions relate to adaptive or maladaptive response patterns among employees. Third, our second and third study extend our knowledge of the relationship between the perceived (psychological) moti- vational climate and employee achievement motivation, per- formance, attitudes, and well-being (cf. Parker et al., 2003). In general, psychological climate perceptions—that is, an ind- ividual’s perception of the work environment—are impor- tant to investigate because they predict a variety of individual outcomes important in organizational behavior (Kuenzi & Schminke, 2009; Parker et al., 2003). In addition, individual- level findings might help inform theory building at the organizational level (Parker et al., 2003).

AGT research has been criticized for its focus on organizational effectiveness rather than individual outcomes (DeShon & Gillespie, 2005). Fourth, our research attempts to contribute to motivational theory and, more specifically, to AGT, by clarifying the impact of the motivational climate on work-related behavior and well-being of individual employees. To facilitate this, we controlled for personal dispo- sition variables (i.e., goal orientation) that have been argued to influence how individuals interpret a given situation (DeShon & Gillespie, 2005).

Perceived (psychological) motivational climate: conceptual and definitional issues

Achievement outcomes have been given considerable attention since the 1970s (e.g., Johnson & Johnson, 1974). However, less attention has been given to the investigation of motivational processes that mediate these outcomes: Motiva- tion had been viewed as a goal in itself (Ames & Ames, 1984b). Ames (1984) argued for the importance of focusing on different goal-reward structures (or climates) and their influence on cognitive and self-evaluative motivational factors and achievement behaviors.

The conceptual development of the motivational climate was accomplished predominantly in the sport and educa- tional domains (Ames, 1992a, 1992b; Jagacinski & Nicholls, 1984) and successfully extended into the sport domain

(Seifriz, Duda, & Chi, 1992). The current studies extend the conceptualization to the work domain. Despite the achieve- ment domains of work, education, and sport representing different universes with different criteria of what demon- strated competence may be and with different valued out- comes, the motivational processes are very similar.Within the domain, the individual assesses the criteria of success and failure and assesses whether he or she has the competence to meet the criteria of success. In sport that may include the per- ception of the sometimes extreme physical and psychological workload to achieve success. In work, it may be the high intellectual and psychological workload to achieve success. However, in all domains, it is the understanding of what it takes to achieve success and to avoid failure that is the crucial element. That is why we are attempting to focus on work setting definitions and to determine the criteria of success that may be extant in work settings.

Existing measurements applied in education and sports have typically emphasized that the motivational climate could be created by the coach/teacher, peers, and/or parents (e.g., Newton, Duda, & Yin, 2000; Papaioannou, 1994; Papaioannou, Marsh, & Theodorakis, 2004). These aspects are commonly included in items measuring the perceived motivational climate. However, it seems important to clearly delineate the motivational climate from its plausible anteced- ents such as leadership behavior. The important issue here is the perception of the athlete/pupil of the criteria of success: What do I have to do to be successful? It is the feedback and behaviors of the coach/teacher/parent that is important and it is these that give rise to the recognition of the achievement behaviors necessary to achieve success. That is the motiva- tional climate. In work, the same process is assumed to exist. The supervisor/leader by his/her feedback and demands of coworkers creates a set of implicit and explicit criteria of what it takes to be successful in that work setting.

More recent approaches to the study of the perceived motivational climate in sports or education also seem to lack a clear operationalization and definition of the motiva- tional climate (e.g., Theodosiou & Papaioannou, 2006; Wang, Liu, Chatzisarantis, & Lim, 2010) in terms of consid- ering what the term climate, in general, represents. The difference between a mastery climate and a performance climate is rather emphasized. Therefore, the perceived (psychological) motivational climate at work is identified as employees’ perceptions of the extant criteria of success and failure, which is emphasized through the policies, practices, and procedures of the work environment (cf. Ames, 1992a; Reichers & Schneider, 1990; Schneider & Reichers, 1983). What does the individual perceive he or she has to do to be successful at work?

Traditional AGT characterizes the motivational climate at work by two dimensions: mastery and performance struc- tures (Ames & Archer, 1988). Mastery (or task-involving)

2232 Development and validation of the MCWQ

climates refer to work structures where the individual per- ceives that demonstrated effort, sharing, and cooperation are valued and the emphasis is on learning and mastery of skills (Ames, 1992b). The individual does not feel that social com- parison processes are emphasized and normative criteria are extant. Rather, the individual perceives achievement when their present level of performance exceeds prior achieve- ments (Ames & Ames, 1984a, 1984b). A mastery climate thereby focuses on self-development and building compe- tence. On the other hand, performance (or ego-involving) cli- mates refer to work structures where the individual perceives that demonstrated superiority and favorable normative com- parisons are made. The perceived focus is on intra-team com- petition and social comparison and public recognition of the demonstration of competence (e.g., Ames & Archer, 1988; Roberts, Treasure, & Conroy, 2007). A performance climate thereby focuses on achieving outcomes and normative competence, only those who are the highest (or best) achiev- ers are acknowledged as being successful (Ames, 1984; Ames & Ames, 1984b). Performance climates have also been described as situations of “forced social comparison,” wherein individuals become overwhelmed with comparative information such as verbal comparisons or ability grouping (Ames & Ames, 1984b, p. 42; Levine, 1983). Such a structure might create a situation of negative interdependence among employees.

In sport and education domains, the evidence is consistent in that a mastery climate promotes more adaptive cognitions and behaviors, such as intrinsic interest, increased effort, positive attitudes, trying hard, and persisting when faced with difficulty (Ntoumanis & Biddle, 1999; Valentini & Rudisill, 2006). In contrast, the evidence is consistent in that a perfor- mance climate promotes more maladaptive cognitions and behaviors, such as decreased motivation (e.g., low effort or persistence), use of ineffective strategies, more worry, per- ceiving stress, seeking easy tasks, or giving up when faced with difficulty (Ntoumanis & Biddle, 1999; Roberts et al., 2007). Because individuals are seen as active participants in their own socialization (Hewstone & Stroebe, 2001), the same extant criteria of success and failure may be perceived quite differently by different employees, making it relevant as an individual-level variable (Cumming, Smoll, Smith, & Grossbard, 2007).

In summary, the two motivational climates are presumed to reflect different value orientations that result in various ways of processing different meanings attached to success and failure, performance information, and various action strat- egies (Ames & Ames, 1984b). Because the values elicited through these environmental structures direct employees toward certain performance information, they suggest differ- ent types of motivation. These aspects are relevant when separating the concept of motivational climate from related or similar constructs.

Motivational climate and related constructs

To fully clarify the concept of perceived motivational climate, it is important to consider how the motivational climate con- ceptually relates to similar constructs.

Mastery climate and cooperative climate

A mastery climate outlines a system of motivation repre- sented by encouraging and rewarding employee equality, effort, learning, task mastery, individual improvement, and cooperation. A cooperative climate mainly describes a situa- tion of positive interdependence among employees, whereby interaction toward mutual goals is encouraged and there is an emphasis on resolving issues for mutual benefit (Chen, Tjosvold, & Liu, 2006; Tjosvold, 1995). Therefore, a mastery climate is more broadly defined because cooperation is only one aspect of a mastery climate.

Performance climate and competitive climate

A competitive climate has been defined as “individual-level perceptions of a work environment resulting from struc- tured competition for rewards, recognition, or status or competition inspired by coworkers within a work unit” (Fletcher & Nusbaum, 2010, p. 107). A performance climate also focuses on normative and social comparison; however, the theoretical conceptualizations differ. A performance climate represents a motivational system (Ames & Ames, 1984a), which defines success and failure based on how employees perform in comparison with others. A competi- tive climate represents a more general competitive system focused on performance outcomes (Fletcher, Major, & Davis, 2008; Tjosvold, Johnson, Johnson, & Sun, 2003).

In summary, the focus of competitive versus cooperative climate research is that the criteria for qualitative compari- sons among these climates reflect whether achievement levels are or will be different: the what of achievement striving. Mastery and performance climates focus on the way employees perceive and interpret contextual information and evaluate their performance: the why of achievement striving. The performance and mastery climate concep- tualizations extend the cooperative and competitive climate conceptualizations by realizing the possibility to additionally question the psychological meaning of success and failure to the employee within different motivational climates (Ames, 1984).

Team/group/collective goal orientation

Some organizational researchers have introduced concepts similar to the motivational climate but are conceptualized as group, team, or collective goal orientation (e.g., Bunderson & Sutcliffe, 2002; Porter, Webb, & Gogus, 2010). Only a few

Nerstad et al. 2233

studies have shown evidence from work domains (e.g., Bunderson & Boumgarden, 2010; Bunderson & Sutcliffe, 2002; Huang, 2009), and the majority has examined the impact of a team-learning orientation on outcomes such as team effectiveness and learning.

The conceptual operationalizations have been based on a mixed blend of AGT approaches—for example, Button, Mathieu, and Zajac (1996), Dweck (1986), Elliot and Church (1997), Elliot and Harackiewicz (1996), and Vandewalle (1997) and, to some extent, Ames (1992b) and Nicholls (1984, 1989)—although without recognizing that some of these theoretical approaches represent different energizing constructs and different arguments for the origin of achieve- ment goals (for a review, see Kaplan & Maehr, 2007; Maehr & Zusho, 2009; Papaioannou et al., 2012; Roberts et al., 2007). On one hand, the person-centered perspective—represented by the approaches of Vandewalle (1997), Elliot (1997; Elliot & Church, 1997), Dweck (1986; Dweck & Leggett, 1988), and Button et al. (1996)—assumes that individuals have goal ten- dencies (Dweck & Elliot, 1983) that guide and determine, more or less, which goals they are likely to endorse in a given situation (DeShon & Gillespie, 2005; Maehr & Zusho, 2009). Thus, there is a focus on how personality or self-related con- structs play the major role in goal adoption, and goal orienta- tion is viewed as a relatively stable personal trait (DeShon & Gillespie, 2005). On the other hand, the situated perspective of Ames (1992a, 1992b) and Nicholls (1984, 1989) empha- sizes how goals are a function of either the situation or an interaction between the person and the situation (Maehr & Zusho, 2009). Certain situations, then—such as emphasis on interpersonal competition, evaluative settings, and pay for performance—may make employees more mindful of their abilities and might also influence their goal endorsement (cf. Jagacinski & Nicholls, 1984; Maehr & Zusho, 2009).

Some have suggested a trichotomous achievement goal perspective (e.g., Vandewalle, 1997) in which performance approach (or prove) and avoidance (or avoid) dimensions have been included as part of team/group goal orientations. This is a result of inconsistent findings for performance goal orientation at both the individual and team levels (Dragoni, 2005; Mehta, Feild, Armenakis, & Mehta, 2009). One of the most important conceptual differences between tradi- tional AGT and the trichotomous model pertains to the energization of the motivational process. The trichotomous as well as the 2 × 2 model (which also includes mastery avoid- ance goals) assume that the goals are the manifestation of needs such as needs of achievement motivation (approach) and the fear of failure (avoid) (Elliot & Church, 1997). Thus, achievement goals represent approaches to self-regulation based on satisfying individual needs that are evoked by situational cues (Kaplan & Maehr, 2002, 2007). However, in traditional AGT, the goals themselves are the critical deter- minants of achievement cognition, affect, and behavior. The

goals give meaning to the investment of personal resources because they reflect the purposes underlying achievement actions in achievement contexts, including work. In addition, there are different conceptual assumptions underlying per- formance approach and avoidance goals in the Elliot (1999), Vandewalle (1997), and Dragoni (2005) approaches. Perfor- mance approach tendencies might be based on demonstrat- ing normative ability, but performance avoidance has been argued to be based on one of three facets: impression man- agement, that of “saving face” (Skaalvik, 1997), a fear of failure (e.g., Elliot & Church, 1997), and/or a focus on avoid- ing demonstrating low ability (Midgley et al., 1998). Empiri- cally, the 2 × 2 model also has difficulty, especially the mastery avoidance dimension. This goal involves focusing on not making mistakes or not performing worse than in a previous performance. However, empirically, it has not been well sup- ported (DeShon & Gillespie, 2005; Payne et al., 2007), and it has even been argued that the concept is more a worrying factor than a mastery avoidance factor (Cumming, Smith, & Smoll, 2008). For these reasons, we chose traditional AGT to investigate the impact of the motivational climate on outcomes.

Measurement issues

To measure team/group/collective goal orientation, existing goal orientation measures have been adapted based on a ref- erent shift procedure (Chan, 1998) or simply by aggregating individual goal orientations to the group/team/collective level of analysis (e.g., Huang, 2009; LePine, 2005). Further, in current work-related team/group/collective goal orientation measures, individuals are not asked about their perceptions of the work environment but rather their individual or team/ group/collective goal orientation. Some studies seem to tap more into measurement of a perception of a between-team comparison (e.g., DeShon, Kozlowski, Schmidt, Milner, & Wiechmann, 2004) instead of within-team climate percep- tions. According to Kuenzi and Schminke (2009), climate measures should first rest on individuals’ perceptions of their work environment. Measures of climate should also concern individuals’ perceptions of the policies, practices, and pro- cedures as the elements in the environment that constitute climate (Kuenzi & Schminke, 2009; Schneider & Reichers, 1983).

Outcomes of the motivational climate

Goal orientation

Achievement goal orientation has been defined as a disposition toward developing and/or demonstrating ability in achieve- ment situations (e.g., work) as a result of socialization (Nicholls, 1984, 1989; Payne et al., 2007). Different theorists

2234 Development and validation of the MCWQ

use different terms; but in essence, goal orientations are described in mastery (or task-involving) and performance (or ego-involving) terms (Ames, 1992b; Hulleman, Schrager, Bodmann, & Harackiewicz, 2010). The difference between mastery- and performance-oriented individuals is reflected through the criteria by which they assess their success (Nicholls, 1989). Individuals who pursue a mastery orienta- tion focus on self-development, exerting effort, and building competence, while individuals who pursue a performance orientation assume success is focused on social comparison and normative comparisons of competence (Nicholls, 1984; Payne et al., 2007).

Because traditional AGT is a social cognitive theory, it eschews traits and/or needs, and assumes that goal orienta- tions are the product of socialization processes (Nicholls, 1989). Consequently, one would expect that a mastery climate would foster mastery goal orientation and that performance climate would foster performance goal orientation (Roberts et al., 2007). Evidence from education and sport supports this assumption (see Roberts, 2012). However, a major assump- tion of the theory is that each goal orientation is independent and orthogonal; they are not on a continuum (Nicholls, 1989). Therefore one can be socialized to develop both orien- tations, not either one or the other. Evidence that both orien- tations can coexist has long been established (e.g., Roberts et al., 2007). Research from elite sports, where elite athletes have to perform on demand in major competitive events, has shown that elite athletes do better if they score high on both performance and mastery goal orientations (e.g., Pensgaard & Roberts, 2000). Athletes possess the competence to be able to switch from one orientation to the other dependent on the context. However, intriguing research has demonstrated that this ability to switch from one orientation to the other is still best accomplished when coaches manage to create mastery environments to allow the athlete to cope with the demands of highly competitive major events (Pensgaard & Roberts, 2002). Mastery climates apparently help athletes cope with the demands of competitive contexts, and that even in highly competitive contests, motivation to perform is enhanced, stress is reduced, well-being is fostered, and burnout is less likely (e.g., Lemyre, Hall, & Roberts, 2008; Ntoumanis & Biddle, 1999).

We are suggesting that this may be analogous to the work- place.To meet performance standards,deadlines,and produc- tion schedules, a performance goal orientation might be necessary in an organizational context if the organization is to be successful (Button et al., 1996; DeShon & Gillespie, 2005). Sometimes,onehas tofocusonperformancecriteria;evidence suggests that forhigh-levelperformers,greatperformanceand high performance expectancies were associated with a high performancegoalorientation(VanYperen&Renkema,2008). However, the argument presented in this study is that a mastery motivational climate might prevent the workers

from the maladaptive characteristics of being performance- oriented, similar to the experience of elite athletes in highly competitive contexts. It is not a case of reducing performance orientation; it is the importance of enhancing mastery goal orientation in organizations,classrooms,and sport settings by creating a mastery motivational climate to trigger the mala- daptivecharacteristicsof performanceorientation(e.g.,Lau& Nie, 2008; Ntoumanis & Biddle, 1999; Van Yperen, Hamstra, & van der Klauw, 2011). Given this reasoning, employee behavior and goal orientation might be dependent on the per- ception of the work-specific criteria extant in the social context, which suggests the following hypothesis:

Hypothesis 1. A perceived mastery climate is positively related to employees’ mastery goal orientation.

While a mastery climate is suggested to foster a mastery goal orientation and balance the performance orientation of indi- viduals, a performance climate typically has been shown to foster more performance orientation (Harwood, Spray, & Keegan, 2008). For example, a 6 week study conducted by Lloyd and Fox (1992) indicated that when individuals with a low performance orientation perceived the motivational climate to contain performance criteria, they became more performance-oriented over the course of the study. There- fore, we hypothesize

Hypothesis 2. A perceived performance climate is posi- tively related to employees’ performance orientation.

Work engagement

Work engagement refers to a positive affective-cognitive work-related state of mind that can be characterized by vigor, dedication, and absorption (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Schaufeli & Bakker, 2004). This concept has received increased empirical attention because of its crucial role in developing employee health and well-being, thus increasing the organization’s human capital (Christian, Garza, & Slaughter, 2011). Work engage- ment is closely related to dimensions of intrinsic motiva- tion, including feelings of enthusiasm, identifying with one’s job, high levels of activation, and carrying out an activity because it is rewarding and meaningful in itself (Salanova, Agut, & Peiro, 2005; Salanova & Schaufeli, 2008). Evidence suggests that a mastery climate leads to greater intrinsic motivation (for a review, see Duda, 2001), while a performance climate has been associated with considerably less intrinsic motivation (Ntoumanis & Biddle, 1999; Weiss, Amorose, & Wilko, 2009). Vallerand, Gauvin, and Halliwell (1986) argued that because performance climates under- mine the self-determination of individuals, they could be considered motivationally detrimental. Thus, we expected to find the following associations:

Nerstad et al. 2235

Hypothesis 3. (a) A perceived mastery climate is posi- tively related to work engagement; and (b) a perceived performance climate is negatively related to work engagement.

Turnover intention

Turnover intention can be defined as the intent to leave an organization, and it is a relevant outcome in organizations for several reasons (Zimmerman, 2008). First, replacing employees might be costly, both in terms of recruiting and training to obtain adequate levels of performance over time (Collins & Smith, 2006). Second, turnover might influence the stability, quality, and consistency of the services that organizations provide to customers and clients (Trevor & Nyberg, 2008). This might again result in customer dissatis- faction with provided services from the organization (Lin & Chang, 2005). Lin and Chang (2005) found that employees with a mastery goal orientation were more likely to leave the organization, perhaps because they are highly concerned with learning, development, and seeking out new challenges. However, Dysvik and Kuvaas (2010) found that the relation- ship between mastery orientation and turnover intention was positive only for those who scored low on intrinsic motiva- tion. Thus, creating work environments that are supportive of intrinsic motivation might help lower levels of turnover intention (Dysvik & Kuvaas, 2010). A mastery climate has been shown to be a strong predictor of intrinsic motivation (Cury et al., 1996; Ntoumanis & Biddle, 1999), while empiri- cal findings from sport settings have indicated that individ- uals who drop out of their sport typically perceive a high performance climate (Sarrazin, Vallerand, Guillet, Pelletier, & Cury, 2002). In a work setting, employees rarely drop out of work completely; instead, they might think about changing their job or simply become a demotivated employee. We therefore hypothesize

Hypothesis 4. (a) A perceived mastery climate is negatively related to turnover intentions; and (b) a performance climate is positively related to turnover intentions.

Work performance

Task performance is indicative of behaviors that are critical for completing task assignments or activities that directly support the accomplishments of tasks involved in the techni- cal core of an organization (Rich, Lepine, & Crawford, 2010). Empirical findings suggest that performance tends to improve when individuals perceive a high-mastery motiva- tional climate (Barkoukis, Koidou, & Tsorbatzoudis, 2010; Lau & Nie, 2008; Valentini & Rudisill, 2006). Work perfor- mance is an important outcome variable for organizational

effectiveness and has been included in previous research on goal orientation (DeShon & Gillespie, 2005). In addition, it has been found that a learning orientation in work teams has positive consequences for team effectiveness (Bunderson & Sutcliffe, 2003). We therefore hypothesized the following:

Hypothesis 5. A mastery climate is more highly posi- tively related to work performance (i.e., work effort and work quality) than a performance climate.

Study 1: development and validation of the MCWQ

Study 1 (pilot study) had two main purposes: The first was to develop a set of items assessing the perceptions of a mastery and a performance climate at work. The second purpose was to verify the factor structure based on exploratory factor analysis (EFA) as well as to assess the reliability of the two subscales.

Procedure and participants

Building upon previous questionnaires within sport, educa- tion, and university research cultures (Ames & Archer, 1988; Roberts, Kavussanu, & Sprague, 2001; Seifriz et al., 1992), as well as other relevant literature, a pool of 30 questions was created. The stem of each question was “In my department/ work group. . . .” Such phrases as “achieve better than others” and “focus on learning and development” were used to reflect mastery and performance climates. We first used a panel of experts—motivation researchers in organizational as well as sport psychology—to select the questions that best met the specified performance and mastery climate criteria of success at work. The experts were given descriptions of mastery and performance work climates and asked to rate each item on a 5-point Likert scale (1 = does not capture the essence of the concept well, 5 = captures the essence of the concept well). Items rated 4 and 5 on the Likert scale were retained. Based on these evaluations, a final pool of 17 ques- tions remained.

We then administered the scale to two different samples.1

The first sample (n = 186) consisted of employees from dif- ferent occupational groups (107 females, 78 males). A group of bachelor students, who were actively engaged in work other than their academic studies, were part of this sample. All the participants gave informed consent and answered the questionnaire using paper and pencil method. The first author collected all the questionnaires personally after completion. The participants ranged from 19 to 60 years (M = 30.8 years; SD = 10.76).

1This part of Study 1 adhered to the ethical guidelines presented by the Nor-

wegian Research Ethics Committee.

2236 Development and validation of the MCWQ

The second sample consisted of employees working in an information technology (IT)-related company. The company’s human resource department distributed the questionnaire to 275 employees through a web-based tool (QuestBack). We received 115 completed responses (42 females, 73 males), achieving a response rate of approxi- mately 42%. Participants ranged from 20 to 67 years with an average age between 31 and 40 years and a mean tenure ranging from 1 to 3 years.

Results and discussion

To establish the validity of the questionnaire, EFA was con- ducted on the data from Sample 1 and Sample 2 with both varimax and oblique rotations. The results were similar, indicating that the factors were orthogonal (Nunnally & Bernstein, 1994; Tabachnick & Fidell, 2001). We therefore decided to use the varimax solution. A two-factor solution

emerged, one representing a performance climate and one representing a mastery climate, where for Sample 1, 34% and 15%, and for Sample 2, 28% and 20% of the variance was explained by the performance and mastery climate factors respectively (see Table 1). After elimination of the items that loaded on both factors (cutoff criteria of .35; Kiffin-Petersen & Cordery, 2003) or with factor loadings less than .50 (Nunnally & Bernstein, 1994), or which decreased the alpha coefficients, a 14-item questionnaire was derived that formed the initial scale (see Table 1). The alpha coefficients for Samples 1 and 2 were .84 and .81 for the performance climate and .85 and .77 for the mastery climate.

Study 1 demonstrated that it was possible to identify a performance and a mastery climate in the workplace. The findings supported the two-factor structure, and indicated adequate psychometric properties of the questionnaire. The next step was to further test the psychometric rigor of the scale and test the person-situation dynamics.

Table 1 Principal Components Analysis with Varimax Rotation (Study 1)

Items

Sample 1 (n = 186) Sample 2 (n = 115) Factors Factors

PC MC PC MC

Performance climate PC8: In my department/work group, it is important to achieve better than others. .73 .73 PC2: In my department/work group, work accomplishments are measured based on

comparisons with the accomplishments of coworkers. .72 .70

PC7: In my department/work group, an individual’s accomplishments are compared with those of other colleagues.

.71 .79

PC3: In my department/work group, rivalry between employees is encouraged. .66 .65 PC6: In my department/work group, one is encouraged to perform optimally to achieve

monetary rewards. .64 .63

PC5: In my department/work group, only those employees who achieve the best results/accomplishments are set up as examples.

.64 .50

PC4: In my department/work group, internal competition is encouraged to attain the best possible results.

.64 .73

PC1: In my department/work group, there exists a competitive rivalry among the employees. .53 .54 Mastery climate

MC1: In my department/work group, one is encouraged to cooperate and exchange thoughts and ideas mutually.

.81 .67

MC2: In my department/work group, each individual’s learning and development is emphasized.

.79 .67

MC3: In my department/work group, cooperation and mutual exchange of knowledge are encouraged.

.79 .74

MC4: In my department/work group, employees are encouraged to try new solution methods throughout the work process.

.75 .65

MC5: In my department/work group, one of the goals is to make each individual feel that he/she has an important role in the work process.

.70 .74

MC6: In my department/work group, everybody has an important and clear task throughout the work process.

.63 .56

Eigenvalues 5.70 2.52 3.87 2.80 % of variance 33.55 14.83 27.61 20.01

Note. PC = performance climate, MC = mastery climate.

Nerstad et al. 2237

Study 2: scale dimensionality and empirical scale validation

The main purpose of Study 2 was to further test the psycho- metric properties of the MCWQ by investigating a more adequate sample based on a longitudinal design.

Method

Participants and procedure

To ensure that the ethical standards were met in Study 2, information about the study’s design, planned sample, and procedure, as well as the questionnaires, was evaluated and approved by the Norwegian Social Science Data Services.

The survey was sent to 33,2752 Norwegian engineers and technologists representing different occupational divisions; (a) research and development; (b) health, safety and the envi- ronment (HSE); (c) IT; (d) consultancy; (e) laboratory; (f) human resource management (HRM); (g) logistics; (h) pro- duction; (i) building and reconstruction; (j) sales and mar- keting; (k) service; and (l) economics. The participants in this longitudinal study—7 months between the Time 1 and Time 2 surveys—were members of a union, and the union was responsible for distributing the questionnaire to members through a web-based tool (QuestBack). We received 8,282 completed responses at Time 1, achieving a response rate of approximately 25%, and 4,040 completed responses at Time 2, representing a response rate of approximately 49%. Due to a technical error in the web-based tool, the union managed to match only 1,081 of the respondents. Therefore, we con- ducted an independent samples t test to test whether there were any differences between the 2,959 respondents we were unable to match and the 1,081 remaining respondents. The results indicated that there were some significant demographical differences in gender, education, and hours worked per week. Therefore, we controlled for these variables in the analyses.

To ensure that the respondents were representative of the total sample, the demographic variables were compared with the union’s statistics representing members’ demographic variables. These statistics are updated constantly by the union. According to these statistics, the union has approxi- mately 66,000 members, of which 78% are men, approxi- mately 58% work in the private sector, and approximately 32% work in the public sector. The mean age is 46.8 years old. In our final sample (Time 1 and Time 2), 75% were men, 53%

worked within the private sector, 32% were public sector employees, the mean age was 45.35 (SD = 10.30). Further- more, on average, employees worked 40 hours per week, the average education was at college level, and the average tenure in their present position was 3 years. These demographic variables are similar to those of the union as a whole, and the study participants seemed representative of the entire popu- lation of the union.

Measures

All measures were included at both Time 1 and Time 2 to attempt to deal with common method variance, making it possible to test the discriminant validity of all included study variables at both time points. The MCWQ and turnover intention items were scored on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree, while the goal orientation and work engagement items were scored on a 7-point Likert scale (goal orientation: ranging from 1 = strongly disagree to 7 = strongly agree; work engagement: ranging from 0 = “never” to 6 = “always/everyday”).

The perceived motivational climate was measured by the 14 items from Study 1.

Goal orientations were measured by nine items adapted from the Norwegian version (Dysvik & Kuvaas, 2010) of the work domain goal-orientation scale, validated by Vandewalle (1997). To facilitate conceptual coherence with traditional AGT, participants were asked to indicate when they perceived themselves as most successful at work. Five of the items measured mastery orientation (e.g., “I enjoy chal- lenging and difficult tasks where I’ll learn new skills”), while four of the items measured performance orientation (e.g., “I am concerned with showing that I can perform better than my co-workers”).

Work engagement was assessed by the Norwegian version (Nerstad, Richardsen, & Martinussen, 2010) of the Utrecht Work Engagement Scale (Schaufeli, Bakker, & Salanova, 2006). The nine scale items were grouped into three subscales reflecting the underlying dimensions of work engagement: vigor (three items, e.g., “At my work, I feel bursting with energy”), dedication (three items, e.g.,“My job inspires me”), and absorption (three items, e.g., “When I am working, I forget everything else around me”).

Turnover intention was measured by five items (e.g., “I often think about quitting my present job”) developed by Kuvaas (2008).

To control for the possibility that sociodemographic differ- ences in the predictor and outcome variables might lead to false relationships, we followed the suggestions of Payne et al. (2007) and Abrahamsen, Roberts, and Pensgaard (2008) by including gender (1 = male; 2 = female) and age (in years) as control variables in the analyses. In the hierarchical regres- sion analyses, we also controlled for goal orientations

2The union’s registration system for members’ e-mail addresses is never com-

pletely up to date. Although the questionnaire was distributed to 33,275

members, the union received several delivery failure messages; however, it did

not keep a record of the number of delivery failures. Therefore, we cannot be

certain about the match between the number of surveys and the respondents

who received them.

2238 Development and validation of the MCWQ

measured at Time 1. This is relevant because, according to the person-centered AGT perspective, dispositional goal orienta- tion is argued to determine how the individual interprets an achievement situation (DeShon & Gillespie, 2005).

Results and discussion

Exploratory factor analysis

To test the psychometric properties of the MCWQ, we decided to split the total Time 1 sample randomly and conduct a principal component exploratory factor analysis on one half (n = 4,141) and a confirmatory factor analysis (CFA) on the other half (n = 4,141). Because latent variables should demonstrate acceptable levels of both convergent and discriminant validity, we decided to conduct the principal component EFA on all the multiple-scale items (see Appendix A) with both varimax and oblique rotation to determine item retention (Farrell, 2010). Items with a strong loading of .50 or higher and cross-loadings of .35 or lower were retained (Kiffin-Petersen & Cordery, 2003; Nunnally & Bernstein, 1994).

The EFA (see Appendix A) indicated a clear two-factor structure. Because the results were the same with both

varimax and oblique rotation methods, the two factors can be considered orthogonal (Tabachnick & Fidell, 2001). There were no cross-loadings, and all the factor loadings were above the stringent criteria of .50 (Nunnally & Bernstein, 1994). The EFA indicated support for discriminant validity (Farrell, 2010). Six factors emerged with eigenvalues greater than 1, accounting for 61.03% of the variance. Each item loaded on its appropriate factor (see Appendix A).

Descriptive statistics and reliability analysis

Means, standard deviations, and bivariate correlations between all of the variables in this Study 2 are presented in Table 2. All scales indicated acceptable reliability estimates ranging from .76 to .93 (Tabachnick & Fidell, 2001). However, the reliability analysis indicated that the Cronbach’s alpha for performance climate would increase if item PC6 were deleted. Therefore, we decided to exclude item PC6 from the further analyses in Study 2.

Confirmatory factor analysis

Initially, we conducted a CFA on the MCWQ (see Table 3 and Fig. 1) based on both Time 1 and Time 2 data using LISREL

Table 2 Descriptive Statistics for Study Variables at Time 1 (n = 8,282) and Time 2 (n = 1,081)

Variable M SD 1 2 3 4 5 6 7 8 9 10

1. Age 44.58 10.92 — −.14** −.07 −.00 −.06 −.16** .09** −.25** −.02 .06* 2. Gender 1.25 .43 −.14** — .07 −.06** −.05 −.01 −.02 .03 −.07* .03 3. Education 3.03 .50 −.07 .07** — .00 .14** .05 .00 .05 .00 .02 4. Hours worked per week 39.63 22.73 −.00 −.06** .00 — .19** .07 .08** −.00 .11** .01 5. Mastery orientation 5.20 .97 −.08** −.05** .07** .04** (.82) .25** .44** −.09** .00 .26** 6. Performance orientation 4.07 1.14 −.14** .00 .04** .03* .29** (.76) .08 .07* .30** −.02 7. Work engagement 5.07 1.10 .09** .00 .02 .03* .41** .06** (.93) −.49** −.16** .51** 8. Turnover intentions 2.10 1.05 −.20** .01 .04 .02* −.02 .09** −.43** (.88) .28** −.46** 9. Performance climate 1.99 .70 .04** −.06** .00 .02* .03** .29** −.14** .23** (.84) −.29**

10. Mastery climate 3.56 .78 .06** .03* .00 .01 .19** −.05** .43** −.39** −.28** (.85)

Note. Time 1 correlations are displayed below the diagonal; Time 2 correlations are displayed above the diagonal. Time 1 coefficient alphas are displayed on the diagonal; gender: male = 1, female = 2; education: 1 = high school, 2 = vocational school, 3 = college, 4 = university, 5 = other. *p < .05. **p < .01. ***p < .001.

Table 3 Summary of Confirmatory Factor Analysis for Time 1 and Time 2 Separately

S-Bχ2 df p RMSEA NNFI CFI SRMR

Time 1 (n = 4,141) One-factor model 13,196.80 65 .00 .22 .70 .75 .18 Two-factor model 1,678.85 64 .00 .08 .96 .97 .08 Second-order factor model 1,678.85 64 .00 .08 .96 .97 .08

Time 2 (n = 4,040) One-factor model 14,007.27 65 .00 .23 .69 .74 .19 Two-factor model 1,774.04 64 .00 .08 .96 .97 .08 Second-order factor model 1,774.05 64 .00 .08 .96 .97 .08

Note. S-Bχ2 = Satorra–Bentler chi-square; df = degrees of freedom; p = level of significance; RMSEA = robust root mean square of approximation; NNFI = non-normed fit index; CFI = robust comparative fit index; SRMR = robust standardized root-mean-square residual.

Nerstad et al. 2239

8.80 (Jöreskog & Sörbom, 1993a, 1993b). Because the vari- ables were ordinal, they were also handled as ordinal in the CFA, as recommended by Jöreskog (2004). Consequently, we applied robust maximum likelihood (RML) estimates.

First, a one- and two-dimensional model, as well as a second-order factor model, was tested separately for Time 1 and Time 2. In the two-dimensional model, the latent vari- ables (performance and mastery climate) were allowed to correlate. We also tested the equality of the factor structure over time using a multigroup analysis approach (cf. Brown, 2006).Accordingly, a series of nested models was tested, start- ing with the baseline model (configural invariance), which does not impose any equality constraints across Time 1 and Time 2 on the factor structure and related parameters. Then, we tested a metric invariance model assuming equality of factor loadings.

Chi-square (χ2)3 has been the traditional measure applied to test the closeness of model fit. However, with large samples,

the chi-square statistics might produce significant differences even when the model fit is quite good (Anderson & Gerbing, 1988; Bollen, 1989). Therefore, we decided to conduct the comparative fit index (CFI) difference test (Vandenberg & Lance, 2000).A nonsignificant chi-square difference test indi- cates equality.A lack of invariance based on the CFI difference test is indicated with a CFI difference less than −.01 (Cheung & Rensvold, 2002).

Model fit was also determined by the root mean square error of approximation (RMSEA). Values of .08 or less indi- cate a reasonably fitting model relative to the model’s degrees of freedom (Browne & Cudeck, 1993; Hu & Bentler, 1999). Additionally, the CFI, the non-normed fit index (NNFI), and the standardized root-mean-square residual (SRMR) were included (Hu & Bentler, 1999). CFI values greater than .95 are indicative of a well-fitting model, for SRMR a value close to .08 is considered good, while NNFI values close to 1.00 indi- cate a good fit (Byrne, 2001; Hu & Bentler, 1999).

3When using the RML estimation method, there is a need for a correction of

the Satorra–Bentler chi-square (S-Bχ2) because the difference between S-Bχ2 for nested models is typically not distributed as chi-square (Byrne & Stewart,

2010; Crawford & Henry, 2003; Satorra & Bentler, 2001).

Figure 1 Two-factor model based on CFA Time 1 (n = 4,141) and Time 2 (n = 4,040). Note. Results from the Time 2 analysis are presented in parenthesis; PC1–8 = performance climate items; MC1–6 = mastery climate items; all numerical values on the left-hand side of the figure are error terms. Item PC6 was excluded from this analysis because of the increase in reliability by removing it.

2240 Development and validation of the MCWQ

The results presented in Table 2 indicated that the two- factor model provided an acceptable incremental fit for the MCWQ, although the Satorra–Bentler chi-square was signifi- cant for both Time 1 and Time 2. This could be explained by the fact that in large samples, the chi-square statistic is very powerful and might produce significance even when the model fit is quite good. It is also rare for the test statistic to meet precise chi-square distribution, and the p value may be prone to error approximation compared with this probability under ideal conditions (Bentler, 2007). Furthermore, upon inspection of residuals and modification indices, there were no important indications of misspecifications. Therefore, we continued to test the measurement equivalence by using a multigroup approach (Brown, 2006; Vandenberg & Lance, 2000), where each group was represented by a different wave of assessment (e.g., Group 1 = Time 1, Group 2 = Time 2; Brown, 2006).

The baseline (or configural) model indicated an acceptable fit (S-Bχ2 = 3,175.05; df = 142, p < .0001; RMSEA = .07; CFI = .97; NNFI = .97; SRMR = .08) of the two-factor struc- ture across Time 1 and Time 2, except for the significance of the Satorra–Bentler chi-square. Next, we tested the metric equivalence model (equality of factor loadings), which also indicated ranges of acceptable fit (S-Bχ2 = 3,392.05; df = 155, p < .0001; RMSEA = .07; CFI = .97; NNFI = .97; SRMR = .08), though the Satorra–Bentler chi-square difference test did not support metric equivalence (ΔS-Bχ2 = 218.38; Δdf = 13). Because the CFI difference is a practical, relevant approach to determining equivalence (Byrne, 2008), our large sample size, other fit indices (RMSEA, NNFI, and SRMR), and the fact that recent research apply the CFI differ- ence test (e.g., Byrne & Stewart, 2010), we found it appropri- ate to rely on the CFI difference test (ΔCFI = .00), which indicated metric equivalence.

Outcomes of the motivational climate

As an important step, prior to testing the hypotheses, we fol- lowed the recommendations of Meyers, Gamst, and Guarino (2006) and examined the Pearson correlations between the variables in the analysis to identify possible multicollinearity conditions. All predictor correlations were well below .70, which is the critical value (Meyers et al., 2006), and no vari- ance inflation factor (VIF) value was greater than 10.0 (the highest VIF value was 1.50), indicating that multicollinearity was not in evidence.

The hypotheses were tested with a hierarchical regression procedure. Step 1 involved entering the control variables (age, gender, education, hours worked per week, and Time 1 goal orientation). In Step 2, performance climate and mastery climate were entered.

Mastery and performance goal orientation, work engage- ment, and turnover intention were regressed on the MCWQ and control variables. Both performance and mastery climate accounted for variance in the four dependent variables over and above the variance explained by the control variables; thus, it should be noted that when controlling for goal orien- tation at Time 1, the variance explained for Time 2 goal orien- tation was rather weak (see Table 4). The results indicated a weak positive association between a mastery climate and a mastery goal orientation, and a slightly positive but nonsig- nificant association between a performance climate and mastery goal orientation. Taken together, the motivational climate variables explained 58% of the variance for mastery goal orientation. Further, a performance climate was posi- tively associated with a performance goal orientation, and a mastery climate was not significantly associated with a per- formance goal orientation (explaining 45% of the variance). Hypothesis 1, predicting that a mastery climate is positively

Table 4 Hierarchical Regression Analyses for the Outcomes of the Motivational Climate at Work

Variable

Mastery orientation (T2) Performance orientation (T2) Work engagement (T2) Turnover intention (T2)

Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2

Age −.02 −.02 −.07** −.07** .11*** .09*** −.25*** −.25*** Gender −.01 −.01 −.02 −.00 .02 .02 −.02 −.01 Education .05** .05** .02 .02 −.04 −.02 .05 .03 Hours worked per week .08*** .08*** .04 .03 .03 .04 .01 −.01 Mastery orientation (T1) .74*** .73*** −.01 −.01 .35*** .27*** −.10** −.02 Performance orientation (T1) −.01 −.02 .66*** .64*** .02 .07* .03 −.04 Mastery climate .06** .02 .31*** −.29*** Performance climate .03 .08*** −.05 .11*** Adjusted R2 .57 .58 .44 .45 .13 .23 .07 .18 ΔR2 .58*** .00* .45** .01** .13*** .10*** .07*** .11*** F 243.75*** 184.596*** 146.98*** 112.53*** 27.75*** 41.183*** 14.09*** 29.67*** ΔF 243.75*** 3.60* 146.98** 5.50** 27.75*** 70.68*** 14.09*** 70.93***

Note. Total n employees = 1,081. Standardized regression coefficients are shown: T1 = Time 1; T2 = Time 2. *p < .05. **p < .01. ***p < .001.

Nerstad et al. 2241

related to employees’ mastery goal orientation, was thereby supported. Hypothesis 2, predicting that a performance climate is positively related to employees’ performance orien- tation, was also supported.

A mastery climate was positively and moderately associ- ated with work engagement, while the relationship between a performance climate and work engagement was nonsignifi- cant but in the expected negative direction. The explained variance was 23%. Thus, Hypothesis 3 predicting that (a) a perceived mastery climate is positively related to work engagement and (b) a perceived performance climate is nega- tively related to work engagement was partly supported.

Lastly, a performance climate was positively associated with turnover intention; on the other hand, a mastery climate was moderately negatively related to turnover intention (18% of the variance explained). Therefore, Hypothesis 4 predict- ing that (a) a perceived mastery climate is negatively related to turnover intentions and (b) a performance climate is posi- tively related to turnover intentions was supported.

Study 3: motivational climate and work performance

The purpose of the third study was to further test construct validity by investigating the relationship between the per- ceived motivational climate and supervisor-rated perfor- mance by again controlling for dispositional goal orientation.

Method

Participants and procedure

Using a web-based tool (Confirmit), 1,300 employees of a Norwegian financial organization were surveyed. The partici- pants were informed that their individual responses would be kept confidential and that their supervisor would evaluate their degree of work effort and quality. Approximately 34% (n = 470) of participants returned questionnaires. Of these, 169 participants could be matched with their supervisors’ ratings. The final sample consisted of 90 men and 79 women,

of whom approximately 62% reported an educational back- ground of 3–6 years at university or business school level. All participants were full-time employees, and approximately 70% did not have any managerial responsibility.

Measures

The motivational climate was assessed with the 14-item measure applied in Study 2.

The scale we applied for assessing work performance was based on items developed by Kuvaas and Dysvik (2010). The scale consists of 10 items intended to measure leader evalu- ated work effort (e.g., “He/she intentionally expends a great deal of effort in carrying out his/her job”) and work quality (e.g., “He/she delivers higher quality than can be expected”). In addition, we controlled for age and gender, as we did in Studies 1 and 2.

Results and discussion

EFA, descriptive statistics, and reliability analyses

We conducted an EFA with both varimax and oblique rota- tion including all multiple items.4 Descriptive analyses were further conducted, and we tested whether multicollinearity was an issue before we conducted hierarchical regression analyses.

The EFA indicated a clear two-factor structure, and the results were the same with both varimax and oblique rotation methods. Again, all the factor loadings were above the strin- gent criteria of .50 (Nunnally & Bernstein, 1994), and there were no cross-loadings. The EFA also indicated support for discriminant validity (Farrell, 2010). Four factors emerged with eigenvalues greater than 1, accounting for 59.27% of the variance, and each item loaded on its appropriate factor.

The descriptive statistics are presented in Table 5. All scales indicated acceptable reliability estimates ranging from .75 to

4Because of space limits, we did not include a table showing the EFA results of

Study 3. The EFA can be obtained from the first author.

Table 5 Descriptive Statistics for Study 3 (n = 169)

Variable M SD 1. 2. 3. 4. 5. 6. 7. 8.

1. Age 43.05 10.40 — 2. Gender 1.51 .50 .02 — 3. Mastery orientation 4.18 .53 −.17** −.05 (.78) 4. Performance orientation 3.50 .73 −.18** −.06 .34** (.75) 5. Performance climate 2.54 .89 −.03 .04 .08 .29** (.88) 6. Mastery climate 3.77 .68 .09 −.10* .04 −.15** −.23** (.87) 7. Work effort 4.25 .68 −.16** −.16** .23** −.01 −.17** .26** (.93) 8. Work quality 3.82 .73 −.23** −.14** .16** .06 −.24** .23** .71** (.92)

Note. Coefficient alphas are displayed on the diagonal. Gender: male = 1, female = 2. *p < .05. **p < .01. ***p < .001.

2242 Development and validation of the MCWQ

.93 (Tabachnick & Fidell, 2001). However, the reliability analysis did not suggest that the Cronbach’s alpha for perfor- mance climate would increase if item PC6 were deleted. Therefore, we decided to include the item in the current analyses.

Hierarchical regression analyses

Following the recommendations of Meyers et al. (2006), examination of the VIF where no value was greater than 10.0 (the highest VIF value was 1.50) indicated that multicollin- earity was not in evidence.

Supervisor-rated work effort and work quality were regressed on the MCWQ and control variables. As in Study 2, both performance and mastery climate accounted for vari- ance in the two dependent variables over and above the vari- ance explained by the control variables (see Table 6). The results indicated a moderate positive association between a mastery climate and work effort, and a negative but nonsig- nificant association between a performance climate and work effort. Taken together, the motivational climate variables explained 15% of the variance for work effort. Further, a mastery climate was positively associated with work quality while a performance climate was significantly negatively associated with work quality (explaining 16% of the vari- ance). Hypothesis 5, predicting that a mastery climate is more highly positively related to work performance (i.e., work effort and work quality) than a performance climate, was thereby partly supported.

General discussion

The purpose of this study was to develop a scale to measure the perceived motivational climate at work in line with the theoretical framework of the traditional AGT. Further, a second purpose was to investigate the predictive influence of the motivational climate over and above dispositional

goal orientation by investigating its impact on employee behavior and well-being, controlling for dispositional goal orientation.

The three studies reported above represent four impor- tant contributions: First, within the constraints of AGT, we demonstrated the viability of a performance and a mastery climate within the workplace and that the measure we developed was able to isolate the two dimensions of the climate. This initial work on the MCWQ has contributed to the development of a valid and reliable measure of the motivational climate at work. The empirical findings of all three studies supported the two-factor structure of the MCWQ and indicated that it has adequate psychometric properties, including support for both configural and metric equivalence. In addition, the two climate dimensions were significantly negatively correlated at −.28, indicating that they are related but separate. The results from these analyses compare favorably with those of other published studies that have measured the dimensions in other achieve- ment contexts (e.g., Papaioannou, Milosis, Kosmidou, & Tsigilis, 2007; Roberts et al., 2001; Seifriz et al., 1992). Second, the present research demonstrated the criterion- related and construct validity of the MCWQ, and the find- ings from the hierarchical regression analyses in both Studies 2 and 3 concurred with previous empirical findings (see Ntoumanis & Biddle, 1999; Roberts et al., 2007) where the motivational climate at work similarly predicted rel- evant outcome variables. These findings are to a great extent supportive of our hypotheses and are empirically and con- ceptually consistent with previous AGT findings (Ames, 1992b; Nicholls, 1984, 1989; Ntoumanis & Biddle, 1999). Though promising, the limitations of the present research (see below) require us to continue to develop the measure and to extend use to other organizational contexts.

Third, Studies 2 and 3 especially contribute to further emphasize the important impact a psychological climate has

Table 6 Hierarchical Regression Analyses of the Relationship between the Perceived Motivational Climate and Supervisor-Rated Task Performance

Variable

Supervisor-rated work effort Supervisor-rated work quality

Step 1 Step 2 Step 1 Step 2

Age −.14 −.15** −.23*** −.23*** Gender −.18** −.16** −.16** −.13 Mastery orientation .27*** .24** .12 .08 Performance orientation −.19** −.11 −.09 .03 Mastery climate .23*** .21*** Performance climate −.13 −.24*** Adjusted R2 .09 .15 .07 .16 ΔR2 .12*** .07*** .09*** .10*** F 5.36*** 6.10*** 4.04*** 6.37*** ΔF 5.36*** 6.82*** 4.04*** 10.13***

Note. Study 3 (n = 169). Standardized regression coefficients are shown. *p < .05. **p < .01. ***p < .001.

Nerstad et al. 2243

on behavioral, attitudinal, and well-being outcomes (cf. Parker et al., 2003) above and beyond the impact of disposi- tional orientations. The positive association between a per- ceived mastery climate and work engagement, suggests that the characteristics of a mastery climate might be prerequisites for the facilitation of a “climate for engagement” (Bakker, Albrecht, & Leiter, 2011). This finding extends previous work-related AGT research by showing which motivational characteristics facilitate employee well-being (cf. DeShon & Gillespie, 2005). Therefore, our study extends the work engagement literature by showing how the concept of work engagement aligns with other motivational theories (cf. Christian et al., 2011). Traditional AGT is a potent theoretical approach that might help explain why employees do or do not become engaged at work or not beyond their personal disposition.

The motivational climate was a relevant predictor of turn- over intentions. A perceived mastery climate was associated with lower turnover intentions while a performance climate positively predicted turnover intention. Employee loyalty is an important commodity in the workplace and the research reported here suggests that employers may be able to affect that through the criteria of success and failure they feature in employee feedback. Therefore, future research should focus on the relationship between the motivational climate, goal orientation, and employee turnover intention (Hom, Caranikas-Walker, Prussia, & Griffith, 1992). Instead of quit- ting a job to seek out greater challenges (Lin & Chang, 2005), mastery-oriented individuals might consider staying because of the employee benefits of a mastery climate for improving their work life quality.

Also noteworthy, Study 3 indicated that a mastery climate facilitates higher work effort and work quality when control- ling for dispositional goal orientation. A performance climate, on the other hand, was negatively related to work effort over and beyond goal orientation. This means that employees’ performance, especially in terms of work quality, might be enhanced mainly in a mastery climate and that employees’ goal orientation does not necessarily determine how the situation is interpreted. This is potentially an impor- tant finding if employers can overcome negative dispositions through enhancing the criteria of a mastery climate. This finding is in line with previous research conducted in other domains such as sports and education (Harwood et al., 2008; Lau & Nie, 2008), suggesting that the situation may be a stronger predictor of behavior than personal dispositions (Maehr & Zusho, 2009).

Fourth, both Studies 2 and 3 contribute to motivational theory—more specifically, AGT—by showing that it is not necessarily individuals’ goal orientations that determine the interpretation of the motivational climate and that the moti- vational climate may be able to change the dispositional ori- entation of the employee. The pertinent finding was in Study

2 when controlling for goal orientation at Time 1, the rela- tionship between a mastery climate and mastery orientation was significantly positive, and the relationship between a per- formance climate and performance orientation also was sig- nificantly positive. Although practical significance may be questioned because of the small beta coefficients, it does indi- cate that the motivational climate may facilitate change in goal orientation over time. The beta coefficient could possibly have increased if we had extended the time interval of 7 months. However, this indication is supportive of the situ- ated perspective (Ames, 1992a) where the climate is assumed to contribute to employee motivation.

In sum, the present findings supported the conclusions of Roberts et al. (2007), who argued that the creation of a mastery motivational climate is important when the aim is to enhance positive (e.g., well-being) and attenuating negative (e.g., burnout) responses within an achievement context. A mastery climate likely creates choice and a focus on self- determined and self-referenced criteria for success. Although the values emphasized in a mastery climate might not align well with organizational realities focused on demonstrating performance (DeShon & Gillespie, 2005; Poortvliet & Darnon, 2010), it may be an important premise for achieving beneficial work outcomes, including work engagement, high performance, and intentions to stay in the organization.

Limitations

Although our results are promising, several limitations need to be addressed. Because Studies 1 and 2 were conducted relying on only self-report measures, the results might have been influenced by common method variance (CMV). We carefully designed the questionnaire in line with common recommendations to avoid CMV (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), and data were gathered at two time points in Study 2, thereby facilitating some control for CMV. Further, the principal component analysis demonstrated that no single factor emerged that accounted for most of the vari- ance. This indicates no serious threats of CMV, and our meas- ures indicated sufficient discriminant validity (Podsakoff et al., 2003; Podsakoff & Organ, 1986). In addition, we strengthened our design by including Study 3, which included objective measures of performance, thus facilitating some additional control for CMV.

Although we compared our sample in Study 2 to the union’s statistics and conducted additional t tests to examine possible differences from the remaining sample that could not be matched, the external validity is weak. This accounts for Study 3 as well, because only a limited number of employees could be matched with their direct supervisor. In addition, because our data is represented by convenience samples, it is impossible to generalize our results to other occupational groups. However, our results might be of

2244 Development and validation of the MCWQ

interest to organizations and to AGT researchers as they emphasize the relevance of the motivational climate in pre- dicting important employee outcomes, including work moti- vation, performance, well-being, and turnover intention (cf. DeShon & Gillespie, 2005; Parker et al., 2003). The results of our study should be interpreted in light of this limitation, and future research is needed to investigate whether our findings may be generalized to other occupational groups. It could then be an advantage to include a different source measure of the motivational climate to facilitate greater control for whether employees’ perception of the motivational climate is influenced by their own motivational structure and to enhance ecological validity.

Because of the large sample size, some of the standardized beta coefficients in Study 2 were significant but very low, and one might question their meaningfulness. As such, the results should be viewed in light of this limitation.

Lastly, to test the discriminate validity of the motivational climate at the individual level of analysis, other perceptual variables—such as trust, image, commitment, and loyalty— should ideally have been included in the presented studies. Future research would further clarify these issues.

Implications and suggestions for future research

An important practical implication of our findings is that leaders and managers can improve employees’ motivation, well-being, performance, and intentions to stay in the organi- zation by giving employees opportunities for growth and

development and trying to instill values of mastery criteria of success in the workplace. However, future empirical attention is needed to establish the predictive strength of the motiva- tional climate variables. Only then would we be able to rec- ommend leader behaviors and a set of criteria to promote positive and productive work environments focused on equality and enhancing motivation at work.

Current goal orientation conceptualizations might lack the richness to provide direction on which aspects of organizational practices that will generate an effective (moti- vational) climate (DeShon & Gillespie, 2005). An interesting path for future research might be to investigate the strategies (e.g., commitment-based HRM practices) that could make the workplace a learning arena with high motivation, through the creation of a mastery climate. We cannot expect everyone to have equal competence and achievement, but under the assumptions of AGT, we can expect everyone to have equal motivation, regardless of his or her talent (Nicholls, 1979).

Another interesting path for future research would be to clarify the person–situation dynamics as hypothesized by Nicholls (1984, 1989). Only then would we advance closer to a clarification of the stability or change of goal orientations. By being conceptually consistent with traditional AGT, our measure might represent a helpful tool to examine such dynamics.

Acknowledgment

The authors would like to thank the editor and anonymous reviewers for their valuable comments and suggestions.

References

Abrahamsen, F. E., Roberts, G. C., & Pensgaard, A.-M. (2008). Achievement goals and gender effects on multidimen- sional anxiety in national elite sport. Psy- chology of Sport and Exercise, 9, 449–464.

Ames, C. (1984). Competitive, cooperative, and individualistic goal structures: A cognitive-motivational analysis. In R. E. Ames & C. Ames (Eds.), Research on motivation in education (Vol. 1, pp. 177– 207). Orlando, FL: Academic Press, Inc.

Ames, C. (1992a). Achievement goals, moti- vational climate, and motivational pro- cesses. In G. C. Roberts (Ed.), Motivation in sport and exercise (pp. 161–176). Champaign, IL: Human Kinetics.

Ames,C. (1992b).Classrooms: Goals, struc- tures, and student motivation. Journal of Educational Psychology, 84, 261–271.

Ames, C., & Ames, R. (1984a). Goal struc- tures and motivation. The Elementary School Journal, 85, 39–52.

Ames, C., & Ames, R. (1984b). Systems of student and teacher motivation: Toward a qualitative definition. Journal of Educa- tional Psychology, 76, 535–556.

Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and motivation processes. Journal of Educational Psychology, 80, 260–267.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in prac- tice: A review and recommended two- step approach. Psychological Bulletin, 103, 411–423.

Bakker, A. B., Albrecht, S. L., & Leiter, M. P. (2011). Key questions regarding work engagement. European Journal of Work and Organizational Psychology, 20, 4–28.

Bandura, A. (1986). Social foundations of thought and action: A social-cognitive theory. Englewood Cliffs, NJ: Prentice- Hall.

Barkoukis, V., Koidou, E., & Tsorbatzoudis, H. (2010). Effects of a motivational climate intervention on state anxiety, self-efficacy, and skill development in physical education. European Journal of Sport Science, 10, 167–177.

Bentler, P. M. (2007). On tests and indices for evaluating structural models. Person- ality and Individual Differences, 42, 825– 829.

Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.

Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guilford Press.

Nerstad et al. 2245

Browne, M. W., & Cudeck, R. (1993). Alter- native ways of assessing model fit. In K.A. Bollen & J. S. Long (Eds.), Testing struc- tural equation models (pp. 136–162). Newbury Park, CA: Sage.

Bunderson, J. S., & Boumgarden, P. (2010). Structure and learning in self-managed teams: Why “bureaucratic” teams can be better learners. Organization Science, 21, 609–624.

Bunderson, J. S., & Sutcliffe, K. M. (2002). Why some teams emphasize learning more than others: Evidence from busi- ness unit management teams. In H. Sondak (Ed.), Research on managing groups and teams: Toward phenomenology of groups and group membership (Vol. 4, pp. 49–84). Oxford: Elsevier Science.

Bunderson, J. S., & Sutcliffe, K. M. (2003). Management team learning orientation and business unit performance. Journal of Applied Psychology, 88, 552–560.

Button, S. B., Mathieu, J. E., & Zajac, D. M. (1996). Goal orientation in organiza- tional research: A conceptual and empiri- cal foundation. Organizational Behavior and Human Decision Processes, 67, 26– 48.

Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20, 872–882.

Byrne, B. M., & Stewart, S. M. (2010). The MACS approach to testing multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling: A Multidisciplinary Journal, 13, 287–321.

Chan, D. (1998). Functional relations among contructs in the same content domain at different levels of analysis: A typology of composition models. Journal of Applied Psychology, 83, 234–246.

Chen, G., Tjosvold, D., & Liu, C. (2006). Cooperative goals, leader people and productivity values: Their contribution to top management teams in China. Journal of Management Studies, 43, 1177– 1200.

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for

testing measurement invariance. Struc- tural Equation Modeling, 9, 233–255.

Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement: A quantita- tive review and test of its relations with task and contextual performance. Person- nel Psychology, 64, 89–136.

Collins, C., & Smith, K. G. (2006). Knowl- edge exchange and combination: The role of human resource practices in the performance of high-technology firms. Academy of Management Journal, 49, 544–560.

Crawford, J. R., & Henry, J. D. (2003). The Depression Anxiety Stress Scales: Nor- mative data and latent structure in a large non-clinical sample. British Journal of Clinical Psychology, 42, 111–131.

Cumming, S. P., Smith, R. E., & Smoll, F. L. (2008). Development and validation of the achievement goal scale for youth sports. Psychology of Sport and Exercise, 9, 686–703.

Cumming, S. P., Smoll, F. L., Smith, R. E., & Grossbard, J. R. (2007). Is winning every- thing? The relative contributions of motivational climate and won-lost per- centage in youth sports. Journal of Applied Sport Psychology, 19, 322–336.

Cury, F., Biddle, S., Famouse, J., Goudas, M., Sarrazin, P., & Durand, M. (1996). Per- sonal and situational factors influencing intrinsic interest in adolecent girls in school physical education: A structural equation modelling analysis. Educational Psychology, 16, 305–315.

Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86, 499– 512.

DeShon, R. P., & Gillespie, J. Z. (2005). A motivated action theory account of goal orientation. Journal of Applied Psychol- ogy, 90, 1096–1127.

DeShon, R. P., Kozlowski, S. W. J., Schmidt, A. M., Milner, K. R., & Wiechmann, D. (2004). A multiple-goal, multilevel model of feedback effects on the regula- tion of individual and team perfor- mance. Journal of Applied Psychology, 89, 1035–1056.

Dragoni, L. (2005). Understanding the emergence of state goal orientation in organizational work groups: The role of

leadership and multilevel climate percep- tions. Journal of Applied Psychology, 90, 1084–1095.

Duda, J. L. (2001). Achievement goal research in sport: Pushing the bounda- ries and clarifying some issues. In G. C. Roberts (Ed.), Advances in motivation in sport and exercise (pp. 129–183). Cham- paign, IL: Human Kinetics.

Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048.

Dweck, C. S., & Elliot, A. J. (1983). Achieve- ment motivation. In P. Mussen & E. M. Heatherington (Eds.), Handbook of child psychology (pp. 643–691). New York, NY: Wiley.

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 265–273.

Dysvik, A., & Kuvaas, B. (2010). Exploring the relative and combined influence of mastery-approach goals and work intrin- sic motivation on employee turnover intention. Personnel Review, 39, 622–638.

Elliot, A. J. (1997). Integrating the “classic” and “contemporary” approaches to achivement motivation: A hierarchical model of approach and avoidance achievement motivation. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in moti- vation and achievement goals (Vol. 10, pp. 143–179). Greenwich, CT: JAI.

Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Edu- cational Psychologist, 34, 169–189.

Elliot, A. J., & Church, M. A. (1997). A hier- archical model of approach and avoid- ance achievement motivation. Journal of Personality and Social Psychology, 72, 218–232.

Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personal- ity and Social Psychology, 70, 461–475.

Elliot, A. J., & McGregor, H. A. (2001). A 2 x 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501–519.

Farrell, A. M. (2010). Insufficient discrimi- nant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63, 324–327.

2246 Development and validation of the MCWQ

Fletcher, T. D., Major, D. A., & Davis, D. D. (2008). The interactive relationship of competitive climate and trait competi- tiveness with workplace attitudes, stress, and performance. Journal of Organizational Behavior, 29, 899–922.

Fletcher, T. D., & Nusbaum, D. N. (2010). Development of the competitive work environment scale: A multidimensional climate construct. Educational and Psychological Measurement, 70, 105–124.

Harwood, C., Spray, C. M., & Keegan, R. (2008). Achievement goal theories in sport. In T. S. Horn (Ed.), Advances in sport psychology (pp. 157–186). Cham- paign, IL: Human Kinetics.

Hewstone, M., & Stroebe, W. (2001). Intro- duction to social psychology: A European perspective (3rd ed.). Oxford: Blackwell.

Hom, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffith, R. W. (1992). A meta- analytical structural equations analysis of a model of employee turnover. Journal of Applied Psychology, 77, 890–909.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equa- tion Modeling, 6, 1–55.

Huang, J.-C. (2009). Unbundling task con- flict and relationship conflict: The mod- erating role of team goal orientation and conflict management. International Journal of Conflict Management, 21, 334– 355.

Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136, 422–449.

Jagacinski, C. M., & Nicholls, J. G. (1984). Conceptions of ability and related affects in task involvement and ego involve- ment. Journal of Educational Psychology, 76, 909–919.

Johnson, D. W., & Johnson, R. T. (1974). Instructional goal structure: Coopera- tive, competitive, or individualistic. Review of Educational Research, 44, 213– 240.

Jöreskog, K. G. (2004). Structural equation modeling with ordinal variables using LISREL. Retrieved April 1, 2007, from

http://202.129.48.214/CD4007/ LISREL88S/PDFfiles/ordinal.pdf

Jöreskog, K. G., & Sörbom, D. (1993a). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software Interna- tional, Inc.

Jöreskog, K. G., & Sörbom, D. (1993b). LISREL 8: Users guide. Chicago, IL: Scien- tific Software.

Kaplan, A., & Maehr, M. L. (2002). Adoles- cents’ achievement goals: Situating moti- vation in sociocultural contexts. In T. Urdan & F. Pajaers (Eds.), Adolesence and education: Academic motivation of adoles- cents (pp. 125–167). Greenwich, CT: Information Age Publishing.

Kaplan, A., & Maehr, M. L. (2007). The con- tributions and prospects of goal orienta- tion theory. Educational Psychological Review, 19, 141–184.

Kiffin-Petersen, S., & Cordery, J. L. (2003). Trust, individualism and job characteris- tics as predictors of employee prefer- ence for teamwork. International Journal Human Resource Management, 14, 93–116.

Kopleman, R. E., Brief, A. P., & Guzzo, R. A. (1990). The role of climate and culture in productivity. In B. Schneider (Ed.), Organizational climate and culture (pp. 282–318). San Francisco, CA: Jossey- Bass.

Kuenzi, M., & Schminke, M. (2009). Assem- bling fragments into a lens: A review, cri- tique, and proposed research agenda for the organizational work climate litera- ture. Journal of Management, 35, 634– 717.

Kuvaas, B. (2008). An exploration of how the employee-organization relationship affects the linkage between perception of developmental human resource practices and employee outcomes. Journal of Man- agement Studies, 45, 1–25.

Kuvaas, B., & Dysvik, A. (2010). Exploring alternative relationships between per- ceived investment in employee develop- ment, perceived supervisor support and employee outcomes. Human Resource Management Journal, 20, 138–156.

Lau, S., & Nie, Y. (2008). Interplay between personal goals and classroom goal struc- tures in predicting student outcomes: A multilevel analysis of person-context

interactions. Journal of Educational Psy- chology, 100, 15–29.

Lemyre, P. N., Hall, H. K., & Roberts, G. C. (2008). A social cognitive approach to burnout in elite athletes. Scandinavian Journal of Medicine and Science in Sports, 18, 221–234.

LePine, J. A. (2005). Adaptation of teams in response to unforsean change: Effects of goal difficulty and team composition in terms of cognitive ability and goal orien- tation. Journal of Applied Psychology, 90, 1153–1167.

Levine, J. (1983). Social comparison and education. In J. Levine & M. Wang (Eds.), Teacher and student perceptions: Implica- tions for learning (pp. 29–55). Hillsdale, NJ: Erlbaum.

Lin, S.-C., & Chang, J. N. (2005). Goal orientation and organizational com- mitment as explanatory factors of employees’ mobility. Personnel Review, 34, 331–353.

Lloyd, J., & Fox, K. (1992). Achievement goals and motivation to exercise in adolecent girls: A preliminary interven- tion study. British Journal of Physical Education: Research Supplement, 11, 12–16.

Maehr, M. L., & Braskamp, L.A. (1986). The motivation factor: A theory of personal investment. Lexington, MA: Lexington Books/Heath.

Maehr, M. L., & Zusho, A. (2009). Achievement goal theory: The past, present, and future. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of Motiva- tion at School (pp. 77–104). New York, NY: Routledge.

Mehta, A., Feild, H., Armenakis, A., & Mehta, N. (2009). Team goal orientation and team performance: The mediating role of team planning. Journal of Manage- ment, 35, 1026–1046.

Meyers, L. S., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications, Inc.

Midgley, C., Kaplan, A., Middleton, M., Maehr, M., Urdan, T., Hicks Anderman, L., et al. (1998). The development and validation of scales assessing students’ achievement goal orientations. Contem- porary Educational Psychology, 23, 113– 131.

Nerstad et al. 2247

Nerstad, C. G. L., Richardsen, A. M., & Martinussen, M. (2010). Factorial valid- ity of the Utrecht Work Engagement Scale (UWES) across occupational groups in Norway. Scandinavian Journal of Psychology, 51, 326–333.

Newton, M., Duda, J. L., & Yin, Z. (2000). Examination of the psychometric prop- erties of the Perceived Motivational Climate in Sport Questionaire-2 in a sample of female athletes. Journal of Sport Sciences, 18, 275–290.

Nicholls, J. G. (1979). Quality and equality in intellectual development: The role of motivation in education. American Psy- chologist, 34, 1071–1084.

Nicholls, J. G. (1984). Achievement motiva- tion: Conceptions of ability, subjective experience, mastery choice and perfor- mance. Psychological Review, 91, 328– 346.

Nicholls, J. G. (1989). The competitive ethos and democratic education. Cambridge, MA: Harvard University Press.

Ntoumanis, N., & Biddle, S. J. H. (1999). A review of motivational climate in physi- cal activity. Journal of Sport Sciences, 17, 643–665.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.

Papaioannou, A. (1994). Development of a questionnaire to measure achieve- ment orientations in physical education. Research Quarterly for Exercise and Sport, 65, 11–20.

Papaioannou, A., Marsh, H. W., & Theodorakis, Y. (2004). A multilevel approach to motivational climate in physical education and sport settings: An individual or group level construct? Journal of Sport & Exercise Psychology, 26, 90–118.

Papaioannou, A., Milosis, D., Kosmidou, E., & Tsigilis, N. (2007). Motivational climate and achievement goals at the situational level of generality. Journal of Applied Sport Psychology, 19, 38– 66.

Papaioannou, A., Zourbanos, N., Kromidas, H., & Ampatzoglou, G. (2012). The place of achievement goal in the social context of sport: A comparison of Nicholls’ and Elliot’s models. In G. C. Roberts & D. Treasure (Eds.), Advances in

motivation in sport and exercise (Vol. 3, pp. 59–91). Champaign, IL: Human Kinetics.

Parker, C. P., Baltes, B. B., Young, S. A., Huff, J. W., Altmann, R. A., Lacost, H. A., et al. (2003). Relationships between psycho- logical climate perceptions and work outcomes: A meta-analytic review. Journal of Organizational Behavior, 24, 389–416. doi: 10.1002/job.198.

Payne, S. C., Youngcourt, S. S., & Beaubien, M. J. (2007). A meta-analytic examina- tion of goal orientation nomological net. Journal of Applied Psychology, 92, 128– 150.

Pensgaard, A.-M., & Roberts, G. C. (2000). The relationship between motivational climate, perceived ability and sources of distress among elite athletes. Journal of Sport Sciences, 18, 191–200.

Pensgaard, A.-M., & Roberts, G. C. (2002). Elite athletes’ experiences of the motiva- tional climate: The coach matters. Scan- dinavian Journal of Medicine and Science in Sports, 12, 54–60.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and rec- ommended remedies. Journal of Applied Psychology, 88, 879–903.

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Man- agement, 12, 531–544.

Poortvliet, P. M., & Darnon, C. (2010). Toward a more social understanding of achievement goals: The interpersonal effects of mastery and performance goals. Current Directions in Psychological Science, 19, 324–328.

Porter, C. O. L. H., Webb, J. W., & Gogus, C. I. (2010). When goal orientations collide: Effects of learning and perfor- mance orientation on team adaptability in response to workload imbalance. Journal of Applied Psychology, 95, 935– 943.

Reichers, A. E., & Schneider, B. (1990). Climate and culture: An evolution of constructs. In B. Schneider (Ed.), Organizational climate and culture (pp. 5–39). San Francisco, CA: Jossey-Bass.

Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents

and effects on job performance. Academy of Management Journal, 53, 617–635.

Roberts, G. C. (2012). Motivation in sport and exercise from an achievement goal theory perspective: After 30 years, where are we? In G. C. Roberts & D. Treasure (Eds.), Advances in motivation in sport and exercise (Vol. 3, pp. 5–58). Cham- paign, IL: Human Kinetics.

Roberts, G. C., Kavussanu, M., & Sprague, R. L. (2001). Mentoring and the impact of the research climate. Science and Engi- neering Ethics, 7, 525–537.

Roberts, G. C., Treasure, D. C., & Conroy, D. E. (2007). Understanding the dynamics of motivation in sport and physical activ- ity. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 3–30). Hoboken, NJ: John Wiley & Sons, Inc.

Salanova, M., Agut, S., & Peiro, J. M. (2005). Linking organizational resources and work engagement to employee perfor- mance and customer loyalty: The media- tion of service climate. Journal of Applied Psychology, 90, 1217–1227.

Salanova, M., & Schaufeli, W. B. (2008). A cross-national study of work engagement as a mediator between job resources and proactive behavior. The International Journal of Human Resource Management, 19, 116–131.

Sarrazin, P., Vallerand, R., Guillet, E., Pelletier, L. G., & Cury, F. (2002). Motiva- tion and dropout in female handballers: A 21-month prospective study. European Journal of Social Psychology, 32, 395–418.

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psycho- metrika, 66, 507–514.

Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engage- ment: A multi-sample study. Journal of Organizational Behavior, 25, 293– 315.

Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire. Educational and Psychological Measure- ment, 66, 701–716.

Schneider, B., & Reichers, A. E. (1983). On the etiology of climates. Personnel Psy- chology, 36, 19–39.

2248 Development and validation of the MCWQ

Seifriz, J. J., Duda, J. L., & Chi, L. (1992). The relationship of perceived motivational climate to intrinsic motivation and beliefs about success in basketball. Journal of Sport and Exercise Psychology, 14, 375–391.

Skaalvik, E. M. (1997). Self-enhancing and self-defeating ego orientation: Relations with task and avoidance orientation, achievement, self-perceptions, and anxiety. Journal of Educational Psychol- ogy, 89, 71–81.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston, MA: Allyn and Bacon.

Theodosiou, A., & Papaioannou, A. (2006). Motivational climate, achievement goals, and metacognitive activity in physical education and exercise involvement in out-of-school settings. Psychology of Sport and Exercise, 7, 361– 379.

Tjosvold, D. (1995). Effects of power to reward and punish in cooperative and competitive contexts. The Journal of Social Psychology, 135, 723–736.

Tjosvold, D., Johnson, D. W., Johnson, R. T., & Sun, H. (2003). Can interpersonal competition be constructive within

organizations? The Journal of Psychology, 137, 63–84.

Trevor, C. O., & Nyberg, A. J. (2008). Keeping your headcount when all about you are losing theirs: Downsizing, voulntary turnover rates, and the moderating role of HR practices. Academy of Management Journal, 51, 259–276.

Valentini, N. C., & Rudisill, M. E. (2006). Goal orientation and mastery climate: A review of contemporary research and insights to intervention. Estudos de Psicologia, 23, 159–171.

Vallerand, R. J., Gauvin, L., & Halliwell, W. R. (1986). Negative effects of competi- tion on children’s intrinsic motivation. Journal of Social Psychology, 126, 649– 657.

Van Yperen, N. W., Hamstra, M. R. W., & van der Klauw, M. (2011). To win, or not to lose, at any cost: The impact of achievement goals on cheating. British Journal of Management, 22, 5– 15.

Van Yperen, N. W., & Renkema, L. J. (2008). Performing great and the purpose of performing better than others: On the recursiveness of the achievement goal

adoption process. European Journal of Social Psychology, 38, 260–271.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measure- ment invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–69.

Vandewalle, D. (1997). Development and validation of a work domain goal orienta- tion instrument. Educational and Psycho- logical Measurement, 57, 995–1015.

Wang, C. K. J., Liu, W. C., Chatzisarantis, N. L. D., & Lim, B. S. C. (2010). Influence of perceived motivational climate on achievement goals in physical education: A structural equation mixture modeling analysis. Journal of Sport and Exercise Psy- chology, 32, 324–338.

Weiss, M. R., Amorose, A. J., & Wilko, A. M. (2009). Coaching behaviors, motiva- tional climate, and psychosocial out- comes among female adolescent athletes. Pediatric Exercise Science, 21, 475–492.

Zimmerman, R. D. (2008). Understand- ing the impact of personality traits on individuals’ turnover decisions: A meta- analytical path model. Personnel Psychol- ogy, 61, 309–348.

Nerstad et al. 2249

Appendix A

Principal Components Analysis with Varimax Rotation (Study 2; n = 4,141)

Items ENG PC MC TI MO EO

ENG-VI2 .80 ENG-DE2 .80 ENG-VI1 .80 ENG-DE3 .78 ENG-AB3. .78 ENG-AB4 .76 ENG-VI3 .74 ENG-DE4 .73 ENG-AB5 .72 PC7 .78 PC3 .71 PC8 .70 PC2 .70 PC4 .70 PC5 .63 PC1 .55 PC6 .56 MC3 .79 MC1 .75 MC5 .73 MC2 .72 MC4 .69 MC6 .61 TI3 .89 TI2 .83 TI4 .81 TI1 .79 TI5 .57 MO3 .85 MO1 .80 MO5 .77 MO2 .75 MO4 .52 PO4 .79 PO3 .76 PO2 .75 PO1 .66 Eigenvalues 9.18 4.72 2.92 2.31 1.94 1.52 % of variance 24.80 12.77 7.90 6.23 5.23 4.10

Note. Factor loadings less than .30 are not shown; bold and underlined loadings included in the final scales. ENG = work engagement (VI = vigor; DE = dedication; AB = absorption); MC = mastery climate; PC = performance climate; PI = personal initiative; TI = turnover intention; WE = work effort; MO = mastery orientation; WQ = work quality; PO = performance orientation.

2250 Development and validation of the MCWQ

Copyright of Journal of Applied Social Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.