What contributes to individual innovativeness : A multilevel perspective

What contributes to individual innovativeness? A multilevel perspective Tahani Z. Aldahdouh * , Vesa Korhonen, Petri Nokelainen Faculty of Education and Culture, Tampere University, 33014 Tampere, Finland article info Article history: Received 9 December 2018 Accepted 28 February 2019 Available online 19 July 2019 JEL classification: O3 I23 Keywords: Innovativeness Organizational culture Goal orientation Implicit theory Higher education Innovation Bayesian approach Multilevel path analysis Mindset Staff abstract It is well-established that individual innovativeness is an indispensable quality for employees working in a variety of workplace environments. However, the interaction between the psychological and organizational factors influencing innovativeness remains unclear. This study seeks to address that research gap by examining a model comprising a mix of psychological factors (implicit theory and goal orientation) and organizational culture. Data were collected from 315 staff members working in 34 different departments/ schools at Tampere University, Finland. The study employed a Bayesian multilevel path analysis that matched the hierarchical structure of the data to test the hypotheses. The results suggest that psychological factors reflecting goal orientation are the most important for interpreting individual innovativeness. Specifically, mastery goal orientation was shown to be a positive predictor and performance-approach goal orientation a negative predictor of innovativeness. Unexpectedly, departmental culture had neither a direct effect on innovativeness nor a moderation effect on the relationships between the psychological variables and innovativeness. Plausible explanations for these results and implications for future research are discussed. ©2019 Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 1. Introduction Prosperity and advancement rely on the capacity of societies, organizations, and individuals to innovate, and research has consistently shown that individual innovativeness is a key factor in initiative success (G€ okçearslan, Karademir, &Korucu, 2017;Hong, Hwang, Ting, Tai, &Lee, 2013;Jin, 2013;Park &Kim, 2010;Si &Wei, 2012). Rogers (2003) describes innovators as change agents, while Kirton (1976) argues that innovators are pioneers of radical change. Innovativeness is of the utmost importance for knowledge workers (Benson &Brown, 2007) because they are expected to expand their expertise, deal with ambiguity, take risks, embrace novel ideas, and respond quickly to knowledge changes (Aldahdouh, Korhonen, & Nokelainen, 2017). In other words, knowledge workers are asked to be innovators (Drucker, 1999). Many studies show remarkably consistent findings regarding the relevance of innovativeness in predicting adaptive outcomes. For instance, the empirical evidence shows that innovativeness predicts technology usage (G€ okçearslan et al., 2017;Hong et al., 2013;Jin, 2013;Park &Kim, 2010), influences the implementation of information and communication *Corresponding author. E-mail addresses: [email protected] (T.Z. Aldahdouh), vesa.korhonen@tuni.fi(V. Korhonen), petri.nokelainen@tuni.fi(P. Nokelainen). Contents lists available at ScienceDirect International Journal of Innovation Studies journal homepage: http://www.keaipublishing.com/en/journals/international-journal-of-innovation-studies https://doi.org/10.1016/j.ijis.2019.06.001 2096-2487/©2019 Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). International Journal of Innovation Studies 3 (2019) 23e39 technology (ICT; Drent &Meelissen, 2008), and is related to perceived competencies in e-learning (Loogma, Kruusvall, & Ümarik, 2012) and techno-pedagogical skills (Çuhadar, Bülbül, &Ilgaz, 2013). Although several studies have sought to identify the antecedents of innovativeness, the results may be described as scattered. Some studies, for instance, have focused on organizational factors (Naranjo-Valencia, Jim enez-Jim enez, &SanzValle, 2016;Shanker, Bhanugopan, van der Heijden, &Farrell, 2017;Si &Wei, 2012), while others have focused on psychological factors (Aldahdouh, Nokelainen, &Korhonen, 2018;Batra &Vohra, 2016;Lu, Lin, &Leung, 2012;Vinarski-Peretz, Binyamin, &Carmeli, 2011). In a review study summarizing the antecedents of innovativeness, Parzefall, Seeck, and Lepp€ anen (2008) pointed out that “most studies have focused on isolated factors, and a holistic perspective is lacking”(p. 166). Among the few studies that have investigated psychological and organizational factors together, Scott and Bruce (1994) found that both psychological and organizational factors interacted and cooperated in shaping innovative behavior. Beyond those few studies (Miron, Erez, &Naveh, 2004;Montani, Odoardi, &Battistelli, 2014;Scott &Bruce,1994), however, evidence of an interactive effect remains inconclusive. Cai (2017) argued that the study of innovation in higher educational institutions (HEIs) does not enjoy the same momentum as studies in the management field. This conclusion adds more uncertainty about whether models identified in prior studies are applicable to the context of HEIs and calls for research to fill this void. This study responds to this need by exploring some of the most often-cited psychological and organizational factors influencing individual innovativeness in HEIs. We are fundamentally motivated by a curiosity about whether individual innovativeness results from employees' psychological attributes or is shaped by their workplace environment. Alternatively, it may be a function of both psychological and organizational aspects. This study addresses two of the most salient psychological factorsdimplicit theories (Dweck, Chiu, &Hong, 1995) and goal orientations (Midgley et al., 1998)dwhile the organizational aspect is represented by the organizational culture (Cameron &Quinn, 2006). In what follows, we review the literature on innovativeness along with its antecedents and develop the study's hypotheses. Next, we describe the study's methodology, including the sample, the measures, and the analysis. Next, the study's most important findings are outlined. Finally, we discuss the study's results, establish connections to the literature, and draw conclusions and implications for researchers and practitioners. 2. Innovativeness Below, we delineate several relevant concepts in the literature and try to demarcate each one. However, the boundaries are by no means clear. The more confusing concepts in the literature include “creativity”’ (Amabile, 1988), “innovation”(West & Farr, 1990), “innovative behavior”(Scott &Bruce, 1994) and “innovativeness”(Goldsmith &Foxall, 2003). Thus, we will begin by locating this study's concept of innovativeness within a sea of foggy conceptualizations. Some scholars have advocated a conceptual differentiation between creativity and innovation (Miron et al., 2004;Rank, Pace, &Frese, 2004), while others argue that innovation implicitly or explicitly encompasses creativity (West &Farr, 1990). The first approach considers creativity as idea generation while conceiving innovation as idea implementation (Rank et al., 2004). The second approach combines the two concepts and considers creativity to be the first phase of the innovation process, thus viewing creativity as “the ideation component of innovation and innovation as encompassing both the proposal and applications of the new ideas”(West &Farr, 1990, p. 10). This study employs the latter approach because our concern is not just about whether employees will implement the ideas generated by others; we also examine their tendency to generate innovative ideas. Regarding measurement, individual innovation has been operationalized through two main methods. The first measurement level relies on Rogers (2003), who defines innovativeness as “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a system”(Rogers, 2003, p. 242). This definition corresponds to a low level of abstraction because it concerns the tracking of individual differences in observed behavior. The literature also uses various terms to describe innovativeness from a behavioral perspective, including actualized innovativeness (Midgley &Dowling,1978), innovative work behavior,orinnovative job performance (Janssen &Van Yperen, 2004;Jong &Hartog, 2007). A second and more abstract measurement level for innovativeness conceptualizes it as a persistent individual characteristic (Yi, Fiedler, &Park, 2006), a latent construct (Goldsmith &Foxall, 2003;Midgley &Dowling,1978), or an underlying personality trait (Hurt, Joseph, &Cook, 1977) that determines the tendency to generate and accept changes and novel ideas. Innovativeness as a personality trait has been referred to as general innovativeness (Marcati, Guido, &Peluso, 2008), life innovativeness (Roehrich, 2004), innate innovativeness (Midgley &Dowling, 1978), and global trait innovativeness (Goldsmith &Foxall, 2003). The measurement of innovativeness as a personality trait has outweighed the use of behavioral measurement because the latter has been identified as a post-facto technique lacking the predictive power of innovations in other domains (Goldsmith &Foxall, 2003). Accordingly, this study adopts the personality trait approach. 2.1. Factors predicting innovativeness Researchers have made several theoretical attempts to identify the factors predicting individual innovativeness (Anderson, Poto cnik, &Zhou, 2014;Frambach &Schillewaert, 2002;Parzefall et al., 2008;Patterson, Kerrin, &Gatto-Roissard, 2009;Wisdom, Chor, Hoagwood, &Horwitz, 2014). These efforts have revealed the key determinants which can be T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e3924 categorized into psychological and organizational types. The sections below review these factors, along with the relationships between them. 2.1.1. Psychological factors Several psychological factors have been identified as contributors to individual innovativeness. The long list includes Big Five personality dimensions, self-efficacy, thinking styles, intrinsic motivation, and attitudes (Anderson et al., 2014;Parzefall et al., 2008;Patterson et al., 2009;Wisdom et al., 2014). Among those factors, the implicit theory (Dweck, 2006) and achievement goal orientation (Midgley et al., 1998) represent the most promising models. Implicit theory (Dweck &Leggett, 1988) concerns an individual's beliefs about the nature of human attributes, including ability, personality, and morality (Dweck et al., 1995). A person may believe in one of two theories about human attributes (Dweck, 2006). For entity theorists, human attributes are fixed, innate, and physical; one cannot surpass these physical limitations. For incremental theorists, human attributes are elastic, stretchable, and malleable; there are no limitations on how far one can go through practice and effort. Scholars have examined implicit theories extensively, with a focus on human abilities such as intelligence (Blackwell, Trzesniewski, &Dweck, 2007). The results of research conducted in several contexts have consistently shown that entity theories are associated with maladaptive outcomes, while incremental theories are related to adaptive outcomes (De Castella &Byrne, 2015;Dweck, 2006;Murphy &Dweck, 2010). Achievement goal orientations is the other psychological factor important to this study. Goal orientations are often described as the reasons why one is striving to achieve a task (Dweck &Leggett, 1988;Midgley et al., 1998). Three main goal orientations have been identified: (1) mastery, (2) performance-approach, and (3) performance-avoidance (Linnenbrink & Pintrich, 2002;Midgley et al., 1998). Individuals adopting mastery goal orientation tend to engage in tasks in order to improve their capacities and sharpen their skills. By contrast, individuals adopting performance-approach goal orientation tend to engage in a task in order to show others how well theycan do or to surpass their peers (Linnenbrink &Pintrich, 2002). Finally, individuals adopting performance-avoidance goal orientation tend to engage in a task in order to avoid appearing incompetent (Elliot &Church, 1997). Mastery goal orientation is often reported to be associated with adaptive behaviors (De Castella &Byrne, 2015;Dweck &Leggett, 1988), whereas performance-avoidance goal orientation is related to maladaptive behaviors (Elliot &Church, 1997). The research findings on performance-approach goal orientation have been inconsistent and contradictory (Elliot &Moller, 2003); while some findings indicate positive effects (De Castella &Byrne, 2015;Elliot & Church, 1997), others indicate negative consequences (Ames, 1992;Dweck &Leggett, 1988). What we know about the relationship between implicit theories and goal orientations relies largely upon empirical studies that repeatedly confirm that incremental theory predicts mastery goal orientation. Meanwhile, the entity theory predicts performance-approach and performance-avoidance goal orientations (Chen &Pajares, 2010;De Castella &Byrne, 2015;Dweck &Leggett, 1988;Robins &Pals, 2002). In 1988, Dweck and Leggett proposed a theoretical framework for future research wherein they identified implicit theories as predictors of goal orientations and goal orientations as predictors of social behaviors. While several theoretical studies have mentioned that implicit theory and goal orientations can be strong predictors of innovativeness (Anderson et al., 2014; Parzefall et al., 2008;Patterson et al., 2009), empirical findings have partially supported this theoretical framework (Keong & Hirst, 2010;Lu et al., 2012). For instance, Lu et al. (2012) found that mastery goal orientation was an indirect positive predictor of innovative performance. On the other hand, Aldahdouh et al. (2018) found that the entity theory of ability predicted the mastery goal orientation but failed to predict the performance-avoidance goal orientation. Even though performanceavoidance goal orientation appeared to be a negative predictor of innovativeness, mastery goal orientation seemed to be a positive predictor. 2.1.2. Organizational factors Previous studies have listed a number of organizational factors that help hinder or foster innovativeness, such as organizational leadership, structure, strategy, resources, size, and climate (Anderson et al., 2014;Parzefall et al., 2008;Patterson et al., 2009;Wisdom et al., 2014). This study examines the role of organizational culture, the most often-mentioned factor amongst those listed. An organizational culture comprises employees' hidden and collective beliefs, assumptions, and thoughts about their institution, all of which implicitly guide their behavior (Cai, 2008). The term “culture”here refers to the shared identity that distinguishes one working unit from all others. The competing values framework (CVF) is one of the most widely used typological frameworks for organizational culture (Cai, 2008;Cameron &Quinn, 2006). The CVF comprises two crossing dimensions with two opposite poles: (1) internal vs. external; and (2) stability vs. flexibility. The crossing dimensions produce four quadrants representing four organizational culture types (Cameron &Quinn, 2006): Clan (focuses on internal flexibility), Hierarchy (focuses on internal stability), Market (focuses on external stability), and Adhocracy (focuses on external flexibility). Several review studies have made a strong case for the role of organizational culture in influencing innovativeness (Ahmed, 1998;Anderson et al., 2014;Frambach &Schillewaert, 2002;Parzefall et al., 2008;Patterson et al., 2009;Wisdom et al., 2014). For example, Patterson et al. (2009) noted that an organizational culture that supports innovation is one that “encourages risk taking and the exchange of ideas, promotes participation in decision making and management, has goals and rewards for innovation, and provides psychological safety in relation to making suggestions”(p. 25). In support of these claims, Raj and Srivastava (2013) revealed that the Clan, Adhocracy, and Market cultures contribute positively to predicting organizational innovativeness through organizational learning. In another study reported by Amabile (1988), interviews with T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e39 25 R&D scientists identified nine work environment characteristics that foster the generation of novel ideas: giving employees a sense of control over their work (freedom); offering supportive managerial practices, such as showing enthusiasm for new ideas and paying attention to employees' needs and expectations (encouragement); and providing constructive feedback and rewards for employees' professional skills and knowledge (recognition). 2.1.3. Psychological and organizational factors Implicit theory and goal orientations were originally studied in academic contexts, where researchers focused on identifying their antecedents and consequences (Dweck &Leggett, 1988;Midgley et al., 1998). Their efforts have pointed to the importance of identifying the school's or classroom's goal structure as a strong predictor of students' goal orientations (Ames, 1992;Midgley et al., 1998). Students who tend to endorse a statement like “in this classroom, only talented students are rewarded”also tend to endorse statements like “my talent is what it is, and there is not much I can do to improve it.” Therefore, they also tend to endorse statements like “One of my goals is to avoid looking not smart in this class.” Like those of their students, teachers' implicit-theories and goal orientations are shaped by their working environments, as several studies have emphasized (Hamstra, Van Yperen, Wisse, &Sassenberg, 2014;Kunst, van Woerkom, van Kollenburg, & Poell, 2018). Murphy and Dweck (2010), for example, showed that two cultural mindsets contribute to employees' cognition, affection, and behavior: a culture of genius and a culture of growth. In a culture of genius, organizations tend to recruit only “intelligent”people and praise employees on their “innate”attributes. They invest little in employee training, which talented people do not need. Contrariwise, in a culture of growth, organizations tend to recruit growth-minded people, praise employees on their efforts, and invest a great deal in employee training. Keating and Heslin (2015) proposed a model in which they identified organizational culture and its climate as antecedents of employees' implicit theories and their job commitment and satisfaction. Several studies have examined organizational culture as a moderator of the relationship among the psychological variables. For instance, Hon and Leung (2011) found that organizational culture moderated the effect of employees' intrinsic motivations on their creative performance. Miron et al. (2004) examined whether cultures serve as moderators between individual creativity and innovation performance, finding that, in a high-innovative culture, individuals' creative ideas are often transformed into innovation, while individuals' creative ideas remain stagnant in a low-innovative culture. 3. Study variables and hypotheses Table 1 summarizes the study variables, along with the corresponding abbreviations and definitions. We posited that the psychological variables (entity theory of ability and goal orientations) and organizational variables (cultures) predict innovativeness while the organizational variables serve as moderators of the relationships among the psychological variables. Specifically, we propose the following: Hypothesis 1. The entity theory of ability and performance-avoidance goal orientation contribute negatively in predicting innovativeness, while mastery goal orientation contributes positively in predicting innovativeness. Hypothesis 2. The entity theory of ability is negatively associated with mastery goal orientation and positively associated with performance-avoidance goal orientation. Hypothesis 3. The Clan and Adhocracy cultures contribute positively in predicting innovativeness, while the Hierarchy culture contribute negatively in predicting innovativeness. Hypothesis 4. Culture moderates the relationship among the implicit theory of ability, goal orientation, and innovativeness such that: Table 1 Study variables, abbreviations, and definitions. Variable Abbreviation Definition Innovativeness INNOV refers to an individual's willingness to change Entity theory of ability ETA refers to an individual's beliefs that the human attributes are fixed, innate and stable Mastery goal orientation MAS refers to an individuals' tendency to engage in a task in order to improve their own capacities and to sharpen skills Performance-approach goal orientation PAP refers to an individuals' tendency to engage in a task in order to show others how well they can do or to overtake their peers Performance-avoidance goal orientation PAV refers to an individuals' tendency to engage in a task in order to avoid appearing incompetent in comparison to their peers Clan culture CLN refers to a culture that focuses on internal flexibility Hierarchy culture HRC refers to a culture that focuses on internal stability Market culture MRK refers to a culture that focuses on external stability Adhocracy culture ADH refers to a culture that focuses on external flexibility T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e3926 Hypothesis 4.1. Cultures supporting flexibility and discretion (Clan and Adhocracy) mitigate the negative effect of the implicit theory of ability and performance-avoidance goal orientation on innovativeness, but they strengthen the positive effect of mastery goal orientation on innovativeness. Hypothesis 4.2. Cultures emphasizing control and stability (Hierarchy) worsen the negative effect of the entity theory of ability and performance-avoidance goal orientation on innovativeness, but they reduce the positive effect of mastery goal orientation on innovativeness. Hypothesis 4.3. Cultures that support flexibility and discretion (Clan and Adhocracy) mitigate the negative effect of implicit theory of ability on mastery goal orientation, but they weaken the positive effect of the implicit theory of ability on performance-avoidance goal orientation. Hypothesis 4.4. Cultures emphasizing control and stability (Hierarchy) reduce the positive effect of the entity theory of ability on mastery goal orientation, but they worsen the negative effect of the entity theory of ability on performanceavoidance goal orientation. The literature has not presented clear evidence about the relationship between the Market culture and innovativeness (Naranjo-Valencia et al., 2016;Raj &Srivastava, 2013). On one hand, the Market culture is defined by stability and control, which may contribute negatively to mastery goal orientation while contributing positively to performance-avoidance goal orientation. On the other hand, it has an external orientation that supports openness and competitiveness, so it may contribute positively to innovativeness. Therefore, we do not hypothesize regarding the effect of the Market culture on innovativeness and its moderation role. Driven by the data, we allowed the Market variable to be associated with innovativeness and to moderate the relationship among the psychological variables and innovativeness. Similar to findings on the Market culture, findings on the effect (adaptive or maladaptive) of performance-approach goal orientation have been inconsistent (Elliot &Moller, 2003). Thus, we allowed performance-approach goal orientation to be associated with innovativeness while remaining neutral regarding its effect. 4. Method 4.1. Participants All the staff members working in 34 different schools/departments at Tampere University, Finland, were invited to participate in the study. Of these, 315 (167 female and 148 male) respondents left valid responses to an online questionnaire. Therefore, the sample was selected using a non-probability sampling method. The age of the participants varied from 20 to 67, with a mean of 46 years (SD ¼11.187). Seventy percent (n ¼221) of the participants were academic staff, while the rest (n ¼94) were administrative workers. Participants had job experience of an average of 177 months in higher education (about 14.75 years; SD ¼116.475). In terms of educational qualifications, 8% (n ¼25) had completed a bachelor's degree, 40% (n ¼128) had completed a master's degree,15% (n ¼46) had completed a doctoral or post-doctoral degree, 17% (n ¼54) were professors or docents, and 20% (n ¼62) were “others.” 4.2. Measures and procedures We distributed an online questionnaire to all staff members from August 2016 to November 2016, using email invitations and the university's intranet. Unless otherwise indicated, a 5-point Likert scale was used, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Cronbach's a provided an estimate of the internal consistency of the scales. We translated the questionnaire into Finnish and piloted it before use. The questionnaire consisted of demographic questions (seven items) and the following measures: innovativeness, organizational culture, goal orientations, and implicit theories of ability. 4.2.1. Innovativeness A shortened version (13 items) of Hurt et al. (1977) Innovativeness Scale was adopted to measure the staff members' orientations towards change (e.g., “I enjoy trying new ideas”). The scale has shown strong psychometric characteristics and has repeatedly demonstrated its usefulness as a valid measure of general innovativeness (Goldsmith, 1990;Pallister &Foxall, 1998). Cronbach's a was 0.848. 4.2.2. Organizational culture We adopted the Organizational Culture Assessment Instrument (OCAI) to assess staff's perceptions of their departments' culture. The OCAI was devised by Cameron and Quinn (2006) and is based on the CVF, the framework most often used to assess culture in the higher education context (Cai, 2008;Kleijnen, Dolmans, Muijtjens, Willems, &Van Hout, 2009). Previous studies have validated the OCAI's validity and reliability in measuring an organization's culture (Cameron &Quinn, 2006; Heritage, Pollock, &Roberts, 2014). The OCAI consists of 24 questions: six for each of the four cultures. Cronbach's a coefficients were as follows: Clan ¼0.805, Hierarchy ¼0.624, Market ¼0.868, and Adhocracy ¼0.822. The reliability of the T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e39 27 Hierarchy culture is under the acceptable level of 0.70 and is consistent with prior findings that revealed that the Hierarchy factor should be adjusted (Heritage et al., 2014). 4.2.3. Goal orientations We adapted a shortened version (10 items) of Midgley and colleagues' (2000) Achievement Goal Orientation (AGO) Scale to measure staff members' goal orientations. The adaptations included replacing “school”with “work.”The resulting AGO Scale consisted of three subscales: MAS (three items; e.g., “One of my goals in work is to learn as much as I can”), PAP (three items; e.g., “One of my goals is to show others that work is easy for me”), and PAV (four items; e.g., “It's important to me that I don't look incapable of doing my work”). Cronbach's a values for MAS, PAP, and PAV were 0.759, 0.787, and 0.815, respectively. 4.2.4. Implicit theories of ability The eight-item person measure developed by Levy, Stroessner, and Dweck (1998) was adapted to capture the implicit theory of ability. The items were re-worded to reflect first-person beliefs about the nature of participants' personal attributes rather than human attributes in general (e.g., for incremental beliefs, “I can significantly change my basic level of talent”; for entity beliefs, “My talent is something very basic about me that I can't change very much”). The items were measured on a 6point Likert scale, ranging from 1 (“strongly disagree”)to6(“strongly agree”). The incremental items were reverse-scored such that larger scores reflected a relatively strong entity theory. The eight items were added up and averaged to create the Entity Theory of Ability scale ( a ¼0.870). 4.3. Analysis 4.3.1. Approach We conducted Bayesian multilevel path analysis using Mplus version 8.0 (Muth en &Muth en, 2017) to test the hypotheses. A multilevel approach was warranted since our data had a nested structure, whereby we collected 315 responses of individuals working in 34 schools/departments. We opted to use path analysis because the study variables were assumed to have structural dependencies among the predictor variables beside their effects on the outcome variable. We followed the within-and-between approach to multilevel path analysis, wherein estimates for the within-covariance matrix (individuallevel) and between-covariance matrix (group-level) are determined separately (Hox, 2010). This makes it possible to partial out the group-level variance from individual-level variables. Multilevel path analyses of both levels were conducted separately but simultaneously. Due to the small number of groups in this study, it was not feasible to conduct the analysis on latent variables. Thus, summary scores of the variables were used in the analysis. The Bayesian approach was chosen because of its superior performance for small samples (Stegmueller, 2013). Unlike inferential techniques, the Bayesian approach does not rely on any distributional assumptions about the data, such as normality (Finch &Bolin, 2017, p. 286). 4.3.2. Settings The Markov Chain Monte Carlo (MCMC) methodology was used to obtain the parameter estimates in Bayesian analysis. The convergence of parameter estimates was assessed by the Potential Scale Reduction (PSR) convergence criterion. A PSR <1.05 for each parameter indicates that convergence of the MCMC sequence has been reached (Finch &Bolin, 2017). The convergence was also monitored using the trace plots. Quick oscillations in the trace plot indicate convergence. Autocorrelation plots were used to check for the correlation between two adjacent MCMC draws and to set the thinning value. Data was thinned every fourth MCMC draw to minimize the correlation to near zero. Model fit was assessed using the Posterior Predictive P-value (PPP) and Credibility Interval (CI). A PPP value close to 0.50 indicates optimal fit(Finch &Bolin, 2017). A 95% CI that contains zero indicates good fit to the data. In addition, we used the Deviance Information Criterion (DIC) to compare between models, where the model with the lowest DIC value is preferable. The analysis was conducted using non-informative or diffuse priors. Two MCMC chains of a minimum of 45,000 iterations were used (Muth en &Muth en, 2017). 4.3.3. Statistical procedures We tested three models while analyzing the data: random intercept, random slopes, and cross-level interaction models. First, we tested the random intercept model (Fig. 1) and assessed the fitness of the model by computing the PPP. Then, we tested the random slopes model (Fig. 2) by allowing the slopes to vary across departments. Finally, we tested the cross-level interaction model (Fig. 3), in which the cultures served as moderators of the relationships between the psychological variables and the outcome (innovativeness). The logic behind this process had two objectives. One was to ensure simplicity by testing the models from simple to more complex structures. The other was to check the PPP model fit value, which was not available except for the random intercept model. We then compared the DIC value of the random intercept model with the DIC values of both the random slopes and the cross-level interaction models. Lower DIC values would indicate a better model fit. T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e3928 4.3.4. Preliminary analyses 4.3.4.1. Missing data analysis. First, we conducted a missing values analysis. A case screening of 342 collected responses resulted in the removal of 27 cases. The missing values per variable were analyzed using the data imputation technique; they were replaced by the mean (for continuous variables) and the median (for the categorical variables). 4.3.4.2. Sample homogeneity. We conducted a series of differences tests to ensure sample homogeneity with respect to the outcome variable, innovativeness. An independent sample t-test showed that there was no significant difference in innovativeness based on gender (male or female) or job type (academic or administrative). The results of a one-way ANOVA also revealed no significant difference in innovativeness related to staff educational levels (p>0.05). Fig. 1. Random intercept model. INNOV ¼Innovativeness; ETA ¼Entity Theory of Ability; MAS ¼Mastery goal orientation; PAV ¼Performance-Avoidance goal orientation. Performance-Approach goal orientation as well as the path from ETA to PAV were omitted based on the correlation findings. Fig. 2. Random slopes model. INNOV ¼Innovativeness; ETA ¼Entity Theory of Ability; MAS ¼Mastery goal orientation; PAV ¼Performance-Avoidance goal orientation. Performance-Approach goal orientation as well as the path from ETA to PAV were omitted based on the correlation findings. T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e39 29 4.3.4.3. Common method variance. Our data could have suffered from a common method bias since the responses for all study variables were collected from the same individuals. We used Harman's one-factor test (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). All items of our nine variables were entered into an unrotated exploratory factor analysis using SPSS and forcing a one-factor solution. The results indicated that the single factor accounted for only 12.7% of the variance. These results suggested that the common method variance was not a major concern in this study. 4.3.4.4. Data aggregation. The intra-class correlation coefficient (ICC1; Bliese, 2000) was calculated to examine whether there were department-level variances in the study variables that necessitated their inclusion in the between-level model. ICC1 represents the proportion of group-level variance in respect to the total variance of the variable. Variables showing ICC1 > 0.05 were included in the between-level model (LeBreton &Senter, 2008). Though we consider the entity theory of ability and goal orientations as individual characteristics, we computed ICC1 for their respective variables because they may differ significantly across departments due to the study's sampling method. As hypothesized, however, the ICCs for those individual variables showed almost no variance according to department membership (All ICC1s <0.03). Thus, they were included only in the within-level model. Furthermore, we calculated ICC1 for the outcome variable (innovativeness) to see whether individual innovativeness was affected by department membership (Bliese, 2000). The results revealed that 10% of the variance in innovativeness was due to department membership. Our intention for the cultural variables was to measure the common perceptions of culture in each department. We were interested in the mean of each department, and not individual perspectives on what the departmental culture was. However, the cultural variables were measured through the ratings given by individuals in the department. To justify the aggregation of those cultural variables to their departments' means, we used a calculator developed by Biemann, Cole, and Voelpel (2012) to compute median Rwg values using the null uniform distribution (Bliese, 2000). The Rwg value indicates the degree of agreement among staff members within a department. Values greater than 0.70 indicate generally accepted agreement among the raters (LeBreton &Senter, 2008). Using the same tool, we determined ICC1 in addition to the reliability of the group means (ICC2). The results were as follows: for Clan culture, 0.88 (Rwg), 0.08 (ICC1), and 0.44 (ICC2); for Hierarchy culture, 0.88 (Rwg), 0.08 (ICC1), and 0.45 (ICC2); for Market culture, 0.89 (Rwg), 0.16 (ICC1), and 0.64 (ICC2); for Adhocracy culture, 0.88 (Rwg), 0.09 (ICC1), and 0.47 (ICC2). The F-ratios associated with the ICC values were all statistically significant at the 0.05 level. The ICC1 and Rwg values of all cultural variables were above the cut-off values. The ICC2 values ranged between 0.44 and 0.64, classified by Fleiss (1986, p. 7) as fair to good reliability estimates (ICC2 values <0.40 are poor, those between 0.40 and 0.75 are fair to good, and those >0.75 are excellent). Based on the results, we decided to aggregate the cultural variables. We followed the recommendation of Enders and Tofighi (2007), who suggested centering the individual-level variables on their group mean when the focus is on inspecting the moderation effect of the group-level variables on individual-level relationships. Department-level variables were centered on their grand mean (Hox, 2010). Fig. 3. Cross-level interaction model. INNOV ¼Innovativeness; ETA ¼Entity Theory of Ability; MAS ¼Mastery goal orientation; PAV ¼Performance-Avoidance goal orientation. Performance-Approach goal orientation as well as the path from ETA to PAV were omitted based on the correlation findings. T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e3930 4.3.4.5. Descriptive statistics. Table 2 displays the means, standard deviations, and correlations among the variables on the individual and department levels. An inspection of the correlations revealed that the relationships between innovativeness and the psychological variables were significant except for PAP. Therefore, PAP was excluded from further analysis. Similarly, ETA showed a non-significant relationship with PAV. Thus, the regression coefficient between ETA and PAV was canceled out in the examined models. 5. Results 5.1. Random intercept model We were guided by the hypotheses and the correlation matrix in specifying the paths between the variables. At the individual level, we examined the model in which ETA, MAS, and PAV were predictors of innovativeness, while ETA was a predictor of MAS. At the department level, we examined the extent to which Clan, Hierarchy, Market, and Adhocracy cultures explain the variance in the random intercept of innovativeness. Equations (1)e(3) below represent the model: INNOVij ¼ b 0jþ b 10 MASij þ b 20 PAVij þ b 30 ETAij þeij (1) b 0j¼ g 00 þ g 01 CLNjþ g 02 HRCjþ g 03 MRKjþ g 04 ADHjþu0j(2) MASij ¼ a 00 þ a 10 ETAij þεij (3) In equation (1), the intercept b 0j is a random effect that varies across departments, while the slopes b 10 , b 20 , and b 30 are fixed. The cultures on the department level predict the intercept of innovativeness b 0j . Equation (3) allows ETA to predict MAS where the intercept a 00 and the slope a 10 are fixed. ETA was not allowed to predict PAV because ETA had no correlation with PAV, as shown in the correlation matrix (see Table 2). The parameter estimates all converged adequately, as the PSR values decreased smoothly over the iterations, reaching a value of 1.010, which is below the cut-off value of 1.05. The trace plot (Fig. 4) displays quick oscillations, while the autocorrelation plot (Fig. 5) displays low autocorrelation near zero, which together indicating good convergence of the MAS estimate. The posterior parameter trace and autocorrelation plots for the other parameters (not reported) were also indicative of good convergence. The model showed a good fit to the data, as the PPP was 0.278, and the 95% CI for the difference between the observed and the replicated c 2 values covered zero, with a lower bound of 16.494 and an upper bound of 32.025. The DIC value was 1143.358. As shown in Table 3, ETA is negatively associated with MAS ( a 10 ¼0.149) and INNOV ( b 30 ¼0.083), while MAS is positively associated with INNOV ( b 10 ¼0.261). As expected, PAV is negatively associated with INNOV ( b 20 ¼0.097). Although the individual-level variables maintained a significant association with INNOV, a significant value of ℮ ij may suggest that there remains a variance in INNOV that has not yet been explained. Contrary to our expectations, none of the cultures explained the variance of the innovativeness's random intercept, even though a random effect of the intercept ( d 2u0j ) pointed to a significant variation in the intercept ( g 00 ) between departments. A significant overall fixed intercept g 00 , which is the expected value of INNOV when all predictors are on their means, suggested that the intercept was significantly different from zero. Table 2 Means, standard deviations, and correlations among the variables on the individual and department levels. 123456789 1. INNOV 1 0.01 0.02 0.04 0.02 2. ETA 0.09* 1 3. MAS 0.13* 0.09* 1 4. PAP 0.00 0.05 0.04 1 5. PAV 0.08* 0.05 0.07 0.54* 1 6. Clan 1 0.00 0.10* 0.07* 7. Hierarchy 1 0.00 0.03 8. Market 1 0.03 9. Adhocracy 1 M3.75 3.73 3.95 2.22 2.67 3.02 2.87 2.49 2.94 SD 0.057 0.049 0.040 0.048 0.054 0.069 0.052 0.092 0.063 Notes: INNOV ¼Innovativeness; ETA ¼Entity Theory of Ability; MAS ¼Mastery goal orientation; PAP ¼Performance-Approach goal orientation; PAV ¼ Performance-Avoidance goal orientation. Values below the diagonal are correlations at the individual level (n ¼315); values above the diagonal are correlations at the department level (n ¼34). *P<0.05. T.Z. Aldahdouh et al. / International Journal of Innovation Studies 3 (2019) 23e39 31 Heslin, P. A., Latham, G. P., & VandeWalle, D. (2005). The effect of implicit person theory on performance appraisals. Journal of Applied Psychology, 90(5), 842e856. https://doi.org/10.1037/0021-9010.90.5.842. Hong, J. C., Hwang, M. Y., Ting, T. Y., Tai, K. H., & Lee, C. C. (2013). The innovativeness and self-efficacy predict the acceptance of using iPad2 as a green behavior by the government's top administrators. 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