From Principals to Teachers to Students: Exploring an Integrative Model for Predicting Students’ Achievements

Abstract

Purpose: This study explored a theoretical model proposing direct and mediated effects for principals’ characteristics—principals’ information-processing mechanisms (PIPMs) and instructional leadership (IL)—with organizational learning mechanisms (OLMs), for schools’ OLMs with teachers’ characteristics—teachers’ affective commitment (TAC), collective teacher efficacy (CTE), and teachers’ job satisfaction (TJS)—and finally, for teachers’ characteristics with students’ achievements on national math and science tests. Design: Data were collected from a multisource survey of a random sample of 130 elementary school principals representing Israel’s full socioeconomic range, 1,700 teachers from those schools, as well as data on those schools retrieved from the Ministry of Education data set. Data were aggregated at the school level for structural equation modeling (SEM) analysis. Findings: Analysis from SEM confirmed that IL emerges as a prominent mediator between PIPMs and OLMs. OLMs emerged as a prominent mediator between IL and the three teachers’ characteristics. TAC and CTE were significantly directly related to students’ math and science achievements. Finally, OLMs promoted students’ math and science achievements only through CTE. Implications: The relationships found for both principal characteristics (PIPMs and IL) with OLMs in schools highlight principals’ potentially important role in promoting collective learning within schools through utilization of OLMs, which can predict critical teacher characteristics (TAC, CTE, TJS), which in turn can predict school effectiveness measures (i.e., students’ achievements).

Keywords

organizational theory organizational learning organizational learning mechanisms leadership school learning

Research in the past few decades has consistently shown that principals are powerful players who can affect school effectiveness and bring about change (Heck & Hallinger, 2014; Sun & Leithwood, 2017). Given the extent of the reforms to which educational institutions have been subjected in the past two decades, as well as ongoing challenges from globalization, technology, and the marketization of education, it is incumbent on the principals themselves to become reflective, lifelong learners who are open to learning and growth, and who are able to facilitate the learning and growth of both their teachers and their students (Murphy et al., 2016). Whereas numerous studies have highlighted the principal’s role and significance in improving school outcomes, such as students’ achievements, principals seem to affect their students primarily in an indirect manner, through the promotion of organizational learning and structures that allow the school teachers to routinely learn from each other and together. In this way, they improve their teaching abilities, which in turn can promote students’ achievements (Bruggencate et al., 2012; Hallinger, 2011; Louis & Robinson, 2012).

Can principals enhance the quality of teaching and learning in schools? This question has consumed the attention of policy makers, school leaders and researchers in the pursuit of more systemic strategies for improving the quality of schooling (Heck & Hallinger, 2014; Kyriakides & Creemers, 2008). Based on a large body of international research, scholars are accepting the assertion that the impact of leadership on student learning is achieved indirectly by shaping conditions that contribute to effective teaching and learning (Boyce & Bowers, 2018; Leithwood et al., 2010; May & Supovitz, 2011; Robinson et al., 2008; Sebastian & Allensworth, 2012). Consequently, over the past two decades, scholarship in this domain has focused primarily on identifying and testing the “paths” by which leadership influences student learning in schools (Mulford & Silins, 2011; Printy, 2008; Robinson et al., 2008).

The investigation of these paths is based on the following interrelated assumptions. First, the effects of school leadership on students are largely indirect. For example, Louis et al. (2010) found that school leadership effects on student outcomes operate largely indirectly via their effects on collaborative learning, instruction, and classroom environments. Supovitz et al. (2010) also found that the importance of principals’ practices for student learning is mainly due to their indirect influence on teachers’ practices, through the fostering of collaboration and communication around instruction. Second, leadership creates conditions that build the capacity for professional learning and change (Kyriakides & Creemers, 2008; Marks & Printy, 2003). Thus, leaders establish “organizational learning” through their work with the staff that has a clear focus on teaching and learning (Robinson et al., 2009). Third, leadership that increases the school’s capacity for improving teachers’ instructional practice based on collaborative learning will enhance student outcomes (Leithwood et al., 2010; Robinson et al., 2008). Fourth, leadership effects on teaching, learning and school improvement unfold over time (Heck & Hallinger, 2010; Ko et al., 2012), and it can take several years for patterns of school improvement to become visible, especially when examining the aggregated performance of many schools (Mulford & Silins, 2011).

Students’ achievements are not likely to improve unless principals set up structures that support teachers’ effective learning (Murphy et al., 2016). Considering the essential role of principals’ leadership in schools, especially in supporting teacher learning (Hallinger, 2011), the current study was framed to contribute to the global discourse on how school principal behaviors/practices contribute to school-learning processes (Kyriakides & Creemers, 2008; Leithwood et al., 2010). More specifically, the research examines the nature of the paths linking school principal learning and leadership, organizational learning, teachers’ attitudes, and student outcomes in schools over a period of time. In this context, we posed two questions, as reflected in the integrative model described below (in Figure 1): What are the key practices/behaviors of school principals that might promote—directly or indirectly—organizational learning at school, and indirectly promote critical teacher attitudes (teachers’ affective commitment [TAC], teachers’ collective efficacy [TCE], and teachers’ job satisfaction [TJS]) that have been found to predict students’ learning outcomes? Can these critical teacher attitudes mediate the possible effect of school organizational learning (as an instructional and pedagogical learning process by teachers) on students’ learning outcomes (students’ achievements on national science and math tests)?

Figure 1.

The hypothesized research model from principals to teachers to students’ achievements (Hypotheses 1–10 are indicated).

The present research explores a proposed theoretical model for predicting school effectiveness (students’ achievements) that links (directly or indirectly) principals’ practices with organizational learning processes in elementary schools. This exploration is in line with emerging research in educational leadership incorporating and integrating principals’ learning and instructional practices, given the similarities in their conceptual foundations (Boyce & Bowers, 2018; Murphy et al., 2016). Within educational leadership research, exploring the proposed theoretical model may identify potentially powerful mediators—school-related variables—that contribute to student learning and are amenable to leadership intervention. This article begins with a review of the literature for each of our study variables and then presents the proposed chain of links from the principal to the teachers to the students, as hypothesized in the research model. Then, we describe the research design and the findings. The article concludes with a discussion of the findings, implications for practice, limitations of the study, and future research avenues.

Theoretical Review and Hypotheses

Principals’ Information-Processing Mechanisms (PIPMs) Within the Learning-Centered Leadership Framework

In the past decade, there has been growing interest in learning as a major concern of school leaders. While responding to social and political pressures, leaders are expected to buffer the staff from counterproductive policies, and build school-improvement initiatives that address external reforms. Principals can develop ongoing learning in school despite distracting social, political, or economic forces. They can work with school community members to assess what they collectively believe makes their schools successful beyond the limits of accountability measures (Schlechty, 2009). Thus, leaders must create a culture of individual and collective learning in schools, especially in times of accountability and standardization (Drago-Severson & Pinto, 2009).

The capacity for organizational learning in schools depends on the organization members’ capacity for learning, particularly that of the principal. “Learning-focused” (Knapp & Feldman, 2012) or “learning-centered” leadership (Murphy et al., 2016) relates to leaders’ contribution to learning among their students, professionals, and systems. The “learning-centered” leader looks outward, searching the environment for opportunities to improve learning, and especially looks inward to develop collective professional learning communities and a school-wide learning-improvement agenda (Knapp & Feldman, 2012). To accomplish this, the principal must provide new information, ensure the transmission of knowledge on instruction (learning and teaching) among teachers, arrange an interactive social environment that is conducive to collaborative planning and learning, ensure that adequate resources are available to support teachers’ learning, and build an organizational climate that encourages and supports learning throughout the school (e.g., Louis & Robinson, 2012; Sebastian et al., 2017).

Based on a construct to assess learning mechanisms in elementary schools (Schechter, 2008; discussed below) from the information-processing perspective, Schechter and Qadach (2016) developed a construct to assess how elementary principals promote structural and procedural learning arrangements in schools. This PIPM construct for collecting, analyzing, storing, disseminating, retrieving, and using school-related information includes four dimensions: (a) storing and retrieving information—principals’ processes and means for storing personal and organizational experiences and then coding them into the school memory and drawing on the encoded information to guide decisions and actions; (b) receiving information (from students, parents, community, and superiors)—principals’ processes and means for acquiring information from stakeholders; (c) disseminating information to teachers—principals’ processes for providing teachers with information to be collectively analyzed; and (d) analyzing external and internal information—the processes whereby the teachers and the school principal meet to discuss and analyze important events and/or usable accumulated data that affect the school, such as evaluating school climate and students’ academic achievements. This perspective is in line with the growing argument that most principal-evaluation instruments assess general management (e.g., implementing vision) or traits, rather than focusing on the leaders’ critical behaviors and actions within the learning-centered leadership framework (Hitt & Tucker, 2016; Leithwood & Sun, 2012).

Principal’s Instructional Leadership (IL)

In line with increasing expectations from school systems to become more accountable in preparing students for 21st-century challenges, IL has drawn considerable interest from principals, educators, and researchers (e.g., Boyce & Bowers, 2018; Hallinger, 2011). Principals’ IL has been shown to be one of the most salient determinants in supporting effective instruction and improving school performance, based on extensive international research (e.g., Hallinger & Wang, 2015; Sun & Leithwood, 2017). The current study adopted Hallinger and Murphy’s (1985) conceptual framework that proposes three dimensions for the IL construct: defining the school’s mission, managing the instructional program, and promoting a positive school-learning climate. These dimensions are further divided into 10 IL practices, comprising principals’ behaviors that set high expectations and clear goals for students’ and teachers’ performance, monitor and provide feedback regarding the school’s technical core (teaching and learning), provide and promote professional growth for all teachers, and help create and maintain a school climate of high “academic press” (Hallinger & Wang, 2015). In brief, the instructional leader develops the school’s academic learning climate by defining and communicating shared goals that assert high expectations of the students, monitoring and providing feedback on the teaching and learning processes, and promoting professional development aligned with the teachers’ needs and the school’s goals (Neumerski, 2012). Research has shown that the very essence of IL is to transform the school as an organization into an environment where teaching and learning become sustainable, and where teachers and learners can reach their full potential (Boyce & Bowers, 2018).

The research literature has discussed specific aspects of IL, including how instructional leaders allocate their time and select behaviors. Thus, instructional leaders’ daily decision making is often described as focusing on improvements to teaching and learning and on creating structures to facilitate teachers’ work in ways that strengthen their organizational belief system and that, in concert, foster student learning (Leithwood et al., 2010). A body of research on IL has identified the effectiveness of principals’ strong directive leadership concerning curriculum and instruction, even in at-risk, poor urban communities (e.g., Hallinger, 2011). The literature espouses the notion that strong instructional leaders establish a school vision (Hallinger & Wang, 2015), build a school culture (Heck & Hallinger, 2014), create a positive instructional climate (May & Supovitz, 2011), and engage in curriculum and instructional issues with teachers (Horng & Loeb, 2010), among other behaviors. Indeed, instructional investment cannot be undertaken without attention to maintaining a high level of organizational management in the school, which multiple studies have linked to positive school outcomes (Grissom & Loeb, 2011).

A previous study (Schechter & Qadach, 2016) revealed a positive and significant association between PIPMs (as the latent variable) and all three factors of IL—defining the school’s mission, managing the instructional program, and promoting a positive school-learning climate. Most of the PIPM factors (except “analyzing external and internal information”) revealed a positive (moderate) and significant correlation with principals’ IL factors. We may assume that leaders provide a supportive environment in which the educational staff can expand its capacity to learn together in order to focus on student achievement (Seaton et al., 2008). Printy (2008) suggested that leaders act as agenda setters for teacher learning and school improvement, and as knowledge brokers who support teacher learning through the acquisition, assimilation, exploitation, and sharing of both internal and external information on an ongoing basis.

Organizational Learning Mechanisms (OLMs)

To keep pace with a dynamic and uncertain environment, learning processes and structures need to be developed that can both lead to new and diverse knowledge bases and nurture a faculty’s shared belief in its capabilities (Berson et al., 2015). In this sense, organizational learning has been conceptualized as a critical component of school effectiveness, especially in view of today’s continual increase in available information. As learning organizations, schools develop processes, strategies, and structures that enable them to learn and react effectively in uncertain and dynamic environments (Fullan, 2016; Kruse, 2003). In this regard, Louis and Robinson (2012) and Kruse (2003) argued that a school’s capacity for innovation and reform relies on its ability to collectively process, understand, and apply knowledge about teaching and learning, with an emphasis on the processes of collective and continuous acquisition, creation, dissemination, and integration of knowledge.

From the perspective of educational change, Dantow et al. (2007) asserted that the most significant element in actively restructuring schools is the extent to which collective learning mechanisms are utilized (e.g., circulating instructional reference materials and reports), thus generating interactions among staff members revolving around issues related to curriculum and instruction. A school’s ability to collectively process, understand, and apply knowledge about teaching and learning requires the development and maintenance of system structures and processes that facilitate members’ continuous collective learning (Kruse, 2003; Louis & Robinson, 2012; Schechter, 2008). To learn and react effectively in uncertain and dynamic environments, schools need to establish systemic structures and procedures for teachers to collectively think about and share information on a regular basis (Mulford & Silins, 2011).

These structural–social arrangements are focused on gathering and processing information, generally known as the management science perspective. From this perspective, organizational learning requires the existence of OLMs, which are institutionalized structural and procedural arrangements for collecting, disseminating, analyzing, storing, retrieving, and using information that is relevant to the performance of the organization and its members (Lipshitz & Popper, 2000). OLMs function as concrete social arenas, where knowledge can be analyzed and shared by individual members and then becomes the property of the organization (in our research, the school) through dissemination and changes in standard routines and procedures. In this regard, scholars have argued that learning begins with individuals’ intuition and interpretations, which then become integrated, institutionalized, and embedded in systems, structures, or routines (e.g., Berson et al., 2015). Lipshitz et al. (2002) claimed that organizations have cognitive systems that enable them to acquire, perceive, and interpret information in a manner similar, but not identical to the individual learning process. These cognitive systems can be perceived through organizational routines, which resemble individuals’ cognitive procedural memories (Cohen & Bacdayan, 1996).

Based on Lipshitz and Popper (2000), Schechter (2008) found a four-factor construct of OLMs in elementary schools: (a) information analysis refers to the process of giving meaning to incoming information through collective sense-making, such as discussing school goals or working together to modify subject matter for students. As a result, school members decide whether to incorporate the analyzed information into organizational routines; (b) information storage, retrieval, and putting to use refers to the processes and means by which organizational experiences are stored and coded into the school memory. For this purpose, staff meetings can make use of summary reports from previous meetings. Concurrently, school personnel draw on the encoded information to guide their decisions and actions; (c) receiving and disseminating information refers to the process by which school personnel are provided with information, and share it with various school stakeholders. The faculty members can report professional changes and innovations or supply professional and pedagogical reference materials; and (d) seeking information is a process of actively searching for information. Faculty members attempt to learn from the strategies and technologies of other organizations (environmental scanning), as well as by gathering information from colleagues at their own schools.

According to Schechter’s (2008) study, elementary school teachers’ sense of collective efficacy and commitment to their organizations (schools) were significantly and positively related to the extent of OLMs in their schools. The level of change in the system’s properties was significantly and negatively related to the extent of OLMs, whereas the complexity (diversity) of the system’s properties was not related to the extent of the OLMs used in elementary schools. Moreover, although OLMs, through storing, retrieving, and putting information to use, served as a prominent mediating variable between teachers’ perceived uncertainty and TCE in the urban school context (turbulent and competitive environment), they did not play a mediating role in the research model for the suburban school context, with its more placid environment (Schechter & Qadach, 2012). At the secondary school level, teachers’ sense of collective efficacy was found to be significantly and positively related to the extent of OLMs that they reported to exist in their schools, as found for elementary schools. Similarly, secondary school TAC to their organizations (schools) was significantly and positively related to the extent of the OLMs that they reported to exist in their schools, as found for elementary schools (Schechter, 2008). As for the principal’s role, a mediation regression analysis demonstrated that school vision is a significant predictor of a faculty’s OLMs (the information-processing framework) and functions as a partial mediator between a principal’s transformational leadership style and a faculty’s OLMs (Kurland et al., 2010).

Teachers’ Attitudes

Three teachers’ attitudes have been pinpointed as playing a role in predicting students’ achievements: TCE, TAC, and TJS. Rationales for the selection of these variables in the current study are presented in the section delineating their predicted roles in the hypothesized research model below.

TCE

TCE is defined as the “group’s shared belief in its conjoint capabilities to organize and execute the courses of action required to produce given levels of attainments” (Bandura, 1997, p. 477). TCE can also be defined as

a belief system that includes mutual recognition of the various agents (e.g., home, school, and community)—each of which has a valuable and distinctive role in promoting success—that together, and only together, have the abilities to create environments conducive to the student’s optimal development. (Henderson et al., 1998, p. 4)

Thus, TCE represents the teachers’ perception in a specific school that the faculty as a whole can execute the courses of action needed to positively affect students’ achievements (Goddard et al., 2015).

TAC

Organizational commitment has emerged as a leading construct in organizational research due to its relationship with important work-related concepts. It has been defined as “the relative strength of an individual’s identification with and involvement in a particular organization” (Mowday et al., 1979, p. 226), and as a bond linking the individual to the organization (Mathieu & Zajac, 1990). Allen and Meyer (1996) asserted that commitment includes three dimensions: affective, continuance, and normative. In the present study, we chose to concentrate on TAC, as several studies have found it to be more strongly linked to organizational outcomes than other types of organizational commitment (e.g., Trammell, 2016). Affective commitment is defined as “positive feelings of identification with, attachment to, and involvement in the work of the organization” (Meyer & Allen, 1984, p. 375). In affective commitment, the employee’s values and goals match those of the organization. The better the match, the more the employee feels that he/she wants to stay and work in that organization. In other words, employees develop affective commitment to the extent that the organization satisfies their psychological needs, lets them realize their goals, and lives up to their expectations (Allen & Meyer, 1996).

TJS

Job satisfaction, the most intensively covered attitude in the organizational psychology literature (Judge & Kammeyer-Mueller, 2012), has been defined in various complementary ways that implicitly contain both affects (feelings) and cognitions (evaluations, beliefs). Thus, job satisfaction represents individuals’ emotional state related to their work or work environment, resulting from positive or negative appraisal of their job or job experiences (Robbins & Judge, 2016). Job satisfaction has been suggested to be related to perceived fulfillment of one’s needs through work and of one’s job-related needs at work for teachers in schools (Hsieh, 2016; Hulpia et al., 2009).

The Hypothesized Chain of Links in the Proposed Research Model

We adopted a direct-mediated-effects model to test the hypothesized chain of effects between PIPMs, IL, OLMs, teachers’ attitudes (TAC, TCE, TJS) and students’ achievements (scores on national science and math tests). Figure 1 presents the variables’ relationships in the hypothesized research model.

Our examination of both relationships and mediations, as described below, are based on recent encouragement to examine the relationships between IL and organizational learning (Boyce & Bowers, 2018; Murphy et al., 2016), as well as on the need to expand our understanding of how organizational learning relates to teachers’ attitudes (Boyce & Bowers, 2018), consequently affecting students’ achievements.

The relationship between PIPMs and IL

Principals’ knowledge of teaching and learning will lead them to set up learning structures that support effective instruction, thus enacting their IL (Hallinger & Wang, 2015). In fact, Robinson et al. (2008) concluded that principals’ focus on sharing, acquiring, and interpreting information related to teaching and learning could result in their improved instructional capacities, which could in turn affect teachers’ engagement in collaborative learning frameworks, subsequently affecting student outcomes. Because of their central role in the school, principals are often in a unique position to receive and distribute information and knowledge that are critical for managing the instructional programs. PIPMs represent a dynamic capacity that allows principals to create value and to gain and sustain a competitive advantage through the management of external knowledge (Flatten et al., 2015; Lane et al., 2006). This dynamic capacity, as reflected in a series of routines and processes through which principals acquire and exploit information to create a competitive advantage, allows them to assimilate and share the new information with teachers, with the aim of obtaining coherent and aligned instructional systems. Accordingly, leaders who engage in learning processes more extensively in their schools may encourage teachers to invest more time and effort in formal and informal instructional practices, thus promoting academic learning climate to achieve the school’s goals (Hallinger, 2011). Accordingly, we formulated Hypothesis 1: PIPMs will be positively correlated with IL.

The relationship between IL and OLMs

There is an implicit assumption that principals are the guiding force behind school learning; however, there is only scarce research delineating specific IL activities that promote systemic structures and procedures for teachers to collectively think about and share information related to curriculum and instruction (Mulford & Silins, 2011). As already noted, principals are responsible for arranging the interactive social environment and making adequate resources available for teachers’ learning (e.g., Louis & Robinson, 2012). Principals can build communities fostering teachers’ collaboration, dialogue, and learning. Becoming instructional leaders, principals guide learning improvements that are tailored to school contexts. As indicated by Krüger and Scheerens (2012(, principals as instructional leaders are directly involved in the students’ education, by carefully evaluating teachers’ classroom performance, developing and sustaining collaborative learning processes, and creating organizational mechanisms for instructional data circulation and analysis. Thus, we formulated Hypothesis 2: IL will be positively correlated with OLMs.

The relationships between OLMs and teachers’ attitudes

TCE

Strong TCE beliefs can improve the effectiveness of teachers’ work as they modify the nature and practices of their organizations (Goddard et al., 2004). Conversely, according to Schechter and Qadach (2012), when schools incorporate OLMs intensively as information-processing mechanisms, they develop and sustain a collective memory (causal maps, strategies) that can nurture teachers’ shared sense of efficacy. In an examination of two theoretical models (one for urban elementary schools and one for rural elementary schools) that linked the four subscales of OLMs described earlier with two subscales of TCE (for instruction and for discipline), OLMs were found to be strong predictors of the TCE subscales in both types of schools (Schechter & Qadach, 2012). In light of this, the assumption is that the extent of teachers’ use of OLMs in elementary schools will be positively associated with teachers’ sense of collective efficacy. Hence, we formulated Hypothesis 3a: OLMs will be positively correlated with TCE.

TAC

Growing evidence suggests that extensive use of collective learning mechanisms related to curriculum and instruction (OLMs) promotes greater teacher commitment. For example, Schechter’s (2008) study of organizational learning in elementary schools found that OLMs were significantly, strongly, and positively related to elementary teachers’ organizational commitment. Eno-Attarchi (2011) adapted Schechter’s (2008) OLM measure to high schools and examined its possible relationship with four types of organizational commitment (affective, continuance, normative, and their combination, termed “latent”). Eno-Attarchi (2011) found a significant, positive, moderate-strength relationship between high school OLMs and TAC. However, OLMs were not significantly correlated with continuance, normative, or latent organizational commitment, leading to Hypothesis 3b: OLMs will be positively correlated with TAC.

TJS

The concept of OLMs is relatively new and its relationship with TJS has scarcely been investigated. One recent study (Kurland & Hasson-Gilad, 2015) examined the relationships between organizational learning, TJS, and extra effort (a component of motivation) put forth by elementary school teachers. They found that school organizational learning affected TJS, which in turn affected teachers’ extra effort. Between 57% and 61% of the variance in TJS was explained by organizational learning. The authors reasoned that teachers’ engagement in organizational learning processes—which include teamwork, participation in decision-making, and experiential learning—may enhance their opportunities for growth, their sense of contribution to the organization, and their sense of accountability for organizational outcomes. In other words, organizational learning in schools affects and increases teachers’ motivation. This finding coincides with other studies linking TJS primarily to intrinsic rewards (Bogler & Nir, 2014), involvement in decision making, pedagogical autonomy, and sense of achievement (Hsieh, 2016). Hence, we formulated Hypothesis 3c: OLMs will be positively correlated with TJS.

The relationships between teachers’ attitudes and students’ achievements

TCE and students’ achievements

Bandura (1997) has argued that a teaching faculty’s sense of efficacy regarding their ability to affect students’ achievements contributes significantly to the school’s general level of achievement. In this regard, Tschannen-Moran and Barr (2004) found positive relationships between TCE and students’ achievements (specifically, Grade 8 standards of learning in math, writing, and English). Studies in which TCE has been reported to be predictive of math students’ achievements include those of Cooper (2010) and Garcia (2004). Similarly, reports by Goddard et al. (2004) indicated that TCE is a predictor of science students’ achievements. Finally, Goddard et al.’s (2015) recent study revealed that TCE is a strong predictor of fourth graders’ math and reading achievements, even after controlling for school and student background characteristics and prior levels of student achievement. Eells’s (2011) meta-analysis showed a strong, positive predictive relationship between TCE and students’ achievements, regardless of subject area and even across different instruments and varying contexts. In light of the theoretical and empirical evidence regarding TCE and students’ achievements, we formulated Hypotheses 4a and 4b: TCE will be positively correlated with students’ achievements on the national science exam (Hypothesis 4a) and on the national math exam )Hypothesis 4b).

TAC and students’ achievements

In the context of educational organizations, Kushman (1992) found that TAC is directly linked to students’ achievements, and other studies found that it mediates the link between principal leadership and students’ achievements (Ross & Gray, 2006). Affective commitment is most likely to predict teachers’ engagement, which promotes student learning (Trammell, 2016). Hence, in the current study, we reasoned that teachers with a high level of affective commitment will guide students to make greater efforts, resulting in higher grades, giving Hypotheses 5a and 5b: TAC will be positively correlated with students’ achievements on the national science exam )Hypothesis 5a) and on the national math exam )Hypothesis 5b).

TJS and students’ achievements

As reviewed by Moè et al. (2010), job satisfaction represents an evaluation of one’s job that can beneficially affect organizational outcomes. Satisfied teachers show higher levels of motivation which, in turn, positively affect students’ motivation, inducing them to improve and stand out (Ruth, 2014). Job satisfaction has been found to have a positive and significant effect on teaching quality, student learning (Michaelowa, 2002), student academic performance, and school effectiveness (Somech & Drach-Zahavy, 2000). The level of TJS also affects students’ learning, because it is associated with higher self-efficacy, a greater commitment to organizing and carrying out educational practices, and a better climate in the classroom and at school (Griffith, 2004). Therefore, we formulated Hypotheses 6a and 6b: TJS will be positively correlated with students’ achievements on the national science exam )Hypothesis 6a) and on the national math exam )Hypothesis 6b).

The mediating role of IL in the relationship between PIPMs and OLMs

As already noted, principals need to employ shared IL strategically among teachers (Rigby, 2014). One of the important leadership capacities for principals in the 21st century is the ability to initiate collective learning among school members (Hallinger, 2011; Louis & Robinson, 2012). For this reason, when the school’s principal is perceived by the school’s teachers as a learning-centered leader, thus extensively using structural and procedural learning arrangements that are primarily focused on teaching and learning, they will tend to participate in the school’s learning mechanisms (OLMs). In other words, principals need to become self-efficacious in knowledge and skills pertaining to curriculum development through their learning processes, which sends a message about their importance to the staff (Printy, 2008). Nevertheless, this possible impact on teachers’ engagement in school-learning mechanisms is anchored in IL practices, such as defining and communicating shared goals that assert high expectations of the students, monitoring and providing feedback on the teaching and learning processes, and promoting professional development aligned with the teachers’ needs and the school’s goals (Leithwood et al., 2010; Neumerski, 2012).

New perspectives on school-learning management have emphasized that “learning-centered” leaders look outward, searching the environment for opportunities to improve learning, and especially look inward, to develop collective professional learning communities and a school-wide learning-improvement agenda (Knapp & Feldman, 2012). To link these external and internal information processes, the principal’s instructional role is critical in providing adequate resources to support teachers’ learning, and in building a positive school-learning climate that encourages and supports learning throughout the school (e.g., Louis & Robinson, 2012; Sebastian et al., 2017). Therefore, we formulated Hypothesis 7: IL will mediate the relationship between PIPMs and OLMs.

The mediating role of OLMs in the relationship between IL and teachers’ attitudes

The results of a previous study examining IL, TCE, and student achievement in 53 high schools in New Jersey (Fancera & Bliss, 2011) suggested that the principal’s IL does not directly influence TCE. Furthermore, the literature does not include any examination of the influence of specific IL behaviors and tasks on TCE, or of the relationship between IL and TAC. On the other hand, in elementary schools, the prevalence of OLMs initiated by teachers was significantly and positively related to TCE and to TAC at school (Schechter, 2008). Moreover, Eno-Attarchi (2011) found a significant, positive, moderate-strength relationship between OLMs and TAC in secondary schools.

To our knowledge, very few studies have examined the direct relationship between IL and TJS. A significant contribution to the exploration of this relationship was made by Shatzer (2008), who suggested that IL predicts a meaningful and significantly large amount of the variance in TJS. The leadership functions that were associated with increased TJS were supervision and instruction-evaluation processes, promoting professional learning mechanisms for teachers’ development, and providing incentives for collaborative learning. Tubin (2011) proposed an interesting narrative explanation which begins with the principal’s declaration of academic achievements as the school’s main goal, with the teachers mapping the students, analyzing the results, and changing their instructional practices accordingly, while the principal actively searches for student data, institutionalizes professional forums for data analysis, provides resources for personalized education, and maintains a suitable work environment. In light of the above, we formulated Hypotheses 8a, 8b, and 8c: The relationships between IL and TCE (Hypothesis 8a), between IL and TAC (Hypothesis 8b), and between IL and TJS (Hypothesis 8c) will be mediated by OLMs.

The mediating role of teachers’ attitudes between OLMs and student achievement

With regard to the final mediating link in the chain, as mentioned above, OLMs have been found to be strong predictors of TCE (Schechter & Qadach, 2012), TAC (Schechter & Atarchi, 2014), and TJS (Kurland & Hasson-Gilad, 2015). Similarly, a large body of research links these teacher attitudes directly to students’ achievements and school improvement (e.g., Fancera & Bliss, 2011; Ruth, 2014). No previous studies have explored whether or which of these teachers’ attitudes (TCE, TAC, TJS) play a mediating role between the processes created by the teachers (at the school level) for learning (OLMs) and students’ achievements. In the current study, we formulated Hypotheses 9a, 9b, and 9c: The relationship between OLMs and students’ math achievements will be mediated by TCE (Hypothesis 9a), TAC (Hypothesis 9b), and TJS (Hypothesis 9c); and Hypotheses 10a, 10b, and 10c: The relationship between OLMs and students’ science achievements will be mediated by TCE (Hypothesis 10a), TAC (Hypothesis 10b), and TJS (Hypothesis 10c).

Method

Setting

Israel’s national school system serves more than 2 million students (nearly 5,000 K-12 schools), with approximately 73% of them in the Jewish sector and 27% in the Arab sector (Israeli Central Bureau of Statistics, 2016). According to the Gini coefficient, which measures a nation’s distributional inequality, Israel has one of the largest gaps between the rich and the poor, alongside the United States and the United Kingdom (Organization for Economic Co-operation and Development, 2016). Mindful of the great diversity among school populations, recent educational policy in Israel has been directed to achieving high levels of equality in education outcomes across the board, thus narrowing the achievement gap upward through growing performance pressure. In practice, however, Israeli student achievement is at a low level, with a growing gap (between low and high achievers), as evidenced in various comparative international studies (BenDavid-Hadar, 2016).

Participants and Procedures

To test the proposed relationships, we utilized a multisource survey design from a sample of 1,700 teachers (randomly selected) and their principals from 130 elementary schools in Israel, which were randomly selected from a school list on the educational system website (from all of Israel); 23% of the studied schools were from the Arab sector and 77% were from the Jewish sector (proportional to the sectors’ representation in the school system). School size was based on the number of enrolled teachers with an average of 34 (SD = 11.43). We ensured a random sampling minimum of 30% of the teaching staff at each school.

As for the participants, 66% of the principals were women. Their average tenure as principal was 9.5 years (SD = 7.02) and their average tenure as principal at the present school was 7.90 years (SD = 6.21). Their average tenure in teaching was 22.0 years (SD = 8.60). As for education, 24% had a BA degree, 70% had an MA degree, and 65% were graduates of a principal training course. Regarding teachers, 92% were women, their average age was 36.24 years (SD = 7.12) and their average tenure in teaching was 15.34 years (SD = 9.61).

Data were collected during the 2011 and 2013 academic years (explained below in the Measures section). Data collection was performed in several steps. After the research project was approved by the Ministry of Education, schools were randomly chosen from a list on the Ministry’s website. After obtaining the school’s agreement to participate, a research team member administered the questionnaire. The study purpose was explained in general terms, anonymity was guaranteed, and the importance of candid responses was emphasized. Principals completed the PIPM measure. Teachers completed the measures assessing IL, OLMs, TCE, and TAC. TJS and students’ achievements were obtained online from the Ministry of Education’s data set. All participants provided demographic variables. Only teachers and principals who had worked in the organization for more than 1 year were included, to ensure that all respondents had sufficient time to develop perceptions about their school and their co-workers.

Measures

We used three different sources of data (principals, teachers, and schools’ scores on the Ministry of Education’s website) to avoid same source and common method bias.

Self-reported PIPMs (n = 130)

Assessing how elementary school principals promote structural and procedural learning arrangements in schools (explained above), this PIPM construct for collecting, analyzing, storing, disseminating, retrieving, and using school-related information includes four dimensions reflecting information processing as a property of the leader. Principals completed a 22-item questionnaire (Schechter & Qadach, 2016) developed specifically for the education system. Principals rated items on a 5-point Likert-type scale ranging from never (1) to always (5). The 22-item measure consisted of four subscales: (a) storing and retrieving information (five items, e.g., “I document meetings with the superintendent, municipal authorities, and parents,” α = .80); (b) receiving information from stakeholders (eight items, e.g., “I meet with students to learn about difficulties, special requests, comments, and insights,” α = .85); (c) disseminating information to teachers (five items, e.g., “I disseminate reports concerning professional changes and innovations among the staff,” α = .82); and (d) analyzing external and internal information (four items, e.g., “I examine external data (such as national exams) with the teaching staff,” α = .80). A confirmatory factor analysis (CFA), using the AMOS 20.0 software program, was conducted. We used fit indices recommended by Byrne (2013): the values of the comparative fit index (CFI), incremental fit index (IFI), and Tucker–Lewis index (TLI) are recommended to be greater than .90; root mean square error of approximation (RMSEA) is recommended to be up to .05, and acceptable up to .08 (Steiger, 2007). The four-factor construct showed acceptable fit indices, χ²(199) = 279.08, RMSEA = .04, CFI = .97, TLI = .96, IFI = .98.

Teacher-rated principal’s IL (n = 1,700, aggregated to n = 130)

Teachers completed a questionnaire based on Berger’s (2010) work, which adapted Hallinger and Murphy’s (1985) Principal Instructional Management Rating Scale to the elementary school level. The questionnaire, which was reduced to 31 items and validated for the Israeli education system, captured three scales, which tapped 10 leadership functions. This questionnaire preserved the theoretical meaning of the IL components. In the current study, these factors comprised: defining the school’s mission (12 items, e.g., “Evaluates teachers on reaching academic goals that are directly tied to school objectives,” α = .90); managing the instructional program (10 items, e.g., “Locates students whose exam results indicate that they need tailored teaching methods,” α = .90); and promoting a positive school-learning climate (9 items, e.g., “Praises students for high achievements through reinforcements such as prestigious roles or mentioning them in the school paper or on the school website,” α = .94). Teachers rated items depicting their principal’s IL on a 5-point Likert-type scale ranging from never (1) to always (5). CFA results indicated that the three first-order latent constructs of IL provided good fit indices, χ²(418) = 1102, RMSEA = .05, CFI = .96, TLI = .94, IFI = .95.

Teachers’ (self-reported) OLMs (n = 1,700, aggregated to n = 130)

As OLMs are institutionalized structural and procedural arrangements for collecting, disseminating, analyzing, storing, retrieving, and using information that is relevant to the performance of the organization (Lipshitz & Popper, 2000), the four-dimension OLM construct of information processing (described above) reflects the organization/school. Teachers completed Schechter’s (2008) 27-item measure of the OLMs at the elementary school level, depicting four factors: (a) analyzing information (9 items, e.g., “Teachers work together to modify subject matter for students,” α = .87); (b) storing, retrieving, and putting information to use (10 items, e.g., “Each curriculum/project has an updated instructional file,” “Staff meetings make use of summary reports of previous meetings,” α = .91); (c) receiving and disseminating information (5 items, e.g., “Reports about professional changes and innovations are circulated,” α = .77); and (d) seeking information (3 items, e.g., “Teachers observe other teachers’ lessons for learning purposes,” α = .73). Teachers rated items on a5 -point Likert-type scale ranging from doesn’t exist (1) to extensively exists (5). CFA results indicated that the four latent constructs of OLMs provided good fit indices, χ²(249) = 2141, RMSEA = .05, CFI = .96, TLI = .93, IFI = .97.

Teachers’ [self-reported] TCE (n = 1,700, aggregated to n = 130)

Teachers completed Tschannen-Moran and Barr’s (2004) 12-item Collective Teachers’ Beliefs Scale (Hebrew adaptation: Schechter & Tschannen-Moran, 2006). The 12-item measure consisted of two subscales: collective efficacy for instructional strategies (six items, e.g., “How much can teachers in your school do to produce meaningful student learning?” α = .88) and collective efficacy for student discipline (six items, e.g., “How much can teachers in your school do to respond to defiant students?” α = .82). Teachers rated items on a 5-point Likert-type scale ranging from nothing (1) to a great deal (5). CFA results indicated good fit indices, χ²(49) = 120.35, RMSEA = .04, CFI = .98, TLI = .95, IFI = .97.

Teachers’ [self-reported] affective commitment (N = 1,700, aggregated to N = 130)

Teachers completed the eight-item affective commitment subscale of Meyer and Allen’s (1997) 22-item organizational commitment survey. Teachers rated items (e.g., “I feel an emotional connection to this school,” α = .78) on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5). CFA results indicated good fit indices, χ²(8) = 199.39, RMSEA = .03, CFI = .97, TLI = .94, IFI = .96.

TJS [self-reported] (n = 130)

TJS in the participating schools was retrieved from three items in the school climate and pedagogical environment section of the Israeli national standardized Measure of Scholastic Efficiency and Growth data sets. Teachers rated three items tapping satisfaction—from teaching, working in school, and how the school functions (e.g., “I feel satisfied with my work as a teacher in this school”)—on a percentage rating scale (range 0% to 100%) of the extent of teachers’ acceptance of the items. We retrieved mean scores of teachers’ satisfaction, given as mean score per school.

Students’ achievements (n = 130)

Students’ achievements were based on the schools’ mean scores for students’ science and math achievements in the national standardized Measure of Scholastic Efficiency and Growth. For each participating school, students’ math and science achievement scores were located, given as a mean score per subject per school (individual student achievement results in both science and math were not available) on the Ministry of Education webpage. The external national standardized student achievement tests, administered every 2 years, examine the extent to which elementary schools meet expected student mastery levels for national curricula in four core subjects, two subjects per testing year (math and native language and science and English language, meaning that each of the four subjects is administered every 4 years as an external exam at a specific school). Hence, in this study, we tested student learning outcomes—national student achievements—in 2011 for science subject matter and in 2013 for mathematics subject matter, keeping all other research model variables collected in 2011. Moreover, we examined math and science rather than the other two nationally tested core subjects—native language (Hebrew/Arabic) and English—because the former subjects are universal and culturally independent, whereas the languages are not uniform in Israel’s education system (e.g., English is a second language for Jews but a third language for Arabs).

Control variables

We controlled for the effects of school principal seniority (i.e., number of years in post) and school size (i.e., number of teachers in each school), variables that have been shown to be potentially relevant to school outcomes, such as students’ achievements and teachers’ satisfaction from the school (e.g., Berson et al., 2015). In addition, in leadership research, managers’ seniority and number of employees have been shown to be potentially relevant for their effects on organizational processes and outcomes (Berson et al., 2015). More specifically, we controlled for number of years in post because principals’ leadership has been found to vary with career stage, with those who are just starting out struggling to overcome insecurity and develop a sense of confidence, whereas the veteran, better established principals appear to be more competent and tend to use a participative leadership style as a means of developing learning-centered schools (Louis & Robinson, 2012). Moreover, the early years of the principalship are primarily marked by technical management tasks, rather than instructional tasks (Early & Weindling, 2004). Furthermore, OLMs—disseminating, retrieving, distributing, analyzing information—functioning as the school memory (Kruse, 2003) may potentially be influenced by the number of teachers who are professionally interacting in a specific school (Louis & Murphy, 2017).

Data Aggregation

As discussed in the research hypotheses, the school is identified as the unit of analysis. Therefore, (a) PIPMs were assessed by the principals responding to structural and procedural learning arrangements in their schools, (b) TJS and students’ achievements in math and science were assessed at the school level (retrieved as a school score from the Ministry’s data set). TJS examines the extent of teachers’ satisfaction from work at their school. It is part of the school climate and pedagogical environment measure as outlined in the national standardized Measure of Scholastic Efficiency and Growth data sets (school level), (c) OLMs, IL, and the teachers’ attitudes of TAC and TCE were represented by aggregating school members’ responses. Regarding TAC, previous research has aggregated team identification to examine its effect on team performance (Kearney & Gebert, 2009). For example, Tremblay et al. (2019) aggregated affective commitment (e.g., “I feel like part of the family at my organization”) to the group level in order to examine group-level perceived organizational support as a mediator between leadership and affective commitment. The aggregation is based on a rationale that TAC represents teachers’ psychological bond to their school. Therefore, teachers’ psychological constructs, such as TAC and TJS, can influence organizational actions in the same way that individual-level processes can mediate organizational actions (Kramer & Staw, 2003).

Because in our analysis, we were interested in the school level, we calculated average rWG values (James et al., 1993), which indicate the degree of agreement among team members within a school. Thus, for the latter variables, it was critical to demonstrate high within-school agreement to justify using the school average as an indicator of school-level variables; a value of .70 or greater is suggested as an adequate amount of within-group interrater agreement (rWG, James et al., 1993). In the current study, all scales exceeded this level. Mean rWG for OLMs was .78; for IL .92; for TCE .93; and for TAC .90. These results provided sufficient statistical justification for aggregating to the school level.

Aiming to identify potentially powerful mediators—school-related variables—that contribute to students’ achievements, our analysis created latent variables composed of several more specific, correlated variables. These highly correlated variables forming latent variables may resolve two difficulties that are usually encountered when testing indirect effects on student achievement. First, they reduce the size of the data set needed to test a model of such influence because they reduce the number of variables included in the model. Second, the creation of latent variables can help identify the most important underlying leadership mediators among arrays of competing individual, highly correlated variables (Sun & Leithwood, 2017). Nevertheless, prior to aggregating individual-level scores to the group level by mean, intraclass correlations (ICC) were calculated to examine whether these variables, measuring teachers’ perceptions in each school, cluster significantly at the school level. ICC1 examines the within-group variance by answering the question: “To what extent can variability in the measure be predicted from organization membership?” ICC2 examines the between-group variance by answering the question: “How reliable are the organization means within a sample?” (Bliese & Halverson, 1996). Values were ICC1 = .19, ICC2 = .77 for IL; ICC1 = .14, and ICC2 = .57 for OLMs; ICC1 = .20, and ICC2 = .70 for TCE; ICC1 = .20, and ICC2 = .79 for TAC. As indicated by Bliese (2000), ICC1 generally ranges from 0 to .50 with a median of .12 and the acceptable ICC2 values should be larger than .60 by convention (Cohen, 2003). All scales slightly exceeded the median score. Taken together, these results indicated the appropriateness of aggregating the data to the school level.

Data Analysis

We analyzed the survey data using structural equation modeling (SEM) techniques with the AMOS 20 program. This line of analysis is appropriate for a number of reasons and preferable to the commonly used Baron and Kenny (1986) method. First, it allows for the testing of full as well as partial mediation (James et al., 2006). Second, a simultaneous test of the significance of both the path from an initial variable to a mediator and the path from the mediator to an outcome (the test applied by SEM) provides the best balance of Type I error rates and exhibits greater statistical power relative to other approaches (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). Third, SEM can test the relationships among multiple variables simultaneously, enabling us to estimate direct, indirect, and total effects for various variables of interest. Because SEM is primarily based on model fitting and selection, several statistics were used to specify how well the estimated models described the input data set. To gauge model fit as recommended by researchers (e.g., Jöreskog & Sörbom, 1993), we used several goodness-of-fit indices in assessing the fit of the research model. We report χ² values, which provide a statistical basis for comparing the relative fit of models. We tested the mediation effects by comparing alternative models: the fully mediated model (hypothesized model) and a partially mediated model.

Furthermore, to provide a more rigorous test of whether the mediated effects found in the model are statistically significant, we conducted bootstrap analyses tests, recommended by Preacher and Hayes (2008). The bootstrapping approach to analyzing indirect effects is recommended by a growing number of researchers when testing for mediation in small samples. This is due to biased variance and standard error estimates using conventional mediation approaches (Shrout & Bolger, 2002). Thus, bootstrapping is a recommended procedure as a means to avoid power problems caused by asymmetries and other nonnormality issues associated with directly estimating indirect effects. The advantage of this procedure lies in evaluating the confidence interval measure of the population from the data (Preacher & Hayes, 2008). A nonzero confidence interval means that mediation exists. In the current study, bootstrap analyses were based on 2,000 repeated bootstrap samples (Shrout & Bolger, 2002).

Results

Means, standard deviations, and ranges (minimum to maximum) of research variables are presented in Table 1.

Table 1.

Means (Ms), Standard Deviations (SDs), and Ranges (Min to Max) of Research Variables (n = 130).

Variable	Subscale	Min	Max	M	SD
Principals’ self-reported information-processing mechanisms (PIPMs)	1. PIPMs (1): Storing and retrieving information	1.67	5.00	4.20	0.54
	2. PIPMs (2): Receiving information	2.13	5.00	3.80	0.62
	3. PIPMs (3): Disseminating information to teachers	1.80	5.00	4.40	0.57
	4. PIPMs (4): Analyzing external and internal information	3.00	5.00	4.47	0.45
	5. PIPMs: (All)	2.23	5.00	4.20	0.44
Teachers’ rating of principals’ instructional leadership (IL)	6. IL: Climate	2.80	4.70	3.90	0.32
	7. IL: School’s mission	2.90	4.80	4.00	0.39
	8. IL: Instructional program	2.81	4.70	3.93	0.43
	9. IL: (All)	2.91	4.71	3.98	0.40
Teachers-rated organizational learning mechanisms (OLMs)	10. OLMs (1): Analyzing information	2.91	4.92	4.16	0.40
	11. OLMs (2): Storing, retrieving, and putting information to use	2.15	4.68	3.60	0.49
	12. OLMs (3): Receiving and disseminating information	1.80	4.44	3.31	0.56
	13. OLMs (4): Seeking information	1.43	4.41	2.90	0.63
	14. OLMs: (All)	2.13	4.55	3.50	0.48
Teachers-rated teachers’ collective efficacy (TCE)	15. TCE for instructional strategies	3.45	4.83	4.16	0.36
	16. TCE for student discipline	3.24	4.90	4.12	0.38
	17. TCE: (All)	3.44	4.87	4.14	0.36
Teachers’ self-reported affective commitment (TAC)	18. TAC	2.51	5.00	4.18	0.50
Teachers’ self-reported job satisfaction (TJS)	19. TJS	48	92	72.81	9.11
Students’ achievement	20. Science achievement	35	82	54.67	9.54
Students’ achievement	21. Math achievement	32	85	59	10.72

The results in Table 2 show significant positive intercorrelations (p < .01) between PIPM factors (.42 < r < .56, p < .01), IL factors (.88 < r < .90, p < .01), OLM factors (.62 < r < .92, p < .01) and finally, positive significant intercorrelation between both TCE factors (i.e., collective efficacy for instructional strategies and for student discipline; r = .92, p < .001).

Table 2.

Correlations Among Study Variables at the School Level (n = 130).

	1	2	3	4	5	6	7	8	9
1. PIPMs: (All)	—
2. IL: (All)	.27***	—
3. OLMs: (All)	.22***	.78***	—
4. TCE: (All)	−.11	.48***	.78***	—
5. Teachers’ affective commitment (TAC)	.22**	.18**	.19**	.01	—
6. Teachers’ job satisfaction (TJS)	.09	.18	.16*	.30***	.00	—
7. Science achievement	.19	.01	.08	.40*	.36*	.02	—
8. Math achievement	.49	−.15	−.02	.35*	.3*	.29	—	—
9. Number of teachers	−.20**	−.11	−.08	−.2**	−.09	.38	.10	.28**	—
10. Principal seniority	−.04	.02	.94	−.11	−.09	−.11	−.07	.09	.13

Note. PIPMs = principals’ information processing mechanisms; IL = instructional leadership; OLMs = organizational learning mechanisms; TCE = teachers’ collective efficacy.

p ≤ .05. **p ≤ .01. ***p ≤ .001.

Hypothesis Tests

Figure 2 summarizes SEM results of the hypothesized model. SEM results confirmed the hypothesized model for prediction of students’ achievements, showing a good model fit for the data, χ²(133) = 239.60, CFI = .97, IFI = .97, TLI = .94, RMSEA = .05 (Byrne, 2013; Steiger, 2007). The model explained 21% of the variance in students’ achievements in math, and 29% of the variance in students’ achievements in science.

Figure 2.

Standardized path coefficients from principals to teachers to student achievement (n = 130).

As seen in Figure 2, the relationship between principals’ practices (PIPMs and IL) was significant and positive (β = .30, p < .001), confirming Hypothesis 1. Examination of the relationship between principals’ IL and OLMs also revealed a significant positive relationship (β = .80, p < .001), thus confirming Hypothesis 2. Regarding the relationships between OLMs and teachers’ attitudes, all three parts of Hypothesis 3 (a, b, c) were confirmed; OLMs were significantly associated with TCE (β = .50, p < .001), TJS (β = .16, p < .05), and TAC (β = .20, p < .01).

Regarding the relationships between teachers’ attitudes and students’ achievements: a significant positive relationship emerged between TCE and students’ achievements, both in science (Hypothesis 4a: β = .40, p < .05) and in math (Hypothesis 4b: β = .35, p < .05). Similarly, significant relationships emerged between TAC and students’ achievements, in both science (Hypothesis 5a: β = .36, p < .05) and math (Hypothesis 5b: β = .30, p < .05). Therefore, Hypothesis 4 and Hypothesis 5 were confirmed. However, for Hypothesis 6, TJS was not significantly related to students’ achievements in either science (p > .05) or math (p > .05).

Mediation Effects: Principals’ Practices, OLMs, and Teachers’ Attitudes (Hypothesis 7–Hypothesis 10)

Hypothesis 7 concerns the mediating role of IL in the relationship between PIPMs and OLMs. To support the full-mediation model, we compared the hypothesized mediated model with a partially mediated model, which is the same model, but with additional paths that enable testing for a possible direct effect between PIPMs and OLMs. Although the latter model exhibited good fit indices, χ²(132) = 239.00, CFI = .96, TLI = .92, IFI = .96, RMSEA = .06, partial-mediation model did not show any significant improvement compared with full-mediation model, Δχ²(1) = .60, p > .05), confirming that the IL is a full mediator in the relationship between PIPMs and OLMs.

As noted above, to provide a more rigorous test of whether the mediated effects found in the model were statistically significant, we conducted bootstrap analyses. The indirect effect path of PIPMs to OLMs through IL was .24 (p < .01); the 99.5% confidence interval [lower .5% and upper .5%] ranged between .11 and .43 (2,000 repeated bootstrap samples; Shrout & Bolger, 2002), thus confirming Hypothesis 7.

Hypothesis 8 concerns the mediating role of OLMs in the relationship between IL and teachers’ attitudes of TCE, TAC, and TJS. To support the full-mediation model, we compared it with the partially mediated model, to test for a possible direct effect between IL and the three teachers’ attitudes (TCE, TAC, and TJS). Although the partial-mediation model exhibited good fit indices, χ²(130) = 232.46, CFI = .96, TLI = .91, IFI = .96, RMSEA = .06, it did not show any significant improvement over the full-mediation model, Δχ²(3) = 7.14, p > .05, confirming that OLMs are full mediators in the relationships of IL to teachers’ attitudes of TCE, TAC, and TJS.

As aforementioned, we performed bootstrap analyses (Shrout & Bolger, 2002). The indirect effect path of IL to TCE through OLMs was .40 and significant (p < .01); the 99.5% confidence interval [lower .5% and upper .5%] ranged between .14 and .46 and was a nonzero value (Shrout & Bolger, 2002), thus confirming Hypothesis 8a. The indirect effect path of IL to TAC through OLMs was .16 and significant (p < .05); the 99.5% confidence interval [lower .5% and upper .5%] ranged between .30 and .46, thus confirming Hypothesis 8b. Finally, the indirect effect path of IL to TJS through OLMs was .13 and significant (p < .05), and the confidence interval [lower .5% and upper .5%] ranged between .02 and .35, thus confirming Hypothesis 8c.

Hypothesis 9 and Hypothesis 10 examined the mediation between OLMs and students’ achievements (math and science) through TCE, TAC, and TJS. We found mediation between OLMs and students’ achievements (math and science) through TCE only (hypotheses of mediation through TAC and TJS were not confirmed, p > .05). To support the full-mediation model, we compared it with the partially mediated model, to test for a possible direct effect between OLMs and students’ achievements (math and science). Although this latter model exhibited good fit indices, χ²(131) = 239.39, CFI = .96, TLI = .91, IFI = .96, RMSEA = .06, it showed no significant improvement over the full-mediated model, Δχ²(2) = 0.21, p > .05, confirming that TCE is a full mediator in the relationship between OLMs and students’ achievements (math and science).

Based on bootstrap analyses to test mediated effects (Shrout & Bolger, 2002), the indirect effect path of OLMs on math achievement through TCE was .17 and significant (p < .01); the 99.5% confidence interval [lower .5% and upper .5%] ranged between .18 and 2.08 and was a nonzero value (Shrout & Bolger, 2002), thus confirming Hypothesis 9a. Finally, the indirect effect of OLMs on students’ achievements in science through TCE was .20 and significant (p < .01). The 99.5% confidence interval [lower .5% and upper .5%] ranged between .06 and 1.16 and was a nonzero value (Shrout & Bolger, 2002), thus confirming Hypothesis 10a.

Discussion

This study contributes to the international knowledge base on the investigation of models for predicting students’ achievements by testing and elaborating on the “paths” that link school principal behaviors/practices of leadership and learning with OLMs, teachers’ attitudes and students’ learning outcomes. More specifically, the findings offer insights into strategic targets that instructional leaders can use to enhance OLMs directly and teachers’ attitudes indirectly through organizational learning. Moreover, this study recognizes the significant importance of OLMs in predicting students’ achievements through TCE. Interestingly, the direct effect of TCE on science students’ achievement (2011) was also found with math students’ achievements (2013). Furthermore, the indirect effects between OLMs and both science (2011) and math (2013) students’ achievement through TCE were significant. Thus, these results both support and extend findings from prior empirical models that attempted to suggest paths to predict students’ achievements in elementary schools (e.g., Hallinger & Wang, 2015).

In the first part of the research model, we investigated the relationships between PIPMs, IL, and OLMs. With regard to principals’ key practices, our multiple data sources revealed a significant, positive relationship between PIPMs and principals’ IL as rated by the teachers. This finding suggests that when elementary principals perceive themselves as more proficient at information processing, such as evaluating, retrieving, and analyzing information that they receive and disseminating it appropriately to parents, students, teachers, and external parties such as the Ministry of Education, teachers will perceive such principals as demonstrating stronger IL. In other words, such principals are more likely to be seen as instructional leaders (Hallinger & Wang, 2015)—those who articulate a clear school mission, promote a positive school-learning climate, and properly manage the instructional program. Thus, presumably, to create and lead effective learning and teaching (instructional) environments, principals need to extensively process, create, store, and share the information that they receive on a daily basis (Schechter & Qadach, 2012).

A significant, strong positive relationship also emerged between IL and OLMs. Thus, when teachers perceive principals as instructional leaders, the teachers seem to utilize OLMs more intensively in the school. Such OLMs are critical components for achieving school effectiveness, as schools that function as learning organizations have been shown to perform better (Louis & Robinson, 2012). Moreover, considering that OLMs and PIPMs share a similar information-processing perspective, it was assumed that they would also be strongly related. However, their significant but weak relationship indicated that this was not the case. In as much as the PIPM construct was related to both IL and OLMs, we examined the possibility of mediation and found that, indeed, IL mediates the relationship between PIPMs and OLMs. In other words, the use of information-processing mechanisms by the current principals was only able to promote OLMs through mediation by principals’ IL (i.e., their articulation of the school vision, management of the instructional program, and promotion of a positive learning climate). This can be explained by the similarities in the conceptual foundations of IL and leadership for learning (Boyce & Bowers, 2018; Murphy et al., 2016).

Furthermore, as we expected, OLMs in elementary schools, which represent daily opportunities for teachers to discuss and process information concerning their practice in order to improve it, were found to be significantly and positively related to all three studied teachers’ attitudes: TCE, TAC, and TJS. The few studies that have examined relationships among these constructs reached similar results. For instance, in developing the OLM measure, Schechter (2008) found that it was positively correlated to both TCE and teachers’ commitment, concluding that teachers’ participation in learning forums enhances their sense of loyalty to the organization. In a later study of elementary schools, Schechter and Qadach (2012) found that OLMs positively predicted TCE for instruction and TCE for discipline. Schechter and Atarchi (2014) also found that their measure of OLMs for secondary schools positively correlated with TCE and with teachers’ commitment (affective and normative). To the best of our knowledge, the relationship between OLMs as defined here and TJS has never been examined in the school context. Yet other studies employing different organizational learning measures (e.g., Kurland & Hasson-Gilad, 2015) or conducted in non-educational contexts (e.g., Joo & Park, 2010) yielded similar links between organizational learning and employee satisfaction.

Next in our research model, in as much as OLMs were related to teachers’ attitudes and IL was related to OLMs, it was possible to test these relationships for potential mediation. The analysis demonstrated that OLMs indeed mediate the relationships between IL and all three teachers’ attitudes. Thus, at this point, we were able to establish a chain of relationships in which PIPMs were linked to IL, which was linked to OLMs, which in turn was linked to the teachers’ attitudes, with IL mediating between PIPMs and OLMs, and with OLMs mediating between IL and the teachers’ attitudes.

The next link in the chain, found between teachers’ efficacy and students’ achievements, supports assertions made by several researchers regarding TCE as a predictor of students’ achievements in math (e.g., Cooper, 2010) and science (Goddard et al., 2004). These studies suggested that school success depends on teachers’ collective belief that they can improve students’ achievements, regardless of those students’ low-socioeconomic status, lack of ability, or family background. It is important to emphasize that in the current study, TCE showed the strongest direct relationship to students’ achievements in science and math, compared with the other teachers’ variables (TAC and TJS) and to the rest of the variables in the research model. Another interesting finding was that TCE was the teacher attitude most strongly related to OLMs. This may be in line with Louis et al.’s (2010) findings that TCE in professional learning communities mediated the relationship between leadership (principal IL) and student outcomes, highlighting the significant role of TCE in mediating between OLMs and student achievement.

TJS has rarely been studied in relation to student achievement, and even then, results have been mixed. Michaelowa (2002), for example, found a significant relationship between TJS and students’ achievements, although the study also reported that some factors positively influencing TJS (such as type of contract) negatively influenced students’ achievements. Our insignificant finding substantiated that of Caprara et al. (2006), who did not find any significant correlations between TJS and student achievement and concluded that the latter is based mainly on teachers’ competence and not on their satisfaction with their job. As job satisfaction has many components, definitions, and measurements, further research should explore if and how its components affect student achievement. This is especially important because, in the current study, TJS was significantly related to TCE, which was significantly related to students’ achievements, indicating that the role of TJS in student outcomes may be complex and indirect.

With regard to the final mediation link in the chain, we examined our assumption that OLMs would predict students’ achievements (through teachers’ attitudes). The results revealed interesting findings in which OLMs positively predict students’ achievements (in math and science) only through TCE, while the other attitudes (TAC and TJS) did not have a mediating role. As aforementioned, prior research (Louis et al., 2010) found TCE (teachers’ beliefs in students’ academic ability) in professional learning communities mediating the relationship between leadership (principal IL) and student outcomes. Finally, these findings obtained from the final (direct and mediated effects) model resemble Hallinger and Heck’s (2010) analysis of longitudinal data, revealing that collaborative leadership positively affected growth in student learning indirectly through building academic capacity in schools.

Implications

PIPMs at the school level describe how principals’ daily operations of gathering, storing, analyzing, and distributing information are the first link in a chain of events that affect teachers’ procedures (OLMs) and ultimately, students’ achievements. Previous research established that principals affect school learning in an indirect manner (Hallinger, 2011). The PIPM construct demonstrates how this indirect effect on learning operates in schools on a daily basis, through the PIPMs’ effect on IL, which affects OLMs, which in turn affect important teachers’ attitudes, which are ultimately related to higher student achievement. The study also confirms the important role of IL in facilitating OLMs and in improving the important teachers’ attitudes, that is, TAC, TJS, and TCE, through the OLM mediation. In this regard, the current empirical outcomes establish the importance of the relatively new construct of OLMs for creating a school-learning environment which may enhance teachers’ attitudes that affect students’ achievements. In particular, this study confirms the OLMs’ ability to predict TCE, as well as the latter’s ability to predict students’ achievements, substantiating previous studies (Eells, 2011; Goddard et al., 2015). Furthermore, by finding the mediating role of TCE between OLMs and students’ achievements (in math and science), we emphasize the significant role of teachers’ collective belief in their ability to elicit progress in students’ academic ability as a connecting bridge between the school academic/learning capacities (i.e., OLMs) and student achievement.

In an environment that is constantly changing and routinely inundated with information, principals must be able to gather, store, retrieve, analyze, and disseminate performance-relevant information on an ongoing, orderly basis. Principals can adopt specific OLMs to create an environment in which school learning becomes routine, consequently affecting teachers and students in significant ways. In light of the built-in fragmentation of the school structure (e.g., division by subject matter), principals need to orchestrate a time and place for the OLMs, creating and sustaining networks of learning arrangements. Principals are key figures in introducing both learning spaces and forums into the ongoing school processes and promoting a learning culture, which is necessary for productive information processing (Schechter & Atarchi, 2014).

Limitations and Future Research

The major strength of the present study is that the likelihood of common method variance was low because data were collected from three sources (Urick & Bowers, 2014): principals, teachers, and the educational system (Ministry of Education’s data set). However, several limitations in the study warrant attention. The research design was suitable for initial exploration of an integrative model examining a chain of links from principals (information processing and IL), to teachers’ learning within the organization, to teachers’ attitudes (TAC, TCE, and TJS), to students’ academic achievements. Due to the design of the present study, the data could not provide direct evidence of causal links between the proposed variables. Accordingly, we adopted the approach of other studies (e.g., Kammeyer-Mueller et al., 2013; Piccolo et al., 2010) in which hypotheses and argumentation are expressed in terms of relationships. Therefore, conceivably, the causal order could be reversed, and reciprocal causality cannot be ruled out. As this model does not provide a detailed view of the factors’ dynamics, future research would do well to use a longitudinal qualitative research approach to gain a deeper understanding of these interactions.

Moreover, to evaluate the causal relationships among data derived from nonexperimental longitudinal research designs, we have to use the cross-lagged approach (Da’as, 2020), while controlling the variables at different points in time. However, as explained above, because the external national standardized student achievement tests for a particular subject are administered once every 4 years, student achievement data were collected between 2011 and 2013; thus data pertaining to principals’ practices, school-learning processes, and teacher attitudes were collected in the 2011–2012 academic year; data pertaining to science as a subject matter were collected in 2011, and those pertaining to mathematics were collected in 2013. In this context, more complex models of school leadership (in our study IL and PIPMs) are needed to model the complexities of schooling processes over time; thus, it is important to explore how leadership effects vary through time-nested longitudinal studies. Furthermore, individual student achievement results were not available on the Ministry’s website. Hence, future research may use hierarchical linear modeling to examine nested effects as well as examine this model at a different analytical level (multi-SEM) to determine whether processes at the school level affect individual-level outcomes or vice versa.

As the focus of this study was elementary schools, a larger study across school levels might reveal differences that play a significant role in shaping principals’ practices, OLMs, and teachers’ attitudes. Different models in future studies might predict student outcomes at each level in different ways, as middle and especially high schools place greater emphasis on specialization and division of labor (mostly subject matter-oriented) than elementary schools. Moreover, compared with elementary schools, middle and high schools are more loosely linked (e.g., decentralization of power), possibly influencing the links between principals’ practices, especially within the “learning-centered” leadership framework (Murphy et al., 2016), OLMs and teachers’ attitudes.

Differences between urban and suburban schools were not examined in this study. Schools do not operate in a vacuum; they function as part of a larger social system, including the school district and the local community in which they are embedded (e.g., Rumberger, 2004). Schechter and Qadach (2012) previously showed, for example, that OLMs do not have the same effect in urban versus suburban schools. Future research on the school–environment interface should examine the fit between the research model that was validated here and these two types of schools—in the more turbulent, uncertain urban environment versus the more placid, certain suburban one. Within this future line of research, it is important to note that if an information-processing perspective is used in various national and international contexts, the items under each of the four broad OLM categories may be notably different.

Furthermore, future study needs to compare the research model through two different research populations: Arab and Jewish faculty members in Israeli schools. The Arab population is characterized by collectivism and high power distance, whereas the Jewish sector is characterized by individualism and low power distance, reflected in the relationships between school principals and teachers (Da’as, 2017; Da’as & Zibenberg, 2019). In addition, future research should differentiate Arab and Jewish students’ achievements, in light of national reports from previous years revealing significant differences (BenDavid-Hadar, 2016). Such a study might make use of two separate, large population samples to determine possible differences in the theoretical model as it applies to the two student populations.

Finally, growing research in educational leadership seeks to explore the means by which principals can affect student learning, primarily by focusing on how leadership fosters teachers’ professional learning (Hallinger, 2011; Robinson et al., 2008). This research examined an integrated model, attempting to advance our field regarding the relationships between IL and leadership for learning as a systemic anchor in influencing teachers’ attitudes and consequently affecting students’ achievements. Is it possible that PIPMs are a more fined-grained facet of a leader’s IL practice? For example, might all three aspects of IL necessitate PIPMs to be more robustly expressed or evidenced? Thus, we would encourage scholars to further explore the connections and extensions of “learning-focused” (Knapp & Feldman, 2012) and “learning-centered” leadership frameworks (Hallinger, 2011; Murphy, 2006) in relation to IL (both quantitatively and qualitatively) in diverse cultural contexts.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Mowafaq Qadach

Author Biographies

Mowafaq Qadach, PhD, senior lecturer at MBA faculty, Department of Education, Ono Academic College, Israel. His research areas include teachers’ collective efficacy, organizational learning mechanisms, instructional leadership, principals’ learning, students’ achievements and self-regulated learning.

Chen Schechter, PhD, professor, leadership, organizational development and policy in education, School of Education, Bar-Ilan University, Israel. He is the editor-in-chief of the Journal of Educational Administration. He serves as head of MOFET National Institute for Research, Curriculum, and Program Development for Teachers and Teacher Educators. He also serves as chair of the board of directors at the Institute for the Advancement of Teaching, Learning and Social Integration. His research interests include leadership development/preparation, educational change/reform in accountability systems, learning from success, collaborative learning strategies, and systems thinking leadership.

Rima’a Da’as, PhD, senior lecturer at Department of Education, Al-Qasemi, Academic College of Education, Israel. Her research areas include organizational learning and change, educational leadership, leaders’ cognitive complexity, absorptive capacity, leadership characteristics and school outcomes, and statistical methods for educational research.

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