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The purpose of this paper is to examine the independent effects of principal background, training and experience as well as teacher academic qualifications on school proficiency growth through time.

The authors analyzed the entire population of all elementary and middle schools in the state of Illinois, n=3,154 schools, from 2000 to 2001 through 2005-2006 using growth mixture modeling. The authors examined growth at the school level in the percentage of students meeting or exceeding standards on the Illinois Standard Achievement Test, analyzing separate models for Chicago and non-Chicago schools.

The results suggest that there are two statistically significantly different latent school proficiency trajectory subgroups through the six-year time period, one high and one low, for both Chicago and non-Chicago schools. In addition, the models suggest that teacher academic qualifications, principal training, principal experience as a principal and an assistant principal, and experience of the principal as a teacher previously in their schools are significantly related to school proficiency growth over time, dependent upon school context.

Recent studies on the independent effects of principal experience, training and teacher academic qualifications have shown inconsistent results on school achievement growth. The authors demonstrate that principal training and background may have an effect on school-level proficiency score growth.

This study is one of the first to examine statistically different proficiency growth trajectories using an entire state-wide data set over a long-term, six-year timeframe.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

Previous literature has recognized the variability of job performance, calling attention to the inter-individual differences in performance change. Building on Murphy’s (1989) theoretical model of performance, the purpose of this paper is to verify the existence of two distinct classes of performance, reflecting stable and increasing trends, and to investigate which personal conditions prompt the inclusion of individuals in one class rather than the other.

Overall job performance was obtained from supervisory ratings for four consecutive years for 410 professionals of a large Italian company going through significant reorganization. Objective data were merged with employees’ organizational tenure and self-efficacy. Growth Mixture Modeling was used.

Two main groups were identified: the first one started at higher levels of performance and showed a stable trajectory over time (stable class); the second group started at lower levels and reported an increasing trajectory (increasing class). Employees’ with stronger efficacy beliefs and lower tenure were more likely to belong to the stable class.

Through a powerful longitudinal database, the nature, the structure and the inter-individual differences in job performance over time are clarified. The study extends Murphy’s (1989) model, showing how transition stages in job performance may occur also as a result of organizational transformation. Moreover, it demonstrates the essential role of self-efficacy in maintaining high performance levels over time.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

Outsourcing of accounting increasingly attracts research interest, but research concerning the impact of the benefits of outsourcing on firm capabilities and performance across firms remains limited. This paper aims to reveal the unobservable latent classes of firms that outsource their accounting functions by testing a research model concerning the topic.

The authors build on accounting outsourcing research and adapt a research model from the literature on business services outsourcing. The authors analyze the data from 261 small and medium-sized enterprises in Europe using finite mixture structural equation modeling (FMSEM) and additional methods.

The authors reveal three latent classes with different research models. Thriving outsourcers (N = 103) have a positive attitude toward accounting outsourcing and associate competitive capabilities with mediating the relationship from outsourcing benefits to firm performance. Annoyed outsourcers (N = 143) are dissatisfied with their accounting service provider and only associate outsourcing benefits with competitive capabilities. Convenient outsourcers (N = 15) feel comfortable with their current accounting service provider and associate outsourcing benefits with neither capabilities nor with firm performance.

The study initiates the discussion about the unobservable heterogeneity among accounting outsourcers. The study introduces the use of the FMSEM method in accounting outsourcing research.

The study offers novel insights concerning accounting outsourcers and proposes original explanations for their outsourcing decisions that would help both the outsourcers and accounting service providers.

The study might be the first to categorize accounting outsourcers using FMSEM.

This article aims to describe patterns of change in innovation networks and to clarify the roles of time and history in shaping network trajectories. The authors test seven predictor variables and their interactions to examine their influences on network performance over time.

A fuzzy simulation of innovation networks and investigations of different network types, using two classes of growth modeling techniques, help refine understanding of innovation as an interactive, developmental process.

Innovation network trajectories are influenced by self-reinforcing, contradictory and damaging forces. History affects network trajectory development, particularly with regard to financial resource access. The temporal processes reveal three contrasting classes of developmental trajectories for innovation networks.

The study methodology can account for theoretically derived factors leading to innovation, in and across types of networks and for changes over time; it moves beyond a cross-sectional approach. Although the model structure is generic, the parameters are based on a radical innovation, so the findings may not transfer directly.

Managers in innovation business networks can use the identified variables to improve network performance, by facilitating processes that inject financial capital and integrating heterogeneous skills that focus on a wider variety of skills that generate both exploratory and exploitative knowledge development.

This article contributes to discourses on network trajectories through an analysis of processes that influence the growth and decline of innovation business network performance. An original methodology generates and analyzes dynamic longitudinal network data.

The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field.

Different approaches to treat heterogeneity in PLS path models are introduced, critically evaluated and classified into a methodological taxonomy. Future research directions are derived from a comparison of benefits and limitations of the procedures.

The review reveals that finite mixture‐PLS can be regarded as the most comprehensive and commonly used procedure for capturing heterogeneity within a PLS path modelling framework. However, further research is necessary to explore the capabilities and limitations of the approach.

Directions for additional research, common to most latent class detection procedures include the verification and comparison of available approaches, the handling of large data sets, the allowance of varying structures of path models, the profiling of segments and the problem of model selection.

Whereas modelling heterogeneity in covariance structure analysis has been studied for several years, research interest has only recently been devoted to the question of clustering in PLS path modelling. This is the first contribution which critically consolidates available approaches, discloses problematic aspects and addresses significant areas for future research.

The purpose of the paper is the analysis of the evolution of students’ satisfaction over time in a large Italian university and the effects on it because of some characteristics of the teachers: didactic practices, beliefs and needs with regard to teaching and learning.

The first step of the analysis identifies a latent construct, measured with items composing the questionnaire, and proposes a reduced set of indicators to measure satisfaction and to model its evolution over time (information collected in three consecutive academic years is available). A second step clusters teachers in homogenous groups with reference to their opinions, beliefs and needs, collected with a new survey conducted at the University of Padova, with the aim of developing strategies to support academic teachers. Then, a mixture conditional latent growth model is estimated with covariates affecting the latent parameters and class membership.

Model estimation identifies a large group of university courses with a high level of satisfaction, which stays constant over time, and a small group of problematic courses with low satisfaction, moreover, that decreases over the three considered academic years. Interesting significant effects of covariates related to both the teacher and the didactic activity are estimated.

Statistical analyses show that the implementation of innovative didactic practices and commitment to quality of teaching are important factors to be encouraged by the university management. On the contrary, the traditionalist way of teaching and a low passion for teaching do not improve students’ satisfaction.

This research aims to identify trajectories of employees' perceptions of organizational support (POS) over the course of an eight-month period and to document associations between these longitudinal trajectories and several outcomes related to employees' well-being (i.e. job satisfaction), attitudes (i.e. turnover intentions, affective commitment) and behaviors (i.e. voice behaviors).

POS ratings provided each four months by a sample of 747 employees were analyzed using person-centered growth mixture analyses.

Results revealed that longitudinal heterogeneity in POS trajectories was best captured by the identification of four distinct profiles of employees. Two of these profiles followed stable high (67.2%) and low (27.3%) POS trajectories, whereas the remaining profiles were characterized by increasing (2.2%) or decreasing (3.3%) POS trajectories. Our results showed that, by the end of the follow-up period, the most desirable outcome levels were associated, in order, with the increasing, high, low and decreasing trajectories.

This research has important implications by showing that perceptions of organizational support fluctuate over time for some employees and help better predicting valuable work-related outcomes.

These findings shed a new perspective on organizational support theory by adopting a dynamic perspective, and revealing that changes over time in POS are more potent predictors of valuable work-related outcomes than stable POS levels.

This chapter presents Infosys’ approach to leader development that includes the practical benefits of psychometric and statistical methods commonly used by other disciplines, such as Rasch measurement and latent growth modeling. Infosys is beginning to use these with other individualized leader development practices such as coaching, intervention bundling, and evaluation. When combined, these elements have the potential to personalize developmental processes to each leader and improve microlevel leadership theory with the overarching purpose of enhancing global leadership at Infosys and promoting the science of individual leader development.