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The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is an increasing body of research based on complexity theory (CT) suggesting that constructs such as these should be viewed as variables in a complex system with inter-dependencies, interactions, and potentially nonlinear relationships. Despite the significant body of conceptual research related to CT, there is a lack of methodological research into these potentially nonlinear effects. The purpose of this paper is to demonstrate the theoretical and practical importance of non-linear terms in a multivariate polynomial model as they become more significant predictors of firm success in collaborative environments and less significant in more rigidly controlled work environments.

Multivariate polynomial regression methods are used to examine the significance and effect sizes of interaction and quadratic terms in operations scenarios expected to have varying degrees of complex and complex adaptive behaviors.

The results find that in highly collaborative work environments, non-linear and interaction effects become more significant predictors of success than the linear terms in the model. In more rigid, less collaborative work environments, these effects are not present or significantly reduced in effect size.

This study shows that analytical methods sensitive to detecting and measuring nonlinearities in relationships such as multivariate polynomial regression models enhance our theoretical understanding of the relationships between constructs when the theory predicts that complex and complex adaptive behaviors are present and important.

This study demonstrates that complex adaptive behaviors between management and the workforce exist in certain environments and provide greater understanding of factor relationships relating to firm success than more traditional linear analytical methods.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

This study explores the factors that explain the adoption of innovative teaching practices within schools and how this is determined by the different perceptions of principals and teachers.

The authors use the self-other agreement to measure the difference between the principal and teachers' rating based on the responses of 255 principals and 10,415 teachers, applying polynomial regression with surface analysis to examine the in-agreement/disagreement of self- and other-ratings.

Results indicate that schools where principals and teachers agree on the level of collaborative culture, learning climate, professional development and instructional leadership are associated with higher innovative teaching practices, creating opportunities for stimulating learning environments. In addition, the adoption of innovative professional practices is more likely to result when there is disagreement with teacher over-rating the factors.

It has practical implications for developing strategies aimed at encouraging the implementation of innovative teaching practices among teachers and it extends the research on teachers' professional practices by using self-other agreement data collection method and surface analysis.

The vast collection of data provide a unique investigation opportunity of the effects of collaborative culture, learning climate, professional development and instructional leadership on innovative teaching in Italy.

The purpose of this paper is to better understand the indirect link between high-performance work systems (HPWSs) and firm performance in Chinese professional service firms (PSFs) by investigating the mediating role of organizational ambidexterity, i.e. a firm’s capability to simultaneously explore new ideas and exploit existing resources.

Data were collected from 120 Chinese accounting firms. The authors used hierarchical and polynomial regression analyses to test their hypotheses.

The proposed positive link between the HPWS and organizational ambidexterity was found. Further, the results showed a non-linear relationship between organizational ambidexterity and organizational performance.

The present study is limited in terms of small sample size, single industry and self-report data.

Firms which reported a higher level of HPWS demonstrated better performance due to their organizational capability to explore new ideas and exploit existing resources. In the Chinese context, firms that had high levels of exploration (exploring new resources) and exploitation (exploiting existing resources) or that had a high level of exploration experienced higher performance. The authors can conclude from these findings that without exploration, organizational success is difficult to achieve for PSFs.

This is the first study examining the underlying mechanism of organizational ambidexterity in the indirect relationship between HPWS and firm performance in Chinese PSFs. It advances the authors understanding of HPWS and firm performance relationship in an Eastern country and an emerging context of PSFs. This is also the first study to use polynomial regression to operationalize organizational ambidexterity.

Congruence serves as a key framework in many leader–follower dyad theories. This paper aims to introduce polynomial regression analysis with response surface methodology (PRA with RSM) as a statistical technique for investigating research questions concerning leader–follower dyadic relationships in the hospitality context.

First, this paper illustrates the necessity of applying PRA with RSM to more effectively address the research issues related to leader–follower dyadic relationships. Next, this paper presents an overview and the key concepts of PRA with RSM. Critical issues that need to be noted and two recent hospitality leadership studies that have used PRA with RSM are discussed. Third, an empirical example in the hotel context is provided to illustrate the application of PRA with RSM.

By applying this methodology to the study of hospitality leader–follower dyadic relationships, researchers will be able to address a range of topics related to dyadic theory, such as leader–member exchange and value congruence.

PRA with RSM reveals that congruence effects vary within leader–follower dyads. Industry professionals can promote a better leader–follower fit by incorporating dyadic surveys to understand mutual agreement and perceptions regarding same-workplace phenomena.

The paper addresses the misalignment between leader–follower dyadic theory and the methodology used in hospitality leadership studies.

Drawing on the transactional cost economics (TCE) perspective, we aim to investigate the effects of the balance and imbalance between contractual and relational governance on a firm's bridging responses to supply chain disruptions. By adopting the institutionally contingent perspective, we further examine the moderating effect of cultural distance on the relationship between governance mechanisms and bridging responses.

Based on data collected from 183 firms in China, we use polynomial regression and response surface analyses to test our research model.

The bridging responses increase along with an increasing balance level between contractual and relational governance and decrease along with an increasing imbalance level between contractual and relational governance. Moreover, the positive effect of balance between contractual and relational governance is strengthened by a large cultural distance. We also find that a large cultural distance amplifies the negative effect of the combination of high relational governance and low contractual governance yet weakens that of the combination of high contractual governance and low relational governance.

Our study provides nuanced insights into the effects of the balance and imbalance between contractual and relational governance on bridging responses and into the cultural boundary conditions under which these effects vary.

The goal of this paper is to model the activated carbon adsorption capacity of cadmium using a polynomial regression model. The properties that influence the removal of heavy metals by activated carbon must be taken into consideration in order to synthesize materials specific to the characteristics of the pollutant.

Multivariable analysis applications allow a comprehensive description of the relationship between activated carbon surface properties and heavy metal adsorption.

The authors use a second-grade polynomial regression model to determine the influence of porosity and surface acidity in cadmium adsorption by activated carbon.

The authors propose a statistic analysis to correlate the carbon properties with its cadmium adsorption capacity. Model coefficient analysis will give a better comprehension of the influence of activated carbon porosity and surface acidity of cadmium removal.

Role congruity theory and gender stereotypes research suggests men are expected to engage in agentic behavior and women in communal behavior as leaders, and that role violation results in backlash. However, extant gender and leadership research does not directly measure expectations–behavior incongruence. Further, researchers have only considered one condition of role incongruence – display of counter-role behavior – and have not considered the outcomes of failing to exhibit role-congruent behavior. Additionally, few studies have examined outcomes for male leaders who violate gender role prescriptions. The present study aims to address these shortcomings by conducting a novel empirical test of role congruity theory.

This experimental study used polynomial regression to assess how followers evaluated leaders under conditions of incongruence between follower expectations for men and women leaders’ behavior and leaders’ actual behavior (i.e. exceeded and unmet expectations). Respondents read a fictional scenario describing a new male or female supervisor, rated their expectations for the leader’s agentic and communal behavior, read manipulated vignettes describing the leader’s subsequent behavior, rated their perceptions of these behaviors, and evaluated the leader.

Followers expected higher levels of communal behavior from the female than the male supervisor, but no differences were found in expectations for agentic behavior. Regardless of whether expectations were exceeded or unmet, supervisor gender did not moderate the effects of agentic or communal behavior expectations–perceptions incongruence on leader evaluations in polynomial regression analyses (i.e. male and female supervisors were not evaluated differently when displaying counter-role behavior or failing to display role-congruent behavior).

In addition to providing a novel, direct test of role congruity theory, the study highlighted a double standard in gender role-congruent behavior expectations of men and women leaders. Results failed to support role congruity theory, which has implications for the future of theory in this domain.

The purpose of this paper is to amend and extend the emerging research that has utilized an employee-focused approach to examining the service recovery process. In doing so, the authors examine the influences of supervisor and coworker support for error management on two measures of employee service performance: service recovery performance and helping behaviors during service failure and recoveries. Specifically, this study examines the linear and non-linear interaction effects of supervisor and coworker support for error management on the outcome variables.

To examine the proposed relationships, the authors conducted a field study that utilized survey data from a sample of 243 restaurant employees and their immediate supervisors. Employee ratings of supervisor and coworker support for error management were matched with the data gathered for the two dependent variables (i.e. supervisory ratings of service recovery performance and helping behaviors). Structural equation modeling was used to examine the linear interaction effects on the outcome variables. To examine the non-linear interaction effects on the outcome variables the authors utilized polynomial regression and response surface modeling.

The results showed that the interaction effects of supervisor and coworker support for error management was significantly positively related to both service recovery performance and helping behaviors. In addition, an alternative analysis of the shape of the interaction effects using polynomial regression and response surface modeling showed that the moderating effects may be better conceptualized as non-linear.

These findings offer new insights about the roles and impact of various forms of support in the service recovery process. First, the current study focuses specifically on supervisor and coworker support for error management and the impact on employees’ service recovery performance and helping behaviors. Second, this research investigates the interaction effects of these two forms of support on service recovery performance and helping behaviors. Third, along with linear interaction effects, the current work examines non-linear interaction effects. These relationships examined in this study have not been tested before. Thus, the findings of this research make a unique contribution to research in service management. The findings of this study provide more prescriptive insights about the means to prevent and respond effectively to service errors.