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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 paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.

Crop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.

The framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.

This paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

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.

Goal orientation shapes employees’ approach to and interpretation of workplace aspects such as supervisors’ behavior. However, research has not fully examined the effect of goal orientation as an antecedent of abusive supervision. Drawing from victim precipitation theory, this study aims to fill this research gap by investigating how employees’ goal orientation influences their perception of abusive supervision.

Two studies were conducted to test the hypotheses. In Study 1, 181 employees in 45 departments participated in the survey, and multilevel confirmatory factor analysis, two-level path model and polynomial regression were used. In Study 2, 108 working adults recruited from a professional online survey platform participated in a two-wave time-lagged survey. Confirmatory factor analysis, hierarchical linear regression and polynomial regression were used.

This study found that employees’ learning goal orientation was negatively related to their perception of abusive supervision. In contrast, performance-avoidance goal orientation was positively related to their perception of abusive supervision, whereas performance-approach goal orientation was unrelated to this perception. Moreover, employees’ perception of abusive supervision was greater when learning and performance-approach goal orientation alignment occurred at lower rather than higher levels, and when performance-avoidance and performance-approach goal orientation alignment occurred at higher rather than lower levels.

This research identified two novel victim traits as antecedents of abusive supervision – employees’ learning goal orientation and performance-avoidance goal orientation. Furthermore, adopting a multiple goal perspective, the authors examined the combined effects of goal orientation on employees’ perception of abusive supervision.

Network coverage is crucial for the adoption of advanced Smart Home applications. The commonly used log-based path loss model is not able to accurately estimate WiFi signal strength in different houses, as it does not fully consider the impact of building morphology. To better describe the propagation of WiFi signals and achieve higher estimation accuracy, this paper studies the basic building morphology characteristics of houses.

A new path loss model based on a decision tree was proposed after measuring the WiFi signal strength passing through multiple housing units. Three types of regression models were tested and compared.

The findings demonstrate that the log-based path loss model fits small houses well, while the newly proposed nonlinear path loss model performs better in large houses (area larger than 125 m2 and area-to-perimeter ratio larger than 2.5). The impact of building design on path loss has been proven and specifically quantified in the model.

Proposed an improved model to estimate indoor network coverage. Quantify the impacts of building morphology on indoor WiFi signal strength. Improve WiFi signal strength estimation to support Smart Home applications.

This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and dissimilarity in psychological resilience on the follower's absenteeism in times of organizational crisis and (2) the moderating effect of relational demography (gender and age similarity) in these relationships.

Polynomial regression and response surface analysis were performed using data from 510 followers and 149 supervisors in a financial firm in Canada.

The results overall support the similarity–attraction perspective, but not the resource complementarity perspective. Dissimilarity in resilience was predictive of followers' absenteeism, and similarity in surface-level conditions (gender and age) attenuates the relational burdens triggered by resilience discrepancy.

The findings reiterate the importance of developing employees' resilience, while shedding light on the importance for managers of being aware of their potential misalignment with subordinates resilience.

The results (1) suggest that it is the actual (di)similarity with the leader, rather than leader's degree of resilience, that shapes followers' absenteeism and (2) add nuance to the resilience literature.

This cross-culture study aims to investigate how two cultural values, collectivism (COL) and long-term orientation (LTO), integrate with each other to trigger green consumption.

Using data from three consumer surveys in Vietnam, South Korea and Poland, this study employs polynomial regression with response surface analysis as a methodological approach to assess the complementary, balanced and imbalanced effects of cultural dimensions (COL and LTO) on consumers' green purchase intention (GPI) and behaviors.

First, this study found that, in Vietnam, both COL and LTO had a significant positive effect on GPI. However, only COL demonstrated a significant effect on GPI in South Korea and Poland. Second, this study also revealed that, in all three countries, when COL and LTO were in agreement, the degree of GPI was higher when COL and LTO were higher. The findings also showed that an increase in the imbalance between COL and LTO integration in the Vietnam sample led to a decrease in consumers' GPI. However, this relationship was insignificant in the South Korea and Poland samples.

This study enriches the understanding of green purchase behavior (GPB) and its underlying cultural factors within a cross-cultural framework. In particular, it enhances the knowledge of the debated relationship between different facets of cultural values (specifically, COL and LTO) and pro-environmental behavior, shedding light on this complex relationship in the context of the three different countries.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

Peer-to-Peer Accommodation Service (P2PAS) has emerged as a novel paradigm that enables consumers to book temporary accommodation through P2PAS platforms (online transaction), and then reside in hosts' rooms (offline consumption). Due to potential variance in performance and conflict of interest between hosts and platforms, consumers may differ in their trust perceptions of the two parties, which in turn affects consumers' continuous usage of P2PAS. To this end, the authors endeavor to unravel the effect of consumers' trust incongruence on continuance intention, and to further elucidate the moderating influence of transaction and consumption risks on this relationship. This paper aims to discuss the aforementioned objectives.

This study collected data through an online survey of 408 P2PAS consumers. Polynomial modeling and response surface analysis were conducted to validate the hypothesized relationships.

Response surface analysis reveals that trust incongruence did not significantly affect consumers' continuance intention. However, continuance intention would be greater when TP was higher than TH compared with when TH was higher than TP. Furthermore, the analytical results suggest that trust incongruence exerts greater negative effect on continuance intention when transaction and consumption risks were high.

First, the study marks a paradigm shift in conceptualizing the incongruence between TP and TH as a determinant of consumers' continuance intention toward P2PAS. Second, the authors derive a typology of risks that is contextualized to P2PAS. Finally, the authors establish transaction and consumption risks as boundary conditions influencing the effects of trust incongruence on consumers' continuance intention toward P2PAS.

We examine the relationships between clients’ level of coaching readiness and trust in their executive coach and increases to both personal learning improved work performance. Distance relationships, the setting for this study, epitomize the norms of the New World of Work (NWoW), but also provide particular challenges for building trust and recognizing similarities between client and coach.

This study investigates distance coaching relationships in matched-pairs, longitudinal investigation of formal executive coaching.

Results support the proposed moderated mediation path. Findings reveal that both coaches’ perceptions of client readiness for coaching and client trust in coach each predict both client personal skill development and performance improvement.

While important toward gaining a better understanding of the relational functioning of distance coaching relationships, inclusion of only distance relationships may truncate the generalizability of our findings.

The study’s findings have practical implications for organizations that invest in executive coaching with regard to the importance of evaluating the candidates' readiness for coaching before the assignment, trust-building throughout distance coaching relationships and perceptions of similarity on client coaching outcomes.

Distance relationships, the setting for this study, provide particular challenges for building trust and recognizing similarities between client and coach and the current investigation points to the relevance of these relational mechanisms to client outcomes. In so doing, this study explores how perceptions of deep-level similarity between a coach and client may serve as moderators of these relationships.