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Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

Despite the extensive body of research on absolute corporate social responsibility (CSR) performance, limited attention has been given to the distinct concepts of optimal and aggressive CSR engagement, as well as their associations with CSR awarding. This study aims to differentiate between optimal and aggressive CSR engagement and examine their relationship with CSR awarding while considering the moderating influence of board characteristics from the perspectives of stakeholder and agency theories.

This empirical analysis draws on an international dataset comprising 43,803 observations from nine sectors across 41 countries. We employ a least squares dummy variable regression approach that accounts for country, industry and year effects to conduct the analysis.

The results reveal that engagement in aggressive CSR activities beyond the optimal level leads to the generation of a social reputation through CSR awarding. However, the influence of board characteristics on this relationship is significant. Specifically, the presence of a dedicated CSR committee encourages CSR awarding in the context of aggressive CSR engagement. Conversely, board independence constrains the relationship between aggressive CSR engagement and CSR awarding. Notably, board gender diversity does not have a discernible impact on this connection.

Our evidence provides valuable insights to help firms seeking to enhance their social reputation through CSR activities better allocate their resources and avoid unnecessary financial commitments.

This study advances the current understanding by exploring the relationship between aggressive CSR engagement and the recognition of CSR awards. Furthermore, it scrutinises the factors that dictate when such aggressive CSR engagement translates into enhanced social reputation, as evidenced by the attainment of CSR awards.

This study investigates CSR reporting’s role in debt access and cost of debt with the moderating role of external assurance and GRI adoption in emerging markets. Such an investigation will help facilitate external fund flow to firms in better terms.

We collected data from 16 emerging markets between 2008 and 2019 from the Thomson Reuters Eikon and ran fixed effects regression analysis and robustness tests by addressing endogeneity concerns, adopting alternative sample and integrating additional control variables.

The results show that CSR reporting has a positive association with access to debt and a negative association with the cost of debt. Furthermore, both external assurance and GRI adoption do not significantly moderate between CSR reporting and access to debt and cost of debt. Hence, creditors in emerging markets are not interested in CSR report assurance and GRI framework adoption and do not integrate them into their lending decisions.

Emerging markets are unique settings characterized by high growth rates, limited capital availability, high debt costs and weak institutional environments. Thus, reaching debt with convenient conditions is critical for emerging market firms to finance their growth. Hence, our study will help emerging market firms reach external funding more easily and in better terms via CSR transparency. Besides, our investigation is based on a broad sample of emerging markets, and hence updates prior emerging market studies conducted in single-country settings. Lastly, we test the complementarity of third-party assurance and GRI adoption to CSR reporting in loan contracting.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.