Using CRM to Model Firm Performance in a Business-to-Business Market

Customer relationship management (CRM) is the widely accepted approach for gathering, examining, understanding and translating information related to customers into managerial action. CRM is investigated in the context of new product performance (NPP). CRM enhances NPP as well as firm performance. This study investigates the impact of CRM on NPP through the moderation of top management support and an innovative culture, as well as the impact of CRM on firm performance through the mediation of NPP. A questionnaire survey is used for data collection from marketing managers of 159 firms in Pakistan in the B-to-B market. Hypotheses were tested using SEM in SMART PLS. This research shows that CRM directly affects firm performance, while NPP partially mediates the relationship of CRM and firm performance. These findings have significant implications for the practitioner. This study delivers insights to managers and academicians about the role of CRM in enhancing NPP and improving firm performance. In general, the study provides new insights into CRM by integrating top management support and an innovative culture. The research extends our understanding that top management support and innovative culture do not moderate the relationship of CRM with new product performance in a B-to-B context.     

Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability. For this purpose, M5′ model trees method (M5′) is introduced and applied to classify the crash data and then calibrate a model for each homogenous class. The data for 1134 and 345 randomly selected links on urban arterials in the city of Charlotte, North Carolina was used to develop and validate models, respectively. The outputs from the hybrid approach are compared with the outputs from cluster-based negative binomial regression (NBR) and general NBR models. Findings indicate that M5' has high predictability and is very reliable to interpret the role of different attributes on crash frequency compared to other developed models.