Purpose: The purpose of this paper is to investigate the effects of consumers’ perceptions towards volume discount (VD) benefits on their intention to purchase grocery products under this scheme using deal proneness as a moderator.Design/methodology/approach: Data were gathered through a survey of 583 Malaysian consumers, and were analysed using the partial least squares technique.Findings: The results showed that perceived savings, self-expression value, and convenience positively affected consumers’ intention to purchase grocery products under VDs. Deal proneness negatively moderated the relationship between perceived quality, innovation, and consumers’ intention to purchase under VDs.Practical implications: The findings of the study will help retailers to understand the effects of consumers’ perceptions of VD benefits on their intention to purchase products under VDs, thereby allowing retailers to promote products under VDs more effectively.Originality/value: Although the VD is a common type of sales promotion, this topic is rarely explored. This study thus contributes to the advancement of knowledge by determining the effects of the potential benefits of VDs on consumers’ intention to purchase.

The first field experiment with intelligent speed adaptation (ISA) in Malaysia was held in December 2010 in the State of Penang. Eleven private cars were instrumented with an advisory system. The system used in the present study included a vocal warning message and a visual text message that is activated when the driver attempts to exceed the speed limit. When the driver decreases the speed, the warning stops; otherwise it is continuously repeated. The test drivers drove the vehicles for three months with the installed system, and the speed was continuously logged in all vehicles. The warning was however only activated in the second month of the three month period. The present study aimed to evaluate the effects of an advisory ISA on driving speed, traffic safety, and drivers' attitude, behavior, and acceptance of the system. To examine these effects, both the survey and the logged speed data were analyzed and explored. The results show a significant reduction in the mean, maximum and 85th percentile speed due to the use of the system. However, there was no long-lasting effect on the speed when the system was deactivated. In the post-trial survey, drivers declared that the system helped them well in following the speed limits and that it assisted them in driving more comfortably. Furthermore, the warning method was more accepted compared to a supportive system, such as active accelerator pedal (AAP). After the trial, most drivers were willing to keep an ISA system

Since the last two decades, several modeling approaches have been developed in road safety literature to establish the relationship between traffic accidents and road characteristics. However, to the best of the authors’ knowledge, no extensive research work has been published on application of Adaptive Neuro-fuzzy Inference System (ANFIS) on road accident modelling. Therefore, the present paper aims to develop an ANFIS technique for modelling traffic accidents as a function of road and roadside characteristics. To achieve the objective, accident data and road characteristics were collected over a two-year period along the Qazvin-Loshan intercity roadway in Iran. The candidate set of explanatory variables included the Mean Horizontal Curvature (MHC), Shoulder Width (SW), Road Width (RW), Land Use (LU), Access Points (AP), Longitudinal Grade (LG), and Horizontal Curve Density (HCD). The results showed that RW, SW, LU, and AP significantly affected accident frequencies. Using statistical performance indices, the ANFIS model was compared with the Poisson, negative binomial, and non-linear exponential regression models. Based on the comparative results, the proposed model had higher prediction performance than the other three traditional models which has been widely used in the literature. To conclude, the proposed model could be used as a robust approach to handle uncertainty and complexity existed in accident data. In general, ANFIS model can be an effective tool for transportation agencies since intervention decisions and plans aiming at improving road safety depend on the prediction capabilities of a system.

This study focused on pedestrian crashes occurred in a 543-km sample of Malaysia federal roads in which roadways mostly pass through rural areas. To do this, four count models including Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models were developed to establish the relationship between vehicle-pedestrian crashes and the relevant road infrastructure and environmental features. Data on pedestrian crashes were collected for a four-year period from 2007 through 2010. The result indicates that ZIP model performed best, in terms of the comparative criteria

Globally, as many as 50 million road users are injured in road traffic crashes and about 1.35 million of them die. Between 2002 and 2030, global deaths resulting from injuries caused by road traffic accidents are predicted to increase by over 40%. Speeding has a relatively eminent relationship with accident involvement and severity. The experiences of pioneers have revealed that the intelligent speed adaptation (ISA) system reasonably has brought a promising future to speed management and road safety. The findings from the earlier studies disclose that the effect of the advisory ISA on drivers’ behavior, particularly their driving speed choice, is positive and that it is the most desirable system among the drivers. Nevertheless, the system does not have a long-lasting effect, and when the system is removed or deactivated, the effect gradually disappears. Speed limit compliance index (SLCI) is a conceptual method in the form of a novel performance-based indicator and a mathematical formulation that would be potentially a feasible solution to address the abovementioned defect, and it could be an effective countermeasure to improve the efficiency of such a system. This study mainly aims to discuss and illustrate the structure of this method.

Background: This study examines how Green Knowledge Integration Capability (GKIC) influences supplier performance within sustainable supply chains by balancing exploration (acquiring new knowledge) and exploitation (refining existing knowledge) strategies. Methods: Based on Social Exchange Theory, Relationship Motivation Theory, and Absorptive Capacity Theory, a conceptual model was developed and tested using cross-sectional survey data collected from 398 managers representing 240 multinational corporations (MNCs) operating in Pakistan. Structural Equation Modelling (SEM) was employed to analyse the relationships among exploration and exploitation focus, Green Management Innovation (GMI), GKIC, Green Absorptive Capacity (GAC), and supplier performance. Results: The findings indicate that exploration and exploitation strategies significantly enhance supplier performance, with GKIC acting as a mediating mechanism linking strategic focus and innovation to performance outcomes. Moreover, GAC strengthens the impact of the exploration and exploitation focus on performance but exhibits limited moderating effects for GMI and GKIC pathways. Conclusions: The results highlight GKIC’s critical role in translating strategic and innovation initiatives into supplier performance gains. This study contributes to the sustainable supply chain literature and provides actionable insights for managers and policymakers to enhance sustainability outcomes through knowledge integration and absorptive capacity development.