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In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function.

Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function.

In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy.

The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

The purpose of this paper is to focus specifically on the examination of factors influencing the effectiveness of product placement in social media.

Two field experiments were used to test research models and questions. In each experiment, random sampling was used to assign volunteers into groups, controlled by different experimental settings. Questionnaires were distributed to the volunteers in order to collect their attitude toward advertisement, brand impression, and intention to click the advertisement. Their browsing behavior was measured by click through rate, browse depth, and browsing time.

The paper found that the effects of product placement conducts (product prominence and presentation) in social media are similar to the effects of product placement in other media. Also, a match between the vehicle and product would create deeper browsing depth and longer browsing time on the product web site. Product placement on a higher awareness vehicle would be more effective for marketing.

This study makes its contribution in understanding the effectiveness of product placement in social media on delivering a marketing message. The results further highlight the importance of “integrating” the product placement content with its context and its vehicle.

India’s retail sector is going through a significant transitional period with the internet as a new distribution channel becoming more common. Given the barriers to adoption, the purpose of this paper is to understand the role played by trusting beliefs when engaging with an online retailer. In the Indian market, online vending is expected to increase at a rate of 35 per cent per annum, and by understanding trusting beliefs retailers will be able to develop their market share by developing appropriate and/or innovative strategies.

The work is based on a sample of more than 200 internet customers in India. In understanding the data and the relationships that emerged from the modelling, the authors used a range of tools to analyse the data, including CFA and structural equation modelling. The authors also used descriptive statistics to provide a holistic overview of response profiles.

The study reveals that trusting beliefs are negatively influenced by an uncertainty avoidance culture and positively influenced by a firm’s image and a customer’s price awareness. Moreover, purchase intentions (PIs) are significantly enhanced by trusting beliefs in an online environment. Hence, it leads us to conclude that PIs can be augmented by facilitating and ensuring good quality service by placing special emphasis on timeliness, accuracy and conditions of the order, security and privacy paradigms, aftersales services, etc.

As can be seen, the internet is an emergent tool for retailers in India. By understanding trusting beliefs, retailers will be able to better understand customers’ behaviour and thus design management strategies accordingly. Although this is likely to take more than a decade, as internet retailing becomes embedded it may have a detrimental effect on the historical channel to market, thus altering the country’s retail landscape which is currently dominated by small retailers.

The work’s findings are insightful for those seeking to maximize the opportunities presented by the internet as a channel to market. The works shows how the channel is influenced and thus how it can be managed. In making the contribution the authors provide guidance in terms of operational activity to engage with potential customers.

This paper examines trusting beliefs when using the internet as a channel to market and in doing so it makes a new contribution because it establishes links with culture and other factors. For the research venue the authors use a developing market and therefore the findings are applicable to markets with similar characteristics.