Optimization of dynamic product offerings on online marketplaces: A network theory perspective

The fierce competition amongst brands on online marketplaces makes the optimization of offerings within this context a significant challenge. To address this challenge, we draw upon network theory and model the degree of competition through consumers’ consideration sets. We use a large empiricaldataset from one of the biggest online marketplaces to explore the dynamic relationship between network position and the degree of competition, and we depict the redistribution of market share of related offerings after adjusting their array. In doing so, we provide a theoretical reference on when and how brands should optimize their product offerings on online marketplaces. We further demonstrate that intra-brand cannibalization relations have a significantly greater impact on the degree of competition compared to inter-brand ones, while intra-brand cannibalization relations represent the main reason for fluctuations in the degree of competition. Hence, contrary to existing theoreticalinsights and practical intuitions, our findings demonstrate that brands should minimize the number and heterogeneity of their offerings within a market segment to increase their sales on online marketplaces

An Optimal Method For Product Selection By Using Online Ratings And Considering Search Costs

With the collecting and publishing data about consumers purchasing and browsing products at the platform of online, this data prodives new ways to better understand the consumers search behavior before purchase. How to base on consumers online search behavior and simutaneously consider offline experience costs is worth studying. An optimal method based on the utility of the attribute of product is proposed. The proposed method follows steps below. Firstly, based on the multi-attribute utility theory, the overall utility of product is calculated by using ratings data. Secondly, the overall utility is combined into the original sequential search model to find the optimal selection strategy. Thirdly, the candidate product sets arranged in descending order of the reservation utilities are finally obtained. Finally, taking the online ratings data provided by a comprehensive automobile website as an example, lastly the proposed method is simulated and compared with other method. The result shows that the proposed method is feasible and effective

Researching Dynamic Brand Competitiveness Based on Consumer Clicking Behavior

Analyzing brand dynamic competition relationship by using consumer sequential online click data, which was collected from JD.com. It is found that the competition intensity of the products across categories is quite different. Owing to the purchasing time of durable-like goods is more flexible, that is, the purchasing probability of such products changes more obviously over time. Therefore, we use the Local Polynomial Regression Model to analyze the relationship between the brand competition of durable-like goods and the purchasing probability of the specific brand. Finding that when brands increase at a half of the total market share for consumers cognition preference, the brands’ competitiveness is peak and makes no significant different from one hundred percent for consumer to complete a transaction. The findings contribute to brand competitiveness for setting up marketing strategy from the dynamic and online consumer behavior’s perspective

Modeling Consumers’ Sequential Browsing Behavior Considering the Path Dependence

Due to the relatively low cost in searching and switching, the competition among brands is extremely fierce in online platforms. To accurately analyze brands’ competitiveness in online platforms, this research examines the heterogeneous consumer groups’ browsing path dependence among related brands, deploying the clickstream data of 2,563 consumers with 18,212 browsing records from one of the largest online platforms in China. We use the duration analysis method to scrutinize how path dependence can better characterize different consumer groups’ browsing behavior in different product categories. Our analyses demonstrate the high accuracy of using the consumers’ browsing path dependence to explain the pattern of consumer behavior, as well as identify the spell of the behavior of heterogeneous consumer groups. These results provide nuanced implications to strategically manage the branding, marketing, and customer management in online platforms

Dynamic competition identification through consumers’ clickstream data

Brands that use online marketplaces face challenges on identifying the market structure and analyzing their competitiveness. We address that lacuna by modeling online consumers’ behavior using clickstream data and considering the interdependence of brands using network analysis. We draw on a dataset of 6,549,484 records over a period of 10 weeks from one of the biggest online marketplaces in China and employ spatial auto-regressive models and network structural properties of brands to predict sales. Our findings indicate that intra-brand competition is more intense than inter-brand one and is the main reason for the fluctuations of sales. Concurrently, we demonstrate the redistribution of market shares of related products after the firm adjusts the length of the production line, so as to provide a reference for how to adjust the length intra-brand. By exploring the relationship between the structural position in the network and brand sales, we show that the span of structural holes of a brand negatively influences sales, while betweenness and degree centrality has a positive impact on sales. Our study contributes to the better understanding of brand competition on online marketplaces and presents both theoretical and practical implications. We discuss the significance of our findings for brand competition on online marketplaces and platforms, while we draw an agenda for future research on the topic

Intravoxel Incoherent Motion MR Imaging for Staging of Hepatic Fibrosis.

To determine the potential of intravoxel incoherent motion (IVIM) MR imaging for staging of hepatic fibrosis (HF).We searched PubMed and EMBASE from their inception to 31 July 2015 to select studies reporting IVIM MR imaging and HF staging. We defined F1-2 as non-advanced HF, F3-4 as advanced HF, F0 as normal liver, F1 as very early HF, and F2-4 as significant HF. Then we compared stage F0 with F1, F0-1 with F2-3, and F1-2 with F3-4 using IVIM-derived parameters (pseudo-diffusion coefficient D*, perfusion fraction f, and pure molecular diffusion parameter D). The effect estimate was expressed as a pooled weighted mean difference (WMD) with 95% confidence interval (CI), using the fixed-effects model.Overall, we included six papers (406 patients) in this study. Significant differences in D* were observed between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 2.46, 95% CI 0.83-4.09, P = 0.006; WMD 13.10, 95% CI 9.53-16.67, P < 0.001; WMD 14.34, 95% CI 10.26-18.42, P < 0.001, respectively). Significant differences in f were also found between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 1.62, 95% CI 0.06-3.18, P = 0.027; WMD 5.63, 95% CI 2.74-8.52, P < 0.001; WMD 3.30, 95% CI 2.10-4.50, P < 0.001, respectively). However, D showed no differences between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 0.05, 95% CI -0.01─0.11, P = 0.105; WMD 0.04, 95% CI -0.01─0.10, P = 0.230; WMD 0.02, 95% CI -0.02─0.06, P = 0.378, respectively).IVIM MR imaging provides an effective method of staging HF and can distinguish early HF from normal liver, significant HF from normal liver or very early HF, and advanced HF from non-advanced HF

Comparing stage F0 with F1, F0-1 with F2-3, and F1-2 with F3-4 using <i>f</i>.

<p>We used influence analysis to drop a study exerted excessive influence on the overall estimate and therefore to decrease the heterogeneity. Abbreviations: WMD = weighted mean difference; CI = confidence interval.</p

The baseline characteristics of included studies and patients.

<p>The baseline characteristics of included studies and patients.</p

Comparing stage F0 with F1, F0-1 with F2-3, and F1-2 with F3-4 using D*.

<p>We used influence analysis to drop a study exerted excessive influence on the overall estimate and therefore to decrease the heterogeneity. Abbreviations: WMD = weighted mean difference; CI = confidence interval.</p