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Volume 17
Issue 3
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J. Theor. Appl. Electron. Commer. Res., Volume 17, Issue 3 (September 2022) – 18 articles

Cover Story (view full-size image): Three-dimensional models are indispensable in various industries today, ranging from architecture to manufacturing and the future creation of a metaverse. However, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. Therefore, this study identifies the relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models based on a machine learning approach to derive a virtual 3D asset price prediction tool. The findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. Thereby, the results partly contradict the pricing recommendations of virtual 3D asset marketplace providers. View this paper
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24 pages, 4256 KiB  
Article
Service Decisions in a Two-Echelon Retailing System with Customer Returns
by Mohannad Radhi
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1219-1242; https://doi.org/10.3390/jtaer17030062 - 15 Sep 2022
Cited by 3 | Viewed by 2410
Abstract
Many manufacturers and retailers have already opened online stores to sell their products. Thus, manufacturers are competing as sellers, and retailers are transforming into dual-channel retailers (DCRs). Such an expansion in business scope and the wide spread of lenient return policies trigger tremendous [...] Read more.
Many manufacturers and retailers have already opened online stores to sell their products. Thus, manufacturers are competing as sellers, and retailers are transforming into dual-channel retailers (DCRs). Such an expansion in business scope and the wide spread of lenient return policies trigger tremendous return volume that requires great deal of logistical efforts. The service levels offered within online stores greatly affect channels’ demand. However, they also influence the channel choice of return for online customers, if applicable, when their purchases are unsatisfactory. Therefore, this paper studies the optimal service level for a centralized DCR. In addition, it examines the optimal levels for a decentralized two-echelon system through the implementation of Nash and Stackelberg theoretical frameworks. Important properties of optimal solutions and vital managerial insights have been devised through analytical and sensitivity analysis. It is found that a DCR may have the following tradeoff: decrease service level to increase the reward from the physical store or increase service level to enhance competitiveness of the online store. The optimal decision depends greatly on how sensitive the customers’ return behavior is to service level. In addition, as the exogenous price increases, the change in the retailer’s offered level depends significantly on the different rates of return. Full article
(This article belongs to the Collection Utilizing Models for e-Business Decision-Making: From Data to Wisdom)
►▼ Show Figures

Figure 1

Figure 1
<p>Forward and backward flow of products in a two-echelon retailing system.</p> Full article ">Figure 2
<p>Effect of online store’s rate of return on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>600</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Effect of physical stores’ rate of return on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>600</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Effect of selling price on: (<b>a</b>) manufacturer’s profit; (<b>b</b>) retailer’s profit. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is low. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>900</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5 Cont.
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is low. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>900</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is high. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>900</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6 Cont.
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is high. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>900</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mi>v</mi> <mi>a</mi> <mi>r</mi> <mi>i</mi> <mi>a</mi> <mi>b</mi> <mi>l</mi> <mi>e</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is high. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo> </mo> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mi>variable</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>600</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 7 Cont.
<p>Effect of physical store’s salvage price on: (<b>a</b>) manufacturer’s decision; (<b>b</b>) retailer’s decision; (<b>c</b>) manufacturer’s profit; (<b>d</b>) retailer’s profit, when <math display="inline"><semantics> <mi>ε</mi> </semantics></math> value is high. The following market condition is used: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo> </mo> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>m</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>o</mi> </msub> <mo>=</mo> <msub> <mi>α</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mi>variable</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>600</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>420</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∅</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">
15 pages, 1137 KiB  
Article
Do Online Firms Individualize Search Results? An Empirical Analysis of Individualization on Amazon
by Rasha Ahmed, Sahar Al Seesi and Gerardo Ruiz Sánchez
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1204-1218; https://doi.org/10.3390/jtaer17030061 - 31 Aug 2022
Cited by 2 | Viewed by 2345
Abstract
Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household [...] Read more.
Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior. Full article
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<p>Frequency distribution of search results by ASIN.</p> Full article ">Figure 2
<p>Number of coupons offered by ASIN.</p> Full article ">Figure 3
<p>Example gross and net prices for a 6-pack of Colgate Cavity Protection Toothpaste with Fluoride (ASIN B01BNEWDFQ). Keepa (<a href="https://keepa.com/" target="_blank">https://keepa.com/</a>) was used to track gross prices. Keepa indicates that gross prices may vary across time but not across participants, which was confirmed by our data.</p> Full article ">Figure 4
<p>Comparison between the distribution of the overlap coefficient for searches undertaken on the same day and searches undertaken on different days, for each of the three products. Each bar shows the percentages of participant pairs in the specified category (same day/different days) having an overlap coefficient of 0, 0.33, 0.66, and 1. Same-day pairs, <span class="html-italic">n</span> = 5749. Different-day pairs, <span class="html-italic">n</span> = 46,901.</p> Full article ">
19 pages, 1105 KiB  
Article
Execution of Omni-Channel Retailing Based on a Practical Order Fulfillment Policy
by Ke Wang, Yitian Li and Yulin Zhou
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1185-1203; https://doi.org/10.3390/jtaer17030060 - 30 Aug 2022
Cited by 4 | Viewed by 2983
Abstract
With the rapid development of the retail industry and its transition to omni-channel, a critical challenge that how to fulfill customer orders by choosing the proper channels arises for the retailers. In omni-channel retailing, customers can make a purchase online or offline, and [...] Read more.
With the rapid development of the retail industry and its transition to omni-channel, a critical challenge that how to fulfill customer orders by choosing the proper channels arises for the retailers. In omni-channel retailing, customers can make a purchase online or offline, and the online customers are offered the options of home delivery or collection at a specified store, delivering immediately or during an appointed time window, and accepting split delivery or not. For the effective execution of omni-channel retailing in such a circumstance, this paper proposes an intuitive order fulfillment policy, aiming to gain lower service cost and higher customer satisfaction, as a reference for the retailers’ operation management. Via experimental analyses under various service costs and demand forecasts, their influences on channel selection and the policy performance are illustrated. Furthermore, the comparison of the performances of the omni-channel with independent channels quantitatively reveals one crucial reason for the surge of omni-channel. Full article
(This article belongs to the Special Issue Multi-Channel Retail and Its Applications in the Future of E-Commerce)
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<p>Options of order fulfilment.</p> Full article ">Figure 2
<p>Measurement of satisfaction for online customers.</p> Full article ">Figure 3
<p>Process of selecting channels.</p> Full article ">Figure 4
<p>Setting of experimental scenarios.</p> Full article ">Figure 5
<p>Channel selection under different store service cost.</p> Full article ">Figure 6
<p>Impact of store cost on the average cost of order fulfilment.</p> Full article ">Figure 7
<p>Results of the cost comparison.</p> Full article ">Figure 8
<p>Results of the customer satisfaction.</p> Full article ">Figure 9
<p>Historical demand with a rising trend.</p> Full article ">Figure 10
<p>Cost comparison in the scenario with increasing demand.</p> Full article ">Figure 11
<p>Customer satisfaction comparison in the scenario with increasing demand.</p> Full article ">
23 pages, 3142 KiB  
Review
Discovering Themes and Trends in Digital Transformation and Innovation Research
by Pengbin Gao, Weiwei Wu and Ying Yang
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1162-1184; https://doi.org/10.3390/jtaer17030059 - 24 Aug 2022
Cited by 15 | Viewed by 8686
Abstract
In recent years, the relationship between digital transformation and innovation became very popular topics, attracting extensive attention, and inspiring a number of documents. Although much literature discusses the intersection of both fields, most works offer neither a complete nor a truly objective overview [...] Read more.
In recent years, the relationship between digital transformation and innovation became very popular topics, attracting extensive attention, and inspiring a number of documents. Although much literature discusses the intersection of both fields, most works offer neither a complete nor a truly objective overview of the current state of research. Therefore, there is a need for a comprehensive and objective review of research themes to analyze the intersection. For this purpose, based on the literature collected from the Web of Science (WoS) database published between 1994 and 2021, co-word analysis was carried out to explore research themes and identify the most salient themes in digital transformation and innovation research. The results of scientific output show that digital transformation and innovation is attracting increasing academic interest of scholars from many countries and different fields. The distribution of high-frequency keywords shows that the research in this field is multidisciplinary, including not only many economic and management fields, but also many classical theories and research methods. The clustering results of keywords reveal five clusters of themes: diffusion and adoption of technology and innovation, digital innovation management, digital transformation management, digital platform and ecosystem, and digital entrepreneurship and economy. According to the results of strategic diagram and performance analysis, digital innovation management and digital transformation management are the mainstream of research, while digital platform and ecosystem and digital entrepreneurship and economy have strong development potential. This study provides a snapshot of the thematic development of digital transformation and innovation research, enabling researchers to better master the current situation and suggesting the development trend in the future. Full article
(This article belongs to the Section Entrepreneurship, Innovation, FinTech Accounting and Industry 4.0)
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<p>Process of data collection.</p> Full article ">Figure 2
<p>Frequency distribution of papers and journals analyzed by year of publication.</p> Full article ">Figure 3
<p>Clusters of keywords identified in the digital transformation and innovation research.</p> Full article ">Figure 4
<p>Strategic diagram based on number of papers published.</p> Full article ">Figure 5
<p>Strategic diagram based on number of times the papers was cited.</p> Full article ">Figure 6
<p>Analysis of streams of research on the most prominent keywords.</p> Full article ">Figure 7
<p>Analysis of streams of research on the most prominent clusters.</p> Full article ">Figure 8
<p>Longitudinal analysis of the most significant keywords.</p> Full article ">Figure 9
<p>Longitudinal analysis of the most significant clusters.</p> Full article ">
19 pages, 2447 KiB  
Article
Blockchain Technology as an Ecosystem: Trends and Perspectives in Accounting and Management
by Thomas Kitsantas and Evangelos Chytis
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1143-1161; https://doi.org/10.3390/jtaer17030058 - 19 Aug 2022
Cited by 21 | Viewed by 6506
Abstract
A plethora of studies have examined the emerging technology of blockchain and its applications in accounting, management, and enterprise resource planning systems (ERPs). Blockchain technology (BT) can change the architecture of today’s ERPs and overcome the limitations of these centralized systems. The aim [...] Read more.
A plethora of studies have examined the emerging technology of blockchain and its applications in accounting, management, and enterprise resource planning systems (ERPs). Blockchain technology (BT) can change the architecture of today’s ERPs and overcome the limitations of these centralized systems. The aim of this study is twofold. First, this paper defines and analyzes the deployment of an innovative architecture of a Blockchain as an Ecosystem (BaaE) platform proposing a conceptual model of the Triple Entry Accounting (TEA) transforming the current accounting practices. Second, the paper explores the integration of cost management, supply chain, and inventory management on BT providing the significant challenges and benefits and suggesting an agenda for future research. The authors conduct an exploratory qualitative analysis of an extensive body of literature, from 81 journals. The paper’s innovative contribution and primary objective is to explore, address, and employ this emerging BaaE platform technology that could potentially be integrated with TEA. Further, the study examines the theoretical, technical, and business aspects regarding TEA, since there is limited research evidence in this field. Additionally, the study tries to identify the implications of BaaE in the area of cost management, supply chain, and inventory management from an ecosystem perspective. This effort can assist organizations and practitioners in understanding and further examining this emerging technology. Full article
(This article belongs to the Special Issue Recent Research and Developments of Digitalization Transformation and innovation in Businesses)
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<p>The research methodology of the framework.</p> Full article ">Figure 2
<p>The architecture of cloud computing and the BaaE platform.</p> Full article ">Figure 3
<p>The integration of BT vertically and horizontally in the value chain.</p> Full article ">Figure 4
<p>The conceptual model of cloud computing and BaaE platform integrating TEA.</p> Full article ">Figure 5
<p>The Decentralized Cost Management within the BaaE. Source.</p> Full article ">Figure 6
<p>The Supply Chain and Inventory Management within cloud computing and BT.</p> Full article ">
19 pages, 2133 KiB  
Article
Schema Incongruity: A Framework for Distributing Service Forms of FMCG Brands via a Digital Channel
by Mathew Parackal, Damien Mather and Rory Redman
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1124-1142; https://doi.org/10.3390/jtaer17030057 - 9 Aug 2022
Viewed by 2823
Abstract
This paper reports an extension of schema incongruity theory to a service form of fast-moving consumer goods (FMCG) distributed via a digital platform. According to this theory, an FMCG brand’s service form would be incongruent compared to its traditional form available via supermarkets. [...] Read more.
This paper reports an extension of schema incongruity theory to a service form of fast-moving consumer goods (FMCG) distributed via a digital platform. According to this theory, an FMCG brand’s service form would be incongruent compared to its traditional form available via supermarkets. Based on the relevancy and expectancy dimensions, the level of incongruence for such a service was classed as moderately incongruent. The study used qualitative research to investigate whether the moderate incongruence appealed to modern-day customers. The findings revealed that a subscription to receive a regular supply of the FMCG test brand appealed to the respondents. The moderate incongruity employed in this study was of an optimal stimulation level, enabling respondents to see the added values offered. The values recognised included convenience, family bonding, health and variety. The study observed schema incongruity theory operating for the service form of the FMCG brand. As the study used a qualitative methodology, the findings are specific to the brand and context tested. However, the high interest observed suggests schema incongruity theory could serve as a framework for using a digital distribution system to market service forms of FMCG brands. Full article
(This article belongs to the Special Issue Supply Chain Digitalization)
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<p>Posts with questions about the three shopping channels.</p> Full article ">
17 pages, 4712 KiB  
Article
A Loyalty System Incorporated with Blockchain and Call Auction
by Shu-Fen Tu, Ching-Sheng Hsu and Yan-Ting Wu
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1107-1123; https://doi.org/10.3390/jtaer17030056 - 4 Aug 2022
Cited by 9 | Viewed by 2981
Abstract
A loyalty program is a type of incentive to reward customers’ perceived value and enhance their purchasing behavior. The key to the success of a loyalty program is to allow customers to more actively participate in the program. One possible solution is to [...] Read more.
A loyalty program is a type of incentive to reward customers’ perceived value and enhance their purchasing behavior. The key to the success of a loyalty program is to allow customers to more actively participate in the program. One possible solution is to allow customers to sell out idle loyalty points and buy in the points that they need. On the basis of a call auction, this study designs a peer-to-peer exchange mechanism for customers to realize the above trade. In addition, a blockchain-based system is developed to support the issuance, redemption, and exchange of loyalty points. In this study, Hyperledger Fabric is adopted as the underlying blockchain technology because it has some features that are beneficial to a cross-organizational coalition loyalty program. This study also proposes a feasible multi-host deployment scheme for the Hyperledger Fabric blockchain network that is suitable for our application scenario. Finally, some implementation results are given to demonstrate the system process from the perspective of the application layer. The mechanism proposed in this study is helpful to improve the likelihood of successfully exchanging points, thus accelerating the circulation and use of loyalty points. Full article
(This article belongs to the Special Issue Blockchain Commerce Ecosystem)
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<p>The whole process of the proposed system.</p> Full article ">Figure 2
<p>Interactions between users and the blockchain network.</p> Full article ">Figure 3
<p>Multi-host deployment of Hyperledger Fabric network.</p> Full article ">Figure 4
<p>User interface of registration.</p> Full article ">Figure 5
<p>User interface of issuance.</p> Full article ">Figure 6
<p>User interface of redemption.</p> Full article ">Figure 6 Cont.
<p>User interface of redemption.</p> Full article ">Figure 7
<p>User interface of exchange.</p> Full article ">Figure 8
<p>User interface of list.</p> Full article ">Figure 9
<p>Test results of query operations. (<b>a</b>) 10 queries (max: 153; min: 80; mean: 94); (<b>b</b>) 30 queries (max: 280; min: 232; mean: 253); (<b>c</b>) 50 queries (max: 424; min: 364; mean: 393).</p> Full article ">Figure 10
<p>Test results of invoke operations. (<b>a</b>) 10 invokes (max: 2150; min: 1950; mean: 1998.1); (<b>b</b>) 30 invokes (max: 6090; min: 5804; mean: 5911.1); (<b>c</b>) 50 invokes (max: 9957; min: 9650; mean: 9773.5).</p> Full article ">
32 pages, 6955 KiB  
Article
How Do Consumer Fairness Concerns Affect an E-Commerce Platform’s Choice of Selling Scheme?
by Lin Chen, Guofang Nan, Qiurui Liu, Jin Peng and Junren Ming
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1075-1106; https://doi.org/10.3390/jtaer17030055 - 26 Jul 2022
Cited by 18 | Viewed by 3677
Abstract
Considering consumer fairness concerns, this paper investigates an e-commerce platform’s selling scheme choice when it adopts a wholesale selling scheme or an agency selling scheme to create a contract with a manufacturer. We find that the intensity of the fairness concerns and the [...] Read more.
Considering consumer fairness concerns, this paper investigates an e-commerce platform’s selling scheme choice when it adopts a wholesale selling scheme or an agency selling scheme to create a contract with a manufacturer. We find that the intensity of the fairness concerns and the platform fee are key factors affecting the platform’s optimal selling scheme choice. Specifically, when these two factors are relatively high or low, the wholesale selling scheme outperforms the agency selling scheme in terms of the e-commerce platform’s profit. Otherwise, the e-commerce platform should adopt the agency selling scheme. Moreover, when these two factors are sufficiently large or small, the wholesale selling scheme will yield a win-win result for the players of the e-commerce supply chain. Interestingly, we find that, considering fairness-minded consumers, a larger platform fee may be harmful to the platform. We also extend the baseline model to consider the consumer heterogeneity of fairness concerns, proportional platform fee, fairness concern about the manufacturer’s profit, and endogenous platform fee. We find that the main insights remain qualitatively unchanged under these model extensions. Full article
(This article belongs to the Collection Advances in Supply Chain Management in the Era of Electronic Commerce)
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<p>The sequence of events under the wholesale selling scheme.</p> Full article ">Figure 2
<p>The sequence of events under the agency selling scheme.</p> Full article ">Figure 3
<p>Platform’s profit comparison.</p> Full article ">Figure 4
<p>Comparison of social welfare.</p> Full article ">Figure 4 Cont.
<p>Comparison of social welfare.</p> Full article ">Figure 5
<p>Platform’s profit comparison with respect to fairness concerns.</p> Full article ">Figure 6
<p>Platform’s profit comparison with respect to fairness concerns.</p> Full article ">
18 pages, 2420 KiB  
Article
Why Advertise on Short Video Platforms? Optimizing Online Advertising Using Advertisement Quality
by Weifeng Li, Minghui Jiang and Wentao Zhan
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1057-1074; https://doi.org/10.3390/jtaer17030054 - 26 Jul 2022
Cited by 11 | Viewed by 5201
Abstract
The emergence of short videos has provided a new way for advertisers to place online video advertisements. On short video platforms, the quality of advertisements is the main factor that attracts consumers. This study constructs a model based on advertisers’ advertising behavior and [...] Read more.
The emergence of short videos has provided a new way for advertisers to place online video advertisements. On short video platforms, the quality of advertisements is the main factor that attracts consumers. This study constructs a model based on advertisers’ advertising behavior and formulates their optimal advertising quality strategies. We then expand on the quality strategy by considering advertisers’ pricing factors. In addition, we compare advertisement placement on short video platforms and on general video platforms and analyze how advertisers should choose their advertisement placement strategies. Our research shows that advertisers should improve the quality of their advertisements to gain more profit when the platform is operating well, so that users become more willing to buy and products become more profitable. In addition, advertisers should present their advertisements in the shortest possible duration and show them after a longer program. Users’ price sensitivity negatively affects advertisers’ optimal advertising quality strategies and profits. Furthermore, advertisers’ choice of platform mainly depends on the advertising nuisance cost to users and the platform’s cost of entry. Finally, we find an optimal budget allocation scheme for small companies joining short video platforms to invest in bidding and designing advertisements. Full article
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<p>Distribution of programs and advertisements on video platforms.</p> Full article ">Figure 2
<p>Advertisement design strategy of advertisers on video platforms. <span class="html-italic">Note:</span> <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>l</mi> <mo>=</mo> <mn>600</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>C</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>5000</mn> <mo>,</mo> <mo> </mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ρ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Advertisers’ profits under different numbers of potential users. <span class="html-italic">Note:</span> <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>l</mi> <mo>=</mo> <mn>600</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>C</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>5000</mn> <mo>,</mo> <mo> </mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ρ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>The effect of <math display="inline"><semantics> <mi>k</mi> </semantics></math> on <math display="inline"><semantics> <mrow> <msup> <mi>Π</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> <mo>,</mo> <msup> <mi>q</mi> <mo>*</mo> </msup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The effect of <math display="inline"><semantics> <mi>h</mi> </semantics></math> on <math display="inline"><semantics> <mrow> <msup> <mi>Π</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> <mo>,</mo> <msup> <mi>q</mi> <mo>*</mo> </msup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. <span class="html-italic">Note:</span> <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>l</mi> <mo>=</mo> <mn>600</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>C</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>5000</mn> <mo>,</mo> <mo> </mo> <mi>Λ</mi> <mo>=</mo> <mn>200</mn> <mo>,</mo> <mo> </mo> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Advertisers’ cost allocation decisions and benefits. <span class="html-italic">Note:</span> <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>100</mn> <mo>,</mo> <mo> </mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>l</mi> <mo>=</mo> <mn>600</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mo> </mo> <mi>C</mi> <mo>=</mo> <mn>15000</mn> <mo>,</mo> <mo> </mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ρ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">
22 pages, 1962 KiB  
Article
Decisions and Coordination of Green Supply Chain Considering Big Data Targeted Advertising
by Haiju Hu, Yakun Li and Mengdi Li
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1035-1056; https://doi.org/10.3390/jtaer17030053 - 22 Jul 2022
Cited by 13 | Viewed by 3127
Abstract
The application of big data targeted advertising in the green supply chain makes the green marketing of products more accurate and effective. This paper applies game theory to study the decisions and coordination issues of a green supply chain in which the online [...] Read more.
The application of big data targeted advertising in the green supply chain makes the green marketing of products more accurate and effective. This paper applies game theory to study the decisions and coordination issues of a green supply chain in which the online retailer conducts big data targeted advertising. A centralized model and two Stackelberg game models (an online-retailer-led decentralized model and a manufacturer-led decentralized model) were constructed and solved. The zero wholesale price-side-payment contract and greedy wholesale price-side-payment contract were introduced into the green supply chain for coordination. The study found that: (1) the increase in demand attenuation coefficient, green sensitivity coefficient, and big data targeted advertising sensitivity coefficient will be beneficial to the growth of total consumer demand, supply chain profit, and environmental benefit; (2) supply chain coordination is necessary because greenness, demand, supply chain profit, and environmental benefit under the centralized model are higher than those under two decentralized models; (3) two contracts can achieve the coordination of the green supply chain, and the profits of the manufacturer and online retailer under the contract are greater than those under the decentralized model. The results can provide insights for promoting green supply chain operations. Full article
(This article belongs to the Special Issue Supply Chain Digitalization)
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<p>The structure of the green supply chain.</p> Full article ">Figure 2
<p>Decisions, demand, profits, and environmental benefit change with <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. (<b>a</b>) Greenness and targeted advertising intensity change with <span class="html-italic">λ</span>. (<b>b</b>) Prices change with <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. (<b>c</b>) Demand and environmental benefit change with <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. (<b>d</b>) Profits change with <math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p> Full article ">Figure 3
<p>Decisions, demand, profits, and environmental benefit change with <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>a</b>) Greenness and targeted advertising intensity change with <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>b</b>) Prices change with <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>c</b>) Demand and environmental benefit change with <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>d</b>) Profits change with <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">Figure 4
<p>Decisions, demand, profits, and environmental benefit change with <math display="inline"><semantics> <mi>e</mi> </semantics></math>. (<b>a</b>) Greenness and targeted advertising intensity change with <math display="inline"><semantics> <mi>e</mi> </semantics></math>. (<b>b</b>) Prices change with <math display="inline"><semantics> <mi>e</mi> </semantics></math>. (<b>c</b>) Demand and environmental benefit change with <math display="inline"><semantics> <mi>e</mi> </semantics></math>. (<b>d</b>) Profits change with <math display="inline"><semantics> <mi>e</mi> </semantics></math>.</p> Full article ">
16 pages, 4026 KiB  
Article
It Reminds Me of My Happy Childhood: The Influence of a Brand Logo’s Holiday Atmosphere on Merchandise-Related Nostalgic Preference
by Tingyi Wang and Rong Chen
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1019-1034; https://doi.org/10.3390/jtaer17030052 - 22 Jul 2022
Cited by 3 | Viewed by 2901
Abstract
The holiday atmosphere abounds in marketing campaigns. The present research examines how the holiday atmosphere of a brand logo influences consumers’ merchandise-related nostalgic preference. Across three studies, the authors find that when consumers are exposed to brand logos with a strong (vs. weak) [...] Read more.
The holiday atmosphere abounds in marketing campaigns. The present research examines how the holiday atmosphere of a brand logo influences consumers’ merchandise-related nostalgic preference. Across three studies, the authors find that when consumers are exposed to brand logos with a strong (vs. weak) holiday atmosphere, they prefer nostalgic products (Study 1). This effect occurs because consumers exposed to a strong (vs. weak) holiday atmosphere generate more childhood imagery, resulting in significantly more choices of nostalgic products (Study 2). Additionally, the paper finds that the effect of the holiday atmosphere of a brand logo on merchandise-related nostalgic preference—here, the preference for products of nostalgic brands—does not occur for non-traditional holidays (Study 3). These findings make important contributions to the literature on holiday marketing, logo design and nostalgic marketing. Full article
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<p>Holiday-themed logos vs. regular logos of Google and Apple (China).</p> Full article ">Figure 2
<p>Study 1: the effects of logo’s holiday atmosphere on choices of nostalgic products.</p> Full article ">Figure 3
<p>Choice of nostalgic products vs. non-nostalgic products.</p> Full article ">Figure 4
<p>Childhood imagery as mediator.</p> Full article ">Figure 5
<p>Moderation effect of holiday type (traditional vs. non-traditional). (<b>A</b>) Food brands. (<b>B</b>) Hand cream brands.</p> Full article ">
16 pages, 597 KiB  
Article
Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine
by Sunčica Rogić, Ljiljana Kašćelan and Mirjana Pejić Bach
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1003-1018; https://doi.org/10.3390/jtaer17030051 - 21 Jul 2022
Cited by 14 | Viewed by 4291
Abstract
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number [...] Read more.
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign. Full article
(This article belongs to the Collection Utilizing Models for e-Business Decision-Making: From Data to Wisdom)
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<p>Predictive procedure illustration.</p> Full article ">Figure 2
<p>Sensitivity level before and after data pre-processing.</p> Full article ">
19 pages, 399 KiB  
Article
Online Grocery Shopping in Germany: Assessing the Impact of COVID-19
by Lisa M. Gruntkowski and Luis F. Martinez
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 984-1002; https://doi.org/10.3390/jtaer17030050 - 20 Jul 2022
Cited by 36 | Viewed by 8920
Abstract
Online grocery shopping in Germany has shown a strong growth in the past years and is expected to further develop in the future, especially through the influence of COVID-19. The main purpose of this study was to examine six theoretical customer-oriented factors and [...] Read more.
Online grocery shopping in Germany has shown a strong growth in the past years and is expected to further develop in the future, especially through the influence of COVID-19. The main purpose of this study was to examine six theoretical customer-oriented factors and their influence on consumer online grocery purchase intentions. Additionally, this study compares consumer perceptions before and since the COVID-19 outbreak. Since the health crisis is very recent, the research on its impact on online grocery purchasing behavior is limited. A total of 402 valid questionnaires were collected in Germany. The data were analyzed using the software SPSS IBM 28. The results indicate that perceived risk still has a negative influence on purchase intentions, thus remaining relevant in online grocery shopping. However, the consumers’ perceived risk is considered lower compared to the pre-COVID-19 scenario. Moreover, perceived usefulness, perceived ease of use, perceived trust, convenience, as well as situational factors were found to have a positive relationship with purchase intention, both before the COVID-19 crisis and since then. The COVID-19 pandemic shows a strong reduction in perceived risk, while the remaining characteristics increase at moderate levels. Online grocery businesses could use the insights of this study to reduce perceived risks as well as successfully communicate the benefits of online shopping to consumers. Full article
(This article belongs to the Special Issue Digital Resilience and Economic Intelligence in the Post-Pandemic Era)
35 pages, 6301 KiB  
Article
Behavioral Patterns beyond Posting Negative Reviews Online: An Empirical View
by Menghan Sun and Jichang Zhao
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 949-983; https://doi.org/10.3390/jtaer17030049 - 20 Jul 2022
Cited by 6 | Viewed by 3384
Abstract
Negative reviews on e-commerce platforms are posted to express complaints about unsatisfactory experiences. However, the exact knowledge of how online consumers post negative reviews still remains unknown. To obtain an in-depth understanding of how users post negative reviews on e-commerce platforms, a big-data-driven [...] Read more.
Negative reviews on e-commerce platforms are posted to express complaints about unsatisfactory experiences. However, the exact knowledge of how online consumers post negative reviews still remains unknown. To obtain an in-depth understanding of how users post negative reviews on e-commerce platforms, a big-data-driven approach with text mining and sentiment analysis is employed to detect various behavioral patterns. Specifically, using 1,450,000 negative reviews from JD.com, the largest B2C platform in China, the posting patterns from temporal, perceptional and emotional perspectives are comprehensively explored. A massive amount of consumers across four sectors in recent 10 years is split into five levels to reveal group discrepancies at a fine resolution. The circadian rhythms of negative reviewing after making purchases are found, suggesting stable habits in online consumption. Consumers from lower levels express more intensive negative feelings, especially on product pricing and customer service attitudes, while those from upper levels demonstrate a stronger momentum of negative emotion. The value of negative reviews from higher-level consumers is thus unexpectedly highlighted because of less emotionalization and less biased narration, while the longer-lasting characteristic of these consumers’ negative responses also stresses the need for more attention from sellers. Our results shed light on implementing distinguished proactive strategies in different buyer groups to help mitigate the negative impact due to negative reviews. Full article
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<p><b>Overview of dataset.</b> (<b>a</b>) The stacked histogram of different scores’ proportions for four sectors. (<b>b</b>) Log–log distribution of usefulCount for every review, taking the sector Computers as example. (<b>c</b>) The stacked histogram of identified buyers for five user levels, distinguishing four sectors. (<b>d</b>) The stacked histogram of different scores’ proportions for five user levels, distinguishing four sectors.</p> Full article ">Figure 2
<p>The flow chart of methodological and analysis steps.</p> Full article ">Figure 3
<p><b>Log–log distributions of intervals for RC and PP times, taking the sector Computers as an example.</b> The subplot (<b>a</b>) is for the all-user group. Subplots from (<b>b</b>–<b>f</b>) correspond to a certain user level, from copper to PLUS. For every subplot, the <span class="html-italic">x</span>-axis is the interval in hours on a log scale and the <span class="html-italic">y</span>-axis is the proportion on a log scale. Moreover, results from other sectors are similar and thus omitted due to limited space.</p> Full article ">Figure 4
<p><b>Heatmaps of hours for RC and PP of negative reviews, taking the sector Computers as an example.</b> The <span class="html-italic">x</span>-axis refers to the hour of PP, and the <span class="html-italic">y</span>-axis refers to that of RC. Every patch corresponds with an RC and PP pair, and the color in it refers to its frequency, i.e., the darker red means a greater frequency. The results of the four sectors are consistent.</p> Full article ">Figure 5
<p><b>Distributions of PP or RC times and their intervals derived from uniquely identified consumers on a 2 h scale.</b> (<b>a</b>) PP distributions for four different product categories on an hourly scale. (<b>b</b>) RC distributions for four different product categories on an hourly scale. (<b>c</b>) Distribution for identified consumers’ average intervals between PP and RC times for all four product categories on an hourly scale. <span class="html-italic">Here, we filter out identified consumers in every sector who have no less than three purchases in our sampling</span>.</p> Full article ">Figure 6
<p><b>Proportions of detailed aspects of negative reviews for the three main reasons.</b> In each plot, the <span class="html-italic">x</span>-axis represents different aspects, as illustrated in <a href="#jtaer-17-00049-t002" class="html-table">Table 2</a>, and the <span class="html-italic">y</span>-axis stands for different user levels. The horizontal sum of the patches in every subplot is 1.</p> Full article ">Figure 7
<p><b>Rough distribution of negative review length in characters and effective words for all five user levels.</b> (<b>a</b>,<b>b</b>) are no-fliers boxplots for negative review length, in characters and effective words, respectively. In each plot, the <span class="html-italic">x</span>-axis refers to different user levels rising from left to right and the <span class="html-italic">y</span>-axis refers to review length. Moreover, the growth of reviews with user levels is statistically significant in the one-way <span class="html-italic">t</span>-test.</p> Full article ">Figure 8
<p><b>Semantic network of ‘match’ for five user levels (<math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>).</b> The red node in each network refers to the word ‘match’, and the blue nodes are the words that are the top-four similar synonyms in each level. The sizes of the nodes refer to their degrees.</p> Full article ">Figure 9
<p><b>Similarity in expression habits across user levels.</b> The <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis represent the five user levels, and the numbers in the patches refer to the average similarity with the standard deviation in brackets behind it. Patches in the grid refer to the similarity between two user levels, with darker colors indicating greater similarity. The repeating frequency was set as 1000 and the size as 500.</p> Full article ">Figure 10
<p>(<b>a</b>) <b>Average proportions of positive and negative emotion words in negative reviews of 2017</b>. (<b>b</b>) <b>Distribution of user emotion from different user levels in the four sectors</b>. (<b>a</b>) The bars on the upper side of the <span class="html-italic">x</span>-axis represent the percentage of positive emotion words from five user levels, while those on the underside indicate that of negative ones. The absolute value of the <span class="html-italic">y</span>-axis means the percentage of emotional words. The error bar is calculated by the sample standard deviation. (<b>b</b>) A stacked histogram for emotion distribution of user levels. Bars of different colors represent the percentage of different degrees of emotion, which is divided into positive, neutral, negative and extremely negative according to <math display="inline"><semantics> <msub> <mi>i</mi> <mrow> <mi>p</mi> <mi>o</mi> <mi>l</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>. The <span class="html-italic">x</span>-axis refers to the five user levels, and the four bars on a certain user level are for the sectors of Gifts and Flowers, Clothing, Computers and Phone and Accessories.</p> Full article ">Figure 11
<p><b>Normalized continuous probability for every score pair with a threshold of an hour.</b> The <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis are the scores of the previous reviews and the postreviews, respectively. For every bubble, we normalized it by dividing the initial probability by the corresponding postscore’s proportion. The colored bubbles with a value larger than one are blue, and others are red. Therefore, the size, along with the color of the bubbles, represents the extent of the influence that comes from the emotional basis of previous reviews.</p> Full article ">Figure 12
<p><b>Proportions of emotion shifts in negative reviews of all users and user levels, taking the Computer sector as an example.</b> The <span class="html-italic">x</span>-axis shows three types of emotion shifts. The heights of different-colored bars refer to the percentage of a certain kind of emotion shift in all after-use comments with the initial score equal to 1. The results from other sectors are consistent.</p> Full article ">Figure A1
<p><b>Distribution of review-creation time and product-purchase time on an hourly scale. All data represent negative reviews.</b> All data represent negative reviews. The <span class="html-italic">x</span>-axis represents the hour, and the <span class="html-italic">y</span>-axis represents the proportion of consumers that purchase or post negative reviews at the corresponding hour.</p> Full article ">Figure A2
<p><b>Different performances in periodic intervals between weekdays and the weekend, taking the sector Computers as an example.</b> The meanings the of <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis are both corresponding with those in <a href="#jtaer-17-00049-f004" class="html-fig">Figure 4</a>. The identification standard of a weekday or weekend is according to PP time in subplots (<b>a</b>,<b>b</b>), while it is according to RC time in (<b>c</b>,<b>d</b>). The results of other sectors are the same.</p> Full article ">Figure A3
<p><b>Proportions of main reasons for negative reviews from different user levels. This is a stacked histogram for the proportion of the main reasons for negative reviews.</b> The <span class="html-italic">x</span>-axis corresponds to the five user levels, and the <span class="html-italic">y</span>-axis corresponds to proportions. Additionally, every four bars above every user level represent different categories: from left to right, Gifts and Flowers, Clothing, Computers and Phone and Accessories.</p> Full article ">Figure A4
<p><b>Proportions of main reasons for negative reviews on an hourly scale.</b> (<b>a</b>) is the stacked histogram for the sector Computers and (<b>b</b>) is for Clothing. The <span class="html-italic">x</span>-axis is hours, from 0 to 23, and the <span class="html-italic">y</span>-axis is the proportion of a certain reason at the corresponding hour.</p> Full article ">Figure A5
<p><b>Proportions of main reasons for negative reviews on an hourly scale for the other two sectors.</b> (<b>a</b>) is a stacked histogram for sector Phone and Accessories and (<b>b</b>) is for Gifts and Flowers. The <span class="html-italic">x</span>-axis is hours in a day, from 0 to 23, and the <span class="html-italic">y</span>-axis represents the proportion at the corresponding hour. Compared with the two sectors in <a href="#jtaer-17-00049-f0A4" class="html-fig">Figure A4</a>, it can be concluded that Phone and Accessories are products with strong personal features such as Clothing, while peaks in the sector Gifts and Flowers often appear at 10–12, 16 and 21–22 o’clock, which is often delivery time.</p> Full article ">Figure A6
<p><b>Parameter selection.</b> (<b>a</b>–<b>c</b>) are curves for the main reasons of logistics, customer service and marketing, respectively. The <span class="html-italic">x</span>-axis corresponds to the number of topics for model training, and the <span class="html-italic">y</span>-axis represents the value of multipliers. It is worth mentioning that this curve was not the only basis of parameter selection. The topic content and topic number were also considered.</p> Full article ">Figure A7
<p><b><math display="inline"><semantics> <msub> <mi>r</mi> <mi>c</mi> </msub> </semantics></math> curve for all five user levels.</b> The <span class="html-italic">x</span>-axis shows <span class="html-italic">N</span>, the number of the most similar words chosen to form the network, and the <span class="html-italic">y</span>-axis shows the value of <math display="inline"><semantics> <msub> <mi>r</mi> <mi>c</mi> </msub> </semantics></math>. Five curves with different colors represent the five user levels. For the selection of <span class="html-italic">N</span>, we aimed at the one with or near the largest second derivative on the curve, which refers to the most effective or representative part that is contained in the network. Therefore, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> was determined, with <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mn>6</mn> <mo>,</mo> <mn>7</mn> </mrow> </semantics></math> to prove the robustness.</p> Full article ">Figure A8
<p><b>Semantic similarity among the five user levels in the bootstrapping method (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">N</mi> <mo>=</mo> <mn mathvariant="bold">5</mn> <mo>,</mo> <mn mathvariant="bold">6</mn> <mo>,</mo> <mn mathvariant="bold">7</mn> </mrow> </semantics></math>).</b> The similarity is measured through the Kendall correlation coefficient of 500 randomly selected words that are repeated 1000 times. The <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis represent five user levels, and the numbers in the patches refer to the average with the standard deviation in brackets behind it. Patches in the grid refer to the similarity between two user levels, with darker colors indicating greater similarity. In addition, the outcomes here illustrate the reliability of the semantic similarity among user levels.</p> Full article ">Figure A9
<p><b>Average proportions for positive and negative emotion words in negative reviews of 2013–2016.</b> The bars on the upper side of the <span class="html-italic">x</span>-axis represent the percentages of positive emotion words, while those on the underside are the negative ones. The five groups, from left to right, refer to user levels, that is, copper, silver, golden, diamond and PLUS. The absolute value of the <span class="html-italic">y</span>-axis means the percentage of emotional words. Moreover, the error bar is calculated by the sample standard deviation. As the four bar plots show, the same tendency is performed in the negative degree and is regular in the positive degree in 2015–2017. Regarding the compared performances in consecutive years, it reflects that JD has emphasized the paid users’ experience more and provided more rights or guarantees for them in recent years.</p> Full article ">Figure A10
<p><b>Continuous probability for PP time and RC time in a day of identified buyers.</b> The <span class="html-italic">x</span>-axis represents four sectors in our dataset, and the <span class="html-italic">y</span>-axis is the continuous probability. Moreover, it can be seen that the continuous probability for RC time is often larger than that for PP time.</p> Full article ">Figure A11
<p><b>Proportion of emotion shifts in all reviews and all reviews with different scores, taking the Computer sector as an example.</b> The <span class="html-italic">x</span>-axis shows the three types of emotion shifts defined above. The heights of different-colored bars refer to the percentage of a certain kind of emotion shift in all after-use comments. Note that the results from other sectors are the same.</p> Full article ">Figure A12
<p><b>Average lags in an hour of five user levels’ emotion shifts in all negative reviews, taking the Computer sector as an example.</b> The <span class="html-italic">x</span>-axis shows five user levels’ emotion shifts. The heights of different-colored bars refer to the average lag of a certain kind of user levels’ emotion shift in all after-use comments with initial scores equal to 1. Note that the results from other sectors are the same. Note that the upward trend of lags for increasing emotion in user level is statistically significant in the one-way <span class="html-italic">t</span>-test, which proves negative momentum again.</p> Full article ">
25 pages, 2389 KiB  
Article
Towards Virtual 3D Asset Price Prediction Based on Machine Learning
by Jakob J. Korbel, Umar H. Siddiq and Rüdiger Zarnekow
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 924-948; https://doi.org/10.3390/jtaer17030048 - 7 Jul 2022
Cited by 10 | Viewed by 2951
Abstract
Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the [...] Read more.
Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators. Full article
(This article belongs to the Section e-Commerce Analytics)
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<p>Three-dimensional model characteristics: (<b>a</b>) mesh/body; (<b>b</b>) body with texture; (<b>c</b>) body with texture and material settings (rendered); (<b>d</b>) body with rigged geometry for animation.</p> Full article ">Figure 2
<p>Histograms of <span class="html-italic">model_price</span> value distributions: (<b>a</b>) <span class="html-italic">model_price</span> value distribution; (<b>b</b>) <span class="html-italic">model_price</span> value distribution after 0,99 quantile criterion; (<b>c</b>) <span class="html-italic">model_price</span> value distribution after logarithmic transformation and normalization.</p> Full article ">Figure 3
<p>Relationships between the target and categorical variables.</p> Full article ">Figure 4
<p>Hyperparameter tuning.</p> Full article ">Figure 5
<p>Feature importance scores (FISs) of the random forest regression model.</p> Full article ">Figure A1
<p>Virtual 3D asset price prediction tool.</p> Full article ">
15 pages, 1003 KiB  
Article
Evolution of the Online Grocery Shopping Experience during the COVID-19 Pandemic: Empiric Study from Portugal
by Sofia Gomes and João M. Lopes
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 909-923; https://doi.org/10.3390/jtaer17030047 - 6 Jul 2022
Cited by 32 | Viewed by 11364
Abstract
Online shopping has intensified in the last decade. The COVID-19 pandemic has imposed circulation limitations and more restrictive behaviors on consumers due to fears of contracting the virus, boosting online grocery shopping. This study aims to assess the relationship between the online food [...] Read more.
Online shopping has intensified in the last decade. The COVID-19 pandemic has imposed circulation limitations and more restrictive behaviors on consumers due to fears of contracting the virus, boosting online grocery shopping. This study aims to assess the relationship between the online food purchasing experience during the pandemic and the intention to purchase food online after the pandemic. The sample of this quantitative study is composed of 358 Portuguese consumers who carried out grocery shopping online during the pandemic and was collected through an online questionnaire. First, a cross-sectional description of the variables was applied to this sample and then an analytical cross-sectional survey was carried out using the partial least squares method. Due to health concerns, food and beverage consumption behaviors changed positively during the pandemic compared to before. Healthier consumer behavior towards food and beverages during the pandemic, compared to before the pandemic, may influence a greater propensity for online grocery shopping. Sociodemographic characteristics (age, education, income) were also determinants of the propensity to shop online during the pandemic. Specifically, the results of this study demonstrate a positive influence of young male consumers, with higher levels of education and income, regarding the online supermarket shopping experience. The results also demonstrate that a good online shopping experience during the pandemic can positively influence online shopping intentions after the pandemic. This study makes it possible to identify determinants of the online food shopping experience, serving as guidance and preparation for strategic marketing for retail grocery companies that wish to position themselves online. It also helps marketers and policymakers understand the potential influence of sociodemographic characteristics such as age, income, and education on building a relationship with consumers. Finally, the relationship between personal characteristics and the online grocery shopping experience requires further substantiation and this study contributes to this gap in the literature. Full article
(This article belongs to the Special Issue Digital Resilience and Economic Intelligence in the Post-Pandemic Era)
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<p>Structural model.</p> Full article ">Figure 2
<p>PLS model.</p> Full article ">
16 pages, 421 KiB  
Article
The Impact of Offline Service Effort Strategy on Sales Mode Selection in an E-Commerce Supply Chain with Showrooming Effect
by Xiangsheng Wang and Temuer Chaolu
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 893-908; https://doi.org/10.3390/jtaer17030046 - 24 Jun 2022
Cited by 10 | Viewed by 3441
Abstract
In practice, several e-commerce platforms offering online channels not only act as resellers but also serve as the marketplace. However, the existing literature rarely explores the impact of the offline service effort strategy with the showrooming effect on the platform’s optimal sales mode. [...] Read more.
In practice, several e-commerce platforms offering online channels not only act as resellers but also serve as the marketplace. However, the existing literature rarely explores the impact of the offline service effort strategy with the showrooming effect on the platform’s optimal sales mode. Considering a supply chain consisting of a manufacturer and a platform, we examine the interplay between the manufacturer’s offline service effort strategy and the platform’s online sales modes. We derive conditions under which each of the four scenarios (adopting the service effort strategy under the agency or reselling modes, not adopting the service effort strategy under the agency or reselling modes) emerges in equilibrium. Our results show that the service effort strategy with the showrooming effect can induce the platform’s sales mode selection. Specifically, when the referral fee is low and the showrooming effect is moderate, the platform may choose the agency mode instead of the reselling mode, while when the referral fee is sufficiently high and the showrooming effect is moderate, the platform may adopt the reselling mode instead of the agency mode. Furthermore, when the competition intensity and showrooming effect are sufficiently small, the service effort strategy will be beneficial to the manufacturer and the platform, creating a win-win situation. When the competition intensity or showrooming effect is sufficiently large, the service effort strategy may cause a prisoner’s dilemma for the manufacturer and the platform. In addition, the supply chain consisting of a manufacturer, an offline store and an online platform is also studied in the extension section, and we find that our main results are valid. Full article
(This article belongs to the Special Issue Blockchain Commerce Ecosystem)
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<p>The game sequence.</p> Full article ">Figure 2
<p>The comparisons of retail prices (<math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.1</mn> </mrow> </semantics></math>). (<b>a</b>) under the agency mode. (<b>b</b>) under the reselling mode.</p> Full article ">Figure 3
<p>The profit change of the platform (<math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.1</mn> </mrow> </semantics></math>). (<b>a</b>) under the agency mode. (<b>b</b>) under the reselling mode.</p> Full article ">Figure 4
<p>The profit change of the whole supply chain (<math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.1</mn> </mrow> </semantics></math>). (<b>a</b>) under the agency mode. (<b>b</b>) under the reselling mode.</p> Full article ">Figure 5
<p>The equilibrium outcomes (<math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.1</mn> </mrow> </semantics></math>). (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>.</p> Full article ">
18 pages, 2006 KiB  
Article
New Practice of E-Commerce Platform: Evidence from Two Trade-In Programs
by Qiang Hu, Tingyuan Lou, Jicai Li, Wenjin Zuo, Xihui Chen and Lindong Ma
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 875-892; https://doi.org/10.3390/jtaer17030045 - 21 Jun 2022
Cited by 5 | Viewed by 3042
Abstract
In the context of developing the digital platform economy, trade-in programs have become an effective strategy for e-commerce platforms to stimulate consumption. Many head e-commerce platforms have launched their own trade-in programs. However, the existing research on trade-in programs is still stuck in [...] Read more.
In the context of developing the digital platform economy, trade-in programs have become an effective strategy for e-commerce platforms to stimulate consumption. Many head e-commerce platforms have launched their own trade-in programs. However, the existing research on trade-in programs is still stuck in the traditional trade-in model. The purpose of this study is to explore whether there is a new and more beneficial trade-in program. In this paper, we construct the Stackelberg game model between a brand owner and a B2C e-commerce platform under two trade-in programs and use optimization theory to obtain the equilibrium results of the model. The results indicate that the performance improvement of the new-generation product will promote the increase in two-generation products’ price under traditional trade-in programs, the price of the new-generation product will increase, and the price of the previous-generation product will decrease under new trade-in programs. The brand owner always prefers traditional trade-in to new trade-in. However, the e-commerce platform prefers traditional trade-in to new trade-in just when the previous-generation product is durable enough and the performance improvement of a new-generation product is small enough; otherwise, it prefers new trade-in to traditional trade-in. These findings are beneficial to the operational practices of e-commerce platforms and brand owners. Full article
(This article belongs to the Collection Advances in Supply Chain Management in the Era of Electronic Commerce)
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<p>The model framework. (<b>a</b>) ON model; (<b>b</b>) VPN model.</p> Full article ">Figure 2
<p>Decision timeline.</p> Full article ">Figure 3
<p>Consumers’ purchasing decisions in ON model.</p> Full article ">Figure 4
<p>Consumers’ purchasing decision in VPN model.</p> Full article ">Figure 5
<p>Impacts of main parameters on the optimal pricing strategy set {<math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>1</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>2</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mi>m</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> }. (<b>a</b>) The impact of <span class="html-italic">s</span> on {<math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>1</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>2</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mi>m</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> }; (<b>b</b>) The impact of <span class="html-italic">α</span> on {<math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>1</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>2</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mi>m</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> }; (<b>c</b>) The impact of <span class="html-italic">τ</span> on {<math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>1</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>2</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mi>m</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> }; (<b>d</b>) The impact of <span class="html-italic">β</span> on {<math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>1</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mn>2</mn> <mi>r</mi> </mrow> <mrow> <mi>M</mi> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mi>m</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> }.</p> Full article ">Figure 6
<p>Impacts of improvement rate <span class="html-italic">r</span> on the pricing with two trade-in programs. (<b>a</b>) The impact of <span class="html-italic">r</span> on the pricing under ON model; (<b>b</b>) The impact of <span class="html-italic">r</span> on the pricing under VPN model.</p> Full article ">Figure 7
<p>Impacts of remaining rate <span class="html-italic">α</span> and improvement rate <span class="html-italic">r</span> on optimal profits with two trade-in programs. (<b>a</b>) Impacts on the brand owner’s optimal profit; (<b>b</b>) Impacts on the e-commerce platform’s optimal profit.</p> Full article ">Figure 8
<p>The dominant scenario of trade-in program (varying the trade-in and retail prices).</p> Full article ">
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