Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System
Abstract
:1. Introduction
2. Literature Review
3. Model Formulation
- (1)
- Two retailers sell homogeneous products to meet the different regional markets, and two markets are independent of each other. Retailer Ri predicts demand and figures out the optimal order quantity (OOQ) to maximize his/her utility, . The upstream agent also predicts demand and figures out his/her OOQ .
- (2)
- The upstream agent decides her marketing strategies according to retailers’ different states of sharing information, and recommends an order quantity to the retailers; this is denoted as , . Where θ is an increment, it can also be taken as an index of telling lies. Because of asymmetrical information, the agent intends to report unreal information to maximize her/his profit. is the agent’s ROQ for retailer Ri.
- (3)
- Then, the retailer adjusts his/her order quantity and decides on the final actual ordering quantity (AOQ) . The decision is made based on his OOQ , the agent’s ROQ , and his trust in the agent, .
- (4)
- After two retailers submit their order quantity to the manufacturer through the agent, the manufacturer produces the products and delivers them to retailers R1 and R2, respectively.
- (5)
- Finally, two retailers figure out the profit and compare the difference value between the recommended quantity and the actual demand , with the difference value between the optimized quantity and the actual demand at the end of the period, respectively. The difference value is used to update the trust value for the next period. We translate these dynamics into the model below.
3.1. Demand Prediction and Profit Model
3.2. ROQ Distribution Model
3.2.1. Pattern 1: None of the Retailers Share Demand Prediction
3.2.2. Scenario 2: One Retailer Shares His/her Demand Prediction
3.2.3. Pattern 3: All Retailers Share Their Demand Prediction
3.3. Trust Model
4. Design of Experiments
5. Experimental Results
5.1. The Agent Predicts More Accurately Than the Two Retailers
5.2. Both Retailers Predict More Accurately Than the Agent
5.3. The Agent Predicts More Accurately than One of the Two Retailers and Less Accurately Than the Other One
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Paired Differences | t | df | Sig. (2-Tailed) | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Std. Deviation | Std. Error Mean | 95% Confidence Interval of the Difference | ||||||
Lower | Upper | ||||||||
Model 1 | R1–R2 | −0.03526 | 0.03659 | 0.00732 | −0.05036 | −0.02015 | −4.818 | 24 | 0.000 |
Model 2 | R1–R2 | 0.03780 | 0.04524 | 0.00905 | 0.01913 | 0.05648 | 4.178 | 24 | 0.000 |
Model 3 | R1–R2 | −0.01259 | 0.02293 | 0.00459 | −0.02205 | −0.00312 | −2.745 | 24 | 0.011 |
R1 | Model 1–Model 2 | −0.05456 | 0.05964 | 0.01193 | −0.07917 | −0.02994 | −4.574 | 24 | 0.000 |
R1 | Model 1–Model 3 | −0.05825 | 0.06373 | 0.01275 | −0.08456 | −0.03195 | −4.571 | 24 | 0.000 |
Paired Differences | t | df | Sig. (2-Tailed) | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Std. Deviation | Std. Error Mean | 95% Confidence Interval of the Difference | ||||||
Lower | Upper | ||||||||
Model 1 | R1–R2 | −0.01044 | 0.01724 | 0.00345 | −0.01756 | −0.00333 | −3.029 | 24 | 0.006 |
Model 2 | R1–R2 | 0.27599 | 0.06870 | 0.01374 | 0.24763 | 0.30434 | 20.087 | 24 | 0.000 |
Model 3 | R1–R2 | 0.00618 | 0.01106 | 0.00221 | 0.00161 | 0.01075 | 2.792 | 24 | 0.010 |
R1 | Model 1–Model 2 | −0.23160 | 0.06376 | 0.01275 | −0.25792 | −0.20528 | −18.163 | 24 | 0.000 |
R1 | Model 1–Model 3 | −0.00653 | 0.00974 | 0.00195 | −0.01055 | −0.00251 | −3.353 | 24 | 0.003 |
Paired Differences | t | df | Sig. (2-Tailed) | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Std. Deviation | Std. Error Mean | 95% Confidence Interval of the Difference | ||||||
Lower | Upper | ||||||||
Model 1 | R1–R2 | −0.65565 | 0.22430 | 0.04486 | −0.74824 | −0.56306 | −14.615 | 24 | 0.000 |
Model 2 | R1–R2 | −0.37299 | 0.17917 | 0.03583 | −0.44695 | −0.29903 | −10.409 | 24 | 0.000 |
Model 3 | R1–R2 | −0.67734 | 0.25751 | 0.05150 | −0.78364 | −0.57105 | −13.152 | 24 | 0.000 |
R1 | Model 1–Model 2 | −0.25191 | 0.06425 | 0.01285 | −0.27843 | −0.22538 | −19.603 | 24 | 0.000 |
R1 | Model 1–Model 3 | 0.25752 | 0.09025 | 0.01805 | 0.22027 | 0.29477 | 14.268 | 24 | 0.000 |
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Retailer R2 | |||
---|---|---|---|
Retailer R1 | Share | Do not share | |
Share | −2, −2 | 1, 2 | |
Do not share | 2, 1 | −1, −1 |
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Fu, X.; Han, G. Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System. Sustainability 2017, 9, 710. https://doi.org/10.3390/su9050710
Fu X, Han G. Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System. Sustainability. 2017; 9(5):710. https://doi.org/10.3390/su9050710
Chicago/Turabian StyleFu, Xiao, and Guanghua Han. 2017. "Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System" Sustainability 9, no. 5: 710. https://doi.org/10.3390/su9050710