Zhou HE and his team proposed strategies for restaurants in O2O markets using an agent-based model

Prof. Zhou HE and his team published a paper in the International Journal of Production Economics. This paper uses an agent-based model to put forward the optimist decisions of independent restaurants in O2O food ordering and delivery markets.

Abstract: In the booming online-to-offline (O2O) food ordering and delivery market, numerous independent restaurants are competing for orders placed by customers via online food ordering platforms. The food quality and location decisions are deemed the two principal considerations of restaurants in this emerging market. To investigate restaurants’ evolutionary food quality and location behaviors, we propose an agent-based O2O food ordering model (AOFOM) consisting of three types of agents: customers, restaurants, and the online food ordering platform. We explicitly model their adaptive behaviors by optimizing the agents’ benefits. We find that customers’ behaviors significantly impact the restaurants’ food quality decisions. Besides, the relationship between the restaurant’s location decisions and customers’ waiting time is less significant in the O2O food ordering market due to an equalizing delivery service provided by the online platform. We also examine the characteristics of the best restaurants and the impacts of different delivery policies on the food quality and location decisions of restaurants.

Keywords: Agent-based model; Online-to-offline; Food ordering; Food delivery; Location

1. Background
Modern information technologies provide people with a new option of eating under online-to-offline transactions. Restaurants, customers, and online platforms can all benefit from these transactions. However, due to the difficulty of attracting online takeaway orders and the time-sensitive nature of takeaway food, the restaurant's sales territory is bounded by an online food ordering platform. Therefore, how should restaurants' operations management (OM) be optimized in competitive O2O food ordering and delivery markets becomes a question for researchers and restaurant managers. With rich survey data and annual reports from iResearch and researchers’ observations, the article suggests that food quality and location decisions are the two principal considerations of restaurants. In the article, researchers employ the agent-based modeling (ABM) technique to create an agent-based O2O food ordering model (AOFOM) grounded in complex adaptive system (CAS) theory which helps to study the optimal decisions of independent restaurants in O2O food ordering and delivery markets.
2. Literature Review
As firms have increasingly adopted the O2O approach, researchers have devoted much research to this topic. In general, research on the O2O approach can be divided into four streams, namely channel-related, product/service-related, customer-related, and technology-related.
Competitive location models:
As the first study on competitive location problems, Hotelling (1929) has spurred voluminous subsequent research on this topic, which can be generally grouped into two divisions according to the competition type, i.e., static or dynamic. Static spatial competitions are often framed as one-stage Operations Research (OR) problems (Kress and Pesch, 2012), which seek the optimal location(s) for one competitor without considering the others' reactions. However, it is increasingly difficult for such analytical models to capture dynamic spatial interactions between facilities and customers. Players in dynamic locational competitions repeatedly re-optimize their locations simultaneously (such as Hotelling, 1929) or sequentially (pioneered by Hakimi, 1983). However, due to the complexity of dynamic spatial competitions with many players, such models are usually complicated to solve using game-theoretic approaches.
Food-related operations management:
From the OM viewpoint, food-related studies generally focus on three research strands. The first is food quality management due to its importance to public health. The second research strand is food supply chain management, particularly the distribution network design. The third research strand concerns service competition among food service providers.
Agent-based modeling:
An agent can be viewed as an abstract entity, and multiple agents form a complex adaptive system (CAS). In recent years, CAS theory has received considerable attention due to a growing need to tackle difficult issues in market competition. Currently, the most similar study is He et al. (2018a), in which an empirically-grounded agent-based framework is proposed for the OM of mobile application startups. Apart from the similarities with the AOFOM model in this paper, there are fundamental differences between these two models. Therefore, this paper is the first study on the service OM of restaurants in the emerging O2O food ordering and delivery market.
3. Modelling
With the basis of explicitly defining each agent's attributes and behaviors interacting with other agents, the flow of AOFOM is generally divided into five stages: model and agent initialization, customers place orders, food preparation, and delivery, restaurants make new decisions, and information update and model termination. In the stage of model and agent initialization, agents are created. In the stage of customers placing orders, customers search for information from the platform and then evaluate alternatives and place orders. In the food preparation and delivery stage, the platform sends orders to restaurants and receives estimated pick-up time. Then, the foods are prepared by restaurants and delivered by the platform. In the stage of making new decisions, Restaurants make further decisions on food quality and location. Customers send feedback in the information update and model termination stage, and the platform updates information. If it meets termination criteria, the model terminates. If not, it goes back to the location of customers placing orders.
4. Conclusion
The following conclusions can be drawn from this study—first, customers' preference behaviors significantly impact the restaurants' food quality decisions. For example, if customers prefer higher-quality food, the restaurants will increase their quality. Second, the restaurant's food quality will be fine with improving food preparation efficiency because it is mainly based on customers' choices. Forth, the best restaurants have features of having higher food quality and more significant uncertainty in decision-making.
5. Contribution
This study has three main contributions. First, this paper is the first study on the service OM of restaurants in the emerging O2O food ordering and delivery market. Therefore, the findings offer timely and meaningful insights into numerous independent restaurants' food quality and location strategies. Second, researchers employ the agent-based modeling technique to model the market rather than mathematical approaches. Therefore, the complex, dynamic, and non-linear agent interactions can be well captured. Third, based on the accumulated order count, researchers divide all restaurants into two groups (the best ones and others) and analyze the differences in their decisions to understand what strategies are more likely to help restaurants succeed in the competition.
6. Insights for future research
This study has three main insights for future research. First, price, minimum order quantity, and other elements neglected in our model can be considered for a more realistic result. Second, more profound research on the operations management of the online food ordering platform could be exciting and necessary as it plays a vital role in the market. Finally, it is worth collecting and using individual-level behavioral information and other empirical data for further agent-based studies on the O2O food ordering and delivery market.
7. Citation
He, Z., Han, G., Cheng, T. C. E., Fan, B., & Dong, J. (2019). Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: An agent-based approach. International Journal of Production Economics, 215, 61–72. https://doi.org/10.1016/j.ijpe.2018.05.008
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