Online reputation management: estimating the impact of management responses on consumer reviews

We investigate the relationship between a firm’s use of management responses and its online reputation. We focus on the hotel industry and present several findings. First, hotels are likely to start responding following a negative shock to their ratings. Second, hotels respond to positive, negative, and neutral reviews at roughly the same rate. Third, by exploiting variation in the rate with which hotels respond on different review platforms and variation in the likelihood with which consumers are exposed to management responses, we find a 0.12-star increase in ratings and a 12% increase in review volume for responding hotels. Interestingly, when hotels start responding, they receive fewer but longer negative reviews. To explain this finding, we argue that unsatisfied consumers become less likely to leave short indefensible reviews when hotels are likely to scrutinize them. Our results highlight an interesting trade-off for managers considering responding: fewer negative ratings at the cost of longer and more detailed negative feedback.Accepted manuscrip

Extending snBench to Support Hierarchical and Configurable Scheduling

It is useful in systems that must support multiple applications with various temporal requirements to allow application-specific policies to manage resources accordingly. However, there is a tension between this goal and the desire to control and police possibly malicious programs. The Java-based Sensor Execution Environment (SXE) in snBench presents a situation where such considerations add value to the system. Multiple applications can be run by multiple users with varied temporal requirements, some Real-Time and others best effort. This paper outlines and documents an implementation of a hierarchical and configurable scheduling system with which different applications can be executed using application-specific scheduling policies. Concurrently the system administrator can define fairness policies between applications that are imposed upon the system. Additionally, to ensure forward progress of system execution in the face of malicious or malformed user programs, an infrastructure for execution using multiple threads is described

You get what you give: theory and evidence of reciprocity in the sharing economy

We develop an analytical framework of peer interaction in the sharing economy that incorporates reciprocity, the tendency to increase (decrease) effort in response to others’ increased (decreased) effort. In our model, buyers (sellers) can induce sellers (buyers) to exert more effort by behaving well themselves. We demonstrate that this joint increased effort can improve the utility of both parties and influence the market equilibrium. We also show that bilateral reputation systems, which allow both buyers and sellers to review each other, are more responsive to reciprocity than unilateral reputation systems. By rewarding reciprocal behavior, bilateral reputation systems generate trust among strangers and informally regulate their behavior. We test the predictions of our model using data from Airbnb, a popular peer-to-peer accommodation platform. We show that Airbnb hosts that are more reciprocal receive higher ratings, and that higher rated hosts can increase their prices. Therefore, reciprocity affects equilibrium prices on Airbnb through its impact on ratings, as predicted by our analytical framework.Accepted manuscrip

The rise of the sharing economy: estimating the impact of Airbnb on the hotel industry

Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. We explore the economic impact of the sharing economy on incumbent firms by studying the case of Airbnb, a prominent platform for short-term accommodations. We analyze Airbnb's entry into the state of Texas, and quantify its impact on the Texas hotel industry over the subsequent decade. We estimate that in Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8-10% range; moreover, the impact is non-uniform, with lower-priced hotels and those hotels not catering to business travelers being the most affected. The impact manifests itself primarily through less aggressive hotel room pricing, an impact that benefits all consumers, not just participants in the sharing economy. The price response is especially pronounced during periods of peak demand, such as SXSW, and is due to a differentiating feature of peer-to-peer platforms -- enabling instantaneous supply to scale to meet demand.Accepted manuscrip

First confirmed record of Elodea canadensis Michx. (Hydrocharitaceae) in Greece

The paper confrms the presence of Elodea canadensis Michx. in Greece andoutlines the history of contradictory relevant reports. Tis is also the frst report ofthe species’ presence in the transboundary lake Great Prespa

The supply and demand effects of review platforms

Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.Published versio

Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks

Resource-disaggregated data centre architectures promise a means of pooling resources remotely within data centres, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business. This can be accomplished by means of using an optically circuit switched backbone in the data centre network (DCN); providing the required bandwidth and latency guarantees to ensure reliable performance when applications are run across non-local resource pools. However, resource allocation in this scenario requires both server-level \emph{and} network-level resource to be co-allocated to requests. The online nature and underlying combinatorial complexity of this problem, alongside the typical scale of DCN topologies, makes exact solutions impossible and heuristic based solutions sub-optimal or non-intuitive to design. We demonstrate that \emph{deep reinforcement learning}, where the policy is modelled by a \emph{graph neural network} can be used to learn effective \emph{network-aware} and \emph{topologically-scalable} allocation policies end-to-end. Compared to state-of-the-art heuristics for network-aware resource allocation, the method achieves up to $20\%$ higher acceptance ratio; can achieve the same acceptance ratio as the best performing heuristic with $3\times$ less networking resources available and can maintain all-around performance when directly applied (with no further training) to DCN topologies with $10^2\times$ more servers than the topologies seen during training.Comment: 10 pages + 1 appendix page, 8 figure