Pricing and trust

We experimentally examine the effects of flexible and fixed prices in markets for experience goods in which demand is driven by trust. With flexible prices, we observe low prices and high quality in competitive (oligopolistic) markets, and high prices coupled with low quality in non-competitive (monopolistic) markets. We then introduce a regulated intermediate price above the oligopoly price and below the monopoly price. In monopolies volume increases and so does quality, such that overall efficiency is raised by 50%. Somewhat surprisingly, the same pattern emerges in oligopolies. In fact, across all market forms transaction volume and traded quality are maximal in regulated oligopolies

Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers

This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns

Status versus Reputation as Motivation in Online Communities

The continued active participation of contributors is crucial for online knowledge exchange communities. In many communities, virtual credit scores measure contributions and play a pivotal role in motivating active participation over time. We use status theory to characterize virtual credit as a double-edged sword to participation dynamics. We hypothesize that virtual scores reflect status rather than reputation and produce a non-linear effect by motivating contributions when participants are of low status but demotivating participants once they achieve high status. We test our theorizing on a dataset of a large Q&A community. Consistent with our hypotheses, we find robust evidence that status-seeking is a positive source of motivation but self-depletes, meaning that cumulating status in the community reduces the motivational drive of status-seeking. This study contributes to the literature on the motivations to participate in voluntary online knowledge exchange communities by offering an explanation of the dynamics of continued active participation

Information Management and Market Engineering. Vol. II

The research program Information Management and Market Engineering focuses on the analysis and the design of electronic markets. Taking a holistic view of the conceptualization and realization of solutions, the research integrates the disciplines business administration, economics, computer science, and law. Topics of interest range from the implementation, quality assurance, and advancement of electronic markets to their integration into business processes and legal frameworks

LEARNING FROM INTERORGANIZATIONAL PRODUCT FAILURE EXPERIENCE IN THE MEDICAL DEVICE INDUSTRY

Much research examines the causes of product failures such as the Ford Pinto gas tank design. Research also examines the consequences of product failures such as new product introductions resulting from the need to improve failed products. However, little is known about how the causes and consequences of product failures interact across different firms, and generate inter-organizational learning, within the same industry. Specifically, limited research has examined if a firm learns to reduce its own annual rate of product failures (e.g., experiences fewer product-related adverse events) by attending to the product failures and new product introductions of its competitors. In addition, we also do not know (1) how delayed reporting of product failure influences interorganizational learning, and (2) how the introduction of new products by one company impacts another firm’s effort to learn from this competitor’s product failures. To address these gaps, this dissertation develops and tests relationships between (1) inter-organizational learning from product failures, (2) product failure reporting delays, and (3) new product introductions. Regression analysis of 98,576 manufacturing firm-year observations from the medical device industry over a ten-year period (1998 to 2008) supports the proposed model. Specifically, the analysis supported two insights: (1) As expected, a competitor’s reporting delays can inhibit learning from others’ failures by increasing the chance of making poor inferences about the failure. Unexpectedly, however, delays can also improve inter-organizational learning because in reports that have taken longer to file, a clearer understanding of the failure’s cause-effect relationships is developed. iii (2) As expected, a competitor\u27s new product introductions positively impact interorganizational learning by transferring knowledge of product design between firms. Unexpectedly, a competitor’s new product introductions can also negatively impact inter-organizational learning from product failure by distracting the observing firm’s attention away from the competitor’s failures. The thesis contributes to the inter-organizational learning literature by: (1) modelling learning from others’ product failures, (2) highlighting the effects of reporting delays, and (3) showing how others’ new product introductions can distract. This thesis shows that learning from others’ product failures and new product introductions has significant benefits because it prevents serious injury and death among device users

Using agent-based modelling and simulation to model performance measurement in healthcare

One of the priority areas of the UK healthcare system is urgent and emergency care, especially accident and emergency departments (A&E departments). Currently, there is much interest in studying the unintended consequences of the current UK healthcare performance system. Simulation modelling has been proved to be a useful tool for modelling different aspects of the healthcare systems, particularly those related to the performance of A&E departments. Most of the available literature on modelling A&E departments focus on supporting operational decision-making and planning in specific healthcare units to study particular problems such as staff scheduling, resource utilisation, and waiting time issues. That is, most simulation studies focus on analysing how different configurations of healthcare systems affect their performance. However, to our knowledge, few simulation studies focus on explaining how human behaviour affects the performance of the system, and very few have studied how, in turn, performance targets set for A&E departments affect human behaviour in healthcare systems. Some aspects of human behaviour have been incorporated within existing simulation models, though with limitations. In fact, most studies have aimed to study patients’ behaviour, and few have included some aspects of the behaviour of clinical staff. Here we consider how to model clinician behaviour in relation to the performance of A&E departments. This thesis presents an exploratory study of the use of agent-based modelling and simulation (ABMS) and discrete event simulation (DES) to demonstrate how to model clinician behaviour within an A&E department and how that behaviour is related to waiting time performance. Clinical behaviour, incorporated in the simulation models developed here, employs a framework called PECS that assumes that behaviour is influenced by Physical (P), Emotional (E), Cognitive (C) and Social (S) factors. A discussion of the advantages and limitations of the use of ABMS and DES to model such behaviour is included. The findings of this research demonstrate that ABMS is well suited to simulate human behaviour in an A&E department. However, it is not explicitly designed to model processes of complex operational and queue-based systems such as accident and emergency departments. In addition, this research work also demonstrates that DES is an adequate tool for modelling A&E’s processes and patient flows, that can, in fact, incorporate different aspects of human behaviour. Furthermore, the process of modelling human behaviour in DES is complex because, though most DES software allows the representation of reactive behaviour, they make it difficult to model other types of human behaviour The main contributions of this thesis are: 1) a comparison and evaluation of how suitable ABMS and DES are for modelling clinical behaviour, 2) an approach to model the relationship between human behaviour and waiting time performance, considering four aspects of human behaviour (physical, emotional, cognitive and social)

Improving shared access to Cloud of Things resources.

Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed. Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants. The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability