Three analytics-based essays examining the use and impact of Intelligent Voice Assistants (IVA) and Health Information Technologies (HIT) in service contexts

Recent advancements in information technology (IT) innovation, such as artificial intelligence (AI) and machine learning (ML), are changing the dynamics in the service sector by driving smart reinvention of service tasks and processes. Additionally, organisations are leveraging the capabilities of emerging information systems (IS) to make their services more efficient and customer centric. However, the decision to use recent advancements in IT can be challenging for organizations since the required initial investment for implementation is often high and the economic value and impact on service performance cannot be gauged with certainty (Kwon et al. 2015). This forces many organizations to prioritise which IT functionalities may best be suited for their needs. To support the decision making process of organizations, regarding the adoption and use of innovative IT, scholars in the information systems (IS) and related fields are called to improve knowledge and understanding about various IT components and functionalities as well as their corresponding impact on individual users and organizations. Scholars are also expected to provide the means by which businesses can meaningfully predict the potential impact and economic value of innovative IT (Ravichandran 2018). In this three essay dissertation, we investigate how the use of various components and functionalities of innovative information systems can individually (or together) impact the quality of service delivered to end consumers. The essays are broadly based on the intersection of artificial intelligence (AI), machine learning(ML) and services. In the first study, we found that during encounters between eService consumers and Intelligent Voice Assistants (IVAs), typically powered by artificial intelligence, machine learning and natural language processing, the following dimensions are important for the perceived quality of service: IVA interactivity, IVA personalization, IVA flexibility, IVA assurance and IVA reliability. Among the five dimensions of IVA encounter, we found that IVA interactivity, IVA personalization and IVA reliability had positive impacts on the effective use of IVAs. In study 2, we investigated performance of hospitals in the health service sector. We proposed a smart decision support system (DSS) for predicting the performance of hospitals based on the Health Information Technology (HIT) functionalities as applied and used in these hospitals for patient care and in improving hospital performance. We found that the predictive performance of our proposed smart DSS was most accurate when HIT functionalities were used in certain bundles than in isolation. In study 3, we investigated the effect of hospital heterogeneity on the accuracy of prediction of our proposed smart DSS as we recognize that not all hospitals have the same set of context, opportunity, location and constraints. We found that the following sources of variations in hospitals had significant moderator effects on the accurate prediction of our smart DSS: hospital size, ownership, region, location (urban/rural) and complexity of cases treated. In summary, this dissertation contributes to the IS literature by providing insight into the emergent use of artificial intelligence and machine learning technologies as part of IS/IT solutions in both consumer-oriented services and the healthcare sector

Strategic decisions under uncertainty: Supplier quality improvement and exit in duopoly

This dissertation consists of three interrelated essays on firm-level decision problems when the exterior environment (e.g. product quality or market prospect) is uncertain and there are strategic interactions with other firms (e.g. competitors). The first essay (Chapter 2) studies a buyer’s decision to improve its supplier’s quality when the focal supplier is shared by another buyer who competes in the same market. Each buyer’s investment is a way to outperform the other buyer. However, the investment opportunity comes with spillover risk via the shared supplier. Given this risk-benefit tradeoff, we characterize the conditions under which the optimal timing of the first investment in shared suppliers is earlier (or later) than in sole suppliers. Also, we find that learning moderates the impact of competition and spillover on investment decisions, which suggests that the interplay between learning, spillover, and competition should be carefully examined to build sound investment strategies. The second essay (Chapter 3) also examines buyers’ investment decisions in a buyer-supplier-buyer triad. However, we consider the case when market competition is not an integral part of the problem so that a buyer strives to free-ride on the other buyer’s investment in the shared supplier. Moreover, because the improved quality deteriorates over time by organizational forgetting, buyers should make such an investment decision repeatedly. This problem is thus a repeated free-rider problem. The main finding of this essay is that each buyer delays its investment in the hope of free-riding on the other only if the game is repeated and there is a unique equilibrium entailing inefficient delays. Due to this uniqueness of the equilibrium, we are able to construct the well-defined measure for the inefficiency from free-riding incentives and estimate this inefficiency by using primary data from a field study of an automotive manufacturer. The results from this estimation indicate that the inefficiency can be substantial although it greatly varies depending on the supplier sectors. The third essay (Chapter 4) investigates firms’ exit decision problems under uncertainty by employing the similar mathematical framework used in the second essay: The first firm to exit the market concedes the monopolist’s profit to the remaining firm. The extant literature in economics has predicted that the firms stay in the market longer than necessary. We revisit this problem with two realistic perturbations – firms are asymmetric in their exit barriers and the market evolves stochastically. In contrast to the findings of the previous literature, we find that this perturbed model does not admit an MPE (Markov perfect equilibrium) resulting in inefficient (i.e. longer than necessary) stays. Therefore, this asserts the instability of an equilibrium with inefficient stays, which provides a novel rationale for selecting an equilibrium over the others

STOCHASTIC MODELS FOR RESOURCE ALLOCATION, SERIES PATIENTS SCHEDULING, AND INVESTMENT DECISIONS

We develop stochastic models to devise optimal or near-optimal policies in three different areas: resource allocation in virtual compute labs (VCL), appointment scheduling in healthcare facilities with series patients, and capacity management for competitive investment. A VCL consists of a large number of computers (servers), users arrive and are given access to severs with user-specified applications loaded onto them. The main challenge is to decide how many servers to keep “on”, how many of them to preload with specific applications (so users needing these applications get immediate access), and how many to be left flexible so that they can be loaded with any application on demand, thus providing delayed access. We propose dynamic policies that minimize costs subject to service performance constraints and validate them using simulations with real data from the VCL at NC State. In the second application, we focus on healthcare facilities such as physical therapy (PT) clinics, where patients are scheduled for a series of appointments. We use Markov Decision Processes to develop the optimal policies that minimize staffing, overtime, overbooking and delay costs, and develop heuristic secluding policies using the policy improvement algorithm. We use the data from a local PT center to test the effectiveness of our proposed policies and compare their performance with other benchmark policies. In the third application, we study a strategic capacity investment problem in a duopoly model with an unknown market size. A leader chooses its capacity to enter a new market. In a continuous-time Bayesian setting, a competitive follower dynamically learns about the favorableness of the new market by observing the performance of the leader, and chooses its capacity and timing of investment. We show that an increase in the probability of a favorable market can strictly decrease the leaders expected discounted profit due to non-trivial interplay between leaders investment capacity and timing of the dynamically-learning follower.Doctor of Philosoph