INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Modelling reservation-based shared autonomous vehicle services: A dynamic user equilibrium approach

Shared Autonomous Vehicles (SAVs) are expected to be used for regular and pre-planned trips. Such trips are suitable for reservation-based services, wherein the customer needs to book for a trip in advance. Systems enabling reservation of trips can allow for better planning of routes and schedules, and if optimally designed, enable higher efficiency. The primary objective of this research is to model the effects of such a system, by formulating and solving the combined Dynamic User Equilibrium and Shared autonomous vehicle Chain Formation (DUESCF) problem. The problem is formulated as a bilevel model based on game theory, involving road users and SAV service operator. Given a situation where conventional private and shared autonomous vehicles co-exist, road users select paths and departure times to maximize a perceived utility (commonly treated as minimizing a disutility) by forming a DUE (fixed point problem), and the SAV service operator tries to maximize the performance by forming appropriate SAV chains (combinatorial problem). The final objective of this bilevel model is a traffic assignment that includes SAV chain formation, such that both road users and SAV service operator obtain optimal solutions by reaching a Nash equilibrium, where no player is better off by unilaterally changing their decisions. A solution approach, based on Iterative Optimization and Assignment (IOA) method, is proposed with path flow and SAV performance changes as convergence criteria. Furthermore, the solution approach is tested for its robustness, and a scenario analysis is carried out to evaluate the impacts of reservation-based SAV services. The results show that a ridesharing SAV system is better compared to a carsharing and a mixed system consisting of both, in terms of total system travel time, congestion levels, total vehicle kilometres travelled and vehicle requirements

Fault tolerant mechanism design for general task allocation

We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account (i.e., fault tolerant). To achieve this, we show that the post-execution verification (PEV)-based mechanism presented by Porter et al. (2008) is applicable if and only if agents' valuations are risk-neutral (i.e., the solution is almost universal). We then consider a more advanced setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still applicable if and only if the trust aggregation is multilinear. Given this characterisation, we further demonstrate how this mechanism can be successfully applied in a real-world setting. Finally, we draw the parallels between our results and the literature of efficient mechanism design with general interdependent valuations