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

Merging Prospect Theory with the Analytic Hierarchy Process: Applications to Technology Markets

abstract: This thesis presents a model for the buying behavior of consumers in a technology market. In this model, a potential consumer is not perfectly rational, but exhibits bounded rationality following the axioms of prospect theory: reference dependence, diminishing returns and loss sensitivity. To evaluate the products on different criteria, the analytic hierarchy process is used, which allows for relative comparisons. The analytic hierarchy process proposes that when making a choice between several alternatives, one should measure the products by comparing them relative to each other. This allows the user to put numbers to subjective criteria. Additionally, evidence suggests that a consumer will often consider not only their own evaluation of a product, but also the choices of other consumers. Thus, the model in this paper applies prospect theory to products with multiple attributes using word of mouth as a criteria in the evaluation.Dissertation/ThesisMasters Thesis Applied Mathematics 201

Beyond the user preferences:Aligning the prototype design to the users' expectations

It is important for practitioners to conceptualize and tailor a prototype in tune with the users’ expectations in the early stages of the design life cycle so the modifications of the product design in advanced phases are kept to a minimum. According to user preference studies, the aesthetic and the usability of a system play an important role in the user appraisal and selection of a product. However, user preferences are just a part of the equation. The fact that a user prefers one product over the other does not mean that he or she would necessarily buy it. To understand the factors affecting the user's assessment of a product before the actual use of the product and the user's intention to purchase the product we conducted a study, reported in this article. Our study, a modification of a well-known protocol, considers the users’ preferences of six simulated smartphones each with different combination of attributes. A sample consisting of 365 participants was involved in our analysis. Our results confirm that the main basis for the users’ pre-use preferences is the aesthetics of the product, whereas our results suggest that the main basis for the user's intention to purchase are the expected usability of the product. Moreover, our analysis reveals that the personal characteristics of the users have different effects on both the users’ preferences and their intention to purchase a product. These results suggest that the designers should carefully balance the aesthetics and usability features of a prototype in tune with the users expectations. If the conceptualization of a product is done properly the redesign cycles after the usability testing can be reduced and speed up the process for releasing the product on the market

Improved Methods for Network Screening and Countermeasure Selection for Highway Improvements

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities

A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research

Assessing and predicting the students’ systems thinking preference: multi-criteria decision making and machine learning

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an approach that helps individuals better understand and effectively solve modern complex systems problems by encouraging holistic thinking. Systems thinking consists of two approaches holistic and reductionist views. This dissertation aims to study college engineering and non-engineering students’ preference for holistic thinking versus reductionist thinking, their ranking to the systems thinking dimensions, and whether this preference varies depending on demographics and general factors. Additionally, this study investigates the possibility of predicting the students’ preference for holistic thinking. The study uses the multi-criteria decision-making method, the Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process to determine the student’s preferences, and uses statistical analysis such as independent sample t-test and ANOVA to evaluate the factors. Also, the study uses machine learning classification models such as Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Trees, voting classifiers, Bagging, and Random Forest to predict and evaluate the most predicting model. The results of the dissertation conclude that overall students prefer the reductionist approach and report the students’ preference towards dimensions of complexity, independence, uncertainty, systems worldview, and flexibility and the ranking difference based on some factors. Lastly, the results show that the students’ preference for holistic thinking can be predicted with a 77% accuracy using the Random Forest classifier

The Application of the EDAS Method in the Parametric Selection Scheme for Maintenance Plans in the Nigerian Food Industry

Nowadays, maintenance performance in organizations has become compelling due to competitiveness in the global market and the inclusion of more legislation issues (such as safety and health regulations) in assessments. In this article, the purpose is to formulate in maintenance problem for a food processing unit as a multicriteria problem and solve it using the evaluation based on distance from the average solution (EDAS) method. To attain this purpose, the authors defined a set of weighted criteria and a set of alternatives, and the solution is the alternative that scores the best in those criteria. Consequently, analysis was done based on the EDAS method and the calculated results from the literature data. Consequently, the parameters considered include the frequency of failure, MTBF, MTTF and MTTR while availability is the response. The EDAS method was used to select the best alternative (MTTR, 0.8802) and this score of 0.8802 is for an alternative. The chief novelty of this article is the unique introduction of an innovative EDAS method, which requires only two measures of the desirability of alternative (positive and negative distances from the average solution) but excluded the evaluation of the idea and nadir solutions for the key performance indicators of maintenance. Consequently, this study initiates a maintenance plan for the food industry referring to the key performance indicators as a cause for poor availability of equipment in the Nigerian food industry

Systematická analýza bankovních služeb pro studenty v České Republice

The aim of this bachelor thesis is to find out which criteria have for students the greatest importance on the current account selection and on the basis of this finding, determine which bank in the Czech Republic best meets the required criteria. Data is gathered via a quantitative approach using a questionnaire, from 133 students of a Technical University of Ostrava in the Czech Republic. These data are processed by multicriteria analysis, more precisely by the WSM and the AHP method. The multi-criteria decision making analysis revealed that current account price, availability of the ATMs and functionality of mobile banking have the biggest influence on students when choosing a current account and vice versa interest on the overdraft has the least important for students at all. These results should help students who will have an overview of the services provided by Czech banks, as well as banks that, based on student preferences, can better meet the needs of students and thus attract more customers.Cílem této bakalářské práce je zjistit, která kritéria mají pro studenty největší význam při výběru běžného účtu a na základě tohoto zjištění určit, která banka v České republice nejlépe splňuje požadovaná kritéria. Data jsou získávána pomocí kvantitativního přístupu pomocí dotazníku 133 studentů Technické univerzity Ostrava v České republice. Tato data jsou zpracována multikriteriální analýzou, přesněji metodou WSM a AHP. Analýza multikriteriálního rozhodování ukázala, že cena běžného účtu, dostupnost bankomatů a funkčnost mobilního bankovnictví mají největší vliv na studenty při volbě běžného účtu a naopak úroky z kontokorentu mají pro studenty nejméně význam. Tyto výsledky by měly pomoci studentům, kteří budou mít přehled o službách poskytovaných českými bankami, stejně jako bankám, které na základě preferencí studentů lépe uspokojí potřeby studentů a přitáhnou tak více zákazníků.154 - Katedra financívýborn