Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

Bu koleksiyon için kalıcı URI

https://hdl.handle.net/20.500.14034/33

Listeleniyor 1 - 20 / 61

Continuous intuitionistic Fuzzy AHP & CODAS methodology for automation degree selection
(Old City Publishing Inc, 2024) Alkan, Nursah; Otay, Irem; Gul, Alize Yaprak; Demir, Zeynep Burcu Kizilkan; Doğan, Onur
The automotive industry's evolution thrives on technological innovation, prioritizing efficiency, safety, and sustainability. Recent improvements in autonomous driving and IoT integration have revolutionized vehicle design, safety, and maintenance with different automation degrees from partial human control to full automation. Selecting these automation degrees involves complicated Multi-Criteria Decision-Making (MCDM) encompassing technical feasibility, societal impact, and regulatory compliance. Utilizing Analytic Hierarchy Process (AHP) and Combinative Distance-Based Assessment (CODAS) offers a structured framework to navigate these complexities. AHP establishes criteria importance, while CODAS handles uncertainties, enabling informed decisions balancing technology with ethical, societal, and regulatory considerations. Fuzzy extensions further refine these methodologies, empowering the industry to adeptly address subjective perceptions and ambiguous data, enhancing the decision-making framework for automotive technology evolution. This paper navigates the intricate landscape of automation degree selection within the automotive industry evolution, employing a structured approach merging fuzzy AHP and fuzzy CODAS methods by utilizing Continuous Intuitionistic Fuzzy Set (CINFUS). This approach not only brings a new perspective to autonomous vehicles but also highlights the importance of choosing the right automation degree. Moreover, a sensitivity analysis involved adjusting the weights assigned to different criteria within the Continuous Intuitionistic Fuzzy (CINFU) AHP framework. By systematically altering these weights and observing their impact on the final automation degree selection, decision-makers can understand the sensitivity of the chosen automation degree to changes in priority among criteria.
Business analytics in customer lifetime value: an overview analysis
(Wiley Periodicals, Inc, 2025) Doğan, Onur; Hızıroğlu, Abdulkadir; Pisirgen, Ali; Seymen, Omer Faruk
In customer-oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision-making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state-of-the-art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan-one of the study's significant contributions-was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.
Enhancing hospital services: Utilizing chatbot technology for patient inquiries
(Springer International Publishing Ag, 2024) Doğan, Onur; Gurcan, Omer Faruk
This paper investigates the implementation and impact of a chatbot system within a hospital environment to address patient inquiries. The study focuses on the development and deployment of a non-medical knowledge-based chatbot designed to respond to a spectrum of queries typically handled by hospital call centers. The chatbot system's primary objective is to provide prompt, accurate, and accessible information to patients, streamlining their interaction process and reducing waiting times. The research explores the technical architecture and functionality of the chatbot, emphasizing its intuitive interface and ability to cater to a diverse range of patient queries. Utilizing a chatbot, data collection includes quantitative analysis of patient inquiries' pre- and post-chatbot implementation, alongside qualitative insights derived from patient feedback. The developed chatbot based on embedding techniques and a pre-trained Large Language Model gives an accuracy of 89%. Preliminary findings emphasized the potential for intelligent virtual assistants to transform patient interactions, enhance information accessibility, and ultimately contribute to the overall improvement of hospital services.
Parallelism-based session creation to identify high-level activities in event log abstraction
(Springer International Publishing Ag, 2024) Doğan, Onur; De Leoni, Massimiliano
Process mining utilizes event data to gain insights into the execution of processes. While techniques are valuable, their effectiveness may be hindered when dealing with highly complex processes that have a vast number of variants. Additionally, because the recorded events in information systems are at a low-level, process mining techniques may not align with the higher-level concepts understood at the business level. Without abstracting event sequences to higher-level concepts, the outcomes of process mining, such as discovering a model, can become overly complex and challenging to interpret, rendering them less useful. Some research has been conducted on event abstraction, often requiring significant domain knowledge that may not be readily accessible. Alternatively, unsupervised abstraction techniques may yield less accurate results and rely on stronger assumptions. This paper introduces a technique that addresses the challenge of limited domain knowledge by utilizing a straightforward approach. The technique involves dividing traces into batch sessions, taking into account relationships between subsequent events. Each session is then abstracted as a single high-level activity execution. This abstraction process utilizes a combination of automatic clustering and visualization methods. The proposed technique was evaluated using a randomly generated process model with high variability. The results demonstrate the significant advantages of the proposed abstraction in effectively communicating accurate knowledge to stakeholders.
Predicting the duration of professional tennis matches using MLR, CART, SVR and ANN techniques
(Springer International Publishing Ag, 2024) Duen, Serdar; Peker, Serhat
This research aims to predict the duration of professional tennis matches by utilizing a dataset that includes player statistics, match characteristics and court attributes. Various machine learning techniques, such as multiple linear regression (MLR), classification and regression trees (CART), support vector regression (SVR) and artificial neural networks (ANN), are applied for this purpose. The study involves a comprehensive dataset spanning professional tournaments from 1993 to 2022. Separate predictive models were developed for tournaments played over 3 and 5 sets employing the corresponding ML techniques and their performances were compared. The findings revealed that the predictive models with MLR and SVR methods excel in best-of-3 set matches, while the ones with SVR and ANN exhibit superior performance for best-of-5 set matches. This research contributes valuable insights into the factors influencing match duration and aids in developing more effective predictive models for tennis events.
Evaluating the relationship between climate change, food prices, and poverty: empirical evidence from underdeveloped countries
(Springer, 2024) Acci, Yunus; Uçar, Emine; Uçar, Murat; Acci, Reyhan Cafri
Climate change is a critical global issue with wide-ranging impacts, particularly on agriculture. This study examines how climate change influences food prices and poverty in underdeveloped countries. Rising temperatures and extreme weather events are diminishing agricultural productivity, leading to increased food prices and worsening poverty. The research involved developing a climate change index using an autoencoder model, which can learn the important features of data and translate it into a lower-dimensional representation. This index was based on variables such as carbon emission rates, annual average rainfall, forest cover, fossil fuel consumption, renewable energy use, and temperature changes. The relationship between this climate change index and food prices and poverty was analyzed using panel causality methods. Additionally, food prices from 2020 to 2030 were projected using various time series forecasting techniques to determine the most accurate predictive model. The findings indicate that while climate change does not significantly affect poverty when considering all countries as a panel, it does have a notable impact on food prices. This underscores the need for effective policy measures to address the effects of climate change on food costs. To mitigate these impacts, it is essential for policymakers to enhance agricultural resilience through sustainable practices and targeted interventions. Future research should expand the dataset and include a broader range of countries to gain a more comprehensive understanding of how climate change affects food prices and poverty.
Temperature-dependent photoluminescence of novel Eu 3+ , Tb3+ , and Dy3+ doped LaCa4 O(BO3)3 : Insights at low and room temperatures
(Pergamon-Elsevier Science Ltd, 2024) Altowyan, Abeer S.; Coban, M. B.; Kaynar, Ümit Hüseyin; Hakami, Jabir; Ayvacikli, M.; Hızıroğlu, Abdulkadir; Can, N.
This study explores the structural and optical qualities of LaCa4O(BO3)3 (LACOB) phosphors doped with Eu3+, Dy3+, and Tb3+ using a microwave-assisted sol-gel technique. It uncovers oxygen-related luminescence defects in LACOB, highlighting emission peaks at 489 and 585 nm for Dy3+, a distinct sharp peak at 611 nm for Eu3+ in the red spectrum, and a notable green emission for Tb3+ due to specific transitions. The photoluminescence (PL) analysis indicates that luminescence is optimized through precise doping, leveraging dipole interactions, and localized resonant energy transfer, which are influenced by dopant concentration and spatial configuration. Temperature studies show emission intensity variations, particularly noticeable below 100 K for Tb3+ doped samples, demonstrating the nuanced balance between thermal quenching and luminescence efficiency. This temperature dependency, alongside the identified optimal doping conditions, underscores the potential of these materials for advanced photonic applications, offering insights into their thermal behavior and emission mechanisms under different conditions.
Optimization of travel requests with process simulation analysis
(Pergamon-Elsevier Science Ltd, 2024) Celik, Atalay; Doğan, Onur
Efficient travel request management is crucial in the modern corporate landscape, impacting decisionmaking, budget control, and overall operational efficiency. This study delves into the intricacies of the travel request management process with process mining and process simulation, aiming to assess the cost structure and efficiency. Three research questions were examined and tested statistically. Using a comprehensive dataset encompassing timestamps, personnel roles, and cost details, the research uncovers significant insights. The findings reveal substantial cost variations across different stages of the travel request process. Rolebased analysis highlights variations in costs and processing times associated with distinct personnel roles. Additionally, delays, bottlenecks, and process variability emerge as key contributors to elevated process costs. Furthermore, process mining and simulation were instrumental in quantifying the impact of process optimizations. The simulations demonstrated that a 20% reduction in response times for Unit Leaders, a 40% reduction for Project Managers, and a 30% reduction for both roles collectively yielded the most significant cost savings. On average, a 0.45% cost improvement was observed for each percentage reduction in response times, reinforcing the importance of efficiency enhancements. These numerical results not only validate the critical role of response time reduction in cost control but also provide actionable insights for organizations to streamline their travel request management processes, improve budget allocation, and enhance operational efficiency in the corporate landscape.
A clustering approach for classifying scholars based on publication performance using bibliometric data
(Cairo Univ, Fac Computers & Information, 2024) Pisirgen, Ali; Peker, Serhat
This study introduces a clustering framework that effectively evaluate scholars' publication performance by utilizing cluster analysis and bibliometric data. In order to capture the various aspects of scholars' publication characteristics, our proposed framework integrates four distinct features, namely APIR which represents Academic age, Productivity, Impact, and Recency. The proposed framework is implemented in a case study focusing on Turkish academia, utilizing a dataset comprising 13,070 scholars from 24 diverse academic divisions across 30 Turkish universities. Cluster analysis yields seven groups of scholars with diverse publishing characteristic based on APIR features and these obtained clusters are profiled as freshmen, stagnant impactful mids, rising stars, stagnant and non-prolific juniors, stagnant impactful seniors, super stars, currently active and prolific seniors. To enhance the cluster analysis results, additional cross analysis is performed based on scholars' certain demographics such as affiliating institutes, divisions, academic titles, and PhD qualification. Scholars in clusters with superior publication performance are often affiliated with top-ranked universities and have academic backgrounds in the fields of Medicine, Engineering, and Natural Sciences. Practically, generated scholar segments and analysis based on these scholar profiles can serve as useful input for policy makers during having decisions about recruitment, promotion, awarding and allocation of funds.
Preeclampsia prediction via machine learning: a systematic literature review
(Taylor & Francis Ltd, 2024) Özcan, Mert; Peker, Serhat
Preeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. We reviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Na & iuml;ve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.
Examining hotel characteristics and facilities influencing customer satisfaction using decision tree analysis
(Emerald Group Publishing Ltd, 2024) Pisirgen, Ali; Erdoğan, Ali Mert; Peker, Serhat
PurposeThis study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.Design/methodology/approachThe research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.FindingsThe analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.Originality/valueThis study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.
Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles
(Emerald Group Publishing Ltd, 2024) Doğan, Onur; Yalcin, Emre; Hızıroğlu, Ourania Areta
PurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library's offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user's rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.
Analysis of countries' performances in individual Olympic Games using cluster analysis and decision trees: the case of Tokyo 2020
(Emerald Group Publishing Ltd, 2024) Cetinkaya, Ahmet; Peker, Serhat; Kuvvetli, Ümit
PurposeThe purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.Design/methodology/approachThis research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.FindingsThe study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.Research limitations/implicationsThe study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.Practical implicationsThe research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.Social implicationsThe research holds significant social implications by contributing insights that extend beyond the realm of sports.Originality/valueThe originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.
The assessment of organizational innovativeness as a mediator between ICT adoption and firm performance in Turkish SMEs
(Sage Publications Inc, 2024) Calli, Busra Alma; Ozsahin, Mehtap; Coşkun, Erman
This study aims to examine the managerial and organizational factors affecting ICT adoption in SMEs, and the link between ICT adoption level and firm performance. In this context, a survey was performed with owners, top-level managers, middle-level managers, and first-line managers of SMEs. The study was carried out in three phases. First, after assessing the ICT adoption level for each participating SME, the effect of owner and manager-related characteristics and organizational attributes on the level of ICT adoption was examined. Second, an analysis was performed to find out if there is a relationship between ICT adoption levels and organizational innovation types. Finally, the effect of ICT adoption level on firm performance and the mediating effect of organizational innovativeness types on this link were investigated. Data obtained from 393 owners and managers of 203 SMEs were analyzed through SPSS. Analysis results show that the owner's/manager's education level and awareness of the benefit of ICT are positively related to ICT adoption level. In contrast, awareness of the costs of ICT is negatively related to ICT adoption in SMEs. Moreover, the results reveal that internal integration and strategic integration level ICT adoption on firm performance is mediated by organizational innovativeness. These findings stand to help SMEs strategically plan their ICT adoption phases based on their organizational needs.
Enhancing e-commerce product recommendations through statistical settings and product-specific insights
(Inderscience Enterprises Ltd, 2024) Doğan, Onur
In the e-commerce industry, effectively guiding customers to select desired products poses a significant challenge, necessitating the utilisation of technology and data-driven solutions. To address the extensive range of product varieties and enhance product recommendations, this study improves upon the conventional association rule mining (ARM) approach by incorporating statistical settings. By examining sales transactions, the study assesses the statistical significance of correlations, taking into account specific product details such as product name, discount rates, and the number of favourites. The findings offer valuable insights with managerial implications. For instance, the study recommends that if a customer adds products with a high discount rate to their basket, the company should suggest products with a lower discount rate. Furthermore, the traditional rules are augmented by incorporating product features. Specifically, when the total number of favourites is below 7,500 and the discount rate is less than 75%, the similarity ratio of the recommended products should be below 0.50. These enhancements contribute significantly to the field, providing actionable recommendations for e-commerce companies to optimise their product recommendation strategies.
Machine selection for inventory tracking with a continuous intuitionistic fuzzy approach
(MDPI, 2025) Cebeci, Ufuk; Simsir, Ugur; Doğan, Onur
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking.
Enhancing e-business communication with a hybrid rule-based and extractive-based chatbot
(MDPI, 2024) Doğan, Onur; Gurcan, Omer Faruk
E-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by AI and NLP technologies. This study focuses on developing a hybrid rule-based and extractive-based chatbot for e-business, which can handle both routine and complex inquiries, ensuring quick and accurate responses to improve communication problems. The rule-based QA method used in the chatbot demonstrated high precision and accuracy in providing answers to user queries. The rule-based approach achieved impressive 98% accuracy and 97% precision rates among 1684 queries. The extractive-based approach received positive feedback, with 91% of users rating it as good or excellent and an average user satisfaction score of 4.38. General user satisfaction was notably high, with an average Likert score of 4.29, and 54% of participants gave the highest score of 5. Communication time was significantly improved, as the chatbot reduced average response times to 41 s, compared to the previous 20-min average for inquiries.
Empowering manufacturing environments with process mining-based Statistical process control
(MDPI, 2024) Dogan, Onur; Areta Hiziroglu, Ourania
The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC-PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the 'Quality Control' activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the 'Return from Waiting' activity to 'Packing and Shipment' is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.
Cart-state-aware discovery of e-commerce visitor journeys with process mining
(MDPI, 2024) Topaloglu, Bilal; Oztaysi, Basar; Doğan, Onur
Understanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success.
Risk assessment of employing digital robots in process automation
(MDPI, 2024) Doğan, Onur; Arslan, Ozlem; Tirpan, Esra Cengiz; Cebi, Selcuk
Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers' optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found Sustainability challenge critical in three processes, Absence of governance management in two, and Security in one. Variability in risk importance arose from process vulnerabilities.

Güncel Gönderiler