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Öğe 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, OnurThe 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.Öğe Business analytics in customer lifetime value: an overview analysis(Wiley Periodicals, Inc, 2025) Doğan, Onur; Hızıroğlu, Abdulkadir; Pisirgen, Ali; Seymen, Omer FarukIn 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.Öğe A clustering approach for classifying scholars based on publication performance using bibliometric data(Cairo Univ, Fac Computers & Information, 2024) Pisirgen, Ali; Peker, SerhatThis 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.Öğe 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, ÜmitPurposeThe 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.Öğe Enhancing e-business communication with a hybrid rule-based and extractive-based chatbot(MDPI, 2024) Doğan, Onur; Gurcan, Omer FarukE-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.Öğe Empowering manufacturing environments with process mining-based Statistical process control(MDPI, 2024) Dogan, Onur; Areta Hiziroglu, OuraniaThe 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.Öğe Cart-state-aware discovery of e-commerce visitor journeys with process mining(MDPI, 2024) Topaloglu, Bilal; Oztaysi, Basar; Doğan, OnurUnderstanding 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.Öğe Risk assessment of employing digital robots in process automation(MDPI, 2024) Doğan, Onur; Arslan, Ozlem; Tirpan, Esra Cengiz; Cebi, SelcukUsing 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.Öğe BSC-Based digital transformation strategy selection and sensitivity analysis(Mdpi, 2024) Oner, Mahir; Cebeci, Ufuk; Doğan, OnurIn today's digital age, businesses are tasked with adapting to rapidly advancing technology. This transformation is far from simple, with many companies facing difficulties navigating new technological trends. This paper highlights a key segment of a comprehensive strategic model developed to address this challenge. The model integrates various planning and decision-making tools, such as the Balanced Scorecard (BSC), Objectives and Key Results (OKR), SWOT analysis, TOWS, and the Spherical Fuzzy Analytic Hierarchy Process (SFAHP). Integrating these tools in the proposed model provides businesses with a well-rounded pathway to manage digital transformation. The model considers human elements, uncertainty management, needs prioritization, and flexibility, aiming to find the optimal balance between theory and practical applications in real-world business scenarios. This particular study delves into the use of SFAHP, specifically addressing the challenge of effectively selecting the most suitable strategy among various options. This approach not only brings a new perspective to digital transformation but also highlights the importance of choosing the right strategy. This choice is crucial for the overall adaptation of businesses. It shows how carefully applying the SFAHP method is key. Combining this with a successful digital transformation strategy is essential. Together, they provide practical and efficient solutions for businesses in a fast-changing technological environment.Öğe Türk üniversitelerinin aday öğrenci web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik açısından değerlendirilmesi(2022) Medeni, Tunç Durmuş; Peker, Serhat; Medeni, İhsan Tolga; Macakoglu, Sevval SerayÜniversitelerin aday öğrenci web sayfaları sosyal ve akademik anlamda paydaşlarına bilgi sağlama konusunda önemli bir iletişim kaynağıdır, bu sebeple bu sayfaların kullanımının kolay, güvenli ve erişilebilirlik standartlarına uygun olması beklenir. Bu makale, Türkiye’deki üniversitelerin aday öğrenci web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik değerlendirmesini sunmaktadır. Bu amaçla, üniversitelerin aday öğrencilerine yönelik 147 adet web sayfası tespit edilmiş ve çeşitli otomatik test araçları ile değerlendirilmiştir. Ayrıca, değerlendirme sonrası üniversitelerin benzer davranış kalıplarının belirlenmesi amacıyla kümeleme analizi yapılmıştır. Sonuçlar, devlet ya da vakıf fark etmeksizin üniversitelerin büyük çoğunluğunun erişilebilirlik ve kullanım kalitesine daha az dikkat ettiğini göstermiştir. Değerlendirilen web sayfalarının WCAG 2.0 yönergesine göre düşük uyumluluk seviyesine sahip olduğu görüldü. Güvenlik açısından kritik bir sorun tespit edilmemiştir ancak geliştirilmesi gereken belirli noktalar bulunmuştur. Ayrıca bu çalışma, web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik ihlallerin çözülebilmesi için geliştiricilere ve yöneticilere bazı değerli öneriler sunmaktadır.Öğe A machine learning framework for data-driven CRM(Springer, 2022) Peker Serhat; Kart ÖzgeIn today’s digital world, enterprises accumulate large quantiles of customer data which drives firms to implement data-driven CRM strategies to manage customer relationships. In CRM, machine learning techniques are widely used as a tool for using customer data and thereby acquiring knowledge from such data. In this context, this research presents a holistic framework for the implementation of machine learning methods in data-driven CRM applications. The proposed framework relies on past transactional data of customers and employs state-of-art machine learning techniques. This research serves as a foundation for future studies on data-driven CRM applications utilizing machine learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Öğe Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market(Emerald Group Publishing Ltd, 2022) Macakoğlu, Şevval Seray; Çınar, Burcu Alakuş; Peker, SerhatPurpose In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets. Design/methodology/approach Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score. Findings The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability. Practical implications The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices. Originality/value This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.Öğe ICT adoption scale development for SMEs(Mdpi, 2022) Özşahin, Mehtap; Çallı, Büşra Alma; Coşkun, ErmanInformation systems research lacks a validated scale for assessing and measuring the adoption of information and communication technologies (ICTs) by small- and medium-sized enterprises (SMEs). The relevant literature is limited in studies mainly concentrating on developing country settings. Furthermore, the emergence of new technological elements and increasing digitalization and digital transformation efforts in the last two years have changed how an organization utilizes and adopts ICTs. Therefore, it is inevitable that the conceptual dimensions proposed in the previous literature and the definitions of these dimensions will also alter. Hence, it is essential to revisit and validate the prior work and enhance it based on current vitality and developments. This study developed and validated a scale for measuring ICT adoption and digitalization for SMEs in a developing country context. The researchers followed an eight-step scale development procedure: (1) comprehensive literature review on ICT adoption and digitalization; (2) identification of dimensions of the level of ICT adoption and digitalization; (3) generation of items; (4) item refinement through focus group; (5) pretest of the measurement; (6) scale purification; (7) data collection; and (8) measurement evaluation. Within the Turkish setting, the ICT adoption scale was determined to have sufficient reliability and validity. Data for this study were gathered from 421 respondents of 219 Turkish SMEs. Supporting the multidimensionality of ICT adoption, 14 items and five dimensions (communication, internal integration, integration with customers, interorganizational integration, and strategic integration) constituted the ICT adoption construct. Considering the dominance of conceptual frameworks that were proposed based on developed countries and the prevalence of unidimensional constructs in the field, the developed multidimensional scale is expected to contribute significantly. Practitioners and policymakers can utilize the suggested scale to discover areas where specific changes are required for the digital transformation in SME utilization efforts that need attention. The outcomes can be applied to industrial sectors and different geographic contexts. By considering stage-based integration, the developed scale can also be used in future studies to investigate the effects of different variables on the extent of ICT adoption and the impact of ICTs on several organizational outcomes.Öğe Do generative leadership and digital literacy of executive management help flourishing micro and small business digital maturity?(Canadian Inst Knowledge Development - Cikd, 2022) Calli, BUsra Alma; Ozsahin, Mehtap; Coşkun, Erman; Arik, Ahmet RifatToday, every firm and organization must digitally transform in order to survive and deal with increasing competition and dynamic market conditions. Digital transformation is not easy to achieve, and many factors play an important role in the successful digital transformation of firms. Leaders' leadership styles and characteristics play a crucial role in digital transformation. This study examines the effects of generative leadership and digital literacy of executive management on the digital maturity of micro and small firms based on the Upper Echelons Theory. Sub-dimensions of digital maturity are also considered and searched to provide a more detailed analysis. The research utilized a survey method and was conducted with 121 upper, middle, and first-line managers of 93 micro and small-size firms operating in the Marmara Region of Turkey. Frequency, factor, regression, and validity and reliability analyses through the SPSS package program were used. The results are two folds. First, generative leadership and digital literacy of executive management help flourish digital maturity when searched independently. Second, the study results indicate that the digital literacy of executive managers has a mediating effect on the relationship between generative leadership and digital maturity. Furthermore, the study proves the mediating effect on digital maturity's technological, strategic, and cultural maturity sub-dimensions. With these findings in micro and small businesses, the study comprehensively contributes to the current knowledge in this domain. (C)CIKD PublishingÖğe Accessibility evaluation of university hospital websites in Turkey(Springer Heidelberg, 2022) Macakoğlu, Şevval Seray; Peker, SerhatHospital web pages serve as an interactive communication resource to meet the needs of patients, healthcare professionals and other stakeholders. The aim of this study is to present the accessibility analysis of 58 university hospital websites in Turkey. For this purpose, websites of the selected university hospitals were analyzed using two different online automated testing tools. The results showed that university hospital websites in Turkey had low compliance levels according to the WCAG 2.0 guidelines. Most of the websites did not even meet the minimum requirements for compliance level A. In addition, almost all of the websites had broken links and about a third of the websites had problems with accessing from mobile devices. Moreover, some important clues that draw attention to the accessibility problems of websites are also discussed in this study. Hence, the results of this study provide feedback to developers and administrators to improve the accessibility of these websites.Öğe Accessibility, usability, and security evaluation of universities' prospective student web pages: a comparative study of Europe, North America, and Oceania(Springer Heidelberg, 2022) Macakoğlu, Şevval Seray; Peker, Serhat; Medeni, İhsan TolgaUniversities' prospective student web pages aim to disseminate information about their academic and social opportunities to their stakeholders; therefore, they must be accessible, of high quality of use and reliable. This article presents the accessibility, usage performance, and security analysis of prospective student web pages of 330 universities from three continents, namely Europe, North America, and Oceania. For this purpose, university websites were selected based on the Webometrics ranking, and online automated test tools were used. The results showed that websites at universities in North America paid more attention to accessibility and quality of use on prospective student web pages, followed by Oceanian and European websites. Evaluated websites had low compliance levels according to the WCAG 2.0 guideline. No major problems were identified in terms of usability and security, but there were certain points for improvement. Moreover, we present and discuss recommendations to developers and administrators for websites to resolve accessibility, usability, and security breaches and provide information equally to all stakeholders. Hence, this analysis report provides feedback to web developers to improve accessibility, quality of use, and security issues of university websites and their prospective student web pages.Öğe Predicting firms' performances in customer complaint management using machine learning techniques(Springer International Publishing Ag, 2022) Peker, SerhatWith the globalization and more intense increasing competition, customer relationship management (CRM) is an important issue in today's business. In this manner, managing customer complaints which is a critical part of CRM presents firms with an is an opportunity to make long-lasting and profitable relationships with customers. In this context, the aim of this paper is to predict firms' performances in online customer complaint management using machine learning algorithms. This study utilizes data obtained from Turkey's largest and well-known third-party online complaint platform and employs three popular machine learning classifiers including decision tree (DT), random forests (RF) and support vector machines (SVM). The results show that the RF algorithm performed better in firms' performance prediction compared to other ML algorithms.Öğe Prioritizing use cases for development of mobile apps using AHP: A case study in to-do list apps(Springer, 2019) Yıldırım, Onur; Peker, SerhatWith the rapid development of communication technologies, the uses of mobile apps have increased in a significant manner over the past few years. Every day many different types of mobile apps are uploaded to mobile application markets. However, it is very difficult for the apps to stay competitive and survive in these marketplaces. Covering the requirements fitting the needs of users is one of significant factors in mobile apps’ success in the market. In this regard, this study aims to use Analytic Hierarchy Process (AHP) to evaluate the use cases for the development of mobile apps. The results show that AHP provides an efficient tool which can be used to determine importance of the requirements in mobile apps considering users’ preferences. © 2019, Springer Nature Switzerland AG.Öğe Customer churn prediction using deep learning(Springer Science and Business Media Deutschland GmbH, 2021) Seymen, Ömer Faruk; Doğan, Onur; Hızıroğlu, AbdülkadirChurn studies have been used for years to achieve profitability and to establish a sustainable customer-company relationship. Deep learning is one of the contemporary methods used in churn analysis due to its ability to process huge amounts of customer data. In this study, a deep learning model is proposed to predict whether customers in the retail industry will churn in the future. The model was compared with logistic regression and artificial neural network models, which are also frequently used in the churn prediction studies. The results of the models were compared with accuracy classification tools, which are precision, recall and AUC. The results showed that the deep learning model achieved better classification and prediction success than other compared models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe The effects of the content elements of online banner ads on visual attention: Evidence from an-eye-tracking study(MDPI, 2021) Peker, Serhat; Menekse Dalveren, Gonca Gokce; İnal, YavuzThe aim of this paper is to examine the influence of the content elements of online banner ads on customers' visual attention, and to evaluate the impacts of gender, discount rate and brand familiarity on this issue. An eye-tracking study with 34 participants (18 male and 16 female) was conducted, in which the participants were presented with eight types of online banner ads comprising three content elements-namely brand, discount rate and image-while their eye movements were recorded. The results showed that the image was the most attractive area among the three main content elements. Furthermore, the middle areas of the banners were noticed first, and areas located on the left side were mostly noticed earlier than those on the right side. The results also indicated that the discount areas of banners with higher discount rates were more attractive and eye-catching compared to those of banners with lower discount rates. In addition to these, the participants who were familiar with the brand mostly concentrated on the discount area, while those who were unfamiliar with the brand mostly paid attention to the image area. The findings from this study will assist marketers in creating more effective and efficient online banner ads that appeal to customers, ultimately fostering positive attitudes towards the advertisement.
