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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Abdimasku : Jurnal Pengabdian Masyarakat International Journal of Social Science Journal of Advances in Information Systems and Technology MERDEKA: Jurnal Ilmiah Multidisiplin Journal of Advances in Information Systems and Technology
Endang Sugiharti
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal of Advances in Information Systems and Technology

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Acceptance of Artificial Intelligence-Based Online Shopping Applications: A Combination of Artificially Intelligent Device Use Acceptance and Online Shopping Service Quality Tiffany Ovilia Dwi Lestari; Endang Sugiharti
Journal of Advances in Information Systems and Technology Vol. 6 No. 1 (2024): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v6i1.2232

Abstract

Nowadays, e-commerce, including Shopee, is often associated with Artificial Intelligence (AI). The use of AI systems triggers the emergence of new marketing methods to reach consumers effectively and offer a better shopping experience. Moreover, the increased use of AI in online commerce occurs because AI is considered an excellent tool to meet rapidly changing consumer demands. Currently, more sellers are using AI-supported features such as chatbots, smart logistics, and personalized recommendations. This makes online channels more competitive and enticing for consumers to make purchases. Despite the numerous benefits of e-commerce and AI, they are not exempt from shortcomings that make customers reluctant to use them. Therefore, this research aims to understand the relationship among factors influencing the acceptance and objection of AI-based Shopee by using a combination of Artificially Intelligent Device Use Acceptance (AIDUA) and Online Shopping Service Quality (OSSQ). The study employs a quantitative method with survey data collection techniques. The collected sample from the survey process consists of 169 respondents, mostly females aged 17-26 years, and students. The results obtained find that factors significantly influencing performance expectancy are social influence, hedonic motivation, anthropomorphism, website design, responsiveness, communication, and trustworthiness. Factors affecting effort expectancy are social influence, reliability, communication, anthropomorphism, and website design. Meanwhile, the factor influencing emotion is performance expectancy. Lastly, the factors influencing willingness to use and objection to use are emotion. Based on the research findings, Shopee developers can enhance the quality of their AI programming algorithms and improve the design quality of Shopee.
Customer Lifetime Value Clustering Using K-Means Algorithm with Length Recency Frequency Monetary Model to Enhance Customer Relationship Management Chairun Nisak; Endang Sugiharti
Journal of Advances in Information Systems and Technology Vol. 6 No. 1 (2024): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v6i1.5011

Abstract

The current era of business growth is fraught with challenges and competition due to rapid technological advancements, rapid market growth, and globalization. This research discusses customer management strategies to enhance Customer Relationship Management (CRM) at PT Digibook Sarana Promosi Indonesia, a company in the digital printing industry. With the emergence of numerous competitors in this challenging business growth era, the k-means algorithm and Length, Recency, Frequency, Monetary (LRFM) model are employed for customer clustering. The results identify two main customer groups. The first group falls into the category of almost lost or uncertain lost customers with the symbol L↓R↑F↓M↓, exhibiting low Customer Lifetime Value (CLV), suggesting a "let go" strategy to focus on more valuable customers. The second group comprises high-value loyal customers with the symbol L↑R↓F↑M↑, demonstrating high CLV, recommending an "enforced" strategy to maintain customer loyalty through loyalty programs. This research indicates that the optimal number of clusters is 2, validated using the ClValid method, with the best values on connectivity, Dunn index, and silhouette.
Analysis of Factors Affecting The Sustainability of Using Online Loan Applications Using The Information System Success Model and Expectation Confirmation Model Kevin Tito Hutahaean; Endang Sugiharti
Journal of Advances in Information Systems and Technology Vol. 6 No. 2 (2024): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v6i2.12035

Abstract

The rapid digitization of financial services has spurred the growth of cashless lending systems, with online lending emerging as a prominent method for individuals and businesses to access funds. This trend is driven by the ease of loan application through online platforms and the challenges associated with traditional bank loans. However, online loans often come with higher interest rates compared to conventional banks, raising concerns about users' long-term engagement with these platforms. This study investigates the factors influencing the continuance intention of users in utilizing online loan applications. Employing a quantitative approach, the research integrates the Information System Success Model (ISSM) and the Expectation Confirmation Model (ECM) to examine 13 variables across 24 hypotheses. Data was collected via Google Forms, distributed through social media, targeting individuals aged 17 to 55 in Indonesia who had previously applied for online loans. After rigorous data screening, 227 valid responses were analyzed using SmartPLS. The findings revealed that out of the 13 variables, use, satisfaction, and debt attitudes significantly influence continuance intention, while other factors like perceived usefulness and impulsive buying were less impactful. Future studies should explore a broader respondent base and incorporate variables such as interest rates and urgency to provide a more comprehensive understanding of users' continued use of online loan applications.
Co-Authors Abas Setiawan Agus Subarkah Agusinta, Lira Chairun Nisak Dini Wagini Elfi Amir Erika Noor Dianti Fajar Ardhi Gibran HM. Thamrin Kevin Tito Hutahaean Metha Yurike Cartika Mirqotussa’adah Mirqotussa’adah Moch Faisal Muzaki Mochamad Faisal Muzaki Much Aziz Muslim Muh Hasbullah Muhammad Nabil Rahadinan Novanka Agnes Sekartaji Novita Ayu Permatasari Prasada Ricardianto Prisma Bayu Nugroho Purwoko, Agus Rini Sadiatmi Riza Arifudin Siti Alimah Tiffany Ovilia Dwi Lestari Togi Adnan Maruli Sinaga Yolanda Wina Soendari Siahaan Yudha Pratidhina