Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers’ needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment  associated to user’s intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren’t familiar with technical features, perceives usefulness is a main factor influencing users’ adoption. Meanwhile, perceived enjoyment plays a role on user’s intention to adopt this interaction model, for users who are familiar with technical features of product

Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization

Due to advances in Artificial Intelligence (AI), it is possible to provide advisory services without human advisors. Derived from judge-advisor system literature, we examined differences in the advice utilization depending on whether it is given by an AI-based or human advisor and the similarity of the advice and their own estimation. Drawing on task-technology fit we investigated the relationship between task, advisor and advice utilization. In study A we measured the actual advice utilization within a guessing game and in study B we measured the perceived task-advisor fit for this game. The findings show that compared to human advisors, judges utilize advices of AI-based advisors more when the advice is similar to their own estimation. When the advice is very different to their estimation, the advices are used equally. Concluding, we investigated AI-based advice utilization and presented insights for professionals providing AI-based advisory services

Explainable software systems: from requirements analysis to system evaluation

The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements

Explainable software systems: from requirements analysis to system evaluation

The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements

Conversational Recommender System: Berbasis pada Kebutuhan Fungsional Produk

Menyatakan kebutuhan berdasarkan fitur teknis produk sering menyulitkan banyak calon pembeli, khususnya untuk produk multi fungsi dan mempunyai banyak fitur, seperti mobil, notebook, smartphone, server, kamera, dan sebagainya, dsb-dan sebagainya. Hal ini dikarenakan tidak semua orang familiar terhadap fitur teknis dari produk-produk tersebut. Menanyakan kebutuhan pengguna aspek kegunaan (kebutuhan fungsional) dari produk yang akan dibeli, adalah cara yang lebih natural dalam menggali kebutuhan pengguna. Oleh karena itu, buku ini menyajikan bagaimana membangun sebuah conversational recommender system (CRS) yang memperhatikan aspek kebutuhan fungsional produk. Ontologi dipilih sebagai pengetahuan dari sistem, karena nature dari struktur ontologi, memungkinkan untuk membuat pemetaan yang lebih fleksibel antara kebutuhan fungsional produk, spesifikasi, dan produk. Selain itu, dalam ontologi, memungkinkan untuk penyusunan masingmasing konsep (entitas) secara hirarkis, dan struktur seperti ini sangat menguntungkan, terutama untuk mendukung pengembangan model pembangkitan pertanyaan. Struktur ontologi ini mempunyai 3 kelas utama, yaitu FuncReq (merepresentassikan kebutuhan fungsional), Specification (merepresentasikan gradasi kualitas fitur teknis) dan Product (merepresentasikan klasifikasi produk). Ontologi merupakan basis pengetahuan dari sistem. Mekanisme interaksi dilakukan melalui dialog tanya jawab, rekomendasi produk dan penjelasan mengapa suatu produk direkomendasikan, seperti layaknya interaksi antara calon pembeli dengan professional sales support. Model komputasional untuk membangkitkan interaksi dikembangkan dengan memanfaatkan eksplorasi relasi semantik dalam ontologi. Dengan model dan struktur ontologi ini, diharapkan pengembangan CRS yang disajikan dalam buku ini, dapat juga diterapkan untuk berbagai domain yang berbeda, khususnya untuk domain produk yang bersifat multi fungsi dan mempunyai banyak fitur (notebook, server, PC, mobil, kamera, smartphone, dan sebagainya, dsbdan sebagainya). iv Conversational Recommender System Berbasis Pada Kebutuhan Fungsional Produk Evaluasi terhadap CRS yang dibangun meliputi evaluasi dari sisi efisiensi maupun efektifitas. Hasil evaluasi menunjukkan bahwa model interaksi dalam CRS berbasis kebutuhan fungsional mampu melakukan mekanisme query requirement dengan efisien, berdasarkan pengurangan jumlah sisa record secara signifikan dalam 4 interaksi. Dalam 4 interaksi, jumlah produk yang direkomendasikan kurang dari 20 dari 288 produk yang ada (< 0.6.9%). Dari sisi efektifitas, dilakukan user study yang melibatkan pengguna yang familiar (expert user) maupun tidak familiar (novice user) dengan fitur teknis produk. Hasil pengujian menunjukkan, CRS berbasis kebutuhan fungsional cukup efektif dalam memandu pengguna. Hal ini ditunjukkan dengan, baik expert maupun novice user lebih menyukai model interaksi CRS berbasis kebutuhan fungsional daripada model interaksi pada aplikasi pencarian produk berbasis pada fitur teknis produk (expert user: 86.67%, novice user: 90%). User study selanjutnya menunjukkan, interaksi dalam CRS berbasis kebutuhan fungsional mampu meningkatkan persepsi positif pengguna, dibandingkan dengan interaksi yang berbasis pada fitur teknis produk, dilihat dari perceived ease of use, perceived enjoyment, trust dan perceived usefulness. Selain itu, model interaksi juga efektif dalam mempengaruhi pengguna untuk tertarik mengadopsi sistem, namun terdapat perbedaan dalam faktor-faktor yang mempengaruhi hal tersebut. Untuk expert user, perceived enjoyment merupakan faktor yang mempengaruhi secara langsung untuk adopsi sistem, sedangkan perceived usefulness merupakan faktor yang secara langsung mempengaruhi adopsi sistem, bagi novice use

Following the Robot – Investigating the Utilization and the Acceptance of AI-based Services

In the past few years, there has been significant progress in the field of artificial intelligence (AI), with advancements in areas such as natural language processing and machine learning. AI systems are now being used in various industries and applications, from healthcare to finance, and are becoming more sophisticated and capable of handling complex tasks. The technology has the potential to assist in both private and professional decision-making. However, there are still challenges to be addressed, such as ensuring transparency and accountability in AI decision-making processes and addressing issues related to bias and ethics, and it is not yet certain whether all of these newly developed AI-based services will be accepted and used. This thesis addresses a research gap in the field of AI-based services by exploring the acceptance and utilization of such services from both individual and organizational perspectives. The research examines various factors that influence the acceptance of AI-based services and investigates users' perceptions of these services. The thesis poses four research questions, including identifying the differences in utilizing AI-based services compared to human-based services for decision-making, identifying characteristics of acceptance and utilization across different user groups, prioritizing methods for promoting trust in AI-based services, and exploring the impact of AI-based services on an organization's knowledge. To achieve this, the study employs various research methods such as surveys, experiments, interviews, and simulations within five research papers. Research focused on an organization that offers robo-advisors as an AI-based service, specifically a financial robo-advisor. This research paper measures advice-taking behavior in the interaction with robo-advisors based on the judge-advisor system and task-technology fit frameworks. The results show the advice of robo-advisors is followed more than that of human advisors and this behavior is reflected in the task-advisor fit. Interestingly, the advisor's perceived expertise is the most influential factor in the task-advisor fit for both robo-advisors and human advisors. However, integrity is only significant for human advisors, while the user's perception of the ability to make decisions efficiently is only significant for robo-advisors. Research paper B examined the differences in advice utilization between AI-based and human advisors and explored the relationship between task, advisor, and advice utilization using the task-advisor fit just like research paper A but in context the of a guessing game. The research paper analyzed the impact of advice similarity on utilization. The results indicated that judges tend to use advice from AI-based advisors more than human advisors when the advice is similar to their own estimation. When the advice is vastly different from their estimation, the utilization rate is equal for both AI-based and human advisors. Research paper C investigated the different needs of user groups in the context of health chatbots. The increasing number of aging individuals who require considerable medical attention could be addressed by health chatbots capable of identifying diseases based on symptoms. Existing chatbot applications are primarily used by younger generations. This research paper investigated the factors affecting the adoption of health chatbots by older people and the extended Unified Theory of Acceptance and Use of Technology. To investigate how to promote AI-based services such as robo-advisors, research paper D evaluated the effectiveness of eleven measures to increase trust in AI-based advisory systems and found that noncommittal testing was the most effective while implementing human traits had negligible effects. Additionally, the relative advantage of AI-based advising over that of human experts was measured in the context of financial planning. The results suggest that convenience is the most important advantage perceived by users. To analyze the impact of AI-based services on an organization's knowledge state, research paper E explored how organizations can effectively coordinate human and machine learning (ML). The results showed that ML can decrease an organization's need for humans’ explorative learning. The findings demonstrated that adjustments made by humans to ML systems are often beneficial but can become harmful under certain conditions. Additionally, relying on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but it requires significant initial setup and coordination with humans. These findings offer new perspectives on organizational learning with ML and can guide organizations in optimizing resources for effective learning. In summary, the findings suggest that the acceptance and utilization of AI-based services can be influenced by the fit between the task and the service. However, organizations must carefully consider the user market and prioritize mechanisms to increase acceptance. Additionally, the implementation of AI-based services can positively affect an organization's ability to choose learning strategies or navigate turbulent environments, but it is crucial for humans to maintain domain knowledge of the task to reconfigure such services. This thesis enhances our understanding of the acceptance and utilization of AI-based services and provides valuable insights on how organizations can increase customers’ acceptance and usage of their AI-based services as well as implement and use AI-based services effectively