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“The Connected User Are You Safe?” - An Exploratory Study About Users Fear of Identity Theft
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceThe surge in digital transactions has led to a notable spike in identity theft cases, incurring enormous expenses for e-commerce companies and users. The safety of online businesses and their users depends on addressing identity theft. We utilise natural language processing to examine Reddit posts related to identity theft to determine the elements influencing individuals’ concerns about identity theft when transacting online. We match these factors with theoretical lexicons to create a unique framework. Further, to extend the study, we intend to validate these factors through statistical analysis to understand their impact on users’ fear of identity theft during online transactions
Critical Success Factors in Data Analytics Projects: Insights from a Systematic Literature Review
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceVarious data analytics applications are increasingly used by organizations to extract insights from data. There are numerous studies exploring the critical success factors (CSFs) in different data analytics fields including Business Intelligence, Artificial Intelligence, Machine Learning, Data Science, and Big Data. Despite the extensive body of research, there remains a gap in identifying a structured CSFs list that offers a holistic view across all these fields. This study addresses this gap by conducting a systematic literature review to investigate CSFs in data analytics projects, aiming to create a comprehensive list that is applicable across various fields. We have categorized CSFs into six key themes: People, Strategy, Technology & Data, Organizational Culture, Process Design and External Factors derived from 28 research papers. By presenting these CSFs comprehensively, this paper seeks to provide a structured approach that will enhance the success rates of data analytics projects, facilitating better strategic alignment and operational efficiency across multiple fields
Hackathon for Skills Development: An Unorthodox Approach for Audit Analytics Implementation
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceTechnical data processing and analysis skills are critical audit analytics (AA) implementation challenges. Nevertheless, there is limited guidance on how to overcome it other than the ‘traditional’ training approach. This paper explores an unorthodox approach to dealing with issues in AA-related skills through a data analytics competition (hackathon). This study observes two hackathons that contribute to addressing AA-related skills issues. The hackathon incorporates the elements of real-world use-case and interactive engagement from the gamification concept and internal and external motivations from the competition notion. Building upon these insights, this paper proposes an emerging framework of a hackathon for skills development in AA implementation efforts. The framework’s purpose is twofold. Scientifically, it assists in understanding the role of gamification and hackathon for skills development based on knowledge creation concepts. Practically, it exemplifies how to utilize hackathons to address skill issues. Furthermore, this research also suggests promising avenues for future studies, including further evaluation to affirm or expand the proposed framework or examination of the impact of detailed elements of the proposed framework
Artificial Intelligence in Supermarkets: A Multiple Analysis About Tasks, Jobs, and Automation
No full textPart 1: Artificial Intelligence Adoption and ImpactInternational audienceThis study aims to analyze the impacts of Artificial Intelligence (AI) and automation in the supermarket sector, focusing on three main areas: tasks, jobs, and automation processes. The research builds on studies about technology adoption and its impact on employment, including Christensen approach on disruptive innovation, Huang & Rust [19] on artificial intelligence in services, and Jarrahi [27] on human-AI symbiosis. A multiple case study approach was employed, involving interviews with four groups: cashiers, managers, customers who use self-checkouts, and customers who do not use self-checkouts. The interviews were analyzed using qualitative methods to identify emerging subcategories. Many customers prefer manned checkouts due to convenience. Self-service checkout technology is primarily used for small purchases, and younger customers find it easier to use. Employees do not currently feel threatened by automation, but there is a trend toward job reduction and relocation to roles requiring analytical, intuitive, and emotional skills. Managers do not see the need to prepare employees for a future without self-service checkouts, focusing instead on current training. Future research should analyze other supermarket functions that could be impacted by AI automation and investigate more deeply the acceptance and impact of these technologies on the labor market
Affordance Actualization of Social Robots: Empirical Evidence from the Hotel Industry
No full textPart 3: Healthcare, Social Well-Being, and EthicsInternational audienceSocial robots exhibit a range of affordances for users. Drawing on the affordance theory, this study examined the affordance actualization of social robots in hotels based on user experience of social robots use. An LDA topic modeling analysis was performed based on online hotel reviews generated by hotel customers on TripAdvisor. We found that users actualize social robots’ affordances such as luggage and storage handling, front desk tasks, delivery tasks, and navigational assistance while providing enjoyment and social interactions during hotel services. This study contributes to the literature by uncovering the actualized utilitarian, social, and hedonic affordances and the specific dimensions of these affordances of social robots in hotels
Integrating Generative AI with TRIZ for Evolutionary Product Design
No full textPart 1: AI-Driven TRIZ and InnovationInternational audienceDemocratizing, scaling, and automating creativity and innovation are essential for boosting competitiveness, efficiency, and cost-effectiveness in product creation. This capability will be a key differentiator for organizations seeking a competitive edge and a catalyst for ensuring a better future for humanity. We demonstrate how the TRIZ methodology and Generative AI provide a solid foundation for solving product design challenges. We show how technology and human feedback enable the automated discovery of product design improvements. To prove this, we have developed a technological proof-of-concept solution that leverages the strengths of both TRIZ and Generative AI. This solution explores product reviews, identifies product strengths and weaknesses, and generates an initial palette of potential product design problems that can be iteratively refined to drive product improvement decisions. We provide the theoretical foundation by utilizing TRIZ’s systematic problem-solving approach and specific examples of generated data, creating a consistent basis for evaluating the tremendous potential of Generative AI and TRIZ in product design and improvement processes
Use of AI in the TRIZ Innovation Process: A TESE-Based Forecast
No full textPart 1: AI-Driven TRIZ and InnovationInternational audienceCurrently, AI is mainly used in the TRIZ innovation process to find solutions to well-defined technical problems using TRIZ tools such as Inventive Principles, Standards and, occasionally, Functional Oriented Search (FOS). In practice, however, problem solving is usually the least time-consuming part of the innovation process, with most effort normally spent defining the overall goal of the innovation, identifying and analyzing the initial problem, selecting the best solution from the set of solutions found, and justifying its feasibility. Therefore, AI would be much more useful if it were introduced into these labor-intensive parts of the TRIZ innovation process as well, and undoubtedly AI developers will eventually try to automate the entire innovation process. The objectives of this paper are (1) to assess the current effectiveness of using AI in real TRIZ projects, (2) to predict the most likely sequence of future AI implementation in different parts of the TRIZ innovation process, and (3) to identify related challenges. The objectives are achieved by analyzing the composition and timing of various activities in a typical TRIZ project and applying the Trend of Decreasing Human Involvement to these activities, where a TRIZ project is considered a technological process that transforms an initial, poorly formulated problem into a viable solution/product. These results can be used by AI and TRIZ specialists to create a roadmap for integrating AI and TRIZ to produce a fully automated innovation process that is applicable to technical systems and, potentially, to business systems
Research on Disruptive Technology Prediction Methods Based on BERT Model and Graph Theory Analysis
No full textPart 1: AI-Driven TRIZ and InnovationInternational audienceWith the acceleration of the technological revolution, disruptive technologies have become a key factor in global technological competition. However, existing prediction methods are limited by single technology fields, semantic analysis limitations, and subjective factors, making it difficult to effectively predict these technologies. In this paper, we studied the current disruptive technology prediction methods using the ideal solution analysis and resource analysis tools of TRIZ theory and proposed a new prediction method. This method combines the BERT model and graph theory analysis for the first time, and it analyzes patent text, mines the inherent relationships between cross-domain technologies, extracts disruptive technology features, and evaluates them through expert judgments. This method fills the gap in existing research. Our method demonstrates unique innovation in cross-domain technology integration and can more accurately predict disruptive technologies. The research results show that the patent technologies selected after being fused perform excellently in terms of performance and advantages, verifying the scientificity and effectiveness of our research framework. This study provides a new and effective method for exploring and predicting disruptive technologies, which is expected to drive further development in related fields
Partner Selection in Additive Manufacturing Networks
No full textPart 4: Mechanism Design for Smart and Sustainable Supply ChainsInternational audienceConsidering the special features of additive manufacturing networks, and with the aim to realize a more efficient functionality of these networks, this work focuses on the issues of partner selection. Based on an in-depth literature review encompassing more than 170 papers, 61 criteria clusters are listed as a general basis. A horizontal and vertical classification of criteria is further conducted, attempting to cover different situations of partner selection in additive manufacturing networks. Here, economic, social, environmental and resilience perspectives are composed as the horizontal view. Key stages of additive manufacturing processes serve as the vertical one. A mathematical algorithm model is further developed to facilitate partner matching. Within the algorithm model, motivations and their relative priorities are considered. This work contributes to enhancing the reliability of partner selection processes, which in the end improves the efficiency of networks’ functionality
Data-Driven Root-Cause Analysis in the Scope of Continuous Improvement Projects
No full textPart 1: Lean Thinking Models for Operational Excellence and Sustainability in the Industry 4.0 EraInternational audienceLean Manufacturing is widely recognized as a prominent methodology for implementing Continuous Improvement (CI) in industrial settings. Root Cause Analysis (RCA) plays a vital role in problem-solving projects, serving as a key component within CI methods like the Plan-Do-Check-Act cycle. However, the RCA process can be time-consuming, relying heavily on the expertise of technicians to manually analyze substantial amounts of data. To address these challenges Industry 4.0 technologies, namely Machine Learning (ML), are being implemented into RCA bringing new challenges such as the need for ML expertise in the lean field. Aiming to contribute to overcoming these challenges, this work presents the development of an Assistance System (AS) that integrates descriptive analysis with ML techniques to support lean technicians in the root cause identification phase. This AS encompasses feature selection, hyperparameter tuning, data balancing and feature importance. Logistic Regression, Decision Tree, Random Forest and XGBoost are the models employed, with F1-Score being the evaluation metric. Besides the ML analysis, the AS also allows for descriptive analysis without ML and data profiling. The results of the ML analysis are presented and compared with the standard descriptive analysis to highlight the effectiveness of the AS in the root cause identification process. The successful integration of descriptive analysis and ML techniques enables a systematic approach to problem-solving, leading to improved overall efficiency and competitiveness in industrial settings