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检索条件"丛书名=Lecture Notes in Business Information Processing 252"

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Data Quality, Product Characteristics, and Product Data Pricing in Manufacturing Enterprises 1

引用

23rd Wuhan International Conference on E-business (WHICEB)

作者： Li, Huiyang Zhang, Meishu Jia, Yu Wang, Nianxin Ge, Shilun Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212003 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Dept Humanities & Social Sci Zhenjiang 212003 Jiangsu Peoples R China

ISBN: (数字)9783031603242

ISBN: (纸本)9783031603266;9783031603242

The pricing of data products is a pivotal aspect of the data factor market's construction process, as it holds the key to unlocking the ten-trillion-dollar market potential. Prior studies primarily centered on pricing personal, public, and financial data. In this study, we delve into a novel type of data product, product data, and aim to construct a profit-maximizing pricing model. Specifically, we address the pricing challenge of manufacturing product data and develop a profit-maximizing pricing model tailored to manufacturing product data, considering factors including data quality utility, product characteristics, and the interplay between market supply and demand. To evaluate our findings, we conducted a simulated pricing analysis using a public dataset. Our findings suggest that product competitiveness exerts a more significant influence on pricing than data quality. As such, product data owners must devise strategic pricing plans to achieve optimal profit levels. The model introduced in this study not only supplements the existing theory and methods of data product pricing but also offers novel perspectives on product data pricing strategies.

关键词： Produce Data Pricing Data Quality Product Characterization Manufacturing Enterprises

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Timed Process Interventions: Causal Inference vs. Reinforcement Learning

Timed Process Interventions: Causal Inference vs. Reinforcem...

引用

21st International Conference on business Process Management (BPM)

作者： Weytjens, Hans Verbeke, Wouter De Weerdt, Jochen Katholieke Univ Leuven Fac Econ & Business Res Ctr Informat Syst Engn LIRIS Leuven Belgium

ISBN: (纸本)9783031509735;9783031509742

The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process interventions are the cornerstones of effective optimization. Prescriptive process monitoring (PresPM) is the sub-field of process mining that concentrates on process optimization. The emerging PresPM literature identifies state-of-the-art methods, causal inference (CI) and reinforcement learning (RL), without presenting a quantitative comparison. Most experiments are carried out using historical data, causing problems with the accuracy of the methods' evaluations and preempting online RL. Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results. Our experiments reveal that RL's policies outperform those from CI and are more robust at the same time. Indeed, the RL policies approach perfect policies. Unlike CI, the unaltered online RL approach can be applied to other, more generic PresPM problems such as next best activity recommendations. Nonetheless, CI has its merits in settings where online learning is not an option.

关键词： Prescriptive Process Monitoring Process Optimization Timed Interventions Causal Inference Reinforcement Learning

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Experiences from the Internet-of-Production: Using "Data-Models-in-the-Middle" to Fight Complexity and Facilitate Reuse

Experiences from the Internet-of-Production: Using "Data-Mod...

引用

21st International Conference on business Process Management (BPM)

作者： van der Aalst, Wil M. P. Rhein Westfal TH Aachen Proc & Data Sci Aachen Germany Celonis Theresienstr 6 D-80333 Munich Germany

ISBN: (纸本)9783031509735;9783031509742

Data-driven approaches play a key role in improving operational processes and production is no exception. The Internet-of-Production (IoP) is an ambitious initiative aiming at cross-domain collaboration in production while exploiting semantically adequate and context-aware data at different levels of granularity. The Internet-of-Things (IoT), in the context of production also referred to as Industry 4.0 or the Industrial Internet of Things, provides a wide range of data assets. However, these are often handled in an ad-hoc manner with little support for reuse. Data pipelines convert machine- or system-specific data into a format suitable for data-science techniques such as machine learning. Based on an analysis of the data used in IoP, we developed so-called "Data-Models-in-the-Middle" (DMMs). Two such models are described in this paper: Measurement and Event Data (MAED) and Object-Centric Event data (OCED). OCED enables Object-Centric Process Mining (OCPM), allowing organizations to view their operational processes from any perspective using a single source of truth. However, OCED is not suitable for low-level machine data that contain a mixture of continuous measurements (e.g., time series data describing position, temperature, force, speed, etc.) and discrete events. Therefore, we also propose MAED as a data format. The combination of both "Data-Models-in-the-Middle" (MAED and OCED) provides a good coverage of many production-related use cases.

关键词： Internet-of-Things Internet-of-Production Process Mining business Process Management Reference Models

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A Quantitative Assessment Method for Microservices Granularity to Improve Maintainability

A Quantitative Assessment Method for Microservices Granulari...

引用

Enterprise Design, Operations, and Computing (EDOC) Forum / IDAMS Workshop / SoEA4EE Workshop / IRESEARCH Workshop / MIDas4CS Workshop

作者： Driessen, Famke Pires, Luis Ferreira Moreira, Joao Luiz Rebelo Verhoeven, Paul van den Bosch, Sander Univ Twente Semant Cybersecur & Serv Grp POB 217 NL-7500 AE Enschede Netherlands Deloitte Consulting POB 58110 NL-1040 HC Amsterdam Netherlands

ISBN: (纸本)9783031547119;9783031547126

The popularity of microservices has increased over the past decade due to their potential benefits for distributed enterprise applications. Developing and maintaining a microservices architecture (MSA) is challenging, amongst others because the size (granularity) of the microservices has an impact on most system properties, such as, e.g., maintainability, performance, and scalability. Currently, architects determine the granularity of the microservices by identifying bounded contexts or business capabilities, which is mainly based on their experience. This paper presents a quantitative assessment method to evaluate the granularity of the microservices for improved system maintainability. The method is based on a set of metrics that are relevant for maintainability, namely change coupling, structural coupling, weighted service interface count, lines of code, service interface data cohesion, and change frequency. By evaluating these metrics, focusing on coupling, cohesion, and size, the method can assess refactors of an architecture in which microservices are merged or decomposed. We validated our method with three open source microservice-based projects with different sizes and structures. Our method can substantiate the design decisions concerning the granularity of microservices, identifying services that are candidates for merging and decomposition towards maintainability evolution, with clear benefits particularly for enterprise applications.

关键词： microservices architecture granularity maintainability service coupling service cohesion change coupling

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Is it Always Better to Be Distinctive? Optimal distinctiveness in the Sharing Economy Platform Market to Maintain Active User 23rd

Is it Always Better to Be Distinctive? Optimal distinctivene...

引用

23rd Wuhan International Conference on E-business (WHICEB)

作者： Zhu, Mengsi Wang, Nianxin Yang, Yuxue Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212100 Jiangsu Peoples R China

ISBN: (纸本)9783031602597;9783031602603

In today's competitive app market, developers must provide a range of user-oriented features to improve app performance. This study extends the theory of optimal differentiation to the sharing economy domain to investigate whether there exists an optimal feature heterogeneity that makes apps perform at their highest. Based on the panel data of eight categories of app information in the sharing economy domain from September 2021 to September 2023, machine learning methods as well as fixed-effects models are used to explore the effect of app functional heterogeneity on app user activity. It is found that there is a significant "inverted U-shaped" relationship between functional heterogeneity and user activity, and we elaborate on the contribution of this finding to the theory of the sharing economy and the competitive strategies of sharing platforms. We also discuss implications for practice and make suggestions for future work.

关键词： Sharing Economy Platform Optimal Distinctiveness Functional Heterogeneity

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The Influence of the Similarity of Digital Transformation Orientation Between Enterprises and Large Customers on Customer Dependence 23rd

The Influence of the Similarity of Digital Transformation Or...

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23rd Wuhan International Conference on E-business (WHICEB)

作者： Wu, Jiang Wang, Yunxu Wuhan Univ Wuhan 430072 Peoples R China

ISBN: (纸本)9783031602597;9783031602603

In recent years, the market has been depressed and large customers have frequently experienced black swan events, posing significant challenges for enterprises' survival and development. Therefore, it is crucial to enhance the stability of large customers. Leveraging text analysis technology, this study empirically examines the impact of digital transformation orientation similarity on customer dependence and its underlying mechanism using data from Shanghai-Shenzhen A-share listed enterprises spanning 2016 to 2021. The findings reveal that: (1) Adopting a digital transformation orientation similar to key customers strengthens customer dependence and enhances customer stability. (2) Trust levels are enhanced, product enterprise standards are standardized and unified, and bargaining power is improved as intermediate mechanisms through which digital-transformation-oriented similarity positively influences customer dependence. (3) Heterogeneity tests considering external environmental factors and internal characteristics of enterprises show that in developed regions with low profitability or non-state-owned status, the positive impact of digital-transformation-oriented similarity on customer dependence becomes more prominent. These conclusions remain robust even after conducting endogenous tests. This paper not only provides new insights into exploring the similarity between enterprises' digital transformation orientations and those of their major customers but also offers empirical evidence supporting the notion that an effective digital transformation strategy can facilitate rapid enterprise development.

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Prediction of Banking Customer Churn Based on XGBoost with Feature Fusion 1

引用

23rd Wuhan International Conference on E-business (WHICEB)

作者： Hu, Zhongyi Dong, Fangrui Wu, Jiang Misir, Mustafa Wuhan Univ Wuhan 430072 Peoples R China Duke Kunshan Univ Kunshan 215316 Peoples R China

ISBN: (数字)9783031603242

ISBN: (纸本)9783031603266;9783031603242

With the increasing competition in the banking industry, accurate prediction of banking customer churn has become an important way in managing customer relationships. To explore efficacy features, enhance the generalization performance of customer churn prediction, this study proposed a XGBoost model with feature fusion for banking customer churn prediction. At first, a feature fusion model based on improved RFM and Affinity Propagation clustering was proposed to extract features representing the long-term and dynamic behavior of customers. By integrating different types of features, a XGBoost model was proposed to predict customer churn. Experimental results demonstrate the superior performance of the proposed model in comparison to other benchmark models.

关键词： Customer churn prediction XGBoost Feature fusion

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Towards a business Case for AI Ethics 14th

Towards a Business Case for AI Ethics

引用

14th International Conference on Software business (ICSOB)

作者： Agbese, Mamia Halme, Erika Mohanani, Rahul Abrahamsson, Pekka Univ Jyvaskyla Seminaarinkatu 15 Jyvaskyla 40014 Finland Tampere Univ Kalevantie 4 Tampere 33100 Finland

ISBN: (纸本)9783031532269;9783031532276

The increasing integration of artificial intelligence (AI) into software engineering (SE) highlights the need to prioritize ethical considerations within management practices. This study explores the effective identification, representation, and integration of ethical requirements guided by the principles of IEEE Std 7000-2021. Collaborating with 12 Finnish SE executives on an AI project in autonomous marine transport, we employed an ethical framework to generate 253 ethical user stories (EUS), prioritizing 177 across seven key requirements: traceability, communication, data quality, access to data, privacy and data, system security, and accessibility. We incorporate these requirements into a canvas model, the ethical requirements canvas. The canvas model serves as a practical business case tool in management practices. It not only facilitates the inclusion of ethical considerations but also highlights their business value, aiding management in understanding and discussing their significance in AI-enhanced environments.

关键词： AI ethics artificial intelligence ethical requirements IEEE Std 7000-2021 ethical requirements canvas software engineering

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The Effect of Anthropomorphic Features of Virtual Streamers on Consumers' Purchase Intention-The Mediating Role of Sense of Trust 23rd

The Effect of Anthropomorphic Features of Virtual Streamers ...

引用

23rd Wuhan International Conference on E-business (WHICEB)

作者： Tong, Yang Diao, Yajing Liu, Bingxue Jiangsu Univ Sci & Technol Zhenjiang 212000 Jiangsu Peoples R China

ISBN: (纸本)9783031602634;9783031602641

Live streaming e-commerce has become increasingly popular due to its instant, interactive, and entertaining features. This is especially true in light of the 2020 epidemic and related policies, which have led to explosive growth in live streaming with goods. Additionally, the rapid development of digital technologies such as artificial intelligence, virtual reality, and three-dimensional images has led to the gradual emergence of virtual streamers in the field of live streaming. The article presents a mediation model of virtual streamer's anthropomorphic features influencing consumer purchase behavior through trust, based on SOR theory. Empirical research is conducted to support the model. The study findings indicate that virtual streamers' affinity has a positive impact on consumers' purchase intention. Additionally, the sense of competence and intimacy also positively affect consumers' purchase intention. However, the mimicry and responsiveness of virtual streamers do not significantly influence consumers' purchase intention. Furthermore, the sense of competence and trust partially mediate the relationship between anthropomorphic features of virtual streamers and consumers' purchase intention. The article's results expand on relevant research regarding virtual streamers in live streaming and provide insights for enterprises and practitioners to better utilize virtual streamers for live streaming with goods.

关键词： virtual streamers anthropomorphic features trust consumers' purchase intention

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Analyzing the Devil's Quadrangle of Process Instances Through Process Mining

Analyzing the Devil's Quadrangle of Process Instances Throug...

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21st International Conference on business Process Management (BPM)

作者： Velasquez, Ignacio Sepulveda, Marcos Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9783031509735;9783031509742

The Devil's Quadrangle is a framework used in business Process Management to describe the inherent process performance trade-offs regarding the time, costs, flexibility, and quality dimensions. In practice, improving a process through one of these dimensions might have a negative effect on the performance of the other dimensions. The dimensions considered by the Devil's Quadrangle are often used for defining indicators that illustrate the overall performance of processes. From a Process Mining perspective, analyzing these dimensions at higher granularity levels, such as for every process instance, is of interest. To achieve this, this work proposes a method for defining Process Mining filters based on metrics related to performance indicators of the four Devil's Quadrangle dimensions. The metrics are calculated for every process instance, which allows using the filters to observe differences in process behavior while considering constraints to the performance indicators and tradeoffs among the four dimensions. It is expected that this visualization will be helpful during exploratory process analysis. It will facilitate the identification of process instances that conform to the filters applied to the performance indicators, as well as the dimensions where improvement is required while considering process instances that do not conform to the applied filters. A Celonis dashboard with the proposed filters has been generated to validate the method.

关键词： Process Mining Devil's Quadrangle Process Analysis Performance Analysis Process Performance Indicators

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