Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Biedebach, Luka; Óskarsdóttir, María; Arnardottir, Erna; Islind, Anna Sigríður

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Topics: mobile system smart watch mobile application smart wearable device healthcare data

Methods: descriptive statistic cluster analysis parametric test ANOVA longitudinal research

Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

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Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment

Roadside Unit-based Unknown Object Detection in Adverse Weather Conditions for Smart Internet of Vehicles

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Yu-Chia; Jhong, Sin-Ye; Hsia, Chih-Hsien

Abstract: For Internet of Vehicles applications, reliable autonomous driving systems usual ... Expand

Abstract: For Internet of Vehicles applications, reliable autonomous driving systems usually perform the majority of their computations on the cloud due to the limited computing power of edge devices. The communication delay between cloud platforms and edge devices, however, can cause dangerous consequences, particularly for latency-sensitive object detection tasks. Object detection tasks are also vulnerable to significantly degraded model performance caused by unknown objects, which creates unsafe driving conditions. To address these problems, this study develops an orchestrated system that allows real-time object detection and incrementally learns unknown objects in a complex and dynamic environment. A you-only-look-once–based object detection model in edge computing mode uses thermal images to detect objects accurately in poor lighting conditions. In addition, an attention mechanism improves the system’s performance without significantly increasing model complexity. An unknown object detector automatically classifies and labels unknown objects without direct supervision on edge devices, while a roadside unit (RSU)-based mechanism is developed to update classes and ensure a secure driving experience for autonomous vehicles. Moreover, the interactions between edge devices, RSU servers, and the cloud are designed to allow efficient collaboration. The experimental results indicate that the proposed system learns uncategorized objects dynamically and detects instances accurately. Collapse

Topics: cloud computing autonomous driving system database system internet technology internet of things

Methods: computational algorithm cluster analysis design artifact experiment artificial neural network

Does the new entrant eat my pie or enlarge my pie? Market entry investigation in the online-to-offline on-demand context

2023 | Decision Support Systems | Citations: 0

Authors: Dai, Hongyan; Ta, La; Xu, Xun

Abstract: The facilitation of information technology has led to the rapid development of o ... Expand

Abstract: The facilitation of information technology has led to the rapid development of online-to-offline commerce, and this mode is attracting increasing businesses to enter the market. Its unique on-demand features affect con­ sumers’ shopping behaviors. This study uses a difference-in-difference approach with propensity score matching and machine learning approaches to examine the impact of a new giant online-to-offline entrant on consumers’ ordering and spending behavior. We find that overall, the new entrant has a negative impact on consumers’ ordering and spending from the perspective of existing providers. This negative impact is amplified if the con­ sumers are highly engaged on the platform. However, the new entrant effect is reduced if the providers use inhouse rather than outsourced deliverymen or consumers have a relatively long distance from the new entrant. Furthermore, the new entrant effect is altered if the platform implements technological innovation. On a provider level, the new entrant has a direct negative impact on consumers’ ordering and spending from homogenous providers; however, a positive spillover effect is observed in terms of increasing consumers’ spending on het­ erogeneous providers. The direct effect occurs immediately, while the spillover effect appears in a time-lagged pattern. On a product level, we find that the new entrant negatively affects consumers’ ordering and spending on substitute products also sold by the new entrant, but that their spending on non-substitute products is boosted. Our findings provide decision support for the new entrant and existing providers to expand the market to stimulate consumers’ purchase from both, to enhance their financial performance. Collapse

Topics: decision support digital innovation innovation management electronic commerce financial performance

Methods: experimental group k-means clustering machine learning computational algorithm Student's t-test

The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Gao, Yang; Rui, Huaxia; Sun, Shujing

Abstract: Text-based customer service is emerging as an important channel through which c ... Expand

Abstract: Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers' perceived social presence on service outcomes and customers' attitudes toward agents. Our identification strategy hinged on Southwest Airlines' sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers' perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers' perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers' emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues. Collapse

Topics: customer service Twitter social presence social media customer satisfaction

Methods: experiment computational algorithm synthetic control method survey support vector machine

Theories: social presence theory big five model

GDPR Privacy Type Clustering: Motivational Factors for Consumer Data Sharing

2023 | European Conference On Information Systems | Citations: 7

Authors: Hanneke, Björn; Baum, Lorenz; Hinz, Oliver

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the dail ... Expand

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the daily life of (online) consumers. Moreover, literature shows that privacy preferences are constantly evolving. This is the first study introducing a GDPR exercising-oriented approach to identify consumer privacy types. Based on a representative sample of the German online population, we cluster consumers according to their privacy importance ("intention to act") and GDPR knowledge ("ability to act") and derive four consumer privacy type clusters: fundamentalists, amateurs, pragmatists, and unconcerned. We investigate motivational factors for changing privacy settings and find significant differences between consumers' intentions and actions for selected factors. This provides evidence for the privacy paradox. Contrarily, intentions and actions align for other factors, which supports the hypothesis that actionbased consent might lower the privacy paradox. Finally, we suggest the development of standardized scales and corresponding clustering methodologies for consumer privacy type clustering to increase comparability over time and across populations. Collapse

Topics: privacy legal context General Data Protection Regulation privacy behavior advertising management

Methods: cluster analysis survey archival research k-means clustering survey design

Consumer Preferences for Privacy Management Systems

2023 | International Conference on Information Systems | Citations: 0

Authors: Hanneke, Björn; Baum, Lorenz; Schlereth, Christian; Hinz, Oliver

Abstract: This work presents insights into consumer preferences regarding Privacy Managem ... Expand

Abstract: This work presents insights into consumer preferences regarding Privacy ManagementSystems in the context of the General Data Protection Regulation (GDPR). The authors perform a Choice-Based Conjoint experiment with consumers (n = 589) to elicit preferences over four attributes and compute usage likelihoods for all product configurations. Results show that data sharing for marketing purposes and discounts are the most important attributes for consumers. Furthermore, consumers prefer digital access to privacy-related information, detailed rights management for data sharing and no data sharing for marketing purposes. Moreover, a cluster analysis reveals differing importance weights across clusters. The study concludes that incorporating consumer preferences into the design and development process of Privacy Management Systems could increase their use and effectiveness, ultimately strengthening consumers' privacy rights and companies' legal compliance. The authors suggest researching legal, business, and consumer requirements more holistically to converge these perspectives to improve Privacy Management Systems adoptions. Collapse

Topics: privacy legal context General Data Protection Regulation marketing management digital dashboard

Methods: experiment cluster analysis survey literature study computational algorithm

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics

2023 | Information Systems Frontiers | Citations: 0

Authors: Kar, Arpan Kumar; Kushwaha, Amit Kumar

Abstract: Data-driven predictions have become an inseparable part of business decisions. A ... Expand

Abstract: Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model. Collapse

Topics: artificial intelligence Twitter innovation management big data social media

Methods: topic model literature study synthesis regression analysis method data transformation

Theories: organizational theory organizational behavior theory management theory technology acceptance model organizational learning theory

IT Project Portfolio Management Tools: Towards Taxonomy-based Archetypes

2023 | HICSS | Citations: 0

Authors: Karrenbauer, Christin; Bergmann, Florian; Breitner, Michael H.

Abstract: To achieve organizational goals and remain competitive, evaluating, selecting, a ... Expand

Abstract: To achieve organizational goals and remain competitive, evaluating, selecting, and managing IT projects and proposals to build a value-driven portfolio is a critical activity. IT project portfolio management (ITPPM) tools assist these portfolio-related activities, support strategic decision-makers, and help complete more IT projects successfully. Despite existing research on this topic, knowledge about the characteristics and design elements of ITPPM tools is still limited. We develop a taxonomy based on scientific literature and 60 real-word ITPPM tools with four perspectives, 20 dimensions, and 51 characteristics. Subsequently, we perform a cluster analysis and identify five ITPPM tool archetypes. Our results and findings contribute to the knowledge base and integrate scientific and practical knowledge to build the basis for further research on ITPPM tools. Further, we structure the ITPPM tool market, guide practitioners in selecting an appropriate ITPPM tool and support the development of new solutions or develop existing ones further. Collapse

Topics: project management knowledge base web page website database system

Methods: cluster analysis literature study qualitative interview theoretical contribution k-means clustering