Enabling Value co-creation with customers through Artificial Intelligence: A case study approach

2023 | European Conference On Information Systems | Citations: 0

Authors: Bejjani, Yorgo

Abstract: The digital movement has radically altered how manufacturing companies interact ... Expand

Abstract: The digital movement has radically altered how manufacturing companies interact with their customers. By using Artificial Intelligence (AI), companies have drastically changed their value co-creation (VCC) strategies. We adopt a case study approach and engage with an original equipment manufacturer (OEM) to get a grasp of the phenomenon. Among the data collection methods we assume, we conduct semi-structured interviews with the company project team and its customer base. In addition, we collect secondary data and run focus groups within the studied firm's management team. This research in progress will advance a framework linking VCC with service maturity and identify the performance metrics required for the AI-based journey. Such a framework may assist practitioners in building services based on AI and VCC. Ultimately, we plan to offer theoretical implications to progress the AI and VCC debate and propose future research suggestions. Collapse

Topics: artificial intelligence supply chain management participatory design predictive maintenance business model

Methods: qualitative interview design artifact case study focus group qualitative content analysis

Valuing Flexibility in Low Carbon Electricity Portfolios

2023 | HICSS | Citations: 0

Authors: Blumsack, S.

Abstract: With the increased focus on responding to climate change by accelerating a tran ... Expand

Abstract: With the increased focus on responding to climate change by accelerating a transition to a low carbon energy system, differing views remain on the combination of energy technologies that will best achieve this goal. Identifying technological pathways is complicated by wide uncertainties in economic and technological factors. Analyses that neglect these uncertainties can produce pathways for a low carbon energy future that are highly granular and specific, but which are based on a particular assumption about future conditions and imply a need to make specific technology commitments over a long period of time. We frame the energy transition problem as the identification of one near-term investment strategy that is flexible across a wide range of possible future costs, followed by many alternative subsequent investment plans, each of which responds to realized future costs to achieve an aggressive emissions reduction target. Using an example of planning a low carbon power system under uncertainty, we demonstrate the option value of not ruling out some energy technologies in the near-term. Collapse

Topics: energy information system IT investment technological change

Methods: support vector machine case study machine learning stochastic model descriptive statistic

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

2023 | European Conference On Information Systems | Citations: 0

Authors: Gunklach, Jonas; Nadj, Mario

Abstract: While the data amount grows exponentially, the number of people with analytical ... Expand

Abstract: While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues. Collapse

Topics: analytical information system data visualization data quality business intelligence artificial intelligence

Methods: exploratory data analysis machine learning literature sample time series analysis longitudinal research

Black-box Models’ Explainability: A Theoretical and Practical Perspective

2023 | Americas Conference on Information Systems | Citations: 0

Authors: He, Yuanqi; Gu, Bin; Li, Chunxiao; Yan, Lingling; Ding, Yanqing

Abstract: The lack of explainability remains a critical challenge to the widespread adopt ... Expand

Abstract: The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. "Understanding brings in trust", while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field. Collapse

Topics: explainable artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making accounting

Methods: computational algorithm principal component analysis data transformation XGBoost experiment

HUK-COBURG: The Implementation of an AI-Enabled Behavioural Insurance Business Model using Geo-Spatial Data

2023 | HICSS | Citations: 0

Authors: Holland, Christopher; John, Daniel

Abstract: Automotive insurance is undergoing digital transformation that exploits new form ... Expand

Abstract: Automotive insurance is undergoing digital transformation that exploits new forms of big data and Artificial Intelligence (AI) systems. Geo-spatial data from GPS and telematics systems enables innovative risk modelling to evaluate driver behaviour and leads to the creation of new insurance services and novel insurance business models. A research framework is proposed to analyse AI-enabled business models and applied to a detailed case analysis of behavioural insurance in HUK-COBURG. The results illustrate the application of geo-spatial data in an insurance context and demonstrate the utility of the research framework to analyse new AI-enabled business models. The analysis identifies important implementation issues and shows that the strategic logic, regulatory and ethical context are important elements of business models. The empirical analysis reveals the strategic properties and effects of the data flywheel concept, which has general applicability. The theory framework and empirical results have important implications for other markets and theoretical contexts. Collapse

Topics: artificial intelligence business model big data innovation management

Methods: case study field study synthesis qualitative interview triangulation

EFFECTS OF DIGITAL TRANSFORMATION ON SOCIAL SUSTAINABILITY – AN EFFECT PATH PERSPECTIVE

2023 | European Conference On Information Systems | Citations: 0

Authors: Kneissel, Katharina; Pedraza-Copete, Mariana; Schäfer, Ricarda

Abstract: Although on top of research and practice agendas, digital transformation is stil ... Expand

Abstract: Although on top of research and practice agendas, digital transformation is still challenging, but promising in addressing economic and ecologic sustainability. However, we lack understanding of the effects of digital transformation on social sustainability and vice versa. Given the relevance of humancentred challenges during digital transformation, a better understanding of the effects of digital transformation on social sustainability is needed. To provide such holistic understanding, we present two effect path models that reflect the effects of digital transformation on social sustainability in the fields of health, education, and equality. Building on a structured literature review and a case study, we find education at the centre of two circular relationships affecting health and equality and highlight the duality of social sustainability as an effect and a driver of digital transformation. Thus, we contribute novel perspectives on digital transformation for social sustainability and aim at inspiring practitioners to foster intra-organisational social sustainability. Collapse

Topics: digital literacy digital transformation organizational context robotic business-to-business

Methods: case study qualitative coding structured literature research qualitative interview mixed method

Toward a campus crisis management system amid the pandemic and beyond

2023 | Information & Management | Citations: 0

Authors: Li, Yaojie; Zhou, Yi; Ge, Linqiang; Chen, Rui; Xiong, Jie

Abstract: This study develops artifact and design principles for a campus-wide crisis mana ... Expand

Abstract: This study develops artifact and design principles for a campus-wide crisis management system amid the COVID19 pandemic and beyond. Drawing upon crisis management literature and the uncertainty reduction theory, we develop a preliminary crisis management system with three fundamental components: dashboard, portal, and forum, geared toward aggregating environmental information, expert knowledge, and stakeholder insights. This immediate, integrated, and interactive campus crisis management system aims to promote organizational learning through knowledge acquisition, dissemination, and utilization while reducing stakeholders’ uncertainty about the crisis. Through comprehensive evaluation, we further synthesize and summarize new design principles for prospective artifacts, including constructing a distributed crisis management network, capitalizing on data mining and knowledge discovery algorithms, and developing the adaptability of the crisis management system in various circumstances. Collapse

Topics: emergency management pandemic organization learning digital dashboard usability

Methods: design principle design science design artifact machine learning kernel theory

Theories: uncertainty reduction theory organizational learning theory theory of abduction

Smart System: Joint Utility and Frequency for Pattern Classification

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

Authors: Lin, Qi; Gan, Wensheng; Wu, Yongdong; Chen, Jiahui; Chen, Chien-Ming

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Thi ... Expand

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFCgen and UFCfast. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFCfast algorithm outperforms the levelwise-based UFCgen algorithm in terms of both execution time and memory consumption. Collapse

Topics: database system production management smart manufacturing operating system Microsoft Windows

Methods: computational algorithm experiment case study machine learning literature study

Predictive End-to-End Enterprise Process Network Monitoring

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Oberdorf, Felix; Schaschek, Myriam; Weinzierl, Sven; Stein, Nikolai; Matzner, Martin; Flath, Christoph M.

Abstract: Ever-growing data availability combined with rapid progress in analytics has lai ... Expand

Abstract: Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organizations. Collapse

Topics: logistics management computer hardware workstation organizational context supply chain management

Methods: event log machine learning case study longitudinal research deep learning

Reinforcing data bias in crisis information management: The case of the Yemen humanitarian response

2023 | International Journal of Information Management | Citations: 0

Authors: Paulus, David; De Vries, Gerdien; Janssen, Marijn; Van De Walle, Bartel

Abstract: The complex and uncertain environment of the humanitarian response to crises can ... Expand

Abstract: The complex and uncertain environment of the humanitarian response to crises can lead to data bias, which can affect decision-making. Evidence of data bias in crisis information management (CIM) remains scattered despite its potentially significant impact on crisis response. To understand what biases emerge in complex crises and how they affect CIM, we conducted a combined interview and document analysis study. Focusing on the largest humanitarian crisis in the world, i.e., the conflict in Yemen, we conducted 25 interviews with managers and analysts of response organizations, and assessed 47 reports and datasets created by response organizations in Yemen. We find evidence of a cycle of bias reinforcement through which bias cascades between field, head­ quarters and donor levels of crisis response. Researchers, as well as practitioners, need to consider these un­ derlying biases and reinforcement loops because they influence what data can be collected when, by whom, from whom, and how the data is shared and used. To the CIM literature, we contribute an in-depth understanding of how four types of data bias emerge in crises: political, accessibility, topical, and sampling bias. Collapse

Topics: information management data quality electronic mail emergency management decision making

Methods: qualitative interview qualitative content analysis qualitative coding case study personal interview