How Can You Verify that I Am Using AI? Complementary Frameworks for Describing and Evaluating AI-Based Digital Agents in their Usage Contexts

2023 | HICSS | Citations: 0

Authors: Alter, Steven

Abstract: This essay explains complementary frameworks for understanding and managing AI i ... Expand

Abstract: This essay explains complementary frameworks for understanding and managing AI in usage contexts. In contrast with broad generalizations about the nature and impact of AI, those frameworks focus on specific AI-based digital agents used by people and/or machines performing purposeful activities in business, home, or societal environments. The agent responsibility (AR) framework helps in describing roles and responsibilities of specific AI-based digital agents in their usage contexts. The agent evaluation (AE) framework identifies six criteria that different stakeholders might use for evaluating AI-based digital agents. Collapse

Topics: artificial intelligence accounting decision making

Methods: natural language processing literature sample simulation operations research computational algorithm

Theories: work systems theory

A Competency Framework for Participatory Modeling

2023 | Group Decision and Negotiation | Citations: 0

Authors: Elsawah, Sondoss; Bakhanova, Elena; Hämäläinen, Raimo P.; Voinov, Alexey

Abstract: Participatory modeling (PM) is a craft that is often learned by training ‘on the ... Expand

Abstract: Participatory modeling (PM) is a craft that is often learned by training ‘on the job’ and mastered through years of practice. There is little explicit knowledge available on identifying and documenting the skills needed to perform PM. In the modeling literature, existing attempts to identify relevant competencies have focused on the specific technical skills required for specific technical model development. The other skills required to organize and conduct the stakeholder process seem to be more vaguely and poorly defined in this context. The situation is complicated by PM being an essentially transdisciplinary craft, with no single discipline or skill set to borrow ideas and recommendations from. In this paper, we aim to set the foundation for both the practice and capacity-building efforts for PM by identifying the relevant core competencies. Our inquiry into this topic starts with reviewing and compiling literature on competencies in problem-solving research areas related to PM (e.g., systems thinking, facilitated model building, operations research, and so forth). We augment our inquiry with results from a PM practitioners’ survey to learn how they perceive the importance of different competencies and how the scope of these competencies may vary across the various roles that participatory modellers play. As a result, we identified five core competency areas essential for PM: systems thinking, modeling, group facilitation, project management and leadership, and, more recently, designing and running virtual workshops and events. Collapse

Topics: project management problem solving internet technology decision making

Methods: survey operations research literature study conceptual modelling survey design

Theories: core competency theory

The Interdependence of Coordination and Cooperation in Information Technology Outsourcing

2023 | Information Systems Research | Citations: 0

Authors: Im, Ghiyoung P.; Ahuja, Manju

Abstract: The success of information technology outsourcing projects largely depends on th ... Expand

Abstract: The success of information technology outsourcing projects largely depends on the extent to which partners are willing to learn through interactions with each other. This study investigates the antecedents and consequences of relational learning between clients and vendors in outsourcing projects. Drawing on the collaboration framework in interfirm relationships research, this study examines the interdependence of coordination and cooperation. The two antecedents of relational learning (i.e., shared domain knowledge and motivational elements) represent coordination and cooperation mechanisms. We investigate the independent (main) and interdependent (moderation) effects of these mechanisms on relational learning from both the client and vendor perspectives. The findings show that the interdependent model is better in explaining the impact of coordination and cooperation mechanisms in outsourcing projects. This finding enables partners to understand relationship dynamics more comprehensively and develop collaboration mechanisms that can bring synergy between coordination and cooperation. Our study also shows that the perceptions of motivational elements are not symmetric between clients and vendors. History: Yulin Fang, Senior Editor; Ning Su, Associate Editor. Funding: This research was supported by the College of Business at the University of Louisville. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1216. Collapse

Topics: outsourcing business process management project productivity computer supported cooperative work resource allocation

Methods: descriptive statistic statistical hypothesis test survey theory development ordinary least square

Theories: goal framing theory

Exploring Artificial Intelligence Bias, Fairness and Ethics in Organisation and Managerial Studies

2023 | European Conference On Information Systems | Citations: 0

Authors: Smacchia, Marco; Za, Stefano

Abstract: Due to the increasing adoption of AI technology in our society, this paper aims ... Expand

Abstract: Due to the increasing adoption of AI technology in our society, this paper aims to develop a complete overview of the current debate on artificial intelligence bias fairness and ethics in organisation and managerial studies. To this end, we adopted the Computational Literature Review (CLR) method to conduct an impact and a topic modelling analysis of the relevant literature, using the Latent Dirichlet Allocation (LDA) technique. As a result, we identified and analysed 18 topics related to the selected domain. We further classified those topics into 5 categories creating a clear distinction between the social and the technical nature of a bias and its origins. Finally, focusing on the emerging topics, we proposed a set of guiding questions that might foster future research directions. This paper provides insights to scholars and managers interested in AI bias and ethical issues and could be used also as a guide to perform CLR. Collapse

Topics: artificial intelligence information management responsible artificial intelligence database system privacy

Methods: literature study literature sample qualitative content analysis topic model computational algorithm

A Machine Learning Model Marketplace to Overcome Low Price Elasticity of Sales Forecasting

2022 | International Conference on Business Informatics | Citations: 0

Authors: Hütsch, Marek

Abstract: Machine learning (ML) sales forecasting applications occupy a special position i ... Expand

Abstract: Machine learning (ML) sales forecasting applications occupy a special position in industry and retail, as used in numerous optimization activities, such as price optimization. While optimizing prices, sales forecasting tools require elasticity regarding the optimization objective. When ML methods are used for sales forecasting, there must be sufficient data to learn the proper elasticity requirements. To overcome the problem of low price elasticity of sales forecasts, when historical price-change data are scarce, we present a sales forecasting model marketplace in which trained ML models are shared across companies. Evaluations of a design science project with 43 retail stores show that a model marketplace with neural networks and dropout profiles achieves good forecasting accuracy and high price elasticity. With these results, companies can implement automated optimization algorithms while avoiding extant cumbersome stovepiped methods. Collapse

Topics: privacy Sustainable Development Goal Python

Methods: case study artificial neural network computational algorithm design science operations research

Deep Reinforcement Learning for Supply Chain Synchronization

2022 | HICSS | Citations: 0

Authors: Jackson, Ilya

Abstract: Supply chain synchronization can prevent the “bullwhip effect” and significantly ... Expand

Abstract: Supply chain synchronization can prevent the “bullwhip effect” and significantly mitigate ripple effects caused by operational failures. This paper demonstrates how deep reinforcement learning agents based on the proximal policy optimization algorithm can synchronize inbound and outbound flows if end-toend visibility is provided. The paper concludes that the proposed solution has the potential to perform adaptive control in complex supply chains. Furthermore, the proposed approach is general, task unspecific, and adaptive in the sense that prior knowledge about the system is not required. Collapse

Topics: supply chain management open source

Methods: experiment computational algorithm reinforcement learning artificial neural network operations research

Theories: control theory

The Role and Value of Service Orchestration in Smart Grid Prosumer Service Systems

2022 | European Conference On Information Systems | Citations: 0

Authors: Merfeld, Tanja; Meisel, Stephan

Abstract: The implementation of smart grid infrastructure as well as the rise of eco-cons ... Expand

Abstract: The implementation of smart grid infrastructure as well as the rise of eco-conscious prosumers in the energy markets are leading to a paradigm shift in the energy sector. Residential households can no longer be viewed as passive market entities, but have to be considered as actors participating in value creation. In this work, we present the co-creation of value in energy markets through the lens of service dominant logic, and highlight the importance of service orchestrators for deriving both design decisions and operational decisions for complex energy systems. For the example of a real-time energy trading service, we assess the value of service orchestration by means of a simulation study. Thereby, we highlight the importance of service orchestration for creating valuable business opportunities, and we provide a transdisciplinary approach that combines service science and service operations research. Collapse

Topics: energy information system price management IT productivity decision making participatory design

Methods: simulation operations research experiment autoregressive moving average model design methodology

Theories: service-dominant logic

Maritime Spare Parts Management: Current State-of-the-Art

2022 | HICSS | Citations: 0

Authors: Pahl, Julia

Abstract: Having the right spare part at the right time to the right place for ship mainte ... Expand

Abstract: Having the right spare part at the right time to the right place for ship maintenance to the minimal possible costs is an exigent management problem that maritime shipping companies face. This is especially challenging in bulk shipping where routes are not fixed, but subsequent port calls depend on spot market dynamics. Thus, spare parts allocation ahead in time is limited, but possible if failures rates of ship components and their timing can be foreseen, so that spare parts can be allocated to hedge against the risk of long waiting times and thus ship downtimes. Companies are very sensitive to the latter due to significant ship operational costs that accrue also during downtime, but without revenues. Thus, reducing ship downtimes by monitoring the condition of components key to the ships performance is essential to the task. However, shipping companies seem far away from applying sophisticated methods for forecasting and planning due to various challenges ranging from data gathering and cultivating an understanding of data quality needs, adaptation to move from preventive towards predictive and condition-based maintenance (CBM) thus enabling the introduction and application of decision support tools for demand forecasting, sourcing, spare parts allocation, and inventory management. In this paper, we investigate the current state of the art of spare parts logistics (SPL) management tightly related to CBM for maritime shipping and discuss the application of methods to the bulk carriage market. Collapse

Topics: knowledge base data quality decision support system logistics management

Methods: survey case study linear optimization operations research literature study

Increasing the Business Value Of Free-Floating Carsharing Fleets By Applying Machine-Learning Based Relocations

2022 | European Conference On Information Systems | Citations: 0

Authors: Prinz, Christoph; Hellmeier, Malte; Willnat, Mathias; Harnischmacher, Christine; Kolbe, Lutz

Abstract: Free-floating carsharing (CS) services provide customers with a fleet of vehicl ... Expand

Abstract: Free-floating carsharing (CS) services provide customers with a fleet of vehicles distributed within an operation area. These services gained popularity because of their positive impact on societal and personal mobility. Understanding determinants of customer demand is a key challenge for developing and applying vehicle relocation strategies to prevent the formation of undersupply areas. In this study, we merge possible features from publicly available data sources with historical demand from CS services situated in three different-sized cities. We train and test a Random Forest (RF) regressor estimating demand based on the enhanced dataset. Building on this demand prediction, we developed a relocation strategy that optimizes vehicle availability at anticipated demand points. Our strategy improved the reservation acceptance ratio in all three reference systems between 7.1 % and 15.6 %. Furthermore, the number of relocations compared to a deterministic relocation strategy could be reduced by 82.3 % and 20.6 % in two cities. Collapse

Topics: ride sharing platform Python organizational value big data knowledge base

Methods: simulation computational algorithm machine learning design artifact design science

Measuring project resilience – Learning from the past to enhance decision making in the face of disruption

2022 | Decision Support Systems | Citations: 0

Authors: Zarghami, Seyed Ashkan; Zwikael, Ofer

Abstract: Although projects are regularly exposed to disruptive events, the literature lac ... Expand

Abstract: Although projects are regularly exposed to disruptive events, the literature lacks an effective measurement system for project resilience. This gap presents challenges for decision makers because of the consequent lack of quantitative information about the level of resilience and its impact on project performance throughout a pro­ ject's life. We argue that managers can be supported by a priori information about past similar projects as well as new data that evolve during disruption and recovery stages to enhance decision making by key project leaders, such as funders when approving new projects, project managers when developing the detailed plan, and project owners when approving corrective actions following a major disruption. Therefore, this paper develops a mathematical model to measure the level of project resilience by predicting disruption and recovery profiles based on past similar completed projects, as well as actual events unique to the project at hand. We illustrate and validate the model based on a portfolio of 43 major projects that faced disruptions from various sources. Our results provide the first empirical evidence to measure the impact of project resilience on the disruption and recovery behavior of real-life projects. The outputs of this research can be used as a decision support system that enables managers to make informed decisions throughout a project's life. Collapse

Topics: project productivity decision making decision support system database system website

Methods: mathematical model operations research hazard function theoretical contribution