User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse

2023 | Information Systems Frontiers | Citations: 0

Authors: Bhatt, Paras; Vemprala, Naga; Valecha, Rohit; Hariharan, Govind; Rao, H. Raghav

Abstract: Online users frequently rely on social networking platforms to transmit public c ... Expand

Abstract: Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects—contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild. Collapse

Topics: privacy Twitter social media pandemic information privacy concern

Methods: topic model qualitative content analysis cluster analysis hierarchical clustering Gensim

Disentangling Human-AI Hybrids

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Fabri, Lukas; Häckel, Björn; Oberländer, Anna Maria; Rieg, Marius; Stohr, Alexander

Abstract: Artificial intelligence (AI) offers great potential in organizations. The path t ... Expand

Abstract: Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration sensemaking system development method

Methods: cluster analysis qualitative interview design artifact design methodology literature study

Theories: sociomaterialism theory co-evolution theory social practice theory

Process and Strategy Mining in Real-Time Strategy Games

2023 | International Conference on Business Informatics | Citations: 0

Authors: Elbert, Nico; Stein, Nikolai; Flath, Christoph M.

Abstract: In this study, we introduce a data-driven approach for process mining in real-t ... Expand

Abstract: In this study, we introduce a data-driven approach for process mining in real-time strategy (RTS) games, which serve as a proxy for corporate decision processes. By analyzing decision sequences in gaming environments, we uncover strategic behaviors impacting performance and match outcomes. Our methodology leverages game data, enriching process mining research and providing implications for strategic planning in business process management. Collapse

Topics: process mining strategic management business process management decision support decision making

Methods: cluster analysis Petri net modeling language XGBoost event log

Formation and Action of a Learning Community with Collaborative Learning Software

2023 | Journal of Management Information Systems | Citations: 0

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor; Lee, Howard

Abstract: This paper explores the formation of a learning community facilitated by custom ... Expand

Abstract: This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model. Collapse

Topics: recommender system collaborative filtering pandemic online learning system open source

Methods: qualitative content analysis descriptive statistic sociometry synthesis mixed method

Theories: cognitive theory

How to design platform ecosystems by intrapreneurs: Implications from action design research on IoT-based platform

2023 | Electronic Markets | Citations: 0

Authors: Inoue, Yuki; Takenaka, Takeshi; Kasasaku, Takami; Tamegai, Tadafumi; Arai, Ryohei

Abstract: The recent technological evolution of the Internet of Things (IoT) and data scie ... Expand

Abstract: The recent technological evolution of the Internet of Things (IoT) and data sciences has created opportunities for intrapreneurs in non-platform firms to expand their businesses into platform ecosystem-related businesses. However, previous studies have typically focused on cases involving entrepreneurs. This study aims to clarify how intrapreneurs’ design for platform ecosystems could be different from that of entrepreneurs. We conducted an action design research (ADR) project in collaboration with a company for 29 months, and designed a platform ecosystem structure based on technological platforms related to radio frequency identifiers (RFID). Our main contribution is the following six-step process that describes how intrapreneurs design platform ecosystems: (1) designing an initial ecosystem structure based on platform ecosystem concepts and certain concepts related to the targeted market; (2) analyzing past business cases; (3) making the ecosystem structure into concrete shape; (4) verifying its validity; (5) elaborating the ecosystem structure; and (6) proposing the designed ecosystem business. Our findings highlight the differences between intrapreneurs’ and entrepreneurs’ designs of the ecosystem. First, when the design processes are underway, the direction of the design of the ecosystem needs to be flexibly modified to align with the strategy of the firm. Second, evidence for the success of the proposed platform ecosystem is required to reduce uncertainty and clarify the legitimacy of the proposition. Third, the structure of a platform ecosystem designed by intrapreneurs becomes a style that supports the existing businesses and networks of the firm. Collapse

Topics: digital ecosystem radio frequency identification digital service logistics management price management

Methods: survey simulation data transformation design process action design research

Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa

2023 | Information Systems Frontiers | Citations: 0

Authors: Joubert, Anke; Murawski, Matthias; Bick, Markus

Abstract: The use of big data promises to drive economic growth and development and can th ... Expand

Abstract: The use of big data promises to drive economic growth and development and can therefore be a value-adding factor, but compared to private or public organisations, the country level is rarely investigated, and that is even more evident for developing countries. Another topic hardly ever considered in the big data research field is ‘big data readiness’, which means the level of preparation and willingness to exploit big data. We address these shortcomings in the literature and focus on the big data readiness of developing countries. Thus, the first research question is: what components are required for an index measuring big data readiness, and how can such an index be designed? We use a design science approach to develop the “Big Data Readiness Index” (BDRI), which is then applied to all African countries to answer our second research question: how do African countries perform in terms of the BDRI? Our analysis yields country rankings that show relatively high BDRI scores for coastal countries, such as South Africa, Kenya and Namibia, and for islands, such as Mauritius. Related implications for both research and policy are discussed. Collapse

Topics: big data missing data open data research and development telephone

Methods: design science cluster analysis hierarchical clustering literature study design process

Do Employees Work Less for Female Leaders? A Multi-Method Study of Entrepreneurial Firms

2023 | Organization Science | Citations: 0

Authors: Kacperczyk, Olenka; Younkin, Peter; Rocha, Vera

Abstract: We propose that female-founded ventures receive a lower amount of employee labor ... Expand

Abstract: We propose that female-founded ventures receive a lower amount of employee labor for equal pay because employees are more likely to decline requests for additional labor by female founders. First, using longitudinal matched employer-employee data covering all founders of new ventures with personnel in Portugal between 2002 and 2012, we confirm that full-time employees contribute fewer regular hours and less overtime work to female-founded firms. Second, using a series of online experiments, we show that this variation in employee labor across female and male-founded firms is partly motivated by a difference in the employee’s expectations of work demands. Specifically, employees perceive female founders’ requests for additional labor to be unfair and more difficult than expected, and both these perceptions explain the lower amount of employee labor supplied in female-founded ventures. Overall, our findings uncover a novel mechanism that helps explain the existence of a gender gap in entrepreneurship beyond the entry stage. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2022.1611. Collapse

Topics: entrepreneurship IT career database system productivity missing data

Methods: experiment quantitative observational study theory development survey descriptive statistic

Theories: sociological 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

How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception

2023 | Information Systems Frontiers | Citations: 0

Authors: Kushwaha, Amit Kumar; Pharswan, Ruchika; Kumar, Prashant; Kar, Arpan Kumar

Abstract: Artificial intelligence (AI) transits from merely adopted technology to fueling ... Expand

Abstract: Artificial intelligence (AI) transits from merely adopted technology to fueling everyday decision-making systems from medication to navigation. With this combination of AI in decision-making systems (ADMS), the present study explores how text-based users' data from social media helps organize the users' perspectives of ADMS? To investigate our research questions, we used a framework consisting of three phases, exploratory, confirmatory, and validatory. We applied hierarchy clustering and topic modeling in the exploratory study, hypothesis building, and empirical analysis during the confirmatory study and support vector machine (SVM) in the validatory study. Our findings suggest that users are primarily concerned about the risk involved in using ADMS. Factors like accountability, self-efficacy, knowledge of ADMS individuals' attitudes towards ADMS impact the perception of ADMS among individuals. This study's theoretical and practical implications have great scope as ADMS is still in its elementary stage. Collapse

Topics: artificial intelligence Twitter social media decision making user attitude

Methods: survey support vector machine topic model regression analysis method statistical hypothesis test

Who needs XAI in the Energy Sector? A Framework to Upgrade Black Box Explainability

2023 | International Conference on Information Systems | Citations: 0

Authors: Lier, Sarah; Gerlach, Jana; Breitner, Michael H.

Abstract: Artificial Intelligence (AI)-based methods in the energy sector challenge compa ... Expand

Abstract: Artificial Intelligence (AI)-based methods in the energy sector challenge companies, organizations, and societies. Organizational issues include traceability, certifiability, explainability, responsibility, and efficiency. Societal challenges include ethical norms, bias, discrimination, privacy, and information security. Explainable Artificial Intelligence (XAI) can address these issues in various application areas of the energy sector, e.g., power generation forecasting, load management, and network security operations. We derive Key Topics (KTs) and Design Requirements (DRs) and develop Design Principles (DPs) for efficient XAI applications through Design Science Research (DSR). We analyze 179 scientific articles to identify our 8 KTs for XAI implementation through text mining and topic modeling. Based on the KTs, we derive 15 DRs and develop 18 DPs. After that, we discuss and evaluate our results and findings through expert surveys. We develop a Three-Forces Model as a framework for implementing efficient XAI solutions. We provide recommendations and a further research agenda.increase the value creation of such information and mitigate the energy transition-associated complexity (Kruse et al. 2021a;Kuzlu et al. 2020;Machlev et al. 2022a). AI is used for load forecasting, power generation and management, demand side management (DSM), and electrical grid operation and control. AI has high relevance regarding decentralized smart energy solutions, e.g., smart grids (SGs) and the modernization of power grids, although AI is not always considered efficient, as traditional methods are less expensive and more sufficient (Kuzlu et al. 2020). The safe integration of renewable energies is enhanced by hardware like smart meters, energy storage devices, SGs, and software like AI and Blockchain. The modernization of power grids enables the collection of information on power quality and energy consumption data, analysis of data for such purposes as forecasting energy demand, optimizing supply and demand, reducing power generation costs and greenhouse gas emissions, improving grid stability and reliability, optimizing energy efficiency, and responding to unforeseen changes (e.g., Alova et al. 2021;Hatzakis et al. 2019;Kruse et al. 2021b;Machlev et al. 2022b). AI-based energy methods contribute to the transformed energy system by optimizing and scheduling flexibilities to increase power efficiency and consumption (Ponnusamy et al. 2021). Implementing AI solutions makes business processes in the energy sector about 28% more efficient (Benbya et al. 2020). Efficient energy distribution improves consumer welfare and energy organizations' services (Antonopoulos et al. 2020). Due to the non-transparent nature of AI methods, explainability is becoming relevant for users, developers, energy providers, decision-makers, and other stakeholders because of the limited explanations and dynamics in decision-making processes. XAI, also known as white or grey box, enables a better understanding of models and systems through interpretability and traceability (Rawal et al. 2021). Explainability is the degree to which humans understand the decisions made by AI systems. XAI in the energy sector refers to the application of AI technologies, algorithms, and models. They make accurate predictions or decisions and provide transparent and interpretable insights into how those predictions or decisions are reached. XAI aims to bridge the gap between the inherent complexity of advanced AI techniques and the need for understanding and trust in decision-making (Angelov et al. 2021; Arrieta et al. 2019;Rawal et al. 2021). XAI techniques in the energy sector enable stakeholders to comprehend and validate the rationale behind AI-driven recommendations, predictions, or optimizations related to energy production, distribution, consumption, and management. By offering comprehensible explanations, XAI enhances transparency, accountability, and stakeholder confidence in AI-powered solutions, allowing for informed and effective decision-making while minimizing the risks of biased or unexpected outcomes (Angelov et al. 2021; Arrieta et al. 2019;Machlev et al. 2022a). Current XAI literature includes programming XAI algorithms (e.g., Ren et al. 2022;Sairam et al. 2020), monoculture guidelines for XAI applications (e.g., Ignatiev 2020; Kuhn et al. 2020), or XAI challenges and requirements (e.g., Angelov et al. 2019;Rawal et al. 2021). There are general principles, guidelines, and frameworks for XAI. For example, Phillips et al. (2021) derived four XAI principles: explanation, meaning, accuracy, and knowledge limits. Some other big organizations like Google, Amazon, and Tesla also derived general principles, requirements, and guidelines for using and implementing XAI. However, these algorithms, applications, principles, requirements, and guidelines cannot be transmitted completely to using and implementing XAI in the energy sector. The energy sector represents a critical infrastructure with complex impacts. AI decisions impact the economy, environment, and society. Sustainability, environmental protection, and societal acceptance and participation are reasons for customized DPs of XAI in the energy sector (e.g., Alova et al. 2021;Kruse et al. 2021b;Kuzlu et al. 2020;Machlev et al. 2022a). To combine the fragmented scientific knowledge base, we analyze relevant literature and expert interviews to develop DPs for XAI applications in the energy sector. We address the research questions (RQs): RQ1:What XAI design requirements in the energy sector are known, today? Collapse

Topics: explainable artificial intelligence artificial intelligence knowledge base database system cloud computing

Methods: qualitative interview natural language processing topic model mixed method literature study