Knowledge Management Systems and Artificial Intelligence Adoption for Increasing Business Sustainability

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Al Halbusi, Hussam; Hassani, Abdeslam; Mosconi, Elaine; Bayiz, Ahmad

Abstract: Sustainable business success is essential for achieving performance in a market ... Expand

Abstract: Sustainable business success is essential for achieving performance in a market in the contemporary technology age. Worldwide, businesses should overcome a number of challenges by using an innovative practices and technologies to achieve long-term business sustainability. This study intended to inspect the relationship between knowledge management systems (KMS) and sustainable business, and to examine the moderating role of artificial intelligence (AI) adoption in the relationship. To achieve this objective, we collect data from Iraqi firms. Using structural equation modelling, the finding of this study show that knowledge management systems significantly increased sustainable business. In addition, AI adoption moderates the relationship between KMS and sustainable business. Hence, this research makes significant implications to literature and policymakers regarding applying and understanding knowledge management systems and AI adoption to enhance sustainable business. Collapse

Topics: artificial intelligence knowledge management system information technology infrastructure computer supported cooperative work technology adoption

Methods: survey partial least squares path modeling structural equation modeling partial least squares regression statistical hypothesis test

A Comparative Analysis of Seven Smart City Development Projects: Institutional, Economic, Technical, and Policy Perspectives

2023 | HICSS | Citations: 0

Authors: Choi, J.; Caicedo, C.

Abstract: This paper argues for the use of a multifaceted, and contextualized approach to ... Expand

Abstract: This paper argues for the use of a multifaceted, and contextualized approach to smart city development by unpacking how individual smart city initiatives have planned and implemented diverse projects based on their distinct environments, stakeholders, and goals. We evaluated and compared the institutional, economic, technical and policy characteristics of seven smart city initiatives (Montgomery, San Diego, New York City, Calgary, London, Vienna, Singapore). Our findings demonstrate three principal implications in smart city development. First, the surveyed smart cities established concrete cases for the use of different project development models in terms of leadership and governance styles, adoption of smart city applications, and planning and management strategies. Second, such differences stemmed from the multifaceted interactions that link environment, stakeholders, and goals. Finally, knowledge management (KM) played a crucial role in ensuring the accumulation and transferability of organizational and policymaking infrastructure within and between smart city initiatives. Collapse

Topics: smart city privacy innovation management mobile system mobile application

Methods: survey synthesis cross sectional research literature study structured literature research

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

2023 | Information Systems Frontiers | Citations: 2

Authors: Dennehy, Denis; Griva, Anastasia; Pouloudi, Nancy; Dwivedi, Yogesh K.; Mäntymäki, Matti; Pappas, Ilias O.

Abstract: example the responsible design and governance (Mäntymäki et al., 2022b) of AI ... Expand

Abstract: example the responsible design and governance (Mäntymäki et al., 2022b) of AI systems. While organisations are increasingly investing in ethical AI and Responsible AI (RAI) (Zimmer et al., 2022), recent reports suggest that this comes at a cost and may lead to burnout in responsible-AI teams (Heikkilä, 2022). Thus, it is critical to consider how we educate about RAI (Grøder et al., 2022) and rethink our traditional learning designs (Pappas & Giannakos, 2021), as this can influence end-users' perceptions towards AI applications (Schmager et al., 2023) as well as how future employees approach the design and implementation of AI applications (Rakova et al., 2021;Vassilakopoulou et al., 2022).The use of algorithmic decision-making and decisionsupport processes, particularly AI is becoming increasingly pervasive in the public sector, also in high-risk application areas such as healthcare, traffic, and finance (European Commission, 2020). Against this backdrop, there is growing concern over the ethical use and safety of AI, fuelled by reports of ungoverned military applications (Butcher and Beridze, 2019; Dignum, 2020), privacy violations attributed to facial recognition technologies used by the police (Rezende, 2022), unwanted biases exhibited by AI applications used by courts (Imai et al., 2020), and racial biases in clinical algorithms (Vyas et al. 2020). The opacity and lack of explainability frequently attributed to AI systems makes evaluating the trustworthiness of algorithmic decisions challenging even for technical experts, let alone the public. Together with the algorithm-propelled proliferation of misinformation, hate speech and polarising content on social media platforms, there is a high risk for erosion of trust in algorithmic systems used by the public sector (Janssen et al., 2020). Ensuring that people can trust in the algorithmic processes is essential not only for reaping the potential benefits from AI (Dignum, 2020) but also for fostering trust and resilience at a societal level.AI researchers and practitioners have expressed their fears about AI systems being developed that are Collapse

Topics: artificial intelligence IT governance decision making digital economy online learning system

Methods: chatbot theory development case study qualitative interview literature sample

Theories: technological frames of reference

PRACTICAL SUPPORT FOR UNLEARNING – A SYSTEMATIC REVIEW TO ORGANIZE THE FIELD

2023 | European Conference On Information Systems | Citations: 0

Authors: Di Maria, Marco; Walter, David; Schoormann, Thorsten; Knackstedt, Ralf

Abstract: Organizations are increasingly concerned with the continuous reassessment and r ... Expand

Abstract: Organizations are increasingly concerned with the continuous reassessment and redesign of their products, processes, and business models to remain competitive in uncertain environments. Thereby, mental models, routines, and behaviors of individuals and entire organizations need to be updated to fit changing demands and environmental factors. 'Unlearning' is a promising approach to achieve this because it allows knowledge to be refined and adapted to novel situations. Unlearning established ways of thinking and doing effectively poses, however, several challenges that could be mitigated by an appropriate tool support. By following a qualitative systematic review of literature from Information Systems and adjacent disciplines, we identified and synthesized existing tools and guidance to assist individuals, teams, and organizations in unlearning. With our study, we contribute to an integrated overview of practical support for unlearning based on a sample of 41 papers as well as useful guidance for practitioners, designers, and researchers. Collapse

Topics: knowledge type change management agile software development organizational context IS discipline

Methods: literature sample design artifact literature study structured literature research synthesis

HUMAN-AI COLLABORATION IN EVERYDAY WORK-LIFE PRACTICES: A COREGULATION PERSPECTIVE

2023 | European Conference On Information Systems | Citations: 0

Authors: Ekandjo, Talitakuum; Cranefield, Jocelyn; Chiu, Yi-Te

Abstract: Driven by the growing integration of Artificial Intelligence (AI) into daily wo ... Expand

Abstract: Driven by the growing integration of Artificial Intelligence (AI) into daily work, this study investigates the Human-Intelligent Personal Assistants (IPAs) coregulation of work-life practices. Guided by an interpretive case study of Microsoft Viva Insights (MVI), we focus on the participation and influence ofIPAs in daily work-life practices. Our findings reveal three coregulatory roles adopted by IPAsrationalist, normalist, and moralist-that influence personal productivity, social bonding and relationship management, self-care, and work-life boundary management practices. By diving deeper into the human-AI relationship from a coregulation perspective, we contribute to the emerging IS literature on the nature and role of AI in transforming how people work. Our research provides valuable insights for practitioners, developers, and scholars aiming to enhance AI design and management, and investigate AI's broader impact on human behaviours at work. Collapse

Topics: artificial intelligence productivity electronic mail privacy digital dashboard

Methods: qualitative interview qualitative coding autoethnography synthesis theory development

An analytical citizen relation management system (CZRM) for social vulnerability mapping and policy recommendation in Brazil

2023 | Decision Support Systems | Citations: 0

Authors: Furtado, Lara Sucupira; Moura, Gustavo; Vasconcelos, Daniel J.R.; Fernandes, Guilherme Sales; Cruz, Lívia Almada; Magalhães, Regis Pires; Coelho Da Silva, Ticiana L.

Abstract: The emerging field of Citizen Relationship Management has shown promise to combi ... Expand

Abstract: The emerging field of Citizen Relationship Management has shown promise to combine data analytics and intuitive digital platforms to help public managers interpret citizen-driven data and draw out appropriate policy. However, research dates back to only the last ten years, and does not commonly focus on families of extreme social vulnerability or how to bring solutions that increase access to public infrastructure. This research addresses the central challenge of setting strategic intervention plans for these families considering their multiple levels of vulnerability and the limited government resources available to address them. The paper builds on the frame­ work for analytical CZRM to prototype and build a tool that analyzes data about income, nutrition, and housing for over 34,000 low-income families in Cear´a, Brazil. Each family is assigned a vulnerability score, which is then used to filter public policy recommendations that address specific vulnerability profiles. The CZRM derives from over one year of interaction with government stakeholders who expressed their needs. Further software vali­ dation was completed with a series of test cases which ensured the tool was working and showing accurate results. The interactive mapping, ranking and filtering tool exemplifies an emerging decision support system to build a citizen portfolio and to set strategies to provide public services to different citizen groups. Collapse

Topics: decision support system database system decision making user interface usability

Methods: design artifact survey cluster analysis

The effects of cyber regulations and security policies on organizational outcomes: a knowledge management perspective

2023 | European Journal of Information Systems | Citations: 0

Authors: Hovav, Anat; Gnizy, Itzhak; Han, Jinyoung

Abstract: Knowledge management (KM) has emerged as an operational and strategic organisati ... Expand

Abstract: Knowledge management (KM) has emerged as an operational and strategic organisational tool. However, the influence of recent cyber regulations (CR) and security policies (SP) on the usefulness of KM, are absent from the literature. This study focuses on the intersection of knowledge management and knowledge fortification. The study employs survey data from a sample of firms and utilises partial least squares (PLS) with extensive post hoc analyses, to examine the influence of CR and SP on KM core processes (acquisition, sharing, and utilisation) and subsequently on the operational effectiveness and strategic performance of firms. Contrary to prior research, we found that external knowledge sharing negatively affected performance. These findings support our assertion that KM processes are tied to contingencies that change the relationship between KM outcomes. Collapse

Topics: knowledge sharing organizational productivity security policy knowledge utilization knowledge acquisition

Methods: survey post-hoc analysis partial least squares regression computational algorithm descriptive statistic

Leveraging Digital Platforms Through Business Model Innovation in SMEs: A Capability Perspective

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Korneeva, Ekaterina; Hönigsberg, Sarah; Dinter, Barbara

Abstract: ... Expand

Abstract: Collapse

Topics: business model digital platform innovation management information technology capability knowledge management

Methods: case study qualitative coding qualitative interview longitudinal research longitudinal case study

Theories: dynamic capabilities theory