Network Centralization and Collective Adaptability to a Shifting Environment

2023 | Organization Science | Citations: 1

Authors: Bernstein, Ethan S.; Shore, Jesse C.; Jang, Alice J.

Abstract: We study the connection between communication network structure and an organizat ... Expand

Abstract: We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors acknowledge financial support from the Division of Research at the Harvard Business School for making this research possible. Collapse

Topics: social influence problem solving internet technology social network debugging

Methods: experiment machine learning simulation Poisson regression logistic regression

Theories: organizational theory

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021

2023 | HICSS | Citations: 0

Authors: Bhatt, P.; Vemprala, N.; Valecha, R.; Hariharan, G.; Rao, H.

Abstract: Polarization, which refers to the formation of opposing groups based on peoples ... Expand

Abstract: Polarization, which refers to the formation of opposing groups based on peoples' beliefs and opinions, is a phenomenon that has existed for eons. However, in the recent past, social media has caused a quantum change in the dynamics of polarization since beliefs may change almost instantaneously on social media because of unfolding events. We investigate social media communication that has resulted in polarized opinions among individuals prior to, during, and after a crisis event; the January 6th US Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observe the evolution of the phenomenon: as the event unfolds, polarization changes. We suggest that understanding the evolution of polarization is important for timely crisis resolution by crisis managers. We utilize three measures, spread, distinctness and regionalization, to examine polarization during a crisis. Based on these measures our findings indicate that social media discussions exhibit an increase in the negative sentiment during the crisis as compared to precrisis, but there is a rebound in terms of positive sentiments emerging in the post crisis period. Also, the results signal reduced diversity in discussion topics during the crisis as compared to pre and post-crisis. Collapse

Topics: social media Twitter terrorist incident artificial intelligence application programming interface

Methods: topic model longitudinal research qualitative content analysis longitudinal survey cluster analysis

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

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

Abstract: ... Expand

Abstract: Collapse

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

Methods: descriptive statistic cluster analysis parametric test ANOVA longitudinal research

Explainable artificial intelligence in information systems: A review of the status quo and future research directions

2023 | Electronic Markets | Citations: 4

Authors: Brasse, Julia; Broder, Hanna Rebecca; Förster, Maximilian; Klier, Mathias; Sigler, Irina

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an ... Expand

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an emerging phenomenon of global interest for academia, business, and society and brought about the rise of the research field of explainable artificial intelligence (XAI). With its pluralistic view, information systems (IS) research is predestined to contribute to this emerging field; thus, it is not surprising that the number of publications on XAI has been rising significantly in IS research. This paper aims to provide a comprehensive overview of XAI research in IS in general and electronic markets in particular using a structured literature review. Based on a literature search resulting in 180 research papers, this work provides an overview of the most receptive outlets, the development of the academic discussion, and the most relevant underlying concepts and methodologies. Furthermore, eight research areas with varying maturity in electronic markets are carved out. Finally, directions for a research agenda of XAI in IS are presented. Collapse

Topics: explainable artificial intelligence artificial intelligence user requirement electronic market user experience

Methods: literature study literature sample cluster analysis synthesis machine learning

Theories: information systems theory activity theory agency theory attribution theory cognitive fit theory

Doing good by going digital: A taxonomy of digital social innovation in the context of incumbents

2023 | Journal of Strategic Information Systems | Citations: 0

Authors: Buck, Christoph; Krombacher, Anna; Röglinger, Maximilian; Körner-Wyrtki, Katrin

Abstract: Digital social innovation (DSI) offers incumbents a strategic field of action to ... Expand

Abstract: Digital social innovation (DSI) offers incumbents a strategic field of action to leverage the op­ portunities of digital technologies to address pressing societal challenges. By proposing a tax­ onomy and 12 clusters of incumbents’ DSI initiatives based on a sample of 296 real-world objects, we develop a unified understanding of DSI and its characteristics. This lays the foundation for further theorising on DSI from an incumbent perspective and for researchers to shape the DSI field. The taxonomy provides incumbents with an orientation to realise DSI’s rich strategic po­ tentials throughout the DSI ideation process and in assessing DSI types. Collapse

Topics: innovation management societal challenge database system value creation digital innovation

Methods: literature sample cluster analysis agglomerative hierarchical clustering structured literature research theory development

Theories: information systems theory

Governance of Digital Health Data on Cooperatively Organized Platforms – a Design Thinking Approach

2023 | HICSS | Citations: 0

Authors: Burmann, Anja; Langer, Henrike; Bosompem, Jennifer; Clemens, Timo; Herten, Benjamin; Kerstan, Leah; Lauer, Romy; Naeve, Peter; Geller, Simon; Huhnt, Jönne; Merkel, Sebastian

Abstract: More and more stakeholders are collecting data for improving their services: fro ... Expand

Abstract: More and more stakeholders are collecting data for improving their services: from scientific research over public administration to commercial enterprises. Existing data management services offer few rights of co-determination for their users. Data cooperatives aim to provide a democratic alternative to this. Through such a platform cooperative members are supposed to be enabled to share their data in a self-determined way. In this paper, we present a design thinking-based user research with stakeholders of data cooperatives in a health-data context. We provide an overview of motivations, expectations, and interfaces between a cooperative and individuals as cooperative members, organizations, representatives from research, and policy makers. In an iterative process, 34 interviews were conducted with different stakeholder groups, from which 7 personas were subsequently derived. For these, 4 prototypes were developed and tested with potential users. Our results show that all interviewed groups were very interested in the concept of data cooperatives. At the same time, it proves challenging to reconcile the conflicting internal and external requirements and to implement attractive value propositions for all stakeholders. Collapse

Topics: design thinking healthcare data user centered design value creation organization structure

Methods: qualitative interview design methodology design artifact literature sample literature study

Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL

2023 | Group Decision and Negotiation | Citations: 0

Authors: Chen, Lu; Hendalianpour, Ayad; Feylizadeh, Mohammad Reza; Xu, Haiyan

Abstract: Based on previous evidence, the use of blockchain for improving Supply Chains (S ... Expand

Abstract: Based on previous evidence, the use of blockchain for improving Supply Chains (SCs) regarding humanitarian projects has received attention over the past five years. The present study is innovative in investigating crucial parameters affecting the using of Blockchain Technology (BT) in Humanitarian Supply Chains (HSCs). More precisely, this study emphasizes parameters that affect blockchain in the HSCs and presents a new fuzzy large-scale group decision-making trial and evaluation laboratory (fuzzy large-scale group-DEMATEL) approach to analyze the interdependence of contributing factors for using BT in HSCs. This method consists of two stages: (1) clustering the large-scale group-experts into small subgroups by their characteristics, and (2) identifying the key factors affecting BT in HSCs with a novel fuzzy large-scale group-DEMATEL approach. According to experts, in this study, among the 25 evaluated factors, disintermediation has been identified as the most important one, followed by anonymity and security. A closer look reveals that 13 and 12 factors have been “cause” and “effect” factors, respectively. Our research can be used to promote the effectiveness of using BT in HSCs, so as to promote the proper distribution of relief materials in practical disasters. Collapse

Topics: blockchain database system risk management anonymity decision making

Methods: cluster analysis computational algorithm cross sectional research literature study

Theories: fuzzy set theory

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

Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Zihan; Sun, Jingyi; Liu, Rong Emily; Mai, Feng

Abstract: Although pervasive spread of misinformation on social media platforms has becom ... Expand

Abstract: Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a realworld dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation. Collapse

Topics: misinformation Twitter filter bubble social media social influence

Methods: machine learning RoBERTa experiment deep learning graph neural network