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Abstract: Abstract Since its nascence, computational organization theory has predominantly relied on classical probability theory to model and simulate organizational properties. However, key assumptions of classical probability theory conflict with empirical observations of organizational behaviors and processes, thereby raising the question if an alternate theoretical basis for probabilistic modeling of organizations might improve the relevancy of computational organization research. In the context of the garbage can model of organizational decision-making, this paper provides two examples—order effects and system measurement—to illustrate the inadequacy of classical probability theory and to stimulate discussion on the merits of incorporating quantum probability theory in computational models. This paper recommends that future work explore the sensitivity of computational organization theory models to probability theories, the impacts associated theoretical assumptions might have on modeling and simulating dynamic organizational interdependencies, and the implications to community practices. PubDate: 2024-06-01 DOI: 10.1007/s10588-023-09378-3
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Abstract: Abstract Employee retention is a problem for organizations of all sizes. Research has shown that transformational leaders improve retention and reduce turnover; however, there has been little research on the effects of transformational leadership on retention over time while also considering employees’ changing employability. We use agent-based modeling to demonstrate these changing relationships while considering the nature of modern organizations. Our model looks at the relationships between transformational leaders, individual employability, and retention. The model uses data from earlier research to define parameters for these variables, showing how workers and leaders interact and affect employability and retention and how these effects change over time. PubDate: 2024-05-03 DOI: 10.1007/s10588-024-09385-y
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Abstract: Abstract Organizations often pursue multiple goals for survival and competitive advantages. We address strategies for the pursuit and achievement of multiple goals from the perspectives of the allocation of resources and attention. Using a computational simulation model, we examine the impact of resource and attention allocation from the perspective of organizational learning, considering it as a critical series of goal-oriented activities, under the argument that the pursuit of the organizational goal is the process of evaluating, searching, and decision making. Our results yield three main strategies that enhance organizational outcomes: sequential attention to goals with more individuals assigned to each goal, scheduling longer times for the achievement of each goal, and managing allowing the organization to proceed with goal-oriented work quickly while individuals take things slowly within an assigned input time as an effective way of using given time. These strategies are pivotal to the successful pursuit and achievement of multiple goals. PubDate: 2024-03-01 DOI: 10.1007/s10588-023-09377-4
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Abstract: Abstract Much of what we know, we know thanks to our interactions with others. There is a variety of ways in which we learn from others. We sometimes simply adopt the viewpoints of those we regard as experts, but we also sometimes change our viewpoints in more subtle ways based on the viewpoints of people we regard as our peers. Both forms of social learning have been receiving increasing attention. However, studies investigating how best to combine them, and how to combine the two with individual forms of learning, are still few and far between. This paper looks at ways to integrate various forms of social learning with learning at an individual level within a broadly Bayesian framework. Using agent-based models, we compare the different ways in terms of accuracy of belief states as well as in terms of evolutionary viability. The outcomes of our simulations suggest that agents are best off spending most of their time engaging in social learning, reserving only a limited amount of time for individual learning. PubDate: 2024-03-01 DOI: 10.1007/s10588-022-09372-1
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Abstract: Abstract Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. Studying organizations as complex systems is a challenge as we must account for hierarchically nested structures, multi-level processes, and changes over time. Informal structures interact with individual attitudes to influence organizational processes such as knowledge sharing, a process vital to organizational performance and innovation. To explore such organizational dynamics, we integrate dynamic social networks, a cognitive model of attitude formation and change, and a physical environment into an agent-based model, the combination of which represents a novel way to study organizations. We use a hospital in southwest Virginia as our case study. The agents in the model are the healthcare workers within the hospital and agent movement occurs over the physical environment of the hospital. Results show that the simulated hospital is resilient to impacts from employee attrition but that communication approaches must be thought through strategically so as not to hinder knowledge sharing. For managers, this type of modeling approach can provide resource and planning guidance in regards to attrition-based strategies and communication approaches. PubDate: 2024-03-01 DOI: 10.1007/s10588-023-09373-8
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Abstract: Abstract Human emergency behaviour and psychological stress response in emergencies are important scientific issues in basic emergency management research. The analysis of the dynamic characteristics of large-scale human behaviour based on electronic footprint data provides a new method for quantitative research on this problem. Previous studies usually assumed that human behaviors were randomly distributed in time, but few studies have studied the psychological stress response of human groups under the influence of emergencies and carried out prediction methods through social media data. Based on the data from five emergencies and daily events in the Qzone, this paper explores the statistical characteristics of human communication behaviors such as time, space and social interaction. The research results reveal the psychological evolution of human groups when they encounter public security emergencies by analysing the causes of individual psychological stress responses in the group. We find that the time interval between people’s posting behaviour and interactive comment behaviour in mobile QQ space before and after an emergency can be approximately described by a power-law distribution. The time interval distribution of Posting and reply is an obvious heavy-tailed distribution. These behavioural characteristics are consistent with people’s psychological stress characteristics. Individual psychological stress responses gradually evolve into social-psychological responses with changes in behavioural characteristics. The greater the social-psychological stress response is, the more panic the public will be, which will cause the outbreak of group irrational behaviour. The research results are theoretically helpful in understanding the impact of emergencies on human communication behaviour patterns and reveal the psychological stress process of mass panic in large-scale emergencies. PubDate: 2024-02-16 DOI: 10.1007/s10588-024-09384-z
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Abstract: Abstract A small number of individuals infected within a community can lead to the rapid spread of the disease throughout that community, leading to an epidemic outbreak. This is even more true for highly contagious diseases such as COVID-19, known to be caused by the new coronavirus SARS-CoV-2. Mathematical models of epidemics allow estimating several impacts on the population and, therefore, are of great use for the definition of public health policies. Some of these measures include the isolation of the infected (also known as quarantine), and the vaccination of the susceptible. In a possible scenario in which a vaccine is available, but with limited access, it is necessary to quantify the levels of vaccination to be applied, taking into account the continued application of preventive measures. This work concerns the simulation of the spread of the COVID-19 disease in a community by applying the Monte Carlo method to a Susceptible-Exposed-Infective-Recovered (SEIR) stochastic epidemic model. To handle the computational effort involved, a simple parallelization approach was adopted and deployed in a small HPC cluster. The developed computational method allows to realistically simulate the spread of COVID-19 in a medium-sized community and to study the effect of preventive measures such as quarantine and vaccination. The results show that an effective combination of vaccination with quarantine can prevent the appearance of major epidemic outbreaks, even if the critical vaccination coverage is not reached. PubDate: 2023-12-01 DOI: 10.1007/s10588-021-09327-y
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Abstract: Abstract Higher education institutions (HEIs) are complex and dynamic organizations in terms of information management, forcing their information systems to respond to enormous challenges and threats. In order to evaluate the HEIs’ information systems, we propose the development of a maturity model capable of supporting the role of HEI’s managers, as well as accreditation agencies, in the assessment of the maturity of these systems, thus, promoting continuous improvement. In this paper, we present and discuss our proposal for an architecture of the maturity model being developed. This one is based on a two-dimensional architecture composed of vertical and horizontal dimensions. We selected a multi-case study approach, based on five Portuguese HEIs, and reviewed the literature to identify the dimensions. This case study was supported by interviews with experts from the selected HEI. The results of this research work were both encouraging and promising amongst the interviewed experts, revealing a high level of acceptance of the general model architecture, as well as positive expectations about its usefulness in the future. The development of our maturity model is carried out by following a formal methodology specially designed to support the construction of this type of model. PubDate: 2023-12-01 DOI: 10.1007/s10588-021-09342-z
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Abstract: Abstract Conspiracy theories (CTs) have thrived during the COVID-19 pandemic and continue to spread on social media despite attempts at fact-checking. The isolation and fear associated with this pandemic likely contributed to the generation and spread of these theories. Another possible factor is the high rate of Twitter users linking to off-platform alternative news sources through URL sharing (Moffitt et al. 2021). In this paper, we compare URLs and their parent domains linked in CT and non-CT tweets. First, we searched the parent domains of URLs shared in conspiracy theory and non-conspiracy theory classified tweets for the presence of Google tracking codes. We then constructed meta-networks linking domains, tracking codes, and Twitter users to find connections between domains and evidence of an eco-system that may have contributed to the cultivation and spread of conspiracy theories during the pandemic. PubDate: 2023-09-04 DOI: 10.1007/s10588-023-09379-2
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Abstract: Abstract The human ability to utilize social and behavioral cues to infer each other’s intents, infer motivations, and predict future actions is a central process to human social life. This ability represents a facet of human cognition that artificial intelligence has yet to fully mimic and master. Artificial agents with greater social intelligence have wide-ranging applications from enabling the collaboration of human–AI teams to more accurately modelling human behavior in complex systems. Here, we show that the Naïve Utility Calculus generative model is capable of competing with leading models in intent recognition and action prediction when observing stag-hunt, a simple multiplayer game where agents must infer each other’s intentions to maximize rewards. Moreover, we show the model is the first with the capacity to out-compete human observers in intent recognition after the first round of observation. We conclude with a discussion on implications for the Naïve Utility Calculus and of similar generative models in general. PubDate: 2023-09-01 DOI: 10.1007/s10588-022-09367-y
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Abstract: Abstract The impact of the COVID pandemic to our society is unprecedented in our time. As coronavirus mutates, maintaining social distance remains an essential step in defending personal as well as public health. This study conceptualizes the social distance “nudge” and explores the efficacy of mHealth digital intervention, while developing and validating a choice architecture that aims to influence users’ behavior in maintaining social distance for their own self-interest. End-user nudging experiments were conducted via a mobile phone app that was developed as a research artifact. The accuracy of social distance nudging was validated in both United States and Japan. Future work will consider behavioral studies to better understand the effectiveness of this digital nudging intervention. PubDate: 2023-09-01 DOI: 10.1007/s10588-022-09365-0
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Abstract: Abstract The development of COVID-19 vaccines during the global pandemic that started in 2020 was marked by uncertainty and misinformation reflected also on social media. This paper provides a quantitative evaluation of the Uniform Resource Locators (URLs) shared on Twitter around the clinical trials of the AstraZeneca vaccine and their temporary interruption in September 2020. We analyzed URLs cited in Twitter messages before and after the temporary interruption of the vaccine development on September 9, 2020 to investigate the presence of low credibility and malicious information. We show that the halt of the AstraZeneca clinical trials prompted tweets that cast doubt, fear and vaccine opposition. We discovered a strong presence of URLs from low credibility or malicious websites, as classified by independent fact-checking organizations or identified by web hosting infrastructure features. Moreover, we identified what appears to be coordinated operations to artificially promote some of these URLs hosted on malicious websites. PubDate: 2023-09-01 DOI: 10.1007/s10588-022-09370-3
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Abstract: Abstract The employment of drone strikes has been ongoing and the public continues to debate their perceived benefits. A question that persists is whether drone strikes contribute to an increase in radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes conducted in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these particular counterterrorism measures. Our exploration and analysis of news reports which discussed drone strikes and radicalization suggest that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We leverage news reports to inform and calibrate an agent-based model grounded in radicalization and opinion dynamics theory. This enabled us to simulate terrorist attacks that approximated the rate and magnitude observed in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and drone strikes. PubDate: 2023-09-01 DOI: 10.1007/s10588-022-09364-1
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Abstract: Abstract Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked accounts compared to unaffiliated accounts. Our analysis reveals that state-linked actors demonstrate significantly higher levels of coordination among themselves compared to their coordination with unaffiliated accounts. Furthermore, the degree of coordination observed between state-linked accounts is more than seven times greater than the coordination observed between unaffiliated accounts. Moreover, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure. PubDate: 2023-08-29 DOI: 10.1007/s10588-023-09382-7
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Abstract: Abstract Open, collaborative mapping initiatives such as OpenStreetMap, a wiki-style map of the world, continually face concerns about the reliability and authority of its data. Based on harnessing the power of millions of volunteers globally, the data production process is decentralized and reflects a mosaic of individual contributors’ skills, motivations, and experiences. Linus’ Law, a widespread assumption within open-source communities, suggests that data quality increases with the number of contributors. In this paper, we evaluate Linus’ Law as applied to the co-production of volunteered geographic information using an agent-based model and examine the effects of knowledge level, variability, and prioritization on emergent production patterns and overall data quality. Our results demonstrate how diminishing returns and the experience of contributors limit Linus’ Law as an intrinsic assessment of data quality. PubDate: 2023-08-21 DOI: 10.1007/s10588-023-09383-6
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Abstract: Abstract The purpose of this study is to review existing research on organization management that applied agent-based modeling and simulation (ABMS). First, we systematically identified 133 relevant articles published between 1998 and 2022 using the Web of Science (WoS) and EBSCOhost database. Second, we analyzed the characteristics of ABMS reported in the 133 articles. The results illustrated that the focal articles made extensive use of ABMS as a means of theory development and the enhancement of transparency was demanded. Third, we used a bibliometric mapping approach to analyze the 133 articles visually. The results identified 36 key terms and four clusters: team behaviors under complex environments, organizational structure and design, knowledge management in organizations, and organizational decision-making. The analysis also showed which key terms are used as research fronts and which terms are emerging. Lastly, we suggest five promising research opportunities that should either be continued or be addressed in organization management. PubDate: 2023-07-24 DOI: 10.1007/s10588-023-09381-8
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Abstract: Abstract The rapid increase in China’s outward digital presence on western social media platforms highlights China’s priorities for promoting pro-Chinese narratives and stories in recent years. Simultaneously, China has increasingly been accused of launching information operations using bot activity, puppet accounts, and other inauthentic activity to amplify its messaging. This paper provides a comprehensive network analysis characterization of the hashtag influence campaign China promoted against the US-hosted Summit on Democracy in December 2021, in addition to methods to identify different types of actors within this type of influence campaign. China uses layers of state-sponsored accounts, bots, and non-bot accounts to promote its messaging. Lastly, we describe how China uses localized campaigns under a more extensive umbrella campaign for information diffusion toward targeted audiences. PubDate: 2023-07-01 DOI: 10.1007/s10588-023-09380-9
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Abstract: Abstract This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network. PubDate: 2023-04-12 DOI: 10.1007/s10588-023-09375-6
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Abstract: Abstract This nation-shaping election of 2020 plays a vital role in shaping the future of the U.S. and the entire world. With the growing importance of social media, the public uses them to express their thoughts and communicate with others. Social media have been used for political campaigns and election activities, especially Twitter. The researchers intend to predict presidential election results by analyzing the public stance toward the candidates using Twitter data. Previous researchers have not succeeded in finding a model that simulates well the U.S. presidential election system. This manuscript proposes an efficient model that predicts the 2020 U.S. presidential election from geo-located tweets by leveraging the sentiment analysis potential, multinomial naive Bayes classifier, and machine learning. An extensive study is performed for all 50 states to predict the 2020 U.S. presidential election results led by the state-based public stance for electoral votes. The general public stance is also predicted for popular votes. The true public stance is preserved by eliminating all outliers and removing suspicious tweets generated by bots and agents recruited for manipulating the election. The pre-election and post-election public stances are also studied with their time and space variations. The influencers’ effect on the public stance was discussed. Network analysis and community detection techniques were performed to detect any hidden patterns. An algorithm-defined stance meter decision rule was introduced to predict Joe Biden as the President-elect. The model’s effectiveness in predicting the election results for each state was validated by the comparison of the predicted results with the actual election results. With a percentage of 89.9%, the proposed model showed that Joe Biden dominated the electoral college and became the winner of the U.S. presidential election in 2020. PubDate: 2023-03-28 DOI: 10.1007/s10588-023-09376-5
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