Innovation failure knowledge sharing plays an important role in reducing the probability of repeated failure of subsequent innovation and improving innovation ability of virtual research organization. However, it is very difficult for members to actively share the innovation failure knowledge without incentives. To promote the sharing behavior of innovation failure knowledge in virtual research organization

The purpose of this research is to analyse the characteristics and behaviour of an organization over the course of 100 months of the courtship, infancy, and go-go stages of its lifecycle. Moreover, the causal feedback loops and stock-and-flow diagrams of each stage of the organizational lifecycle are plotted. The PAEI framework proposed by Adizes and the system dynamics approach are employed to understand

With the rise of online platforms where individuals could gather and spread information came the rise of online cybercrimes aimed at taking advantage of not just single individuals but collectives. In response, researchers and practitioners began trying to understand this digital playground and the way in which individuals who were socially and digitally embedded could be manipulated. What is emerging

Detecting and elucidating botnets is an active area of research. Using explainable, highly scalable Apache Spark-based artificial intelligence, process mining technologies are presented which illuminate bot activity within terrorist Twitter data. A derived hidden Markov model suggests that bot logic uses information camouflage in order to disguise intentions similar to World War II Nazi propagandists

An agent-based model is proposed and tested. This model aims to simulate agency as conceptualized in Bandura's (Am Psychol 37:122–147, 1982; Organ Behav Hum Decis Process 50:248–287; Annu Rev Psychol 52: 1–26) Social cognitive theory. Social cognitive theory has been used to explain the continued underrepresentation of females in certain fields, most notably fields that are associated with engineering

Prior explanations for the observed result that employee cooperation demonstrates both positive and negative relationships with organizational performance often require psychological reactions or cognitions. We use an agent-based model to assume away these effects and demonstrate a possible alternative relying on just three, simple features. These include (1) limits to an employee’s ability to help

Strong demographic faultlines are a potential source of conflict in teams. To study conditions under which faultlines can result in between-group bi-polarization of opinions, a computational model of persuasive argument communication has been proposed. We identify two hitherto overlooked degrees of freedom in how researchers formalized the theory. First, are arguments agents communicate influencing

Diversification involves ongoing decisions about firm boundaries and relatedness. We develop a theoretical model that uses a real-option framework combined with optimal mechanism design to analyze how choices of boundaries and relatedness affect firm performance in the face of tradeoffs between governance and flexibility. We find that: (1) optimal boundaries and relatedness are substitutes in determining

The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that

The knowledge of key network members is generally known to be critical to fuzzy social network analysis. Thus far, most studies aiming to identify critical members have taken network structural centrality measures. Since fuzzy graph cannot effectively depict the multidimensional relationships between the nodes of fuzzy social networks, a fuzzy social network model is developed complying with a mathematical

Even though both collective intelligence and groupthink promote concurrence seeking, studies focused on the relationship between the two are lacking. Therefore, this study aims to explore how to transform groupthink into collective intelligence that contributes to knowledge creation. Three “switching factors”— “knowledge conflict,” “reconsideration,” and “organizational memory”—are defined herein.

Extant research is vertically divided on the question whether exploration and exploitation constitute two ends of a continuum or whether they are orthogonal activities. We suggest that both characterizations are admissible, albeit under different sets of assumptions. Using March’s iconic model, we demonstrate that the continuum conception concerns leveraging an organization’s internal knowledge heterogeneity

We note some future areas for work with cognitive models and agents that as Colbert (I am America (and so can you!), 2007) notes, “so can you”. We present three approaches as something like design patterns, so they can be applied to other architectures and tasks. These areas are: (a) Interacting directly with the screen-as-world. It is now possible for models to interact with uninstrumented interfaces

We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation

Identifying fake news has become an important issue. Increasing usage of social media has led to an increase in the number of people who can be influenced, thus the spread of fake news can potentially impact important events. Fake news has become a major societal issue and a technical challenge for social media companies to identify and has led many to extreme measures, such as WhatsApp deleting two

Ecological models are useful in modeling organizations and their competition over resources. However, the traditional approaches, particularly Blau space models, are restrictive in their dependence on a continuous space. In addition, these models are susceptible to indicating competition in sparsely populated areas of an ecology, resulting in competition being indicated where there are no resources

Online social networks allow users to share a variety of multi-media content on the World Wide Web. The rising popularity of such social networking platforms coupled with limitations in verifying the veracity of shared content has contributed to increase in misinformation on these media. Misinformation content such as fake-news and hoaxes, though often considered innocuous, may have high social cost

We apply a variation of socio-cultural cognitive mapping (SCM) to computer malware features explored previously by Saxe and Berlin that characterized malware binaries as benign or malicious based on 1024 program features derived from a deep neural network-based detection system. In this work, we model the features as attributes within a latent spatial domain using a weighted consensus graph representation

This operations research aims to derive an easy but meaningful method for practitioners to identify key influencers and uncover suppressed narratives within a Twitter topic group. This research employs a new concept called “authentic chatter” (analogous to a grass-roots discourse) in combination with influence metrics, content analysis, and commercial-off-the-shelf social media analysis software (NexaIntelligence)

Social cyber-security is an emergent field defining a multidisciplinary and multimethodological approach to studying and preserving the free and open exchange of information online. This work contributes to burgeoning scholarship in this field by advocating the use of interoperable pipelines of computational tools. We demonstrate the utility of such a pipeline in a case study of Twitter information

This paper describes the design, implementation, and early experiences with a novel agent-based simulator of online media streams, developed under DARPA’s SocialSim Program to extract and predict trends in information dissemination on online media. A hallmark of the simulator is its self-configuring property. Instead of requiring initial set-up, the input to the simulator constitutes data traces collected

The notions of betweenness centrality (BC) and group betweenness centrality (GBC) are widely used in social network analyses. We introduce variants of them; namely, the k-step BC and k-step GBC. The k-step GBC of a group of vertices in a network is a measure of the likelihood that at least one group member will get the information communicated between pairs of vertices through shortest paths within

Although the performance of new product development (PD) is dependent on the structure and formation of design teams, effective configuration of the PD teams remains largely unexplored. According to social network research, teams are often organized in either closely connected or sparse structure. We conceptualize PD projects as collective problem-solving endeavors and develop a computational model

We propose, and test, a model of online media platform users’ decisions to act on, and share, received information. Specifically, we focus on how mental representations of message content drive its spread. Our model is based on fuzzy-trace theory (FTT), a leading theory of decision under risk. Per FTT, online content is mentally represented in two ways: verbatim (objective, but decontextualized, facts)

In this paper, we introduce the concept of social cyber forensics and its usability. Then, we introduce a tool, i.e., Maltego that can be used to study the cross-media affiliation and uncover hidden relations among various online groups. We also provide three stepwise methodologies that leverage Maltego and various open source information to uncover the hidden relationship among (1) Twitter accounts

It is common for organizations to hire workers based on their knowledge, skills, and abilities. However, despite capable workers being hired, productivity may suffer if employees’ motivational needs are not satisfied. We developed an agent-based model to simulate the completion of tasks by teams of workers in an organization. Each worker is described by an ability value and a 3-parameter motive profile

This paper investigates the cause of the collapse of the Second Yatsenyuk Ukrainian government in 2016 and the factions formed throughout the process using a novel method that combines roll call vote analysis and dynamic network analysis. Our findings suggest that a major factor in the dissolution of the coalition government was the 2015 constitutional amendment .We also found that the coalition split

We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations

The Stock market trend prediction is an efficient medium for investors, public companies and government to invest money by taking into account the profit and risk. The existing studies on the development of stock-based prediction systems rely on data acquired from social media sources (sentiment-based) and secondary data sources (financial-sites). However, the data acquired from such sources is usually

In this study a novel population based meta-heuristic, called controlled showering optimization (CSO) algorithm, for global optimization of unconstrained problems is presented. Modern irrigation systems are equipped with smart tools manufactured and controlled by human intelligence. The proposed CSO algorithm is inspired from the functioning of water distribution tools to model search agents for carrying

Automated social media bots have existed almost as long as the social media environments they inhabit. Their emergence has triggered numerous research efforts to develop increasingly sophisticated means to detect these accounts. These efforts have resulted in a cat and mouse cycle in which detection algorithms evolve trying to keep up with ever evolving bots. As part of this continued evolution, our

Forecasting future productivity is a critical task to every organization. However, the existing methods for productivity forecasting have two problems. First, the logarithmic or log-sigmoid value, rather than the original value, of productivity is dealt with. Second, the objective functions are not consistent with those adopted in practice. To address these problems, a fuzzy polynomial fitting and

Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati

Much has been made of the importance of the speed at which disinformation diffuses through online social media and this speed is an important aspect to consider when designing interventions. An additional complexity is that there can be different types of false information that travel from and through different communities who respond in various ways within the same social media conversation. Here

The process of diagnosis, intervention, conservation, restoration and maintenance of built cultural heritage requires complete and accurate knowledge about tangible as well as intangible aspects of their past and present. Comprehensive and precise knowledge about built cultural heritage structures, their functionality, location, environment and any sudden disturbance needs to be digitized for long

Since the stakeholder theory was proposed to explain the interaction among its agents, extensive approaches have been developed. However, the literature continues to suggest the development of new methodologies that allow an analysis of the dynamics and uncertainty of the relationships between each agent. In this sense, this research proposes a novel methodology for the treatment of subjective stakeholder

As America’s opioid crisis has become an “epidemic of epidemics,” Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl analogs, and other non-pharmaceutical opioids on cryptomarkets and assess relationship with the trends in unintentional overdoses in Ohio to provide timely

A significant research topic in the area of computational social choice is the complication of different kinds of dishonest behaviour like manipulation, dominance and bribery. Whereas most of the work on this issue assumes that the opposite party has incomplete knowledge regarding every agent, they did not know the true preferences of other voters. We have analysed the dynamics of voting rules with

Nowadays social media is widely used as the source of information because of its low cost, easy to access nature. However, consuming news from social media is a double-edged sword because of the wide propagation of fake news, i.e., news with intentionally false information. Fake news is a serious problem because it has negative impacts on individuals as well as society large. In the social media the

We describe a computational cognitive model intended to be a generalizable classifier that can provide context-based feedback to semantic perception in robotic applications. Many classifiers (including cognitive models of categorization) perform well at the task of associating features with objects. Underlying their performance is an effective selection of the features used during classification. This

The success of military operations depends on soldiers’ execution of the operation as well as resources used for the operation. However, this does not mean that more men and firepower will ensure victory. Military units, just like any other organization, are collections of distributed elements, and improving the organization or command and control (C2) structure of such elements will ultimately show

Participation in community events such as local festivals can provide people with opportunities to collaborate with community members whom they would have never met otherwise. Furthermore, such collaboration through community events is likely to expand interaction even in daily life. In addition, if the physical properties of a public space in which community events are held change, the number and

Data that continuously track the dynamics of large populations have the potential to revolutionize how we study complex social systems. However, coping with massive, noisy, unstructured, and disparate data streams is not easy. In this paper, we describe a particle filter algorithm that integrates signal processing and simulation modeling to study complex social systems using massive, noisy, unstructured

The emergence of computer-aided diagnostic technology has revolutionized the health sector and by use of medical imaging records, health experts are able to get detailed analysis which enable them in precise diagnosis of gliomas tumors. In this paper, we present an approach that uses domain-specific knowledge together with hybrid image enhancement techniques that provides resulting image(s) with more

The outcome of a work process depends on which tasks are assigned to which employees. However, sometimes optimized assignments based on employees’ qualifications may result in an uneven and ineffective workload distribution. Likewise, an even workload distribution without considering the employee’s qualifications may cause unproductive employee-task matching that results in low performance. This trade-off

As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the

Finding similar objects based on a query and a distance, remains a fundamental problem for many applications. The general problem of many similarity measures is to focus the search on as few elements as possible to find the answer. The index structures divides the target dataset into subsets. With large amounts of data, the volumes of the subspaces grow exponentially, that will affect the search algorithms

The cursive nature, Nastaliq writing style and a large number of different ligatures make ligature recognition very difficult in Urdu. In this paper, we present a segmentation-free approach to holistically recognize Urdu ligatures. We first generate a rich dataset which contains 17,010 ligatures with different orientation and different degrees of noise. Secondly, the ligatures are clustered (categorized)

The literature on the knowledge management relatively ignores an important concept, the individual knowledge management engagement-the degree to which a knowledge worker is involved with the knowledge management-related activities. This concept is imperative for nurturing the productivity of knowledge workers, knowledge management architecture effectiveness, and innovation. Therefore, this study proposes

A multi-agent system is a useful modeling architecture in business process modeling in the sense that we can naturally implement participants in a real company with software agents. However, analyzing and interpreting the simulation results of multi-agent models tends to be difficult due to the inherent complexity of the models. In this regard, another discipline—process mining—is useful for such purposes

Traffic rules violations and accidents on road are major issues now-a-days. Identification of vehicles violating traffic rules and manual monitoring of vehicles is difficult, due to traffic congestion on freeways. A novel mathematical model is proposed to generalize detection of a number of traffic violations on highways. The model, using image processing techniques translates lanes on the road as

Urdu optical character recognition (OCR) is a complex problem due to the nature of its script, which is cursive. Recognizing characters of different font sizes further complicates the problem. In this research, long short term memory-recurrent neural network (LSTM-RNN) and convolution neural network (CNN) are used to recognize Urdu optical characters of different font sizes. LSTM-RNN is trained on

The role of diversity in organizations has been widely discussed in recent decades; nevertheless, both theoretical perspectives and empirical results appear conflicting and inconsistent. Scholars identify many possible reasons such as the definition of diversity, theoretical perspectives, variables, and methodological approaches; this study focuses on the methodological issue of assessing variety.

Big data and its related technologies have become active areas of research recently. There is a huge amount of data generated every minute and second that includes unstructured data which is the topic of interest for researchers now a days. A lot of research work is currently going on in the areas of text analytics and text preprocessing. In this paper, we have studied the impact of different preprocessing

Organizational routines consist of a mix of human actors and artifacts. Indeed, organizational settings are populated by a variety of cognitive artifacts, such as operating standards and prioritization rules, which encapsulate two types of knowledge: standards and regulations constraining individual action and rules sustaining explorative capacities of individuals. In order to investigate the role

Organizations that operate in a dynamic environment must take steps to adapt to their changing circumstances; otherwise, they may collapse entirely. Yet, managers may postpone necessary change to minimize costs, while ignoring the risk that this myopic approach entails to the survival of their organizations. This paper proposes a model that considers failure as a stage-wise process of decline, in which

Productivity is critical to the long-term competitiveness of factories. Therefore, the future productivity of factories must be estimated and enhanced. However, this is a challenging task because productivity can be improved based on a learning process that is highly uncertain. To address this problem, most existing methods fit fuzzy productivity learning processes and convert them into mathematical

In this article, we use Swedish longitudinal register data to study the effect that similarity in organizational properties has on the interaction between organizations. We map out the social space of large organizations in the Stockholm Region and the interplay between social distance and the network communities of employee movements between organizations. We firstly use homogeneity analysis to describe

The strategy of integration known as vendor-managed inventory (VMI), which allows the coordination of inventory policies between producers and buyers in supply chains, has long been considered a strategy for inventory cost reduction. Although the literature acknowledges the importance of understanding the dynamics of VMI implementation through evolutionary games, research in this topic still remains