Question: Combining learning and reasoning services for explainable information integration.

This paper explores the dichotomy between early and late fusion techniques in the context of semantic video analysis, a critical process for indexing and understanding video content at a conceptual level. In the pursuit of this objective, the analysis must synthesize multiple information streams. The timing of this fusion is pivotal and is typically categorized into two primary strategies: early fusion and late fusion. Early fusion integrates the data streams at the feature level, whereas late fusion occurs at the semantic level. Through comprehensive experimental analysis on a substantial dataset of 184 hours of broadcast video, focusing on 20 distinct semantic concepts, the study reveals that late fusion generally yields marginally superior results for the majority of concepts. However, for the concepts where early fusion outperforms late fusion, the performance gap is notably wider. This finding underscores the nuanced relationship between fusion timing and the specific characteristics of the concepts being analyzed, highlighting the importance of tailored approaches in semantic video analysis.

The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., “Chicken meat is healthy”). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on realworld datasets, we demonstrate ClaimEval’s capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

Identifying incorrect content (i.e., semantic error) in text is a difficult task because of the ambiguous nature of written natural language and themany factors that canmake a statement semantically erroneous. Current methods identify semantic errors in a sentence by determining whether it contradicts the domain to which the sentence belongs. However, because thesemethods are constructedon expected logic contradictions, they cannot handle new or unexpected semantic errors. In this paper, we propose a new method for detecting semantic errors that is based on logic reasoning. Our proposed method converts text into logic clauses, which are later analyzed against a domain ontology by an automatic reasoner to determine its consistency. This approach can provide a complete analysis of the text, since it can analyze a single sentence or sets of multiple sentences. When there are multiple sentences to analyze, in order to avoid the high complexity of reasoning over a large set of logic clauses, we propose rules that reduce the set of sentences to analyze, based on the logic relationships between sentences. In our evaluation, we have found that our proposed method can identify a significant percentage of semantic errors and, in the case of multiple sentences, it does so without significant computational cost. We have also found that both the quality of the information extraction output and modeling elements B Dejing Dou dou@cs.uoregon.edu Fernando Gutierrez fernando@cs.uoregon.edu Nisansa de Silva nisansa@cs.uoregon.edu Stephen Fickas fickas@cs.uoregon.edu 1 Department of Computer and Information Science, 1202 University of Oregon, Eugene, OR 97403, USA of the ontology (i.e., property domain and range) affect the capability of detecting errors.

The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the landscape of data integration since the work on rewriting queries using views in the mid-1990's. In addition, we describe two important challenges for the field going forward. The first challenge is to develop good open-source tools for different components of data integration pipelines. The second challenge is to provide practitioners with viable solutions for the long-standing problem of systematically combining structured and unstructured data.

Fusion of hard data with soft data is an issue that has attracted recent attention. An effective fusion strategy requires an analytical framework that can capture the uncertainty inherent in hard and soft data. For instance, computational linguistic parsing of text-based data generates logical propositions that inherently possess significant semantic ambiguity. An effective fusion framework must exploit the respective advantages of hard and soft data while mitigating their particular weaknesses. In this paper, we describe a Dempster-Shafer theoretic approach to hard and soft data fusion that relies upon the novel conditional approach to updating. The conditional approach engenders a more flexible method that allows for tuning and adapting update strategies. When computational complexity concerns are taken into account, it also provides guidance on how evidence could be ordered for updating. This has important implications in working with models that convert propositional logic statements from text into Dempster-Shafer theoretic form.

One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.

Spreadsheets contain a huge amount of high-value data but do not observe a standard data model and thus are difficult to integrate. A large number of data integration tools exist, but they generally can only work on relational data. Existing systems for extracting relational data from spreadsheets are too labor intensive to support ad-hoc integration tasks, in which the correct extraction target is only learned during the course of user interaction.
 This paper introduces a system that automatically extracts relational data from spreadsheets, thereby enabling relational spreadsheet integration. The resulting integrated relational data can be queried directly or can be translated into RDF triples. When compared to standard techniques for spreadsheet data extraction on a set of 100 random Web spreadsheets, the system reduces the amount of human labor by 72% to 92%. In addition to the system design, we present the results of a general survey of more than 400,000 spreadsheets we downloaded from the Web, giving a novel view of how users organize their data in spreadsheets.

We describe theWHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multi-sentential explanations. We explore approaches for achieving a deeper understanding of these explanations and dialogue management approaches and strategies for providing appropriate feedback on them.

One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.

We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.


Question: Combining learning and reasoning services for explainable information integration.

This paper delves into the visualization of image classification models, specifically those learned using deep Convolutional Networks (ConvNets). Two visualization methods are explored, both centered on calculating the gradient of the class score with respect to the input image. The initial technique generates an image that maximizes the class score, effectively visualizing the representation of the class as understood by the ConvNet. The second method creates a class saliency map, tailored to a specific image and class. We demonstrate that these maps can be utilized for weakly supervised object segmentation with classification ConvNets. Furthermore, we establish a link between gradient-based ConvNet visualization methods and deconvolutional networks, highlighting the synergy between these approaches.

We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.

1. o formal representation of plausible inference patterns; such OS deductions, inductions, ond analogies, thot ore frequently employed in answering every doy questions: 2. a set of parameters, such OS conditional likelihood, typicality, and similarity, that affect the certainty of people’s onswers to such questions; ond 3. a system relating the different plorisible inference patterns and the different certainty parameters.

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.

With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert ethicists in the health domain. This may be useful in many applications, especially where machines interact with humans in a medical context. Additionally, when connected to a cognitive model of emotional intelligence and affective decision making, it can be explored how moral decision making impacts affective behavior.

Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.

Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ineffective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide fine-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional effectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model significantly improves two popular multiple-skill knowledge tracing models on all these four aspects.

This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities. 1. MOTIVATION AND SCOPE Comphrehensive machine-readable knowledge bases (KB’s) have been pursued since the seminal projects Cyc [19, 20] and WordNet [12]. In contrast to these manually created KB’s, great advances have recently been made on automating the building and curation of large KB’s [1, 16], using information extraction (IE) techniques and harnessing highquality Web sources like Wikipedia. Prominent endeavors of this kind include academic research projects such as DBpedia [3], KnowItAll [10], NELL [5] and YAGO [29], as well as industrial ones such as Freebase. These projects provide automatically constructed KB’s of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and billions of facts about them. Moreover, several KB’s are interlinked at the entity level, forming the backbone of the Web of Linked Data [14]. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, entity linking, text summarization, deep question answering, and semantic search and analytics over entities and relations in Web and enterprise data (e.g., [2, 6, 8, 13]). Prominent examples of how KB’s can be harnessed include the Google Knowledge Graph [27] and the IBM Watson question answering system [17]. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis is on the twofold role of KB’s for bigdata analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 40th International Conference on Very Large Data Bases, September 1st 5th 2014, Hangzhou, China. Proceedings of the VLDB Endowment, Vol. 7, No. 13 Copyright 2014 VLDB Endowment 2150-8097/14/08. better intelligence with big data. The following sections outline the structure of the tutorial. An extensive bibliography on this theme is given in [30]. 2. BUILDING KNOWLEDGE BASES Digital Knowledge: Today’s KB’s represent their data mostly in RDF-style SPO (subject-predicate-object) triples. We introduce this data model and the most salient KB projects, which include KnowItAll [10, 11], BabelNet [22], ConceptNet [28], DBpedia [3, 18], DeepDive [24], Freebase [4], ImageNet [7], NELL [5], Wikidata [31], WikiNet [21], WikiTaxonomy [26], and YAGO [29, 15]. We briefly discuss industrial projects like the Google Knowledge Graph and related work at Google [9, 13, 25], the EntityCube and Probase projects at Microsoft Research [23, 32], and IBM’s Watson project [17]. Harvesting Knowledge on Entities and Classes: Every entity in a KB (e.g., Steve Jobs) belongs to one or multiple classes (e.g., computer pioneer, entrepreneur). These classes are organized into a taxonomy, where more special classes are subsumed by more general classes (e.g., person). We discuss two families of methods to harvest such information: Wikipedia-based approaches that analyze the category system, and Web-based approaches that use techniques like set expansion. 3. HARVESTING FACTS AT WEB SCALE Harvesting Relational Facts: Relational facts express properties of and relationships between entities. There is a large spectrum of methods to extract such facts from Web documents. We give an overview on methods from pattern matching (e.g., regular expressions), computational linguistics (e.g., dependency parsing), statistical learning (e.g., factor graphs and MLN’s), and logical consistency reasoning (e.g., weighted MaxSat or ILP solvers). We also discuss to what extent these approaches scale to handle big data. Open Information Extraction: Alternatively to methods that operate on a pre-specified set of relations and entities, open information extraction harvests arbitrary SPO triples from natural language documents. It aggressively taps into noun phrases as entity candidates and verbal phrases as prototypic patterns for relations. We discuss recent methods that follow this direction. Some methods along these lines make clever use of big-data techniques like frequent sequence mining and map-reduce computation. Temporal and Multilingual Knowledge: Properly interpreting entities and facts in a KB often requires additional meta-information like entity names in different languages and the temporal scope of facts. We discuss tech-

Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freelyavailable system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.


Question: A complex systems perspective on leadership: Understanding the emergence of leadership at various organizational levels.

Navigating Leadership in the Realm of Complexity: A Systems Perspective

This paper delves into the implications of complexity theory on leadership within organizations, exploring how complex systems and their dynamics influence leadership practices. Complexity theory, as a scientific framework, investigates the intricate interactions and adaptive processes in systems, and their role in emergence, innovation, and overall system health. The paper posits that complexity theory shifts the leadership paradigm from one focused on dictating or directing organizational outcomes to one that facilitates and enables effectiveness. By integrating complexity science into leadership theory, the perspective moves beyond traditional psychological and social psychological models (such as human relations approaches) to encompass the management of dynamic systems and interconnectivity. A definition of organizational complexity is proposed and applied to leadership studies, highlighting strategies for harnessing complexity to enhance effectiveness. The paper also examines the interplay between complexity theory and contemporary leadership theories. Concluding, it outlines potential research directions in the social sciences that could further elucidate the complexities of leadership in dynamic organizational settings.

Co-management, or the joint management of the commons, is often formulated in terms of some arrangement of power sharing between the State and a community of resource users. In reality, there often are multiple local interests and multiple government agencies at play, and co-management can hardly be understood as the interaction of a unitary State and a homogeneous community. An approach focusing on the legal aspects of co-management, and emphasizing the formal structure of arrangements (how governance is configured) runs the risk of neglecting the functional side of co-management. An alternative approach is to start from the assumption that co-management is a continuous problem-solving process, rather than a fixed state, involving extensive deliberation, negotiation and joint learning within problem-solving networks. This presumption implies that co-management research should preferably focus on how different management tasks are organized and distributed concentrating on the function, rather than the structure, of the system. Such an approach has the effect of highlighting that power sharing is the result, and not the starting point, of the process. This kind of research approach might employ the steps of (1) defining the social-ecological system under focus; (2) mapping the essential management tasks and problems to be solved; (3) clarifying the participants in the problem-solving processes; (4) analyzing linkages in the system, in particular across levels of organization and across geographical space; (5) evaluating capacity-building needs for enhancing the skills and capabilities of people and institutions at various levels; and (6) prescribing ways to improve policy making and problem-solving.

The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude. Copyright © 2016 John Wiley & Sons, Ltd.

Consciousness has a number of apparently disparate properties, some of which seem to be highly complex and even inaccessible to outside observation. To place these properties within a biological framework requires a theory based on a set of evolutionary and developmental principles. This paper describes such a theory, which aims to provide a unifying account of conscious phenomena.

In the current information-driven and technologically based global economy, organizations are becoming increasingly dependent on the cumulative knowledge of their employees, suppliers, customers, and other key stakeholders. An organization’s ability to share this knowledge among organizational members is key to its competitive advantage (Bock, Zmud, Kim, & Lee, 2007; Brown & Duguid, 2000; Small & Sage, 2006). Information sharing is critical to an organization’s competitiveness and requires a free flow of information among members that is undistorted and up-to-date (Childhouse & Towill, 2003; Li & Lin, 2006; Moberg, Cutler, Gross, & Speh, 2002; Rahman, 2004; Tan, Lyman, & Wisner, 2002). However, extensive information sharing within organizations still appears to be the exception rather than the rule (Bock et al., 2007; Davenport & Prusack, 1998; Li & Lin, 2006). According to Li and Lin (2006), intensified competition and globalized markets are some of the challenges associated with getting products and services to the right place at the right time and at the lowest cost. These challenges, for instance, have forced organizations to realize that it is not enough to improve their efficiencies; rather, their entire supply chains have to be made competitive. One way for organizations to do this is to support information sharing among their work groups (Li & Lin, 2006). This article explores organizational information sharing as it relates to individuals within and between work groups. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example then illustrates how a social network approach (SNA) was used to explore the process of measuring the social

The emergence of behavioral economics is one of the most prominent conceptual developments in the social sciences in recent decades. The central figure in the field in its early years was Richard Thaler. In this article, I review and discuss his scientific contributions. JEL classification: B2, D9, G1

Many of the problems arjd failures of Management Information Systems (MIS) and Management Science/Operations Research (MS/OR) projects have been attributed to organizational behavioral problems. The millions of dollars organizations spend on MIS and MS/OR development are of little benefit because systems continue to fail. Steps can be taken to understand and solve these behavioral probiems. This article argues that in most oases these behavioral problems are the result of inadequate designs. These bad designs are attributed to the way MIS systems designers view organizations, their members, and the function of an MIS within them. I.e., systems designers' frames of reference. These frames of reference cause faulty design choices and failures to perceive better design alternatives. Seven conditions are discussed which reflect current systems designers' points of view. The discussion of these conditions demonstrates the need to refrawe MIS design methodology within the Socio-Technicai Systems (STS) design approach and change systems designers' perspectives. The STS approach is introduced as a realistic view of organizations and a way to change them. This article is the first of two to appear in consecutive issues of the MIS Quarterly. The purpose of this first article is to demonstrate the need for the srs approaj:h. The second will present the basic concepts and principles of the STS methodology and how it can be utilized in the design of an MIS.

AIM
To analyse the concept of transformational leadership in the nursing context.

BACKGROUND
Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety.

DESIGN
Concept analysis using Walker and Avant's (2005) concept analysis method.

DATA SOURCES
PubMed, CINAHL and PsychINFO.

METHODS
This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context.

IMPLICATIONS FOR NURSING
This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear.

CONCLUSION
Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers’ attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management’s roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT nformation architecture infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area. © 2015 Elsevier Ltd. All rights reserved.

A theory was proposed to reconcile paradoxical findings on the invariance of personality and the variability of behavior across situations. For this purpose, individuals were assumed to differ in (a) the accessibility of cognitive-affective mediating units (such as encodings, expectancies and beliefs, affects, and goals) and (b) the organization of relationships through which these units interact with each other and with psychological features of situations. The theory accounts for individual differences in predictable patterns of variability across situations (e.g., if A then she X, but if B then she Y), as well as for overall average levels of behavior, as essential expressions or behavioral signatures of the same underlying personality system. Situations, personality dispositions, dynamics, and structure were reconceptualized from this perspective.

In past years, research and corporate scandals have evidenced the destructive effects of the dark triad at work, consisting of narcissism (extreme self-centeredness), psychopathy (lack of empathy and remorse) and Machiavellianism (a sense of duplicity and manipulativeness). The dark triad dimensions have typically been conceptualized as stable personality traits, ignoring the accumulating evidence that momentary personality expressions - personality states - may change due to the characteristics of the situation. The present research protocol describes a qualitative study that aims to identify triggers of dark triad states at work by following a grounded theory approach using semi-structured interviews. By building a comprehensive categorization of dark triad triggers at work scholars may study these triggers in a parsimonious and structured way and organizations may derive more effective interventions to buffer or prevent the detrimental effects of dark personality at work.


Question: A complex systems perspective on leadership: The emergence of leadership at various organizational levels.

This seminal work explores the fundamental role of adaptation in both natural and artificial systems, underscoring its critical importance in survival and evolutionary progress. The process of evolution inherently optimizes organisms for their environments, a principle that artificial intelligence (AI) endeavors to emulate. Central themes include:

* Mimicry of biological optimization: AI seeks to replicate the survival mechanisms and adaptive strategies found in nature, focusing on:
  * Exploration and exploitation: Balancing the exploration of new opportunities with the exploitation of existing resources.
  * Niche finding: Identifying and occupying specialized ecological niches.
  * Robustness across changing environments: Demonstrating adaptability and resilience in the face of environmental fluctuations, akin to the differentiation between mammals and dinosaurs.
  * Self-regulation, repair, and reproduction: Emulating the self-sustaining and self-replicating capabilities of living organisms.

In the realm of artificial intelligence, adaptation and modification are considered the bedrock of intelligence. This perspective guides various branches of AI, including non-genetic algorithm (non-GA) approaches:

* Expert Systems: Rule-based systems that employ deductive reasoning to solve complex problems, often mimicking the decision-making processes of human experts.

The essence of AI can be encapsulated in several definitions, reflecting its multifaceted nature and ongoing evolution:

* "Making computers do what they do in the movies": Aspiring to the cinematic portrayal of advanced AI capabilities.
* "Making computers do what humans (currently) do best": Seeking to automate tasks that humans excel at, including pattern recognition, problem-solving, and decision-making.
* "Giving computers common sense; letting them make simple decisions": Endowing machines with the ability to make intuitive judgments, akin to human common sense.
* "Anything too new to be pigeonholed": Embracing the fluid and innovative aspects of AI, where the field continually pushes the boundaries of what is possible.

In summary, adaptation in natural and artificial systems is a foundational concept that drives evolutionary progress and shapes the development of AI, encompassing both GA and non-GA approaches.

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a di cult task. The bene t of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.

The purpose of this paper is to present and test a modeling procedure because researchers have struggled for decades to develop stages-of-growth models that are both theoretically founded and empirically validated. This article presents the concept and hypothesis of stages, the history of stage models, and a procedure that may serve as a useful tool in modeling stages of growth. Based on previous research and lessons learned from case study experience of the government sector in Norway, a procedure for the stages of growth modeling process is suggested and demonstrated. The procedure is used for developing a stage model for e-government interoperability. This article provides new insight into issues and challenges faced when engaging in stages of growth research. The paper proposes a new approach to stages of growth modeling. The utility of the suggested procedure is to improve theory building and empirical validation. The contribution to academia is the modeling process that can be applied in future developments of stages of growth. The contribution to practice lies in the stage hypothesis of organizational development over time.

This paper examines the usefulness of the diffusion of innovation research in developing theoretical accounts of the adoption of complex and networked IT solutions. We contrast six conjectures underlying DOI research with field data obtained from the study of the diffusion of EDI. Our analysis shows that DOI based analyses miss some important facets in the diffusion of complex technologies. We suggest that complex IT solutions should be understood as socially constructed and learning intensive artifacts, which can be adopted for varying reasons within volatile diffusion arenas. Therefore DOI researchers should carefully recognize the complex, networked, and learning intensive features of technology; understand the role of institutional regimes, focus on process features (including histories) and key players in the diffusion arena, develop multi-layered theories that factor out mappings between different layers and locales, use multiple perspectives including political models, institutional models and theories of team behavior, and apply varying time scales while crafting accounts of what happened and why. In general the paper calls for a need to develop DOI theories at the site by using multiple levels of

A key di culty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system con gurations is essential. To this end, we present a taxonomy that classi es multiagent systems according to communication, computational and other capabilities. We survey existing e orts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

This study provides a comparative analysis of seven cases of social entrepre-neurship that have been widely recognized as successful. The paper suggests factors associated with successful social entrepreneurship, particularly with social entrepreneurship that leads to significant changes in the social, political and economic contexts for poor and marginalized groups. It generates hypotheses about core innovations, leadership, organization, and scaling up in successful social entrepreneurship. The paper concludes with a discussion of the implications for the practice of social entrepreneurship, for further research, and for the continued development of support technologies and institutions that will encourage future social entrepreneurship.

The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.


Question: A complex systems perspective on leadership: The emergence of leadership at various organizational levels.

The Revisitation of the Iron Cage: Exploring Institutional Isomorphism and Collective Rationality in Organizational Spheres

In revisiting the concept of the "iron cage," this paper delves into the phenomena of institutional isomorphism and collective rationality within organizational fields. The term "iron cage," originally coined by sociologist Max Weber, refers to the constraining effects of bureaucratic rationality on human freedom and individuality. This study extends Weber's notion by examining how contemporary organizations exhibit patterns of conformity and similarity—termed institutional isomorphism—that lead to a collective rationality, where practices and structures become standardized across various institutions. The exploration of these concepts not only enriches our understanding of organizational behavior but also sheds light on the mechanisms through which organizations align with broader institutional environments. This paper is available for personal, non-commercial use under JSTOR's Terms and Conditions of Use, which can be accessed at http://www.jstor.org/page/info/about/policies/terms.jsp. Prior permission is required for downloading entire journal issues or multiple copies of articles, ensuring compliance with JSTOR's usage policies.

Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern. Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research-literally thousands of studies-focused on understanding and influencing the processes that underlie team effectiveness. Our goal in this monograph is to sift through this voluminous literature to identify what we know, what we think we know, and what we need to know to improve the effectiveness of work groups and teams. We begin by defining team effectiveness and establishing the conceptual underpinnings of our approach to understanding it. We then turn to our review, which concentrates primarily on topics that have well-developed theoretical and empirical foundations, to ensure that our conclusions and recommendations are on firm footing. Our review begins by focusing on cognitive, motivational/affective, and behavioral team processes-processes that enable team members to combine their resources to resolve task demands and, in so doing, be effective. We then turn our attention to identifying interventions, or "levers," that can shape or align team processes and thereby provide tools and applications that can improve team effectiveness. Topic-specific conclusions and recommendations are given throughout the review. There is a solid foundation for concluding that there is an emerging science of team effectiveness and that findings from this research foundation provide several means to improve team effectiveness. In the concluding section, we summarize our primary findings to highlight specific research, application, and policy recommendations for enhancing the effectiveness of work groups and teams.

Typologies are an important way of organizing the complex cause-effect relationships that are key building blocks of the strategy and organization literatures. Here, I develop a novel theoretical perspective on causal core and periphery, which is based on how elements of a configuration are connected to outcomes. Using data on hightechnology firms, I empirically investigate configurations based on the Miles and Snow typology using fuzzy set qualitative comparative analysis (fsQCA). My findings show how the theoretical perspective developed here allows for a detailed analysis of causal core, periphery, and asymmetry, shifting the focus to midrange theories of causal processes.

Executive support is often prescribed as critical for fully tapping the benefits of information technology (IT). However, few investigations have attempted to determine what type of executive support is likely or organizationally appropriate. This article puts forward alternative models of executive support. The models are tested by examining chief executive officers’ behaviors in and perceptions of IT activities. CEOs and information systems executives are surveyed and further data collected from industry handbooks and from chairmen’s annual letters to shareholders. The results suggest that executive involvement (a psychological state) is more strongly associated with the firm’s progressive use of IT than executive participation (actual behaviors) in IT activities. Executive involvement is influenced by a CEO’s participation, prevailing organizational conditions, and the executive’s functional background. CEO’s perceptions about the importance of IT in their firms were generally positive, although they participated in IT activities rather infrequently.

Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.

This study investigated the usefulness of the five-factor model (FFM) of personality in predicting two aspects of managerial performance (task vs. contextual) assessed by utilizing the 360 degree performance rating system. The authors speculated that one reason for the low validity of the FFM might be the failure of single-source (e.g., supervisor) ratings to comprehensively capture the construct of managerial performance. The operational validity of personality was found to increase substantially (50%-74%) across all of the FFM personality traits when both peer and subordinate ratings were added to supervisor ratings according to the multitrait-multimethod approach. Furthermore, the authors responded to the recent calls to validate tests via a multivariate (e.g., multitrait-multimethod) approach by decomposing overall managerial performance into task and contextual performance criteria and by using multiple rating perspectives (sources). Overall, this study contributes to the evidence that personality may be even more useful in predicting managerial performance if the performance criteria are less deficient.

Traditionally, the term ‘crowd’ was used almost exclusively in the context of people who selforganized around a common purpose, emotion, or experience. Today, however, firms often refer to crowds in discussions of how collections of individuals can be engaged for organizational purposes. Crowdsourcing–defined here as the use of information technologies to outsource business responsibilities to crowds–can now significantly influence a firm’s ability to leverage previously unattainable resources to build competitive advantage. Nonetheless, many managers are hesitant to consider crowdsourcing because they do not understand how its various types can add value to the firm. In response, we explain what crowdsourcing is, the advantages it offers, and how firms can pursue crowdsourcing. We begin by formulating a crowdsourcing typology and show how its four categories—crowd voting, micro-task, idea, and solution crowdsourcing—can help firms develop ‘crowd capital,’ an organizational-level resource harnessed from the crowd. We then present a three-step process model for generating crowd capital. Step one includes important considerations that shape how a crowd is to be constructed. Step two outlines the capabilities firms need to develop to acquire and assimilate resources (e.g., knowledge, labor, funds) from the crowd. Step three outlines key decision areas that executives need to address to effectively engage crowds. 1. Crowds and Crowdsourcing Not too long ago, the term ‘crowd’ was used almost exclusively in the context of people who selforganized around a common purpose, emotion, or experience. Crowds were sometimes seen as a positive occurrence– for instance, when they formed for political rallies or to support sports teams— but were more often associated negatively with riots, a mob mentality, or looting. Under today’s lens, they are viewed more positively (Wexler, 2011). Crowds have become useful! It all started in 2006, when crowdsourcing was introduced as ‘‘taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an “open call’’ (Howe, 2006, p. 1). The underlying concept of crowdsourcing, a combination of crowd and outsourcing, is that many hands make light work and that wisdom can be gleaned from crowds (Surowiecki, 2005) to overcome groupthink, leading to superior results (Majchrzak & Malhotra, 2013). Of course, such ambitions are not new, and organizations have long desired to make the most of dispersed knowledge whereby each individual has certain knowledge advantages over every other (Hayek, 1945). Though examples of using crowds to harness what is desired are abundant (for an interesting application, see Table 1), until recently, accessing and harnessing such resources at scale has been nearly impossible for organizations. Due in large part to the proliferation of the Internet, mobile technologies, and the recent explosion of social media (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011), organizations today are in a much better position to engage distributed crowds (Lakhani & Panetta, 2007) of individuals for their innovation and problem-solving needs (Afuah & Tucci, 2012; Boudreau & Lakhani, 2013). As a result, more and more executives–—from small startups to Fortune 500 companies alike–— are trying to figure out what crowdsourcing really is, the benefits it can offer, and the processes they should follow to engage a crowd. In this formative stage of crowdsourcing, multiple streams of academic and practitioner-based literature–—each using their own language–—are developing independently of one another, without a unifying framework to understand the burgeoning phenomenon of crowd engagement. For executives who would like to explore crowdbased opportunities, this presents a multitude of options and possibilities, but also difficulties. One problem entails lack of a clear understanding of crowds , the various forms they can take, and the value they can offer. Another problem entails absence of a well-defined process to engage crowds. As a result, many executives are unable to develop strategies or are hesitant to allocate resources to crowdsourcing, resulting in missed opportunities for new competitive advantages resulting from engaging crowds. To help provide clarity, we submit an overview of the different types of crowdsourcing. Then we introduce the crowd capital framework, supplying a systematic template for executives to recognize the value of information from crowds, therein mapping the steps to acquire and assimilate resources from crowds. Finally, we discuss the unique benefits that can be gained from crowds before concluding with some advice on how to best ‘work a crowd.’ 2. Types of crowdsourcing Crowdsourcing as an online, distributed problemsolving model (Brabham, 2008) suggests that approaching crowds and asking for contributions can help organizations develop solutions to a variety of business challenges. In this context, the crowd is often treated as a single construct: a general collection of people that can be targeted by firms. However, just as organizations and their problems vary, so do the types of crowds and the different kinds of contributions they can offer the firm. The following typology of crowdsourcing suggests that managers can begin by identifying a business problem and then working outward from there, considering (1) what type of contributions are required from members of the crowd and (2) how these contributions will collectively help find a solution to their business problem. First, the types of contributions required from the crowd could either call for specific objective contributions or for subjective content. Specific objective contributions help to achieve an impartial and unbiased result; here, bare facts matter and crowds can help find or create them. Subjective content contributions revolve around the judgments, opinions, perceptions, and beliefs of individuals in a crowd that are sought to collectively help solve a problem that calls for a subjective result. Second, contributions need to be processed collectively to add value. Depending on the problem to be solved, the contributions must either be aggregated or filtered. Under aggregation, contributions collectively yield value when they are simply combined at face value to inform a decision, without requiring any prior validation. For instance, political elections call for people to express their choices via electoral ballots, which are then tallied to calculate the sums and averages of their collective preferences; the reasons for their choices are not important at this stage. Other problems, however, are more complex and call for crowd contributions to be qualitatively evaluated and filtered before being considered on their relative merits (e.g., when politicians invite constituents’ opinions before campaigning). Together, these two dimensions help executives distinguish among and understand the variety of crowdsourcing alternatives that exist today (see Figure 1). Two forms of crowdsourcing rely on aggregation as the primary process: crowd voting and microtask crowdsourcing. In crowd voting, organizations pose an issue to a crowd and aggregate the subjective responses derived from crowd participants to make a decision. Consider the popular television show American Idol, which allows viewers to support their preferred contestants by submitting votes online or via telephone or text. These votes are tallied at the end of the show and contestants with the fewest votes are eliminated from the competition. Similarly, so-called prediction markets (Arrow et al., 2008) activate the wisdom of the crowd through crowd voting. But rather than simply adding up votes, these markets arrive at specific predictions that can exceed the accuracy of experts by averaging the independent responses of crowd participants. For instance, Starwood Hotels and Resorts utilized an internal prediction market by asking a crowd of its own employees to select the best choice among a variety of potential marketing campaigns (Barlow, 2008). In micro-task crowdsourcing, organizations engage a crowd to undertake work that is often unachievable through standard procedures due to its sheer size or complexity. An organization may need to assemble a large data set, have numerous photos labeled and tagged, translate documents, or transcribe audio transcripts. Breaking such work into micro-tasks (Gino & Staats, 2012) allows daunting undertakings to be completed more quickly, cheaply, and efficiently. Consider how Google uses reCAPTCHA (von Ahn, Maurer, McMillen, Abraham, & Blum, 2008) and the little–—and admittedly annoying–—dialogue boxes that ask users to enter the text snippets they see of distorted images onscreen. It is commonly believed that this web utility is only for authenticating human users, thus keeping websites from spambots. However, every time the task of entering characters is completed, individuals are actually digitizing what optical character recognition (OCR) software has been unable to read. In this way, micro-task crowdsourcing is helping to digitize the archives of The New York Times and moving old manuscripts into Google Books. Similarly, crowdfunding (Stemler, 2013) endeavors are a form of micro-task crowdsourcing whereby an overall highly ambitious financial goal is broken into smaller funding tasks and contributions consist of objective resources (herein ‘funds’) that are simply aggregated for each venture. Figure 1. Crowdsourcing alternatives Whether objective or subjective, crowdsourced contributions must be processed to be valuable. In idea crowdsourcing, organizations seek creativity from a crowd, hoping to leverage its diversity to generate unique solutions to problems/issues. An organization may receive many ideas from a crowd, which it will need to filter before one or more can be implemented. For instance, online artist community and e-comm

The relationship between information systems (IS) and organizational strategies has been a much discussed topic with most of the prior studies taking a highly positive view of technology’s role in enabling organizational strategies. Despite this wealth of studies, there is a dearth of empirical investigations on how IS enable specific organizational strategies. Through a qualitative empirical investigation of five case organizations this research derives five organizational strategies that are specifically enabled through IS. The five strategies; (i) generic-heartland, (ii) craft-based selective, (iii) adhoc, IT-driven, (iv) corporative-orchestrated and (v) transformative provide a unique perspective of how IS enable organizational strategy.

While many organizational researchers make reference to Mead’s theory of social identity, none have explored how Mead’s ideas about the relationship between the “I” and the “me” might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead’s “I” and “me” and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.

This article develops a cultural agreement approach to organizational culture that emphasizes how clusters of individuals reinforce potentially idiosyncratic understandings of many aspects of culture including the structure of network relations. Building on recent work concerning Simmelian tied dyads (defined as dyads embedded in three-person cliques), the research examines perceptions concerning advice and friendship relations in three entrepreneurial firms. The results support the idea that Simmelian tied dyads (relative to dyads in general) reach higher agreement concerning who is tied to whom, and who are embedded together in triads in organizations. © 2002 Elsevier Science B.V. All rights reserved.


Question: A complex systems perspective on leadership theory: Understanding emergence at various organizational levels.

Incorporating Diverse Methodologies: The Practical Application of Triangulation

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This document echoes the concept of integrating qualitative and quantitative methods, focusing on the practical implementation of triangulation in research. Triangulation, a methodology that involves the use of multiple methods or data sources to cross-validate findings, is highlighted as a critical approach for enhancing the robustness and reliability of research outcomes. It encourages researchers to engage with diverse data collection techniques, such as interviews, surveys, and observations, to gain a comprehensive understanding of complex phenomena. By employing triangulation, researchers can address the limitations of single-method studies, ensure the consistency of findings across different data sources, and ultimately strengthen the validity of their conclusions. This method is particularly valuable in interdisciplinary research, where the synthesis of various perspectives and methodologies can lead to more nuanced insights and a deeper comprehension of the subject matter.

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted 110 The European Physical Journal Special Topics government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of selforganization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed.

Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media.

Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a core/periphery model as the projects move forward. INtrODUctION The information system development arena has seen many revolutions and evolutions. We have witnessed the movement from structured development to object-oriented (OO) development. Modeling methods, such as data flow diagram and entity relationship diagram, are facing new OO modeling languages, such as the unified modeling language (UML) (see Siau & Cao, 2001; Siau, Erickson, & Lee, 2005; Siau & Loo, 2006) and OO methodologies, such as unified process (UP). The latest development includes agile modeling (see Erickson, Lyytinen, & Siau, 2005), extreme programming, and OSS development. While many of these changes are related to systems development paradigms, methodologies, methods, and techniques, the phenomenon of OSS development entails a different structure for software development teams. Chapter 2.17 Social Network Structures in Open Source Software Development Teams Yuan Long Colorado State University-Pueblo, USA Keng Siau University of Nebraska-Lincoln, USA Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. 646 Social Network Structures in Open Source Software Development Teams Unlike conventional software projects, the participants of OSS projects are volunteers. They are self-selected based on their interests and capability to contribute to the projects (Raymond, 2000). In addition, the developers of OSS projects are distributed all around the world. They communicate and collaborate with each other through the Internet, using e-mails or discussion boards. Therefore, effective and efficient communication and collaboration are critical to OSS success. However, little empirical research has been conducted to study the underlying interaction pattern of OSS teams, especially the dynamics of the social network structures in OSS development teams. To fill this gap, this study examines the evolvement of social structure in OSS teams. The study contributes to the enhancement of the understanding of OSS development, and provides foundation for future studies to analyze the antecedents and consequences of social networks in the OSS context. The remainder of the paper is structured as follows. First, prior studies on social network structures in OSS teams are reviewed. Second, theories related to social structure and social network theory are discussed. Third, the research methodology is presented, and the research results are reported. Next, discussions of the results, the limitations, and the implications are provided. The paper concludes with suggestions for future research.

The classical work of bird-like objects of Reynolds [1] simulates polarized motion of groups of oriented particles, bird-like objects, or simply boids. To do this, three steering vectors are introduced. Cohesion is the tendency of boids to stay in the center of the flock, alignment smoothes their velocities to similar values, and separation helps them to avoid mutual collisions. If no impetus is introduced the boids wander somewhat

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.

Team SA involves a common perspective between two or more individuals regarding current environmental events, their meaning, and projected future status. Team SA has been theorized to be important for resuscitation team effectiveness. Accordingly, multidimensional frameworks of observable behaviors relevant to resuscitation teams are needed to understand more deeply the nature of team SA, its implications for team effectiveness, and whether it can be trained. A seven-dimension team resuscitation SA framework was developed following a literature review and consensus process using a modified Delphi approach with a group of content experts. We applied a pre-post design within a day-long team training program involving four video-recorded simulated resuscitation events and 42 teams across Canada. The first and fourth events represented "pre" and "post" training events, respectively. Teams were scored on SA five times within each 15-minute event. Distractions were introduced to investigate whether SA scores would be affected. The current study provides initial construct validity evidence for a new measure of SA and explicates SA's role in resuscitation teams.

In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-

Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive humanswarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.

Cyberbullying is a relatively new form of bullying, with both similarities and differences to traditional bullying. While earlier research has examined associations between school-contextual characteristics and traditional bullying, fewer studies have focused on the links to students' involvement in cyberbullying behavior. The aim of the present study is to assess whether school-contextual conditions in terms of teachers' ratings of the school leadership are associated with the occurrence of cyberbullying victimization and perpetration among students. The data are derived from two separate data collections performed in 2016: The Stockholm School Survey conducted among students in the second grade of upper secondary school (ages 17-18 years) in Stockholm municipality, and the Stockholm Teacher Survey which was carried out among teachers in the same schools. The data include information from 6067 students distributed across 58 schools, linked with school-contextual information based on reports from 1251 teachers. Cyberbullying victimization and perpetration are measured by students' self-reports. Teachers' ratings of the school leadership are captured by an index based on 10 items; the mean value of this index was aggregated to the school level. Results from binary logistic multilevel regression models show that high teacher ratings of the school leadership are associated with less cyberbullying victimization and perpetration. We conclude that a strong school leadership potentially prevents cyberbullying behavior among students.

The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude. Copyright © 2016 John Wiley & Sons, Ltd.


Question: A complex systems perspective on leadership: Understanding emergence at various organizational levels through a leadership theory.

In high-velocity environments, strategic decision-making processes are critically examined through the lens of four case studies within the microcomputer industry. This analysis delves into the complexities of decision-making under rapid change and uncertainty, revealing the unique challenges and opportunities faced by organizations in dynamic sectors. The study highlights the importance of agility, adaptability, and foresight in strategic planning, emphasizing the need for organizations to continuously reassess and refine their strategies in response to evolving market conditions. By exploring the experiences of companies at the forefront of technological innovation, the research offers insights into effective strategies for navigating high-velocity environments and maintaining competitiveness. The findings underscore the significance of cultivating a culture of innovation and flexibility, fostering robust decision-making capabilities, and maintaining a vigilant awareness of emerging trends and disruptions. This work contributes to the understanding of strategic decision processes in volatile industries, providing valuable lessons for organizations seeking to thrive in rapidly changing landscapes. Please note that the use of this material is subject to the terms and conditions set forth by JSTOR, which can be accessed at http://www.jstor.org/page/info/about/policies/terms.jsp. According to JSTOR's policy, unless explicit permission has been granted, users are prohibited from downloading entire journal issues or multiple article copies, and are restricted to using the content for personal, non-commercial purposes only.

Star performers can be differentiated from average ones by emotional intelligence. For jobs of all kinds, emotional intelligence is twice as important as a person's intelligence quotient and technical skills combined. Excellent performance by top-level managers adds directly to a company's "hard" results, such as increased profitability, lower costs, and improved customer retention. Those with high emotional intelligence enhance "softer" results by contributing to increased morale and motivation, greater cooperation, and lower turnover. The author discusses the five components of emotional intelligence, its role in facilitating organizational change, and ways to increase an organization's emotional intelligence.

Knowledge has become to be considered as valuable strategic assets that can provide proprietary competitive advantages. It is more important for companies to distinguish themselves through knowledge management strategies. Without a constant creation of knowledge, a business is condemned to poor performance. However, it is still unclear how these strategies affect knowledge creation. Knowledge management strategies can be categorized as being either human or system oriented. This paper proposes a model to illustrate the link between the strategies and its creating process. The model is derived on the basis of samples from 58 Korean firms. The model depicts how companies should align the strategies with four knowledge creation modes such as socialization, externalization, combination, and internalization. It is found that human strategy is more likely to be effective for socialization while system strategy is more likely to be effective for combination. Furthermore, the survey result suggests that managers should adjust knowledge management strategies in view of the characteristics of their departments. q 2002 Elsevier Science Ltd. All rights reserved.

Crowdsourcing harnesses the potential of large and open networks of people. It is a relatively new phenomenon and attracted substantial interest in practice. Related research, however, lacks a theoretical foundation. We propose a systemtheoretical perspective on crowdsourcing systems to address this gap and illustrate its applicability by using it to classify crowdsourcing systems. By deriving two principal dimensions from theory, we identify four fundamental types of crowdsourcing systems that help to distinguish important features of such systems. We analyse their respective characteristics and discuss implications and requirements for various aspects related to the design of such systems. Our results demonstrate that systems theory can inform the study of crowdsourcing systems. The identified system types and the implications on their design may prove useful for researchers to frame future studies and for practitioners to identify the right crowdsourcing systems for a particular purpose.

This study explored possible underpinnings of findings from earlier research in which women’s overall leadership competence was less favorably evaluated than men’s. The authors examined perceptions held by senior managers, 34% of whom were CEOs, of women’s and men’s effectiveness at 10 key leadership behaviors. Respondents generally perceived that women were more effective than men at caretaking leader behaviors; and that men were more effective than women at actionoriented, “take-charge” leader behaviors. Notably, male respondents perceived that the behavior at which men leaders most outperformed women was problemsolving. The authors propose that this perception could potentially undermine the influence of women leaders.

The next generation of embedded computing systems will have to meet new challenges. The systems are expected to act mainly autonomously, to dynamically adapt to changing environments and to interact with one another if necessary. Such systems are called organic. Organic Computing systems are similar to autonomic computing systems. In addition Organic Computing systems often behave life-like and are inspired by nature/biological phenomena. Design and construction of such systems brings new challenges for the software engineering process. In this paper we present a framework for design, construction and analysis of organic computing systems. It can facilitate design and construction as well as it can be used to (semi-)formally define organic properties like self-configuration or self-adaptation. We illustrate the framework on a real-world case study from production automation.

In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-

While many organizational researchers make reference to Mead’s theory of social identity, none have explored how Mead’s ideas about the relationship between the “I” and the “me” might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead’s “I” and “me” and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.

Few studies have examined interactions between IT change and organizational change during information systems evolution (ISE). We propose a dynamic model of ISE where change dynamics are captured in four dimensions: planned change, improvised change, organizational change and IT change. This inductively-generated model yields a rich account of ISE and its drivers by integrating the four change dimensions. The model shows how incremental adjustments in IT and organizational processes often grow into a profound change as users improvise. We demonstrate the value of the dynamic model by illustrating ISE processes in the context of two manufacturing organizations implementing the same system over a study period of five years. This paper makes its contribution by holistically characterizing improvisation in the context of IT and organizational change. Our ISE model moves research in organizational and IT change towards a common framing by showing how each affects the other’s form, function and evolution.

When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: new words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions— title words, cited references, and sequence numbers—be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated.

Everybody loves the stories of heroes like Martin Luther King, Jr., Mother Teresa, and Gandhi. But the heroic model of moral leadership usually doesn't work in the corporate world. Modesty and restraint are largely responsible for the achievements of the most effective moral leaders in business. The author, a specialist in business ethics, says the quiet leaders he has studied follow four basic rules in meeting ethical challenges and making decisions. The rules constitute an important resource for executives who want to encourage the development of such leaders among their middle managers. The first rule is "Put things off till tomorrow." The passage of time allows turbulent waters to calm and lets leaders' moral instincts emerge. "Pick your battles" means that quiet leaders don't waste political capital on fights they can't win; they save it for occasions when they really want to fight. "Bend the rules, don't break them" sounds easier than it is--bending the rules in order to resolve a complicated situation requires imagination, discipline, restraint, flexibility, and entrepreneurship. The fourth rule, "Find a compromise," reflects the author's finding that quiet leaders try not to see situations as polarized tests of ethical principles. These individuals work hard to craft compromises that are "good enough"--responsible and workable enough--to satisfy themselves, their companies, and their customers. The vast majority of difficult problems are solved through the consistent striving of people working far from the limelight. Their quiet approach to leadership doesn't inspire, thrill, or provide story lines for uplifting TV shows. But the unglamorous efforts of quiet leaders make a tremendous difference every day in the corporate world.