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This paper aims to investigate the different patterns of organizational behavioural responses to major incidents and develop an original classification of these patterns.
Abstract
Purpose
This paper aims to investigate the different patterns of organizational behavioural responses to major incidents and develop an original classification of these patterns.
Design/methodology/approach
An extensive literature review was made to investigate the different patterns of behavioural responses to major incidents and then to develop an original classification of these patterns. Several sources of information, such as case studies, technical reports, academic journal articles and organizational internal reports were used.
Findings
Organizations respond differently to major incidents. This was clear from the different behavioural patterns investigated and identified. Behavioural patterns determine levels of resilience and ability of organizations to overcome and ultimately survive major incidents.
Practical implications
To promote effective and organized behavioural response patterns to major incidents and improve consistency of responses across the organization, relevant authorities should demonstrate to all private and public enterprises the significance of effective behavioural responses, thus enabling them to better respond to various potential emergencies.
Originality/value
A number of models of human behaviour have been introduced in the literature to understand how people respond to emergency situations. They each take a different perspective on human behaviour but no single theory has emerged as the leading paradigm. This highlights the complexity of understanding human behaviour in such situations and the need for a better classification of behavioural patterns. To the author’s knowledge, this is one of very few studies to investigate, identify and categorize behavioural response patterns to major incidents. This research is expected to be of a substantial value for those interested in improving organizational behaviour during major incidents, as well as those interested in improving organizational resilience.
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Gebeyehu Belay Gebremeskel, Chai Yi, Chengliang Wang and Zhongshi He
Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is…
Abstract
Purpose
Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.
Design/methodology/approach
Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.
Findings
The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.
Originality/value
The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.
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Mingyan Zhang, Xu Du, Kerry Rice, Jui-Long Hung and Hao Li
This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning…
Abstract
Purpose
This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.
Design/methodology/approach
The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.
Findings
Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.
Research limitations/implications
The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.
Originality/value
This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.
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Ahmet Soylu, Felix Mödritscher, Fridolin Wild, Patrick De Causmaecker and Piet Desmet
Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level…
Abstract
Purpose
Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.
Design/methodology/approach
The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.
Findings
The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.
Originality/value
This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.
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Xu Du, Juan Yang, Brett Shelton and Jui-Long Hung
Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning…
Abstract
Purpose
Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.
Design/methodology/approach
This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.
Findings
Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.
Research limitations/implications
A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.
Practical implications
The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.
Originality/value
This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.
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The present research builds on three complementary theories to explore how social influence processes in interaction bring about opinion and sentiment change: expectation states…
Abstract
Purpose
The present research builds on three complementary theories to explore how social influence processes in interaction bring about opinion and sentiment change: expectation states theory, affect control theory, and social influence network theory.
Methodology/approach
An experimental study is used to test intersections between the theories and assess how performance expectations, affective impressions of group members, and emergent perceptions of their influence work together to generate opinion and sentiment change.
Findings
Respondent opinions shifted in the direction of group leaders’ opinions, regardless of behavioral interchange patterns. Opinion change was greater when a third group member shared the leader’s opinion. Change in affective impressions was shaped by the group leader’s opinion, the assertiveness of their behavior, and the support of a third group member. The perceived influence composition of the group predicted opinion and sentiment change, above and beyond the effects of conditional manipulations. Features of the group interaction led to inferences about status characteristics that reinforced the influence order of the group.
Research implications
The chapter tests hypotheses from earlier work and explores status signals not yet tested as predictors of opinion change – behavioral interchange patterns and the degree of support for one’s ideas. In addition, it examines inferences about status characteristics following the group discussion, and influence effects on the prevailing definition of the situation.
Originality/value
This chapter contributes to recent integrative work that explores the relationship between performance expectations, affective impressions, and social influence. Synergistic processes forwarded by earlier research are tested, along with several newly proposed linkages.
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In recent research the strength and nature of the relationship between coaches and executives appears as a critical success factor in successful coaching outcomes. However, little…
Abstract
Purpose
In recent research the strength and nature of the relationship between coaches and executives appears as a critical success factor in successful coaching outcomes. However, little theory has as yet been devoted to an analysis of how relationships are used in executive coaching. Such an analysis requires going from the monadic, individual level of analysis to the dyadic, relational level. The purpose of this paper is to develop a theory of relating in executive coaching at this dyadic level of analysis.
Design/methodology/approach
A conceptual analysis of relating in executive coaching is presented, drawing on a combination of the behavioural approach (Skinner and others) and the systems approach (Bateson and others). A verbatim of a coaching conversation serves as an illustration.
Findings
It is found that the behavioural and systems approaches may be fruitfully combined in one behavioural systems approach. Following this, relating in executive coaching is characterised as systemic, behavioural, communicational, and patterned.
Originality/value
The paper is among the first to study executive coaching at the dyadic level of analysis, and to develop a combined behavioural systems approach towards that purpose. This approach and its outcomes add to and can be clearly distinguished from the more common humanistic, psychodynamic, and cognitive approaches to executive coaching.
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This paper aims to advance an integrative perspective of dynamic relationality in negotiation research by providing a symbiotic solution to modeling the cultural adaptation…
Abstract
Purpose
This paper aims to advance an integrative perspective of dynamic relationality in negotiation research by providing a symbiotic solution to modeling the cultural adaptation process in intercultural negotiations.
Design/methodology/approach
Based on a solution-oriented symbiotic approach, the authors analyze negotiators’ combination strategy to propose the dynamic convergence of dyadic relational negotiation behavior (RNB) both as a descriptive framework and a prescriptive solution to behavioral congruence in intercultural negotiations. The authors use spreadsheet platform with artificial data input to simulate various RNB dynamics between negotiators.
Findings
The authors identify the research gap between the arelational, static paradigm in negotiation literature and the relational, dynamic reality in negotiation practices, develop a fourfold typology of the existing negotiation research and propose the construct of RNB. The authors simulate the dyadic dynamics of RNB in a symbiotic framework. Results illustrate varied dyadic patterns of convergent RNB dynamics, demonstrating the effectiveness of the symbiotic solution to achieving behavioral congruence under multiple conditions. Propositions are then presented to predict negotiators’ initial relational behavior, describe dyadic coevolution of RNB in intercultural negotiations and explicate the relevant chronic consequences regarding relational and economic capital.
Originality/value
This paper fills a significant knowledge gap in the extant cross-cultural negotiation literature by addressing dynamic behavioral adaptation through a relational lens. This symbiotic framework is both descriptive in its predictive capacity to simulate the complexity of non-linear negotiation environment, and prescriptive in its directive capacity to guide negotiators’ plan of action given each other’s observed behavior with a probability estimation.
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This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management…
Abstract
Purpose
This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.
Design/methodology/approach
For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.
Findings
This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.
Practical implications
The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.
Originality/value
Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.
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In today’s world, high-quality economic development is possible through innovative entrepreneurial activity, which has led to the creation of various kinds of innovation…
Abstract
Purpose
In today’s world, high-quality economic development is possible through innovative entrepreneurial activity, which has led to the creation of various kinds of innovation infrastructure facilities that support future high-tech projects. However, the system of selecting start-ups to populate and produce for such organisations does not take into account several factors that exert strong influence on the success of an innovative entrepreneurial project. In this article, the author presents the developed multi-factor methodology of project scoring, which is recommended for use both at the initial stage and in the process of development and implementation of innovative idea.
Design/methodology/approach
The suggested multi-factor methodology is both a qualitative and quantitative methodology that allows evaluation of proposed projects by taking into account individual goals of the innovation infrastructure, serving as a flexible tool for analysing project potential and taking into account the model of human behavioural preferences as a key driver of economic activities.
Findings
As a result of the first (qualitative) stage of the study, the author confirmed the hypothesis that the theoretical model of behavioural preferences corresponds to the demonstrated behavioural characteristics of reference respondents. As a result of the second (quantitative) phase of the research, the author conducted a survey of business incubator residents claiming one of the four models of behavioural preferences, followed by quantitative analysis to determine the extent to which the demonstrated behavioural traits of the respondents correspond to those presented in the theoretical model. The results of the second stage of the study were used in the final scoring of start-ups to identify the most promising projects in terms of development.
Originality/value
The project scoring methodology was tested in two of the largest business incubators in St. Petersburg and clearly demonstrated that the use of qualitative indicators significantly increases the ability of incubator experts to make decisions regarding incoming project information.
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