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Based on a typology of contextualized multiple thinking, this paper aims to elaborate how the levels of thinking (data, information, knowledge, and intelligence), and the types of thinking as a whole, can be used to profile the characteristics of multiple thinking in organizational learning, re‐conceptualize the nature of creativity in organizational action and thinking, and provide a new systematic framework to broaden the possibilities and approaches to developing multiple thinking and creativity in organizational action and learning in education and other sectors.

The paper presents a theoretical framework of multiple thinking and creativity in organizational learning.

Based on the typology of contextualized multiple thinking, a new theoretical framework can be proposed to facilitate understanding and development of multiple thinking and creativity in organizational learning and to enhance the effectiveness of action of individuals and organizations in education and other sectors in a complicated context.

The theoretical framework provides a new direction and new strategies for conceptualizing research, development and practice, designed to promote thinking, creativity and effectiveness in organizational action and learning in education and other sectors in a new era of globalization and great transformation.

This paper aims to clarify the relationship between multiple intelligence (MI) and entrepreneurial opportunity recognition. It discusses to what extent each dimension of MI is considered as an activation driver of entrepreneurial opportunities recognition. This paper also aims to expand the domain of entrepreneurial opportunities recognition via MI by considering the mediating role of neuromarketing perception, adopting a combined perspective of intelligence, entrepreneurship and also neuromarketing to provide a future direction for the creation of interdisciplinary insights in the area of entrepreneurship.

This paper opted for literature synthesis to define key concepts surrounding MI and entrepreneurial opportunities recognition. Besides, it also attempted to identify an influential mediator in explaining the entrepreneurial opportunities recognition phenomenon. Consequently, this paper identified the gaps in current research to draw upon a more holistic conceptual framework. The rationale for the research was justified within the body of research.

This paper suggested research propositions based on the literature synthesis in view of MI and entrepreneurial opportunities recognition. More specifically, it has proposed a conceptual framework, explaining the relationship between a multi-dimensional view of both MI and entrepreneurial opportunities recognition. It is envisaged that the mediating role of neuromarketing perception incorporated in this conceptual work will improve the predictive value of the proposed framework and offer additional insights about factors that advance entrepreneurial opportunities recognition.

This paper suffers from the obvious limitation of lacking empirical investigation. However, it does provide a theoretical rationale for the argument that entrepreneurial opportunities recognition can be advanced if MI are identified and associated with neuromarketing dimensions. Perhaps the most important direction for future research is further extension and validation of this framework by performing an empirical investigation to produce newer insights into this phenomenon.

This conceptual work is different from previous studies on the grounds it has considered unexplored issues in explaining entrepreneurial opportunities recognition. To bridge the critical knowledge gap of the entrepreneurial opportunities recognition phenomenon, a mediating effect of neuromarketing perception is also integrated within the model. The proposed model was neither formulated nor tested empirically in previous studies locally or perhaps globally, therefore it stands out as an original contribution incorporating MI and entrepreneurial opportunities recognition phenomenon while considering the brain activity through neuromarketing perception.

The purpose of this paper is to describe a support system to the selection of enrichment activities in educational environment called RunayaySoft, where Runayay comes from the word Quechua that means develop and Soft as it is an informatics tool that supports the educational institutions and their students, in the selection of activities that allow foster some of their skills based on their interests, learning styles, aptitudes, multiple intelligences, preferences and so on. Moreover, it suggests institutions about the activities that they should make in their building considering student´s characteristics and the agreements that they have.

It does a diagnostic for identifying which characteristics are going to be considered to students and institutions. Then, it generates adaptive profiles with the aim of generating suggestions of enrichment activities that allow to boost some of their skills. For the students were considered their preferences, learning style, aptitude, multiple intelligences and interests. In the case of institutions were the agreements, resources and activities that they develop. Based on this information, it defines the relations for the generation of suggestions of activities toward students, where it does the prioritization of which activities should be considered.

For validating the system, it was done as a functional prototype that generates suggestions to students, as well as educative institutions, through a satisfaction test student assess if they agree or disagree with the suggestions given. With that assessment, it is validated the relationship between student’s characteristics, activity and institution are related for generating activities suggestions.

RunayaySoft generates adaptive profiles for the students, activity and institution. Each profile has information that allows adapt an advice toward students and institutions.

RunayaySoft considers student’s characteristics, activities and educational institutions for generating suggestions for enrichment activities that allow to boost some of their skills. Many times, when activities are generated in educative institutions, they are not considered a learner’s needs and characteristics. For that reason, the system helps institutions to identify activities that should be done in their facilities or with those institutions which they have agreements when the institutions that students come from do not have the required resources.

RunayaySoft suggests enrichment activities to students as well as educative institutions. For students, it suggests disciplinary areas where they can boost their skills; for each disciplinary area are recommended activities based on their preferences. Once students select the disciplinary area and activities, the system suggests educative institutions activities that they can do. If the institutions do not have the necessary facilities, the system shows with which other institutions they can set agreements. Moreover, it supports educative institutions to identify enrichment clusters, where it clusters students based on similar interest, allowing institutions to identify the activities that they should focus on.

Many organizational problems are poorly defined, emotionally laden, and ambiguous. These types of problems rarely have one right answer and the criteria for evaluating the appropriateness of solutions is likely to be context dependent. Further, although cognitive skills are important to effective problem solving, the nature of these problems may also require emotional skills as well. This chapter presents a study which set out to determine whether emotional intelligence as an ability contributes above and beyond cognitive intelligence to the quantity, flexibility, and quality of solutions generated to ill-structured problems. Although support was not found for the notion that emotional intelligence explains the indices of solution generation beyond that of cognitive intelligence, the findings did show that emotional intelligence was a significant predictor of one of the solution metrics, namely the average resolving power of solutions across the two problems. The findings demonstrate that emotional intelligence and cognitive intelligence are separate constructs and suggest that caution be used in proposing the pervasive effects of emotional intelligence. In particular, the results of this study suggest that emotional intelligence may not equally influence all activities, highlighting the need to investigate which steps of the problem-solving process it does indeed impact.

The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.

The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.

The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.

The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.

Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

With a team interaction analysis model, the authors sought to identify a varying range of individual and collective intellectual behaviors in a series of communicative intents particularly expressed with multimodal interaction methods. In this paper, the authors aim to present a new construct (i.e. collective intelligence ratio (CIR)) which refers to a numeric indicator representing the degree of intelligence of a team in which each team member demonstrates an individual intelligence ratio (IR) specific to a team goal.

The authors analyzed multimodal team interaction data linked to communicative intents with a Poisson‐hierarchical generalized linear model (HGLM).

The study found evidence of a distinctive IR for each team member in selecting a communicative method for a certain task, ultimately leading to varying degrees of team CIR.

The authors limited the type and nature of human intelligence observed with a very short list of categories. Also, the data were evaluated by only one subject matter expert, leading to reliability issues. Therefore, generalization should be limited to situations in which teams, with pre‐specified team goals and tasks, are collaborating in multimodal interaction environments.

This study presents potential ways to directly or indirectly optimize team performance by identifying and incorporating IRs and CIRs in team composition strategies.

In the literature of team cognition and performance, the authors offer a new insight on team schema by suggesting a new task‐expertise‐person (TEP) unit integrating information on who uses what communicative methods to best tackle on what cognitive task (i.e. optimum cognition with least cognitive burden). Individual and collective intelligence ratios should be considered as new extensions to conventional transactive memory systems in multimodal team interaction scenarios.

Multiple intelligence, cognitive thinking, and sensory theories provide the framework for understanding how to improve learning and learning environments for students. Research involving the use of a multimodal video game to enhance student learning is discussed. It is proposed in the chapter that multimodal and multiplayer video games make use of sensory-rich interactions to engage users, create meaningful experiences, and often facilitate communication between people located all over the world. Multiple learning theories are addressed and evaluated in order to bring to light issues and areas for investigation when designing technology-rich learning environments. Implications of the findings in relation to connections with gaming technologies, capacity for community building, and next steps for investigating ideal designs for 21st century learning environments are discussed.

This research excursion through shipping companies in Vietnam sought to investigate whether organizational culture, ethics, and emotional intelligence influence knowledge sharing, which in turn enhances competitive intelligence scanning. This paper aims to discuss the above issue.

In total, 401 responses returned from self-administered structured questionnaires relayed to 635 middle level managers were processed through structural equation modeling approach to test hypotheses.

Knowledge sharing was proved to positively relate to clan, market, or adhocracy culture, ethics of care, and high level of emotional intelligence. Knowledge sharing also shows a positive effect on competitive intelligence scanning.

For competitive intelligence scanning to be effective, knowledge should be shared among organizational members, which necessitates the three building blocks: supportive knowledge sharing culture (clan, market, or adhocracy culture), ethics or care, and heightened emotional intelligence.