Habitual Domains Research Articles

Enhancing Depth of Reflection of Family Medicine Residents: Results of an Intervention

The ability to reflect in professional practice is the cornerstone of self-directed learning. Reflective practice in medicine is an important teaching-learning modality for improving decision-making and clinical reasoning. It is a strategy for self-regulation of clinical and academic performance. This study was undertaken with the aim of improving the reflective ability skills of family medicine residents through the introduction of reflective practice at Aga Khan University. This was a case study using a pre-post design. A questionnaire was administered to assess the baseline level of reflection in family medicine residents, followed by a workshop on reflective practice to orient residents about the process and levels of reflection. Residents were asked to document reflective logs on a blog, feedback was provided. Post-intervention data were collected by re-administration of the questionnaire the change in the reflective level. Pre-post questionnaire data were analysed for comparisons using the Wilcoxin Sign Rank test on SPSS version 19. Out of 15 eligible residents, a total of 13 residents were available during the study period. In a pre-intervention questionnaire, reflective levels across the domains indicated that residents used reflection 50% of the time which increased post-intervention to more than 80% of the time. The degree of pre-post difference was lowest in the ‘Habitual Action Domain’ and largest in the domains of ‘Reflection’ and ‘Critical Reflection’ (4.1-4.8 and 3.8-4.5). Reflective practice is an effective strategy to develop residents as reflective thinkers by increasing their depth of reflection. Facilitation of their reflection through feedback enhances their ability to reflect.

Empowering Data Mining Sciences by Habitual Domains Theory, Part I: The Concept of Wonderful Solution

Often big companies and governments fail to produce a successful plan of action in the post-data mining analysis and decision-making stage to face challenging situations, which results in billions of dollars loss. Indeed, this crucial stage is left to the decision makers (DMs) to make decisions based on their experience, available information, mindset and habits without any scientific and systematic approach. In this paper, we present a general formal model for decision-making and new knowledge generation that can be used in post-data mining analysis to derive better decisions. The proposed model is formulated in the framework of Habitual Domains theory. The concepts of optimization in changeable spaces and the related solution, the wonderful solution, are introduced in Part I of the paper. Further, ways on how to find wonderful solutions by expanding DMs Habitual Domains in post-data mining analysis and decision-making are presented with applications in Part II of the paper. The proposed model is a significant departure from the traditional decision models that are based on utility function. It can also be used to empower other sciences such as political sciences, medical sciences, management sciences and research activities in all areas.

Empowering Data Mining Sciences by Habitual Domains Theory, Part II: Reaching Wonderful Solutions

In the Part I of this paper, we presented the main concept of the proposed comprehensive decision model based on Habitual Domains theory, the concept of wonderful solution for solving challenging decision problems that we called decision making in changeable spaces problem (DMCS). In this Part II of the paper, we complete the construction of the model and show that it is operational and effectively empowers DMs in facing challenges. For this purpose, we present the mental principles “7–8–9 principles” that can be used to restructure decision parameters so that new solutions or alternatives could emerge. Then we provide procedures for finding wonderful solutions as sequences of the 7–8–9 principles by solving optimization in changeable spaces (OCS) problems, a new paradigm in optimization. Finally, we present applications of the model to post data mining analysis and decision making. In fact, the proposed model can be used in any area involving decision making and knowledge discovery such as management, politics, health care, technology and research.

Impact of Technology Habitual Domain on Ambidextrous Innovation: Case Study of a Chinese High-Tech Enterprise

To obtain a sustainable competitive advantage in the dynamic environment, it is necessary for Chinese high-tech enterprises to focus on their technology habitual domains in formulating ambidextrous innovation strategy. This study integrates technology habitual domain, exploratory innovation and exploitative innovation within a framework and explores the influence mechanism among them. Based on an in-depth case study on KTE, representing a high-tech enterprise in China, we have several findings. Firstly, we depict the evolution mechanism of technology habitual domain; secondly, we find that the high-tech enterprise’s technology habitual domain will cultivate and develop the firms’ dynamic capabilities; and thirdly, the expansion of technology habitual domain will promote exploitative innovation, while the transformation of technology habitual domain will promote exploratory innovation. These findings can be useful guidance for high-tech enterprises in China who are aiming to achieve ambidextrous innovation to better adapt to the turbulent environment, and thus achieving sustainability.

Impact of Team Leadership Habitual Domains on Ambidextrous Innovation

We studied the formation of leaders' habitual behaviors and the impact of leaders' existing and potential capabilities on ambidextrous (i.e., exploitative and exploratory) innovation activities. Leadership habitual domain (LHD) theory was applied from an endogenous perspective to analyze the impact mechanism of LHD on ambidextrous innovation via the mediating role of dynamic capabilities. We used structural equation modeling to test data collected from 205 team leaders in East China. Results showed that LHD was positively associated with both exploitative and exploratory innovation, and that dynamic capabilities mediated these relationships. Thus, team leaders should renew, reconfigure, and expand their LHD by sensing and seizing opportunities when implementing ambidextrous innovation.

Principles for Deep Knowledge and its Relationships with Integrative Orientation, Motivational Intensity and English Learning Achievement

Nine Principles for Deep Knowledge (PDKs) have been identified as an effective method to expand an individual's Habitual Domains (HDs), which in a broader sense improves learning. This study examines the relationship between Principles of Deep Knowledge and two classical variables, i.e. Integrative Orientation and Motivational Intensity (a.k.a. effort), as well as investigates the relationships among these three constructs and English learning achievement. A total of 194 tertiary education students participated. Overall, the results suggested that the three tested variables correlated and can predict achievement, with more than half of the subscales of Principles for Deep Knowledge. Further analysis showed that Integrative Motivation and Motivational Intensity can predict learning achievement whereas Deep and Down, Alternating and Changing, and Transforming principles can predict Integrative Orientation. Deep and Down and Contradiction can predict Motivation Intensity. The findings show the potential status of PDK as a new alternative measure and predictor, closely relating with the other two constructs. Limitations, pedagogical suggestions, and future study areas are given at the end of this study.

Autonomous Learning and Principles for Deep Knowledge

This study aims to understand whether students have the ability to interpret the connotation of deep knowledge based on afterschool autonomous learning activities, habitual domains and principles for deep knowledge, and to use deep knowledge principles to analyze the relationship between the common autonomous learning activities and knowledge as well as students' rating on various types of deep knowledge. There were 71 participants in this study, and data were collected from group discussions and written records. The results showed that the students have the ability to understand the connotation of deep knowledge and propose a variety of autonomous learning methods. This study explored how these autonomous learning methods are closely connected to the deep knowledge of habitual domains based on the methods proposed by the students. The author also gave teaching suggestions in accordance with the participants' rating on the deep knowledge.

The Learning Feature of Deep Knowledge and Its Relationship With Exercise

Nine Principles for Deep Knowledge of Habitual Domains (HDs) have been identified as an effective approach to expanding and enriching an individual’s HD, or in a broader sense, to improving learning. The purpose of this study was to examine the Principle for Deep Knowledge Survey (PDKS) and the correlations between the PDKS and other individual variables, such as gender, body mass index (BMI), and exercise routines. Seven hundred eighty-five industrial high school students completed the questionnaire. Overall, the results suggested that the psychometric properties of the PDKS were acceptable and also showed a significant relationship between gender and the Principles of Contrasting and Complementing and Cracking and Ripping. In addition, the Principles of Alternating, Changing and Transforming, and Void had a positive correlation with the variable of frequency of exercise. The results showed that exercise could be a mediator in expanding the competence of deep knowledge to improve learning.

Intelligent Knowledge Beyond Data Mining: Influences of Habitual Domains

Data mining is a useful analytic method and has been increasingly used by organizations to gain insights from large-scale data. Prior studies of data mining have focused on developing automatic data mining models that belong to first-order data mining. Recently, researchers have called for more study of the second-order data mining process. Second-order data mining process is an important step to convert data mining results into intelligent knowledge, i.e., actionable knowledge. Specifically, second-order data mining refers to the post-stage of data mining projects in which humans collectively make judgments on data mining models’ performance. Understanding the second-order data mining process is valuable in addressing how data mining can be used best by organizations in order to achieve competitive advantages. Drawing on the theory of habitual domains, this study developed a conceptual model for understanding the impact of human cognition characteristics on second-order data mining. Results from a field survey study showed significant correlations between habitual domain characteristics, such as educational level and prior experience with data mining, and human judgments on classifiers’ performance.

Enterprise Competence Sets Expansion Method Study within Habitual Domain Based on the Fuzzy Rough Neural Network

Enterprise Competence Sets Expansion Method Study within Habitual Domain Based on the Fuzzy Rough Neural Network

Multi-person and Multi-objective Competence Sets Expansion Study within Habitual Domain Facing on Engineering Management Decision

Multi-person and Multi-objective Competence Sets Expansion Study within Habitual Domain Facing on Engineering Management Decision

Habitual domain exploration in inter‐firm networks

PurposeBased on the theory of habitual domain, the purpose of this paper is to explore inter‐firm networks' mechanisms for coping with environmental change and for assisting firms to adapt to their collaborative networks. Business globalization is driving more and more individual firms to form inter‐firm collaborative networks. These networks need to develop new types of strategic capability in order not only to adopt a robust business process that will achieve high efficiency, but also to develop a network system for responding to external environmental changes.Design/methodology/approachWith insights gained from the case study, the paper develops an analytical framework for deconstructing the network behavioral changes based on the network habitual domain concept and discusses the dual roles of the network‐based habitual domain in network behavioral changes.FindingsThis paper demonstrates how a Chinese telecommunications service company and its inter‐firm network became the market leader in one of the provincial capital cities in China within six years.Originality/valueFrom an endogenous perspective, this paper contributes some useful concepts that may assist both academia and managers to identify network‐based habitual domains, improve the domains continuously, enhance their response capabilities, and formulate appropriate strategies according to the competition's requirements.

Quantification and applications of identification spheres

Although the concept of identification has been studied and used in many research areas, including psychology, political science, economics, management science, etc., there is no specific quantitative approach. In this article, we propose a quantitative approach to study the identification conception. Specifically, based on the definition of identification sphere of Habitual Domains Theory, we define and explain the concepts of identification function, degree of identification, identification matrices, identification spheres and dis-identification spheres. The quantitative concepts are then applied to formulate the mathematical models of election of a social group leader. The models consider both degree of identification and that of dis-identification. Different models lead to different results, which enrich our thinking about election. We also introduce mathematical programming models, utilizing identification matrices, and thresholds of identification, to find an optimal combination of key members to influence all targets in a social group. The results are important for marketing promotion of a new product, service or that of a new concept.

Decision Making and Optimization in Changeable Spaces, a New Paradigm

This paper proposes a new decision making/optimization paradigm, the decision making/optimization in changeable spaces (DM/OCS). The unique feature of DM/OCS is that it incorporates human psychology and its dynamics as part of the decision making process and allows the restructuring of the decision parameters. DM/OCS is based on Habitual Domain theory, the decision parameters, the concept of competence set, and the mental operators 7-8-9 principles of deep knowledge. The covering and discovering processes are formulated as DM/OCS problems. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. In addition, some directions of research related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery, and knowledge extraction are provided in the conclusion.

EMPOWER MCDM BY HABITUAL DOMAINS TO SOLVE CHALLENGING PROBLEMS IN CHANGEABLE SPACES

Challenging decision problems in changeable spaces are characterized by existence of complex decision parameters that are changing with time and situations, including criteria and alternatives. Some of these parameters may be critical for their effective solutions, but hidden in the depth of potential domains. In this rapidly changing world, including technology and attitude, without paying attention to the problems in changeable spaces, we could easily commit serious mistakes due to decision blinds, decision traps and/or decision shocks. The article starts with a brief description of the evolution of MCDM toward challenging problems in changeable spaces. Then it briefly sketches a dynamic human behavior mechanism and habitual domain theory which provide an effective list for us to search relevant decision parameters and pave the way for latter discussion. Competence set analysis, derived from habitual domain, is then introduced to exemplify decision blinds, decision traps and decision shocks in challenging decision problems. Checking lists and methods for discovering blinds and traps and for dealing with shocks are also provided. Innovation dynamics, a systematic network of thoughts, is introduced to further look out relevant key parameters in dynamic challenging problems. The related academic subjects in each link of the innovation dynamics are also explained, which allow us to see the complexity and interconnectivities among different challenging problems in changeable spaces. Finally we introduce three habitual domain tool boxes to empower ourselves to expand and enrich our thoughts into the depth of the potential domains of the challenging problems, which allows us to more effectively identify hidden parameters, problems and competence sets to reduce decision blinds, avoid decision traps and solve the problems, or dissolve the problems before they occur.

A Study on the Decision-Making Thinking of the Management of University E-Library in Taiwan

As of end of December 2010, Taiwan reports over 160 colleges and universities and is proclaimed as a country that reports highest college density. Therefore, a heavy impact and acute competition between colleges and universities has made the schools spare no effort to enhance quantity & quality of teaching and research related resources to stay competitive. However, a large improvement is required to the existing university e-libraries in terms of manpower allocation, budget amount, software & hardware facilities, quantity & quality of library collection and management philosophy deficiencies. Therefore, the management of various libraries needs to reinforce librarians’ capabilities in management and decision making to enhance overall library performance. The paper mainly follows various decision-making thinking, decision-making philosophy, decision-making mindset, decision-making methods and decision-making discipline ideas of Habitual Domains, with over 10 years personal experience of being a curator of university library considered to come up with strategies that are beneficial to the decision making thinking of university curators in Taiwan, so as to achieve the performance of library affairs and to expect to enable a new opportunity for the future development of university e-libraries in Taiwan.

N-Person Second-Order Games: A Paradigm Shift in Game Theory

In this paper, we present a new approach to n-person games based on the Habitual domain theory. Unlike the traditional game theory models, the constructed model captures the fact that the underlying changes in the psychological aspects and mind states of the players over the arriving events are the key factors, which determine the dynamic process of coalition formation. We introduce two new concepts of solution for games: strategically stable mind profile and structurally stable mind profile. The theory introduced in this paper overcomes the dichotomy of non-cooperative/cooperative games, prevailing in the existing game theory, which makes game theory more applicable to real-world game situations.

Blinds, fuzziness and habitual domain tools in decision making with changeable spaces

In most challenging decision problems, relevant parameters are changing. Decision blinds, decision traps, and fuzziness, would unavoidably occur in the process of dealing with this kind of problems. To make effective decisions, it is important to be aware of the existence and changing nature of the potential parameters. In this article we first discuss a main cause of fuzziness and unknowns in decision making, and then introduce the relevant parameters including those in human behavioral systems and in decision processes. The concepts of habitual domains, decision blinds and decision traps are explored. The tools of habitual domains are proposed to enable decision makers to more effectively expand and enrich their visions and perceptions on the problems as to reduce decision blinds and fuzziness, and avoid decision traps. Consequentially, effective solutions for decision making in changeable spaces can be located.

DETERMINING KEY ECOLOGICAL INDICATORS FOR URBAN LAND CONSOLIDATION

Urban land consolidation, which reforms land parcels to remove fragmentation and produce ideal blocks, is an effective means of urban renewal. Successful urban land consolidation benefits city officials as well as the general public, in improved city image, increased land value, and more effective land use. However, urban land consolidation can be ecologically detrimental to the environment, while land consolidation has focused solely on development for human benefit. To remove the negative effects of urban land consolidation on the ecological system, this paper establishes a set of criteria for evaluating ecological impacts of an urban land consolidation plan. The current study first identifies key ecological indicators using a special group decision‐making process called “habitual domain analysis” and then records individual weighting of each indicator by an analytical hierarchy process, thus developing an urban ecological evaluation model with four levels and twenty‐three indicators. Santruka Miestu žemes sujungimas, kurio metu pertvarkomi žemes sklypai, pašalinamas susiskaldymas, pateikiami pavyzdiniai blokai, yra veiksmingos miestu atnaujinimo priemones. Sekmingas miestu žemes sujungimas naudingas miestu vadovams, taip pat plačiajai visuomenei, nes pagerina miesto ivaizdi, padidina žemes verte ir veiksmingesni žemes naudojima. Tačiau miesto žemes sujungimas gali būti ekologiškai kenksmingas aplinkai, nes tai atliekama tik žmoniu naudai. Siekiant pašalinti neigiama miestu žemes sujungimo poveiki ekologinei sistemai, šiame straipsnyje nustatomi tam tikri kriterijai, kuriais vadovaujantis vertinamas ekologinis miestu žemes sujungimo poveikis. Dabartinis tyrimas pirmiausia nustato pagrindinius ekologinius rodiklius, taikant specialu grupes sprendimu priemimo metoda, vadinama iprastos aplinkos analize. Tada, atsižvelgiant i analitini hierarchijos procesa, pagal svarba nurodomi atskiri rodikliai ir sudaromas keturiu lygiu ir dvidešimt triju rodikliu miesto ekologinis ivertinimas.

Dynamic multiple criteria decision making in changeable spaces: from habitual domains to innovation dynamics

Human behaviors involve dynamic, evolving, interactive and adaptive processes. Important decision makings usually are dynamic, involving multiple criteria in changeable spaces. This article introduces the behavior mechanism that integrates the findings of neural science, psychology, system science, optimization theory and multiple criteria decision making. It shows how our brain and mind operate and describes our behaviors and decision making as dynamic processes of multiple criteria decision making in changeable spaces. Unless extraordinary events occur or special effort exerted, the dynamic processes will be stabilized in certain domains, known as Habitual Domains. Habitual Domains, which play a vital role in upgrading the quality of our decision making and lives, will be explored. In addition, as important consequential derivatives, concepts of Competence Set Analysis and Innovation Dynamics will also be discussed. Note that these concepts involve transitions between dynamic and static states.