Narrow Results

The paper aims to review the current state of the knowledge in an attempt to renew the interest in studying cognitive side of entrepreneurial ethics. The paper explores how the two streams of the literature — entrepreneurial cognition and entrepreneurial ethics — can possibly be integrated to guide future research. It also reviews the literature at the intersection of entrepreneurial cognitions and ethics. In doing so, the paper draws upon the extant literature to propose a set of questions for future research. Given the ethical implications of entrepreneurial behavior, this paper calls for future interdisciplinary work among entrepreneurial cognition scholars and business ethicists. The extant literature has generally focused on exploring the linkages among entrepreneurial cognitions, moral awareness, and moral judgment. It appears that relatively sparse attention is paid to explore the underlying cognitive patterns of an entrepreneur's actions pertaining to unethical business practices. This gap in the literature at the intersection of cognitions and ethics holds significant potential for future research. The proposed questions for future research include the following: How do cognitive biases and heuristics make an entrepreneur more susceptible to immoral judgment and reasoning? Which of the cognitive schemas is more likely to enhance moral intentions of entrepreneurs? How does distributed cognition shape socially responsible entrepreneurial behavior? Do entrepreneurs prefer rule-based or cost/benefit-based reasoning approach while making moral judgment? Which of the cognitive dimensions of socially responsible behavior — utilitarianism, just, and rights — is more common among entrepreneurs in developed countries in comparison to the entrepreneurs in developing and emerging economies? Do immigrant and/or ethnic entrepreneurs experience identity ambiguity and how does it relate to their unethical actions? How does the level of motivation affect an entrepreneur's reliance on heuristics rather than employing a systematic response to process information for ethical judgment? The proposed questions potentially offer insights into the way in which entrepreneurial cognitions and entrepreneurial ethics are interconnected. Entrepreneurship scholars may enrich their future research efforts by exploring how might insights from entrepreneurial ethics better inform the theoretical developments of entrepreneurial cognitions.

Data lakes are storage repositories that contain large amounts of data (big data) in its native format; encompassing structured, semi-structured or unstructured. Data lakes are open to a wide range of use cases, such as carrying out advanced analytics and extracting knowledge patterns. However, the sheer dumping of data into a data lake would only lead to a data swamp. To prevent such a situation, enterprises can adopt best practices, among which to manage data lake metadata. A growing body of research has focused on proposing metadata systems and models for data lakes with a special interest on model genericness. However, existing models fail to cover all aspects of a data lake, due to their static modeling approach. Besides, they do not fully cover essential features for an effective metadata management, namely governance, visibility and uniform treatment of data lake concepts. In this paper, we propose a dynamic modeling approach to meet these features, based on two main constructs: data lake concept and data lake relationship. We showcase our approach by Megale, a graph-based metadata system for NoSQL data lake exploration. We present a proof-of-concept implementation of Megale and we show its effectiveness and efficiency in exploring the data lake.

In classical genetic algorithm, fitness evaluations are often very expensive or highly time-consuming, especially for some engineering optimization problems. We present an efficient genetic algorithm (GA) by combining clustering methods with an empirical fitness estimating formula. The new individuals are clustered at first, and then only the cluster representatives are really evaluated by its original time-consuming fitness computing processes, and other individuals undergo high efficient fitness evaluating processes by using the empirical fitness estimating formula. To further improve the accuracy of fitness estimations, we present a schema discovery strategy by extracting the common encoding characters from both high-fitness individual group and low-fitness individual group, and then adjust the estimated fitness for each individual based on the matching with the discovered schema. Experiments show that the schema discovery strategy contributes remarkably to the accuracy of fitness estimation. Numerical experiments of some well-known benchmark problems and a practical engineering problem demonstrate that the proposed method could improve the efficiency by over 30% in terms of the times of real fitness evaluations at the similar optimization accuracy of classical genetic algorithm.

This paper examines the roles of external stakeholders in the business model development of new technology-based firms (NTBFs) from the perspective of the founders. Based on a longitudinal study of two Swedish NTBFs, semi-structured interviews, including timeline mapping, were conducted with the founders of each firm over a period of two years, drawing on retrospective data from the first year of founding. The findings reveal that stakeholder interaction is first initiated based on the position the stakeholder has in relation to the firm, whereas what tasks the stakeholder perform in relation to NTBF resource needs has greater consequences for the business model development. The roles of stakeholders further help shape founder perceptions of how to do business, although such influence may be limited over time. The results provide valuable insight into the influence of founders’ perceptions and firms’ business networks on the business configuration of NTBFs, revealing that business model development is both endogenous and exogenous. Specifically, the study provides some original insights around which roles stakeholders play in the early development of the business model and why these roles are necessary at certain points in time.

This chapter discusses how a lesson closure, which should be present in every Mathematics lesson, can be carried out effectively to promote offline metacognition. According to the theory of constructivism, learning is achieved through making connection between the new concept and a learner’s prior knowledge, or schema. Lesson closure is crucial to provide a time and space for students to consolidate their learning and acquire the mathematical concepts. We will introduce a strategy to close a lesson in which the teacher uses students’ reflection of their learning to create a visual representation, known as a closure diagram. This activity provides a structure that allows students to either integrate the knowledge into their schema or identify gaps in their learning. Both situations involve students regulating their thinking, which is metacognitive in nature. Variations of the closure strategy for different purposes will also be discussed and illustrated with artefacts from classroom lessons.

An adaptive step length genetic algorithm (ASLGA) is presented, in this paper, to improve local search efficiency and to prevent premature convergence of the genetic algorithms (GAs). Step length of crossover and mutation are adaptively varied depending on the fitness values of the solutions to improve local search efficiency. Similar solutions are adaptively disrupted by multipoint mutation according to the livingness index of the population to prevent premature convergence. Three typical multimodal functions are used to verify the performance of the ASLGA. Experimental results demonstrate that the ASLGA has good local search efficiency and global search capacity.