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The purpose of the paper is to theorize how to develop student entrepreneurs' ability to reflect by means of a learning activity called the entrepreneurial diary, which seeks to develop self-regulated learners capable of intelligent entrepreneurial action. The importance of self-regulation in entrepreneurship is linked to the individual's ability to make judgments under conditions of uncertainty, which requires reflective thinking.

The paper builds on a synthesized conceptualization of three main literature strands, reflective thinking, cognitive-load theory and experiential entrepreneurship education. In addition to the synthesized conceptualization, it builds on some empirical insights derived from a venture creation master programme in which the learning activity has been developed and refined for the last seven years.

The main finding from the paper is the theoretical justification for why reflective thinking deserves an important place in the educational process and how the entrepreneurial diary as a learning activity can create a bridge between theory and practice in venture creation programmes that take an experience-based pedagogical approach. Furthermore, the study also provides some empirical insights of how students create self-awareness of their learning through the method and the metareflection reports. Self-awareness is foundational for developing conditional knowledge on why and when to make entrepreneurial decisions to balance the often action-oriented processes seen in venture creation programmes.

The paper provides both a practical learning activity to be used in the entrepreneurial classroom and a theoretical contribution on how entrepreneurial experience is transformed into entrepreneurial knowledge to enhance students' judgmental abilities to make entrepreneurial decisions in future entrepreneurial endeavours.

Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making.

This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment.

Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions.

Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

This paper explores the idea of the prudent entrepreneurial self, through re-conceptualizing prudence into the domain of entrepreneurial education, to unite the two processes of becoming enterprising and entrepreneurial. It is argued that developing a capacity for prudence among graduates involves past, present and conjecture forms of knowledge that the authors find in the interplay between individuation and social awareness.

Building on Palmer's idea of wholeness, the authors discuss six poles of paradoxes in entrepreneurial education and in conjunction establish a philosophical argument for the idea of stimulating the development of prudence as fundamentally important to contemporary notions of entrepreneurial education.

The paper presents a model to develop a schema that moves students towards becoming prudent entrepreneurial selves. The model rests on two interrelated developmental processes – individuation and social awareness – conditional for developing the three forms of knowledge (past, present and conjecture) that makes up prudence where developing prudence is a means to handle or cope with the unknown.

This paper argues that for enterprise and entrepreneurship education to realize their potential contributions, both the relationships between each field and the overarching purpose that ties the fields together need to be rethought, and the poles of paradoxes need to be connected to further develop both fields and creating wholeness for the emerging scholarly discipline.

To educate towards the prudent entrepreneurial self means educating towards an unknown end where student development aims to meet both the objectives of individual development and the growth in social awareness required to handle the changing nature of contemporary society.

This study philosophically conceives a united enterprise and entrepreneurship education landscape in which deeper student learning makes possible the notion of the prudent entrepreneurial self.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

We address the following research questions: (1) Is the innovation trajectory of the acquirer affected by previous acquisitions? (2) In which direction knowledge recombination from the acquisition is pushed further? (3) Is the technological acquisition more a means for knowledge exploration and radical innovation or, on the contrary, a way for consolidating previous technological specialization?

The nature of this study is exploratory; therefore, we opted for an inductive approach based on the L'Oréal case study analysis. Data were triangulated from different sources: (a) the L'Oréal website and press releases collected in the 2009–2015 period; (b) journal articles and books on the global cosmetics industry and the insightful work of Jones (2010); (c) the Questel Orbit database containing data on patents; and (d) the Zephyr – Bureau van Dijk database containing information on the acquisitions of firms.

Empirical evidence from a patent data analysis reveals a paradoxical path. On the one hand, acquisitions enable the company to explore new technological spaces; on the other hand, they allow it to reinforce a preexisting technological trajectory, even when the knowledge base of the target is distant from that of the acquirer. Thus, in our case study, the absorption and recombination of knowledge from a variety of domains support specialization more than diversification technology strategies.

We add to innovation management literature a new perspective, by offering a detailed analysis, through patent data, of the knowledge recombination process, led by technological acquisitions.

This study aimed to investigate the learning strategies adopted by Saudi university students and explore the differences in the use of learning strategies due to gender and academic achievement.

The study utilized a cross-sectional descriptive analytic approach and adopted the brief “ACRA-C” learning strategies scale. The study sample consisted of 365 students enrolled at a Saudi university selected using the random clustering technique.

The study revealed that microstrategies and study habits are the most preferred strategies by Saudi university students. Statistically significant differences in the use of learning strategies were found between male and female students in favor of the female students. The study also found that learning strategies are a significant predictor of students' academic achievement.

The study was limited to one college in one Saudi university. Future studies should use larger samples from different colleges and universities in Saudi Arabia and incorporate a variety of measures of academic achievement, such as students' grades in specific courses rather than the overall grade average.

While there are a number of studies that investigated the use of learning strategies by students, there is a lack of such research in the higher education context of Saudi Arabia. Hence, the current study contributes to closing this gap in the literature by looking at the use of learning strategies by university students in Saudi Arabia and the relationship between strategy use, gender and academic achievement.

This study aims at investigating the impact of using Lego Serious Play (LSP) on the effectiveness of teaching supply chain (SC) 4.0 in higher education by going from a traditional 2D approach to a 3D one. LSP in this study is explored as a gamification pedagogical approach that taps into the connection between hands and brain to inspire and engage students to build 3D models using metaphors and storytelling.

An empirical study was conducted among 50 students (over two terms) who were enrolled in a SC 4.0 course and used LSP in their final project that focused on digital SC design. Two questionnaires were designed (one after the standard LSP workshop and the other upon completion of the project) to solicit feedback with respect to how LSP helped students to better understand SC 4.0 topics and fulfill their project. The results were analyzed using Bloom’s taxonomy as well as other pedagogical framework to understand the positive impact of LSP at the cognitive, motivation and social levels.

Results showed that using LSP can enhance the teaching of various SC principles and technologies beyond the abstract point of view (2D) through offering the students an opportunity to apply these principles and technologies in a futuristic project using a hands-on 3D approach. The LSP approach demonstrated its ability to help students navigate through both lower order thinking skills (LOTS) and higher order thinking skills (HOTS) in a meaningful and playful manner. Finally, improving the design skills for students was clear using LSP as it unleashes imagination and taps into internal knowledge together with collective inputs.

The reliance on one case study can be a limitation regarding the generalization of the proposed results. This limitation is attenuated by the representativeness of the case study analyzed. Furthermore, the presented work should encourage future analyses as well as expanding the implementation of LSP to other SC 4.0 teaching contexts and applications.

This paper contributes to the very few literatures regarding using gamification in SC education and specifically how LSP methodology can be adopted in teaching SCM 4.0.

This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

The purpose of this study consisted of an organisational development intervention in a growing small and medium-sized enterprises (SME) where the top management’s objective was to become an attractive workplace for the next generation of employees. The central problem is how to develop a smart working environment (SWE) based on the needs of this target group. The aim is both practical and theoretical.

The action research (AR) approach was used as a frame for the organisational learning process. The problem identification, targets and activities were developed in an iterative process together with the management team and employees. Starting from the main problem, a methodological plan was outlined for the intervention, including several instruments for collecting both qualitative and quantitative data. AR is an emergent process in which data, researchers and participants are equally contributing in deciding on the next steps to be taken.

The theoretical findings pertain to the definition of what an SWE is in this specific context and how it evolved during the intervention period. It is identified as expansive learning of the concept, which is illustrated through the iterative phases allowing for the expansion of understanding and implementing new ways of being, doing and relating in the organisation.

The results are based on a limited and contextually specific sample and are thus descriptive in relation to the organisation subjected to study. Further research is needed to see how the findings are transferable to other contexts.

This study highlights how participative approaches and managerial sensitivity to employees’ needs are valuable for defining and implementing an SWE and how this approach can improve organisational dynamics and contribute to organisational learning.

The study gives insight into factors that the new generation of employees finds most important at work. While prior research on SWE mostly focuses on efficiency and effectiveness framed by digitalisation and workspace, the present findings emphasise the importance of working with the socioemotional dimension at work for ensuring employee sustainability.

This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system.

Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information.

It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market.

There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions.

This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.