The idea that entrepreneurial opportunities exist "out there" is increasingly under attack by scholars who argue that opportunities do not preexist objectively but are actively created through ...subjective processes of social construction. In this article we concede many of the criticisms pioneered by the creation approach but resist abandoning the preexisting reality of opportunities. Instead, we use realist philosophy of science to ontologically rehabilitate the objectivity of entrepreneurial opportunities by elucidating their propensity mode of existence. Our realist perspective offers an intuitive and paradox-free understanding of what it means for opportunities to exist objectively. This renewed understanding enables us to (1) explain that the subjectivities of the process of opportunity actualization do not contradict the objective existence of opportunities, (2) acknowledge the category of agency-intensive opportunites, (3) develop the notion of "nonopportunity," and (4) clarify the ways individuals might make cognitive contact with opportunities prior to their actualization. Our actualization approach serves as a refined metatheory for guiding future entrepreneurship research and facilitates the revisiting of subtle conceptual issues at the core of entrepreneurial theory, such as the nature of uncertainty and "nonentrepreneurs," as well as the role played by prediction in a scientific study of entrepreneurship.
Organizational unlearning Tsang, Eric W.K.; Zahra, Shaker A.
Human relations (New York),
10/2008, Volume:
61, Issue:
10
Journal Article
Peer reviewed
Organizational unlearning is widely considered an important condition for successful adaptation to environmental changes, promoting organizational learning and enhancing a firm's performance. Yet, ...organizational unlearning has received scant attention in the literature, making theory building difficult. This article defines and clarifies the meaning of organizational unlearning, distinguishes it from organizational learning, articulates its dimensions and discusses ways organizations can unlearn.
The case study as a key research method has often been criticized for generating results that are less generalizable than those of large‐sample, quantitative methods. This paper clearly defines ...generalization and distinguishes it from other related concepts. Drawing on the literature, the author shows that case study results may be less generalizable than those of quantitative methods only in the case of within‐population generalization. The author argues that case studies have merits over quantitative methods in terms of theoretical generalization, identifying disconfirming cases and providing useful information for assessing the empirical generalizability of results.
This article reviews the rapidly growing domain of global value chain (GVC) research by analyzing several highly cited conceptual frameworks and then appraising GVC studies published in such ...disciplines as international business, general management, supply chain management, operations management, economic geography, regional and development studies, and international political economy. Building on GVC conceptual frameworks, we conducted the review based on a comparative institutional perspective that encompasses critical governance issues at the micro-, GVC, and macro-levels. Our results indicate that some of these issues have garnered significantly more scholarly attention than others. We suggest several future research topics such as microfoundations of GVC governance, GVC mapping, learning, impact of lead firm ownership and strategy, dynamics of GVC arrangements, value creation and distribution, financialization, digitization, the impact of renewed protectionism, the impact of GVCs on their macro-environment, and chain-level performance management.
Recent research suggests that the distance between countries in terms of culture, institutions, geographic proximity, and economic development matters in the foreign direct investment (FDI) decisions ...made by firms. This study focuses on the historical ties between countries as an additional factor affecting such decisions. In particular, it examines three major historical factors that affect cross-country ties with Vietnam, namely, Chinese occupation and conflict, French colonization, and socialist ideology, and examines the ways in which these historical ties have influenced FDI. The database consists of 631 wholly owned subsidiaries and 1215 joint ventures formed in Vietnam by multinational enterprises from 35 countries and regions between 1989 and 1999. The results indicate that firms from Hong Kong, Taiwan, France, and former and current socialist countries tended to be early movers in Vietnam, whereas firms from Mainland China tended to be late movers. Using the example of Vietnam, this study clearly shows that historical ties can provide additional explanatory power regarding FDI decisions beyond the conventional distance variables.
Many papers have been published recently in the fields of strategy and international business research incorporating the role of organizational knowledge as a basis of firm competitive advantage. ...While such knowledge is normally developed within the firm, it is important that firms possess the ability to learn from others in order to meet the increasing pace of competition. Knowledge transfer, defined here as an event through which one organization learns from the experience of another, has thus become an important research area within the broader domain of organizational learning and knowledge management. This paper presents a theoretical framework, identifies key themes covered by the six articles included in the Special Issue on Inter‐Organizational Knowledge Transfer, and then discusses priorities for future research.
Neighborhood rough sets based attribute reduction, as a common dimension reduction method, has been widely used in machine learning and data mining. Each attribute has the same weight (the degree of ...importance) in the existing neighborhood rough set models. In this work, we introduce different weights into neighborhood relations and propose a novel approach for attribute reduction. The main motivation is to fully mine the correlation between attributes and decisions before calculating neighborhood relations, and the attributes with high correlation are assigned higher weights. We first construct a Weighted Neighborhood Rough Set (WNRS) model based on weighted neighborhood relations and discuss its properties. Then WNRS based dependency is defined to evaluate the significance of attribute subsets. We design a greedy search algorithm based on WNRS to select an attribute subset which has both strong correlation and high dependency. Furthermore, we use isometric search to find the optimal neighborhood threshold. Finally, ten datasets from UCI machine learning repository and ELVIRA Biomedical data set repository are used to compare the performance of WNRS with those of other state-of-the-art reduction algorithms. The experimental results show that WNRS is feasible and effective, which has higher classification accuracy and compression ratio.
•The existing researches on neighborhood rough sets use the same attribute weights.•The attributes that are highly related to decisions should be highlighted.•We introduce partition coefficients of attributes to re-assign weights of attributes.•The results show WNRS can get higher classification accuracy and compression ratio.
Davis's (1971) article “That's interesting! Towards a phenomenology of sociology and a sociology of phenomenology” is regarded by many management researchers as a classic work and a basis for guiding ...management studies; in the wake of its publication, an interesting research advocacy gradually emerged. However, from the perspective of scientific research, Davis's core argument that great theories have to be interesting is seriously flawed. Interestingness is not regarded as a virtue of a good scientific theory and thus has little value in science. Moreover, obsession with interestingness can lead to at least five detrimental outcomes, namely promoting an improper way of doing science, encouraging post hoc hypothesis development, discouraging replication studies, ignoring the proper duties of a researcher, and undermining doctoral education.
•Fe0/CeO2 was first applied to catalytic Fenton oxidation of tetracycline.•Surface-bounded OH plays a critical role in Fe0/CeO2 Fenton system.•CeO2 is beneficial to adsorption of pollutant and ...electron transfer.•Mechanism of tetracycline degradation by heterogeneous Fenton was proposed.
The development of an ultra-efficient heterogeneous Fenton catalyst that has practical application and can be easily separated is of great importance. In this study, a novel nanocomposite, Fe0/CeO2, was successfully synthesised and applied for Fenton oxidation of tetracycline. The results show that Fe0/CeO2 significantly enhanced the removal of pollutants, which suggests a synergistic effect between nanoscale zero-valent iron and CeO2. Furthermore, the composite catalyst exhibited ideal reusability and wide pH adaptation. A radical identification experiment revealed that surface-bounded OH (OHads) played a critical role in Fenton reaction, and compared with bare nanoscale zero-valent iron, Fe0/CeO2 remarkably promoted the generation of OH in the Fenton system, mostly because CeO2 not only increased the adsorption properties of the catalyst, it also accelerated electron transfer to promote the decomposition of H2O2 due to the abundance of surface oxygen vacancies. A reasonable degradation pathway was also proposed for tetracycline on the basis of the detected intermediates. Our findings help to understand the mechanism of tetracycline degradation by heterogeneous Fenton-like, and also provide an efficient system for organic wastewater treatment.
Attribute reduction is one of the most important preprocessing steps in machine learning and data mining. As a key step of attribute reduction, attribute evaluation directly affects classification ...performance, search time, and stopping criterion. The existing evaluation functions are greatly dependent on the relationship between objects, which makes its computational time and space more costly. To solve this problem, we propose a novel separability-based evaluation function and reduction method by using the relationship between objects and decision categories directly. The degree of aggregation (DA) of intraclass objects and the degree of dispersion (DD) of between-class objects are first defined to measure the significance of an attribute subset. Then, the separability of attribute subsets is defined by DA and DD in fuzzy decision systems, and we design a sequentially forward selection based on the separability (SFSS) algorithm to select attributes. Furthermore, a postpruning strategy is introduced to prevent overfitting and determine a termination parameter. Finally, the SFSS algorithm is compared with some typical reduction algorithms using some public datasets from UCI and ELVIRA Biomedical repositories. The interpretability of SFSS is directly presented by the performance on MNIST handwritten digits. The experimental comparisons show that SFSS is fast and robust, which has higher classification accuracy and compression ratio, with extremely low computational time.
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