This study focuses on the phenomenon of using superstition as a decision heuristic in strategic decision-making. We introduce the construct of superstitious heuristic, which is defined as a decision ...shortcut based on superstitious beliefs. The superstitious heuristic is commonly used in strategic decision-making in various cultures and can lead to seemingly puzzling decisions that have strategic consequences for the firm. It is also distinctly different from other major decision heuristics (i.e., heuristics-and-biases, fast-and-frugal heuristics, expert intuition, and simple rules) and can be used over the latter, especially under high uncertainty. Nevertheless, this heuristic type has not yet received much attention in the strategy literature, presumably because its usage in strategic decision-making is less prevalent in Western contexts where most heuristics research takes place. In this study, we initiate an inquiry into this phenomenon through two objectives: (1) introducing the construct of superstitious heuristic with conceptualization and measurement and (2) probing the executive antecedents to the superstitious heuristic. Our study not only brings the superstitious heuristic to the attention of strategy scholars but also lays the conceptual and empirical foundation for advancing strategy research on this heuristic. By delineating the profile of executives who are prone to employing this heuristic in making strategic choices, our study also contributes to upper echelons research on the use of heuristics in strategic decision processes.
Count-Based Research in Management Blevins, Dane P.; Tsang, Eric W. K.; Spain, Seth M.
Organizational research methods,
01/2015, Volume:
18, Issue:
1
Journal Article
Peer reviewed
We review 11 years (2001-2011) of management research using count-based dependent variables in 10 leading management journals. We find that approximately one out of four papers use the most basic ...Poisson regression model in their studies. However, due to potential concerns of overdispersion, alternative regression models may have been more appropriate. Furthermore, in many of these papers the overdispersion may have been caused by excess zeros in the data, suggesting that an alternative zero-inflated model may have been a better fit for the data. To illustrate the potential differences among the model specifications, we provide a comparison of the different models using previously published data. Additionally, we simulate data using different parameters. Finally, we offer a simplified decision tree guideline to improve future count-based research.
BP type UV-fillters and other four widely used UV-fillters are ubiquitous in the child urinary samples (4–6 years, n=53), tap water and commercial distilled water in Hong Kong. BP1, BP2, BP3 and BP4 ...in children urine samples contributed closely to the overall children exposure of UV filters. As a contrast, BP3 was the major substance found in the tap water and distilled bottle water. There were some significant relationships between urinary UV filters and personal characteristics (BMI values, sex, income level, hand washing frequency, and body location usage). Only 2 children applied sun creams in this research, indicating that there were other sources to exposure these chemicals.
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•UV-fillters are ubiquitous in the child urine and drinking water in Hong Kong;•BP1, BP2, BP3 and BP4 contributed closely to the overall children exposure of UV filters;•BP3 was the major substance found in the tap water and distilled bottle water;•There were other sources to exposure these chemicals except suncreams.
Benzophenones (BPs) and other ultra violet (UV) filters (UV-filters) are widely used in sunblock and other personal care products, raising concerns about their adverse health risks to human, especially for children. In the present study, BP-type UV-filters and other four widely used UV-filters were evaluated in the child urinary samples (4–6 years, n = 53), tap water and commercial distilled water in Hong Kong. The results suggested that the target chemicals are ubiquitous in the subject. BP1, BP2, BP3 and BP4 in children urine samples contributed closely to the overall children exposure of UV filters, with detection rates above 58% and geometric means ranging from 44.2 to 76.7 ng/mL. As a contrast, BP3 was the major substance found in the tap water and distilled bottle water, with detection rates of 100% and geometric means of 9.64 and 14.5 ng/L, respectively. There were some significant relationships between urinary UV filters and personal characteristics (BMI values, sex, income level, hand washing frequency, and body location usage), but the health risks associated with UV-filters in Hong Kong children might not be concerning. Only two children applied sun creams in this research, indicating that there were other sources to exposure these chemicals.
Recently, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of cognitive concept learning. We establish a concept hierarchy structure ...based on the existing cognitive concept learning methods. However, none of these methods could obtain the following results: get the concept, recognize objects and distinguish between two different objects. In this paper, our focus is to construct an attribute-oriented multi-level cognitive concept learning method so as to improve and enhance the ability of cognitive concept learning. Firstly, the view point of human cognition is discussed from the multi-level approach, and then the mechanism of attribute-oriented cognitive concept learning is investigated. Through some defined special attributes, we propose a corresponding structure of attribute-oriented multi-level cognitive concept learning from an interdisciplinary viewpoint. It is a combination of philosophy and psychology of human cognition. Moreover, to make the presented attribute-oriented multi-level method easier to understand and apply in practice, an algorithm of cognitive concept learning is established. Furthermore, a case study about how to recognize the real-world animals is studied to use the proposed method and theory. Finally, in order to solve conceptual cognition problems, we perform an experimental evaluation on five data sets downloaded from the University of California-Irvine (UCI) databases. And then we provide a comparative analysis with the existing
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Multi-label learning is a class of machine learning algorithms that study the classification problem of data associated with multiple labels simultaneously. Ensemble-based method is one of the ...representative methods in multi-label learning. In the existing ensemble-based multi-label classification methods, the essence of its base learner is still a binary or multi-class classifier. At present, there is no ensemble-based multi-label method that uses a multi-label classifier based on algorithm adaptation as a base learner. Multi-label algorithms suffer from expensive time costs, and ensemble-based methods further exacerbate the time cost. To address these issues, we propose an efficient multi-label classification method based on kernel extreme learning machine and ensemble learning. Firstly, to solve the problem of high time complexity of multi-label classifiers, we use a Gaussian kernel function to generate a kernel extreme learning machine based multi-label classifier (KELM_ML). Then, based on random sampling, we construct the base classifier, i.e., KELM_ML, in ensemble learning. Finally, to build an ensemble model of multi-label classifiers, an elimination optimization ensemble strategy is proposed by defining an encoding vector of the base classifiers and updating it. Although our method is an ensemble model, it inherits the advantages of good time complexity of ELM, i.e., fast learning time. The experimental and statistical results show that compared with baseline methods and other ensemble-based multi-label methods, our proposed method has better classification performance and stable results.
Double-quantitative decision-theoretic rough sets (Dq-DTRS) provide more comprehensive description methods for rough approximations of concepts, which lay foundations for the development of attribute ...reduction and rule extraction of rough sets. Existing researches on concept approximations of Dq-DTRS pay more attention to the equivalence class of each object in approximating a concept, and calculate concept approximations from the whole data set in a batch. This makes the calculation of approximations time consuming in dynamic data sets. In this paper, we first analyze the variations of equivalence classes, decision classes, conditional probability, internal grade and external grade in dynamic data sets while objects vary sequentially or simultaneously over time. Then we propose the updating mechanisms for the concept approximations of two types of Dq-DTRS models from incremental perspective in dynamic decision information systems with the sequential and batch variations of objects. Meanwhile, we design incremental sequential insertion, sequential deletion, batch insertion, batch deletion algorithms for two Dq-DTRS models. Finally, we present experimental comparisons showing the feasibility and efficiency of the proposed incremental approaches in calculating approximations and the stability of the incremental updating algorithms from the perspective of the runtime under different inserting and deleting ratios and parameter values.
•We study dynamic maintenance approaches for the approximations of Dq-DTRS.•We propose incremental updating mechanisms of Dq-DTRS with the variation of objects.•We design incremental sequential and batch updating algorithms for Dq-DTRS models.•The validity, stability and efficiency of our methods are verified by comparisons.
How do a firm’s internal capabilities and external partnerships contribute to its product and process innovativeness? How do their impacts differ? Based on the theoretical framework of exploitation ...and exploration, we develop an integrative model linking the impact of both internal capabilities and external partnerships on product and process innovativeness. Survey responses from Taiwanese biotechnology firms indicate that research and development (R&D), marketing, and manufacturing capabilities have different effects on product and process innovativeness. Of the four types of external partnerships, only partnerships with universities and research institutes seem to add value, whereas partnerships with suppliers, customers, and competitors do not contribute to innovativeness. Moreover, marketing capability and customer partnerships have a positive interaction effect on product innovativeness, while manufacturing capability and supplier partnerships have a positive interaction effect on process innovativeness.
The construction of fuzzy relations is a key issue of fuzzy rough sets. The fuzzy relations generated by the soft distances between samples are more robust than that generated by the hard distances ...between samples. To improve the ability of fuzzy rough sets in deleting redundant attributes, we propose two enhanced fuzzy similarity relations by fully mining neighborhood information and decision information of samples. Then, we establish the Neighborhood Constrained Fuzzy Rough Sets (NC-FRS) by using the proposed relations to perform attribute reduction. Meanwhile, we design enhanced fuzzy similarity relation-based attribute reduction (EFSR-AR) to select important attributes for classification tasks. Finally, we download three gene expression profiles from NCBI to verify that the proposed algorithm can select genes highly related to tumors, the selected genes are more conducive to tumor classification, and the proposed algorithm has strong anti-noise ability. The comparison results indicate that EFSR-AR does have the ability to combat noise and select some genes highly related to tumors.
•A soft distance is constructed by combining neighborhood and decision information.•Proposing a fuzzy relation to enhance generalization ability of rough sets (RS).•Using proposed relation to estimate attributes and perform attribute reduction.•Proposed algorithm can select attributes highly related to classification tasks.•This study deepens the theory and application of RS from perspective of antinoise.
Purpose: The first purpose of this short essay is to respond to Howells and Scholderer's (2016) harsh critique that organizational unlearning is a superfluous concept. The second purpose is to ...establish a relationship between organizational unlearning and the learning organization. Design/methodology/approach: To respond to Howells and Scholderer's critique, the author carefully examines their arguments--focusing on their comments on the author's previous publications--and checks whether the arguments are logical and coherent. To establish a relationship between organizational unlearning and the learning organization, the author draws on his own research of international joint ventures in China. Findings: Howells and Scholderer seriously miscited the ideas in one of the author's publications, and their main arguments are blatantly flawed. Moreover, they are unaware that many of the faults they find in the organizational unlearning literature are also present in the organizational learning literature. As to the second part of this essay, the study of the acquisition type of joint ventures clearly indicates the presence of organizational unlearning. Moreover, for such ventures to be learning organizations, the unlearning step has to be well managed. Research limitations/implications: As mentioned, the author's response to Howells and Scholderer's critique focuses on their comments on the author's publications. It is highly likely that they have made other erroneous arguments that this essay fails to capture. The author's discussion of unlearning and learning organizations is constrained by the context of acquisition joint ventures. Originality/value: This essay forcefully rebuts Howells and Scholderer's critique, which can become an obstacle in the development of organizational unlearning research. The dynamics of knowledge transfer in acquisition joint ventures suggest that skills of unlearning, and not just learning, are essential to reaching the goal of being a learning organization.
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