• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.49-61
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• 2009

This article provides training exercises for executives into interpreting subroutine maps of executives' thinking in processing business and industrial marketing problems and opportunities. This study builds on premises that Schank proposes about learning and teaching including (1) learning occurs by experiencing and the best instruction offers learners opportunities to distill their knowledge and skills from interactive stories in the form of goal.based scenarios, team projects, and understanding stories from experts. Also, (2) telling does not lead to learning because learning requires action-training environments should emphasize active engagement with stories, cases, and projects. Each training case study includes executive exposure to decision system analysis (DSA). The training case requires the executive to write a "Briefing Report" of a DSA map. Instructions to the executive trainee in writing the briefing report include coverage in the briefing report of (1) details of the essence of the DSA map and (2) a statement of warnings and opportunities that the executive map reader interprets within the DSA map. The length maximum for a briefing report is 500 words-an arbitrary rule that works well in executive training programs. Following this introduction, section two of the article briefly summarizes relevant literature on how humans think within contexts in response to problems and opportunities. Section three illustrates the creation and interpreting of DSA maps using a training exercise in pricing a chemical product to different OEM (original equipment manufacturer) customers. Section four presents a training exercise in pricing decisions by a petroleum manufacturing firm. Section five presents a training exercise in marketing strategies by an office furniture distributer along with buying strategies by business customers. Each of the three training exercises is based on research into information processing and decision making of executives operating in marketing contexts. Section six concludes the article with suggestions for use of this training case and for developing additional training cases for honing executives' decision-making skills. Todd and Gigerenzer propose that humans use simple heuristics because they enable adaptive behavior by exploiting the structure of information in natural decision environments. "Simplicity is a virtue, rather than a curse". Bounded rationality theorists emphasize the centrality of Simon's proposition, "Human rational behavior is shaped by a scissors whose blades are the structure of the task environments and the computational capabilities of the actor". Gigerenzer's view is relevant to Simon's environmental blade and to the environmental structures in the three cases in this article, "The term environment, here, does not refer to a description of the total physical and biological environment, but only to that part important to an organism, given its needs and goals." The present article directs attention to research that combines reports on the structure of task environments with the use of adaptive toolbox heuristics of actors. The DSA mapping approach here concerns the match between strategy and an environment-the development and understanding of ecological rationality theory. Aspiration adaptation theory is central to this approach. Aspiration adaptation theory models decision making as a multi-goal problem without aggregation of the goals into a complete preference order over all decision alternatives. The three case studies in this article permit the learner to apply propositions in aspiration level rules in reaching a decision. Aspiration adaptation takes the form of a sequence of adjustment steps. An adjustment step shifts the current aspiration level to a neighboring point on an aspiration grid by a change in only one goal variable. An upward adjustment step is an increase and a downward adjustment step is a decrease of a goal variable. Creating and using aspiration adaptation levels is integral to bounded rationality theory. The present article increases understanding and expertise of both aspiration adaptation and bounded rationality theories by providing learner experiences and practice in using propositions in both theories. Practice in ranking CTSs and writing TOP gists from DSA maps serves to clarify and deepen Selten's view, "Clearly, aspiration adaptation must enter the picture as an integrated part of the search for a solution." The body of "direct research" by Mintzberg, Gladwin's ethnographic decision tree modeling, and Huff's work on mapping strategic thought are suggestions on where to look for research that considers both the structure of the environment and the computational capabilities of the actors making decisions in these environments. Such research on bounded rationality permits both further development of theory in how and why decisions are made in real life and the development of learning exercises in the use of heuristics occurring in natural environments. The exercises in the present article encourage learning skills and principles of using fast and frugal heuristics in contexts of their intended use. The exercises respond to Schank's wisdom, "In a deep sense, education isn't about knowledge or getting students to know what has happened. It is about getting them to feel what has happened. This is not easy to do. Education, as it is in schools today, is emotionless. This is a huge problem." The three cases and accompanying set of exercise questions adhere to Schank's view, "Processes are best taught by actually engaging in them, which can often mean, for mental processing, active discussion."

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.217-244
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• 2008

Consumers' overall satisfaction on a specific library use is inferred to be primarily accrued from their performance perception and use satisfaction on the library information service system as recent information technology is being rapidly improved and more libraries are being equipped with advanced information technologies. However, prior research has been conducted only on general library service quality and visitors' satisfaction, leaving the important aspects of visitors' library use and information performance perception. Thus, the objectives of this research are to examine the effect of library use patterns such as general visit for book reading and more professional information search, coupled with service quality, on the library users' performance perception on the information system that in turn, affects library use satisfaction on the same information system. More specifically, this study examines whether library visitors perceive differenltly the information system performance according to their library use patterns such that professional library users may have less positive on information system service due to their higher expectation or more positive perception on it due to variety of information uses and positive judgment on advanced information system. Next, three dimensions of service quality, consisting of interaction, outcome, and physical evidence quality in visitors' library use situations, are hypothesized to affect performance perception on library information system. Thirdly, the performance perception on library information system is hypothesized to influence the system use satisfaction while these two constructs are to affect visitors' overall satisfaction. we develop the following research model in accordance with the above theoretical reasoning. All variables used in this study(General Use Patterns, Professional Use Patterns, Interaction Quality, Outcome Quality, Physical Evidence Quality, Information Performance Perception, Information Use Satisfaction, Overall Satisfaction) were defined operationally based on the underlying prior studies. A survey was conducted with prepared questionnaires to about 400 visitors of a specific university library. Among them, 353 proper questionnaires were finally used for the analyses. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was conducted to guarantee the validity and reliability of variables. The results showed that all variables had not only convergent and discriminant validity, but also reliability. Then, research model was examined with a structural equation using LISREL 8.30 version. The fitness of the research model was found to be within the acceptable level. The findings of this study are as follows. The professional library use pattern was found to affect the users' performance perception on the library information system while the general library use pattern was not. Second, three dimensions of service quality (interaction, outcome, physical evidence) were found to influence the information system performance respectively while none of them was not to information use satisfaction. Third, library users' performance perception on the information system operation was found to affect the information system use satisfaction, both of which also influence users' overall satisfaction of the library. The findings of this study suggest that contemporary libraries strengthen their advanced information system operation in a way of user orientation and more importantly maximize their visitors' utilization of information system, accompanying proper material and various program development. This study conceptualized the new constructs of library users' performance perception on the information system and information use satisfaction which could better explain library users' overall satisfaction. Thus, furture study related with library service could utilize the constructs of information system performance and satisfaction as well as the variety of library use patterns in the users' viewpoints.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1285-1295
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• 1997

Background : EGFR is one of the initial step in signal transduction pathway about multistep carcinogenesis. It is homologous to oncogene erbB-2 and is the receptor for EGF and TGF alpha. EGFR has important role in the growth and differentiation of tumor cells. So, EGFR in non-small cell lung cancer was examined to search for possible evidence as clinical prognostic factor. Methods : To investigate the role of EGFR in lung cancer, the author performed immunohistochemical stain of EGFR on 57 resected primary non-small cell lung cancer specimens. And the author analyzed the correlation between EGFR expression, clinical parameters, Sand $G_1$ phase fraction and survival. Results : 1) EGFR were detected in 56% of total 57 patients (according to histologic type, squamous cancer 50%, adenocarcinoma 63%, large cell cancer 75%) (according to TNM stage, stage I 64%, stage II 38%, stage III 55%) (according to cellular differentiation, well 50%, moderately 52%, poorly 65%). All differences were insignificant 2) Using the flow cytometric analysis, mean S-phase fraction of EGFR (+) and (-) group were 22.3(${\pm}10.5$)%. 18.0(${\pm}10.9$)% (p>0.05), mean $G_1$-phase fraction of EGFR (+) and (-) group were 68.4(${\pm}11.6$)%, 71.1(${\pm}12.8$)%, (p>0.05) 3) Two-year survival rate of EGFR (+) and (-) group were 53%, 84%, median survival time of EGFR (+) and (-) group were 26, 53 months. (p<0.05, Kaplan-Meier, generalized Wilcox) Conclusion : EGFR immunostaining may be a simple and useful method for survival prediction in non-small cell lung cancer.