• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.110-122
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• 2010

The emergence of the field of organizational buying behavior in the mid-1960’s with the publication of Industrial Buying and Creative Marketing (1967) set the stage for a new paradigm of thinking about how business was conducted in markets other than those serving ultimate consumers. Whether it is "industrial marketing" or "business-to-business marketing" (B-to-B), organizational buying behavior remains the core differentiating characteristic of this domain of marketing. This paper explores the impact of several dynamic factors that have influenced how organizations relate to one another in a rapidly increasing interdependence, which in turn can impact organizational buying behavior. The paper also raises the question of whether or not the major conceptual models of organizational buying behavior in an interdependent world are still relevant to guide research and managerial thinking, in this dynamic business environment. The paper is structured to explore three questions related to organizational interdependencies: 1. What are the factors and trends driving the emergence of organizational interdependencies? 2. Will the major conceptual models of organizational buying behavior that have developed over the past half century be applicable in a world of interdependent organizations? 3. What are the implications of organizational interdependencies on the research and practice of organizational buying behavior? Consideration of the factors and trends driving organizational interdependencies revealed five critical drivers in the relationships among organizations that can impact their purchasing behavior: Accelerating Globalization, Flattening Networks of Organizations, Disrupting Value Chains, Intensifying Government Involvement, and Continuously Fragmenting Customer Needs. These five interlinked drivers of interdependency and their underlying technological advances can alter the relationships within and among organizations that buy products and services to remain competitive in their markets. Viewed in the context of a customer driven marketing strategy, these forces affect three levels of strategy development: (1) evolving customer needs, (2) the resulting product/service/solution offerings to meet these needs, and (3) the organization competencies and processes required to develop and implement the offerings to meet needs. The five drivers of interdependency among organizations do not necessarily operate independently in their impact on how organizations buy. They can interact with each other and become even more potent in their impact on organizational buying behavior. For example, accelerating globalization may influence the emergence of additional networks that further disrupt traditional value chain relationships, thereby changing how organizations purchase products and services. Increased government involvement in business operations in one country may increase costs of doing business and therefore drive firms to seek low cost sources in emerging markets in other countries. This can reduce employment opportunitiesn one country and increase them in another, further accelerating the pace of globalization. The second major question in the paper is what impact these drivers of interdependencies have had on the core conceptual models of organizational buying behavior. Consider the three enduring conceptual models developed in the Industrial Buying and Creative Marketing and Organizational Buying Behavior books: the organizational buying process, the buying center, and the buying situation. A review of these core models of organizational buying behavior, as originally conceptualized, shows they are still valid and not likely to change with the increasingly intense drivers of interdependency among organizations. What will change however is the way in which buyers and sellers interact under conditions of interdependency. For example, increased interdependencies can lead to increased opportunities for collaboration as well as conflict between buying and selling organizations, thereby changing aspects of the buying process. In addition, the importance of communication processes between and among organizations will increase as the role of trust becomes an important criterion for a successful buying relationship. The third question in the paper explored consequences and implications of these interdependencies on organizational buying behavior for practice and research. The following are considered in the paper: the need to increase understanding of network influences on organizational buying behavior, the need to increase understanding of the role of trust and value among organizational participants, the need to improve understanding of how to manage organizational buying in networked environments, the need to increase understanding of customer needs in the value network, and the need to increase understanding of the impact of emerging new business models on organizational buying behavior. In many ways, these needs deriving from increased organizational interdependencies are an extension of the conceptual tradition in organizational buying behavior. In 1977, Nicosia and Wind suggested a focus on inter-organizational over intra-organizational perspectives, a trend that has received considerable momentum since the 1990's. Likewise for managers to survive in an increasingly interdependent world, they will need to better understand the complexities of how organizations relate to one another. The transition from an inter-organizational to an interdependent perspective has begun, and must continue so as to develop an improved understanding of these important relationships. A shift to such an interdependent network perspective may require many academicians and practitioners to fundamentally challenge and change the mental models underlying their business and organizational buying behavior models. The focus can no longer be only on the dyadic relations of the buying organization and the selling organization but should involve all the related members of the network, including the network of customers, developers, and other suppliers and intermediaries. Consider for example the numerous partner networks initiated by SAP which involves over 9000 companies and over a million participants. This evolving, complex, and uncertain reality of interdependencies and dynamic networks requires reconsideration of how purchase decisions are made; as a result they should be the focus of the next phase of research and theory building among academics and the focus of practical models and experiments undertaken by practitioners. The hope is that such research will take place, not in the isolation of the ivory tower, nor in the confines of the business world, but rather, by increased collaboration of academics and practitioners. In conclusion, the consideration of increased interdependence among organizations revealed the continued relevance of the fundamental models of organizational buying behavior. However to increase the value of these models in an interdependent world, academics and practitioners should improve their understanding of (1) network influences, (2) how to better manage these influences, (3) the role of trust and value among organizational participants, (4) the evolution of customer needs in the value network, and (5) the impact of emerging new business models on organizational buying behavior. To accomplish this, greater collaboration between industry and academia is needed to advance our understanding of organizational buying behavior in an interdependent world.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.14
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• pp.1-40
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• 1973

There is a general tendency to increase nitrogen level in rice production to insure an increased yield. On the other hand, percentage of ripened grains is getting decreased with such an increased fertilizer level. Decreasing of the percentage is one of the important yield limiting factors. Especially the newly developed rice variety, 'Tongil' is characterized by a relatively low percentage of ripened grains as compared with the other leading varieties. Therefore, these studies were aimed to finding out of some measures for the improvement of ripening in rice. The studies had been carried out in the field and in the phytotron during the period of three years from 1970 to 1972 at the Crop Experiment Station in Suwon. The results obtained from the experiments could be summarized as follows: 1. The spikelet of Tongil was longer in length, more narrow in width, thinner in thickness, smaller in the volume of grains and lighter in grain weight than those of Jinheung. The specific gravity of grain was closely correlated with grain weight and the relationship with thickness, width and length was getting smaller in Jinheung. On the other hand, Tongil showed a different pattern from Jinheung. The relationship of the specific gravity with grain weight was the greatest and followed by that with the width, thickness and length, in order. 2. The distribution of grain weight selected by specific gravity was different from one variety to another. Most of grains of Jinheung were distributed over the specific gravity of 1.12 with its peak at 1.18, but many of grains of Tongil were distributed below 1.12 with its peak at 1.16. The brown/rough rice ratio was sharply declined below the specific gravity of 1.06 in Jinheung, but that of Tongil was not declined from the 1.20 to the 0.96. Accordingly, it seemed to be unfair to make the specific gravity criterion for ripened grains at 1.06 in the Tongil variety. 3. The increasing tendency of grain weight after flowering was different depending on varieties. Generally speaking, rice varieties originated from cold area showed a slow grain weight increase while Tongil was rapid except at lower temperature in late ripening stage. 4. In the late-tillered culms or weak culms, the number of spikelets was small and the percentage of ripened grains was low. Tongil produced more late-tillered culms and had a longer flowering duration especially at lower temperature, resulting in a lower percentage of ripened grains. 5. The leaf blade of Tongil was short, broad and errect, having light receiving status for photosynthesis was better. The photosynthetic activity of Tongil per unit leaf area was higher than that of Jinheung at higher temperature, but lower at lower temperature. 6. Tongil was highly resistant to lodging because of short culm length, and thick lower-internodes. Before flowering, Tongil had a relatively higher amount of sugars, phosphate, silicate, calcium, manganese and magnesium. 7. The number of spikelets of Tongil was much more than that of Jinheung. The negative correlation was observed between the number of spikelets and percentage of ripened grains in Jinheung, but no correlation was found in Tongil grown at higher temperature. Therefore, grain yield was increased with increased number of spikelets in Tongil. Anthesis was not occurred below 21$^{\circ}C$ in Tongil, so sterile spikelets were increased at lower temperature during flowering stage. 8. The root distribution of Jinheung was deeper than that of Tongil. The root activity of Tongil evaluated by $\alpha$-naphthylamine oxidation method, was higher than that of Jinheung at higher temperature, but lower at lower temperature. It is seemed to be related with discoloration of leaf blades. 9. Tongil had a better light receiving status for photosynthesis and a better productive structure with balance between photosynthesis and respiration, so it is seemed that tongil has more ideal plant type for getting of a higher grain yield as compared with Jinheung. 10. Solar radiation during the 10 days before to 30 days after flowering seemed enough for ripening in suwon, but the air temperature dropped down below 22$^{\circ}C$ beyond August 25. Therefore, it was believed that air temperature is one of ripening limiting factors in this case. 11. The optimum temperature for ripening in Jinheung was relatively lower than that of Tongil requriing more than $25^{\circ}C$. Air temperature below 21$^{\circ}C$ was one of limiting factors for ripening in Tongil. 12. It seemed that Jinheung has relatively high photosensitivity and moderate thermosensitivity, while Tongil has a low photosensitivity, high thermosensitivity and longer basic vegetative phase. 13. Under a condition of higher nitrogen application at late growing stage, the grain yield of Jinheung was increased with improvement of percentage of ripened grains, while grain yield of Tongil decreased due to decreasing the number of spikelets although photosynthetic activity after flowering was. increased. 14. The grain yield of Jinheung was decreased slightly in the late transplanting culture since its photosynthetic activity was relatively high at lower temperature, but that of Tonil was decreased due to its inactive photosynthetic activity at lower temperature. The highest yield of Tongil was obtained in the early transplanting culture. 15. Tongil was adapted to a higher fertilizer and dense transplanting, and the percentage of ripened grains was improved by shortening of the flowering duration with increased number of seedlings per hill. 16. The percentage of vigorous tillers was increased with a denser transplanting and increasing in number of seedlings per hill. 17. The possibility to improve percentage of ripened grains was shown with phosphate application at lower temperature. The above mentioned results are again summarized below. The Japonica type leading varieties should be flowered before August 20 to insure a satisfactory ripening of grains. Nitrogen applied should not be more than 7.5kg/10a as the basal-dressing and the remained nitrogen should be applied at the later growing stage to increase their photosynthetic activity. The morphological and physiological characteristics of Tongil, a semi-dwarf, Indica $\times$ Japonica hybrid variety, are very different from those of other leading rice varieties, requring changes in seed selection by specific gravity method, in milling and in the cultural practices. Considering the peculiar distribution of grains selected by the method and the brown/rough rice ratio, the specific gravity criterion for seed selection should be changed from the currently employed 1.06 to about 0.96 for Tongil. In milling process, it would be advisable to bear in mind the specific traits of Tongil grain appearance. Tongil is a variety with many weak tillers and under lower temperature condition flowering is delayed. Such characteristics result in inactivation of roots and leaf blades which affects substantially lowering of the percentage of ripened grains due to increased unfertilized spikelets. In addition, Tongil is adapted well to higher nitrogen application. Therefore, it would be recommended to transplant Tongil variety earlier in season under the condition of higer nitrogen, phosphate and silicate. A dense planting-space with three vigorous seedlings per hill should be practiced in this case. In order to manifest fully the capability of Tongil, several aspects such as the varietal improvement, culural practices and milling process should be more intensively considered in the future.he future.