• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.552-564
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• 2002

The data of satellite-observed Microwave Sounding Unit (MSU) channel 1 (Ch1) brightness temperature and General Circulation Model (GCM) reanalyses over the globe have been used to investigate low tropospheric hydrometeors and microwave surface emissivity during the period from January 1981 to December 1993. The average of GCM Ch1 temperature has been reconstructed from three kinds of reanalyses, based on the MSU weighting function. Since the GCM temperature mainly corresponds to the thermal state of the lower troposphere without the difference in the emissivity between ocean and land, it is higher in summer than in other seasons over the regions. The MSU temperature over the ocean shows its maximum at the ITCZ and the SPCZ due to hydrometeors. Over high latitude ocean, the temperature is enhanced because of sea ice emissivity, while it is reduced over the land. The seasonal displacement of the ITCZ and the SPCZ systematically appeared in the difference of Ch1 temperature between the GCM and the MSU. The difference values decrease in the regions of the ITCZ, the SPCZ, and the sea ice because of the increase of the MSU temperature. According to the local minima of the values, the ITCZ moves norhward to 9 N in fall, and the SPCZ moves southward to 12 S in boreal fall and winter. The sea ice in the northern hemisphere is extended southward to 53 N in winter, while the ice in the southern hemisphere, northward to 58 S in boreal summer. We also have discussed the separated contribution from hydrometeors and surface emissivity to the MSU Ch1 temperature, utilizing radiative transfer theory. The increase of 4-6K in the temperature over the ITCZ is inferred to result from hydrometeors of 1-1.5mm/day, and furthermore the increase of 10-30K over the high latitude ocean, ice emissivity of 0.6-0.9.

• Journal of Venture Innovation
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• v.3 no.1
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• pp.117-142
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• 2020

The purpose of this study is to explore the factors affecting the 6th industry experience by Schmitt experience model. The newly introduced variables are the cognitive experience, emotional experience, and social experience that are reconstructed based on Schmitt's experience theory and gender, family as a moderrating variable and trust as a mediation variable. In addition to experience intention. The hypothesis was set as follows. the experience factors that are the cognitive factor, the emotional factor, and the social factor will have a positive(+) influence on the intention to experience. Mooring factors will have a negative(-) effect on intention to experience. For statistical analysis, SPSS 24 and AMOS 23 statistical packages were used to test the research hypothesis. The research was based on 320 questionnaire data and tested by 314 valid responses were analyzed. As a result of the research, First, cognitive, emotional, and social factors had positive(+) effects on experience intention. Among the factors that directly affect the experience intention, the magnitude of influence appeared in the order of cognitive factors > social factors > emotional factors > mooring factors. Second, mooring factors have negative(-) effects on experience intention. Third, Trust has been partially influenced by factors of attraction, cognitive, emotional, and social. Fourth, there are significant statistical differences between men and women in cognitive and mooring factors in the path differences. Fifth, Social factors and mooring factors differed significantly in the composition of the household. Social factors with significant differences in path analysis have also been statistically demonstrated. The results of this study are academically verified that the cognitive, emotional, and social factors have an important influence on the experience intention in the 6th industry experience and the Schmitt's experience model proposed in this study is useful framework of analysis. In practical terms, it could provide implications for what factors should be strategically and marketingly focused to activate the 6th industry experience.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.99-124
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• 2009

Many firms in Korea have adopted and used advanced information technology in an effort to boost efficiency. The process of adapting to the new technology, at the same time, can vary from one firm to another. As such, this research focuses on several relevant factors, especially the roles of social interaction as a key variable that influences the technology adaptation process and the outcomes. Thus far, how a firm goes through the adaptation process to the new technology has not been yet fully explored. Previous studies on changes undergone by a firm or an organization due to information technology have been pursued from various theoretical points of views, evolved from technological and institutional views to an integrated social technology views. The technology adaptation process has been understood to be something that evolves over time and has been regarded as cycles between misalignments and alignments, gradually approaching the stable aligned state. The adaptation process of the new technology was defined as "appropriation" process according to Poole and DeSanctis (1994). They suggested that this process is not automatically determined by the technology design itself. Rather, people actively select how technology structures should be used; accordingly, adoption practices vary. But concepts of the appropriation process in these studies are not accurate while suggested propositions are not clear enough to apply in practice. Furthermore, these studies do not substantially suggest which factors are changed during the appropriation process and what should be done to bring about effective outcomes. Therefore, research objectives of this study lie in finding causes for the difference in ways in which advanced information technology has been used and adopted among organizations. The study also aims to explore how a firm's interaction with social as well as technological factors affects differently in resulting organizational changes. Detail objectives of this study are as follows. First, this paper primarily focuses on the appropriation process of advanced information technology in the long run, and we look into reasons for the diverse types of the usage. Second, this study is to categorize each phases in the appropriation process and make clear what changes occur and how they are evolved during each phase. Third, this study is to suggest the guidelines to determine which strategies are needed in an individual, group and organizational level. For this, a substantially grounded theory that can be applied to organizational practice has been developed from a longitudinal comparative case study. For these objectives, the technology appropriation process was explored based on Structuration Theory by Giddens (1984), Orlikoski and Robey (1991) and Adaptive Structuration Theory by Poole and DeSanctis (1994), which are examples of social technology views on organizational change by technology. Data have been obtained from interviews, observations of medical treatment task, and questionnaires administered to group members who use the technology. Data coding was executed in three steps following the grounded theory approach. First of all, concepts and categories were developed from interviews and observation data in open coding. Next, in axial coding, we related categories to subcategorize along the lines of their properties and dimensions through the paradigm model. Finally, the grounded theory about the appropriation process was developed through the conditional/consequential matrix in selective coding. In this study eight hypotheses about the adaptation process have been clearly articulated. Also, we found that the appropriation process involves through three phases, namely, "direct appropriation," "cooperate with related structures," and "interpret and make judgments." The higher phases of appropriation move, the more users represent various types of instrumental use and attitude. Moreover, the previous structures like "knowledge and experience," "belief that other members know and accept the use of technology," "horizontal communication," and "embodiment of opinion collection process" are evolved to higher degrees in their dimensions of property. Furthermore, users continuously create new spirits and structures, while removing some of the previous ones at the same time. Thus, from longitudinal view, faithful and unfaithful appropriation methods appear recursively, but gradually faithful appropriation takes over the other. In other words, the concept of spirits and structures has been changed in the adaptation process over time for the purpose of alignment between the task and other structures. These findings call for a revised or extended model of structural adaptation in IS (Information Systems) literature now that the vague adaptation process in previous studies has been clarified through the in-depth qualitative study, identifying each phrase with accuracy. In addition, based on these results some guidelines can be set up to help determine which strategies are needed in an individual, group, and organizational level for the purpose of effective technology appropriation. In practice, managers can focus on the changes of spirits and elevation of the structural dimension to achieve effective technology use.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.153-168
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• 2019

Augmented Reality(AR) has been one of the important technologies of the 4th industrial revolution. Consumer acceptance of new technologies is substantial issue for market expansion, but there have been few empirical studies on factors that affect the acceptance or use intention of AR. In this study, we have explored and analyzed the factors influencing technology acceptance based on the extended unified theory of acceptance and use of technology(UTAUT2) model in the AR business and have discussed it with comparison with existing research based on this analysis. The results of this study suggest that the main variables of the existing UTAUT1 model had significant positive effect on the intention to use, such as performance expectancy, effort expectancy, facilitating conditions and hedonic motivation, habits of UTAUT2. In addition, perceived risk introduced in this study had a negative effect on intention to use. Furthermore, the impact between these two factors have been effort expectancy(${\beta}=.294$)>habits(${\beta}=.268$)>hedonic motivation(${\beta}=.266$)>performance expectancy,(${\beta}=.263$)>facilitating conditions(${\beta}=.233$)>perceived risk(${\beta}=-.094$). The impact of social influence did not have a significant effect on intention to use. The intention to use was analyzed to have a significant positive effect on the actual use and recommendation intention. On the other hand, the hypothesis that the age and gender has played a moderating role between independent variables and the intention of use were investigated. Age was found out to play a role as a moderator between social influence, facilitating conditions, hedonic motivation, habits and intention to use. In the same way, gender has been shown to play a moderating role between facilitating conditions, perceived risk and intention to use. Academic and practical implications are suggested based on the results of this study.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.1-12
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• 2014

With continued economic growth, Korea has seen an increase in the nighttime activities of its citizens as hours of activity have extended into night. There is an increasing trend in energy consumption related to citizens' nighttime activities. In order to analyze ideas for an efficient replacement of the power consumption of streetlights and for profit generation by applying grid-type solar systems, this study used an RETScreen model. Through energy analysis and cost analysis, the application benefit and viability of grid-type solar street light systems were analyzed. With analysis result of a total weekly power generation of 114 kWh via a grid-connected solar streetlight system, it was shown that the net present value of a grid-connected solar street light system is 155,362 KRW, which would mean a payback period of about 5.2 years, and as such, it was shown that profit could be generated after about 6 years. In addition, if the grid-connected solar power generation system proposed by this study is to be applied, it was shown that 401,935 KRW in profit could be generated after the 20-year useful life set for the solar system. In addition, the sensitivity analysis was performed taking into account the price fluctuations of SMP, maintenance. As a result, a payback period has increased by 1~2 years, and there were no significant differences. Because the most important factor that affect the economic analysis is the cost of supply certification of renewable energy, a stable sales and acquisition of this certification are very important. the Seoul-type Feed in Tariff(FIT) connected to other institutions will enable steady sales by confirming to purchase the certification for 12 years. Therefore, if those issues mentioned above are properly reflected, Central-grid PV system project will be able to perform well in the face of unfavorable condition of solar PV installation.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.147-187
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• 2008

Online game researchers focus on the flow and factors influencing flow. Flow is conceptualized as an optimal experience state and useful explaining game experience in online. Many game studies focused on the customer loyalty and flow in playing online game, In showing specific game experience, however, it doesn't examine multidimensional experience process. Flow is not construct which show absorbing process, but construct which show absorbing result. Hence, Flow is not adequate to examine multidimensional experience of games. Online game is included in hedonic consumption. Hedonic consumption is a relatively new field of study in consumer research and it explores the consumption experience as a experiential view(Hirschman and Holbrook 1982). Hedonic consumption explores the consumption experience not as an information processing event but from a phenomenological of experiential view, which is a primarily subjective state. It includes various playful leisure activities, sensory pleasures, daydreams, esthetic enjoyment, and emotional responses. In online game experience, therefore, it is right to access through a experiential view of hedonic consumption. The objective of this paper was to make up for lacks in our understanding of online game experience by developing a framework for better insight into the hedonic experience of online game. We developed this framework by integrating and extending existing research in marketing, online game and hedonic responses. We then discussed several expectations for this framework. We concluded by discussing the results of this study, providing general recommendation and directions for future research. In hedonic response research, Lacher's research(1994)and Jongho lee and Yunhee Jung' research (2005;2006) has served as a fundamental starting point of our research. A common element in this extended research is the repeated identification of the four hedonic responses: sensory response, imaginal response, emotional response, analytic response. The validity of these four constructs finds in research of music(Lacher 1994) and movie(Jongho lee and Yunhee Jung' research 2005;2006). But, previous research on hedonic response didn't show that constructs of hedonic response have cause-effect relation. Also, although hedonic response enable to different by stimulus properties. effects of stimulus properties is not showed. To fill this gap, while largely based on Lacher(1994)' research and Jongho Lee and Yunhee Jung(2005, 2006)' research, we made several important adaptation with the primary goal of bringing the model into online game and compensating lacks of previous research. We maintained the same construct proposed by Lacher et al.(1994), with four constructs of hedonic response:sensory response, imaginal response, emotional response, analytical response. In this study, the sensory response is typified by some physical movement(Yingling 1962), the imaginal response is typified by images, memories, or situations that game evokes(Myers 1914), and the emotional response represents the feelings one experiences when playing game, such as pleasure, arousal, dominance, finally, the analytical response is that game player engaged in cognition seeking while playing game(Myers 1912). However, this paper has several important differences. We attempted to suggest multi-dimensional experience process in online game and cause-effect relation among hedonic responses. Also, We investigated moderate effects of perceived complexity. Previous studies about hedonic responses didn't show influences of stimulus properties. According to Berlyne's theory(1960, 1974) of aesthetic response, perceived complexity is a important construct because it effects pleasure. Pleasure in response to an object will increase with increased complexity, to an optimal level. After that, with increased complexity, pleasure begins with a linearly increasing line for complexity. Therefore, We expected this perceived complexity will influence hedonic response in game experience. We discussed the rationale for these suggested changes, the assumptions of the resulting framework, and developed some expectations based on its application in Online game context. In the first stage of methodology, questions were developed to measure the constructs. We constructed a survey measuring our theoretical constructs based on a combination of sources, including Yingling(1962), Hargreaves(1962), Lacher (1994), Jongho Lee and Yunhee Jung(2005, 2006), Mehrabian and Russell(1974), Pucely et al(1987). Based on comments received in the pretest, we made several revisions to arrive at our final survey. We investigated the proposed framework through a convenience sample, where participation in a self-report survey was solicited from various respondents having different knowledges. All respondents participated to different degrees, in these habitually practiced activities and received no compensation for their participation. Questionnaires were distributed to graduates and we used 381 completed questionnaires to analysis. The sample consisted of more men(n=225) than women(n=156). In measure, the study used multi-item scales based previous study. We analyze the data using structural equation modeling(LISREL-VIII; Joreskog and Sorbom 1993). First, we used the entire sample(n=381) to refine the measures and test their convergent and discriminant validity. The evidence from both the factor analysis and the analysis of reliability provides support that the scales exhibit internal consistency and construct validity. Second, we test the hypothesized structural model. And, we divided the sample into two different complexity group and analyze the hypothesized structural model of each group. The analysis suggest that hedonic response plays different roles from hypothesized in our study. The results indicate that hedonic response-sensory response, imaginal response, emotional response, analytical response- are related positively to respondents' level of game satisfaction. And game satisfaction is related to higher levels of game loyalty. Additionally, we found that perceived complexity is important to online game experience. Our results suggest that importance of each hedonic response different by perceived game complexity. Understanding the role of perceived complexity in hedonic response enables to have a better understanding of underlying mechanisms at game experience. If game has high complexity, analytical response become important response. So game producers or marketers have to consider more cognitive stimulus. Controversy, if game has low complexity, sensorial response respectively become important. Finally, we discussed several limitations of our study and suggested directions for future research. we concluded with a discussion of managerial implications. Our study provides managers with a basis for game strategies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of Astronomy and Space Sciences
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• v.12 no.2
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• pp.216-233
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• 1995

The distributions of the ionospheric conductivities, electric potential, ionospheric currents, field-aligned currents, Joule heating rate, and particle energy input rate by auroral electrons along with the characteristics of auroral particle spectrum are examined during moderately disturbed period by using the computer code developed by Kamide et al. (1981) and the ionospheric conductivity model developed by Ahn et al. (1995). Since the ground magnetic disturbance data are obtained from a single meridian chain of magnetometers (Alaska meridian chain) for an extended period of time (March 9 - April 27, 1978), they are expected to present the average picture of the electrodynamics over the entire polar ionosphere. A number of global features noted in this study are as follows: (1) The electric potential distribution is characterized by the so-called two cell convection pattern with the positive potential cell in the morning sector extending into the evening sector. (2) The auroral electrojet system is well developed during this time period with the signatures of DP-1 and DP-2 current systems being clearly discernable. It is also noted that the electric field seems to play a more important role than the ionospheric conductivity the conductivity over the poleward half of the westward electrojet in the morning sector while the conductivity enhancement seems to be more important over its equatorward half. (3) The global field-aligned current distribution pattern is quite comparable with the statistical result obtained by Iijima and Potemra (1976). However, the current density of Region 1 is much higher than that of Region 2 current at pointed out by pervious studies (e.g.; Kamide 1988). (4) The Joule heating occurs over a couple of island-like areas, one along the poleward side of the westward electrojet region in the afternoon sector. (5) The maximum average energy of precipitating electrons is found to be in the morning sector (07∼08 MLT) while the maximum energy flux is registered in the postmidnight sector (02 MLT). Thus auroral brightening and enhancement of ionospheric conductivity during disturbed period seem to be more closely associated with enhancement of particle flux rather than hardening of particle energy.