• 한국노년학
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• v.37 no.1
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• pp.173-200
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• 2017

This study is designed to verify the actual effect on the prevention of the elderly suicide of the integrated crisis intervention service which has been widely provided across all local communities in Gyeonggi-province focusing on the integrated crisis intervention model developed for the prevention of elderly suicide. The integrated crisis intervention model for the local communities and its manual were developed for the prevention of elderly suicide by integrating the crisis intervention theory which contains local community's integrated system approach and the stress vulnerability theory. For the analysis of the effect, the geriatric depression and suicidal ideation scale was adopted and the data was collected as follows; The data was collected from 258 people in the first preliminary test. Then, it was collected from the secondary test of 184 people after the integrated crisis intervention service was performed for 6 months. The third collection of data was made from 124 people after 2 or 3 years later using the backward tracing method. As for the analysis, the researcher used the R Statistics computing to conduct the test equating, and the vertical scaling between measuring points. Then, the researcher conducted descriptive statistics analysis and univariate analysis of variance, and performed multi-level modeling analysis using Bayesian estimation. As a result of the study, it was found out that the integrated crisis intervention model which has been developed for the elderly suicide prevention has a statistically significant effect on the reduction of elderly suicide in terms of elderly depression and suicide ideation in the follow-up measurement after the implementation of crisis intervention rather than in the first preliminary scores. The integrated crisis intervention model for the prevention of elderly suicide was found to be effective to the extent of 0.56 for the reduction of depression and 0.39 for the reduction of suicidal ideation. However, it was found out in the backward tracing test conducted 2-3 years after the first crisis intervention that the improved values returned to its original state, thus showing that the effect of the intervention is not maintained for long. Multilevel analysis was conducted to find out the factors such as the service type(professional counseling, medication, peer counseling), characteristics of the client (sex, age), the characteristics of the counselor(age, career, major) and the interaction between the characteristics of the counselor and intervention which affect depression and suicidal ideation. It was found that only medication can significantly reduce suicidal ideation and that if the counselor's major is counseling, it significantly further reduces suicidal ideation by interacting with professional counseling. Furthermore, as the characteristics of the suicide prevention experts are found to regulate the intervention effect on elderly suicide prevention in applying integrated crisis intervention model, the primary consideration should be given to the counseling ability of these experts.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.