• Title/Summary/Keyword: hypothesis-generation

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Work & Life Balance and Conflict among Employees : Work-life Balance Effect that Reflects Work Characteristics (일·생활 균형과 구성원간 갈등관계 : 직장 내 업무 특성을 반영한 WLB 효과 중심으로)

  • Lee, Yang-pyo;Choi, Chang-bum
    • Journal of Venture Innovation
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    • v.7 no.1
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    • pp.183-200
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    • 2024
  • Recently, with the MZ generation's entry into society and the social participation of the female population, conflicts are occurring between workplace groups that value WLB and existing groups that emphasize collaboration due to differences in work orientation. Public institutions and companies that utilize work-life balance support systems show differences in job Commitment depending on the nature of the work and the activation of the support system. Accordingly, it is necessary to verify the effectiveness of the WLB support system actually operated by the company and present universally valid standards. The purpose of this study is, first, to verify the effectiveness of the support system for work-life balance and to find practical consensus amid changes in policies and perceptions of the working environment. Second, the influence of work-life balance level and job immersion according to work characteristics was analyzed to verify the mutual influence in order to establish standards for WLB operation that reflects work characteristics. For the study, a 2X2 matrix model was used to analyze the impact of work-life balance and work characteristics on job commitment, and four hypotheses were established. First, analysis of the job involvement level of conflict-type group members, second, analysis of the job involvement level of leading group members, third, analysis of the job involvement level of agreeable group members, and fourth, analysis of the job involvement level of cooperative group members. To conduct this study, an online survey was conducted targeting employees working in public institutions and large corporations. The survey was conducted for a total of 9 days from October 23 to 31, 2023, and 163 people responded, and the analysis was based on a valid sample of 152 people, excluding 11 copies that were insincere responses or gave up midway. As a result of the study's hypothesis testing, first, the conflict type group was found to have the lowest level of job engagement at 1.43. Second, the proactive group showed the highest level of job engagement at 4.54. Third, the conformity group showed a slightly lower level of job involvement at 2.58. Fourth, the cooperative group showed a slightly higher level of job involvement at 3.80. The academic implications of the study are that it subdivides employees' personalities into factors based on the level of work-life balance and nature of work. The practical implications of the study are that it analyzes the effectiveness of WLB support systems operated by public institutions and large corporations by grouping them.

Application of a Single-pulsatile Extracorporeal Life Support System for Extracorporeal Membrane Oxygenation -An experimental study - (단일 박동형 생명구조장치의 인공폐 적용 -실험연구-)

  • Kim, Tae-Sik;Sun, Kyung;Lee, Kyu-Baek;Park, Sung-Young;Hwang, Jae-Joon;Son, Ho-Sung;Kim, Kwang-Taik;Kim. Hyoung-Mook
    • Journal of Chest Surgery
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    • v.37 no.3
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    • pp.201-209
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    • 2004
  • Extracorporeal life support (ECLS) system is a device for respiratory and/or heart failure treatment, and there have been many trials for development and clinical application in the world. Currently, a non-pulsatile blood pump is a standard for ECLS system. Although a pulsatile blood pump is advantageous in physiologic aspects, high pressure generated in the circuits and resultant blood cell trauma remain major concerns which make one reluctant to use a pulsatile blood pump in artificial lung circuits containing a membrane oxygenator. The study was designed to evaluate the hypothesis that placement of a pressure-relieving compliance chamber between a pulsatile pump and a membrane oxygenator might reduce the above mentioned side effects while providing physiologic pulsatile blood flow. The study was performed in a canine model of oleic acid induced acute lung injury (N=16). The animals were divided into three groups according to the type of pump used and the presence of the compliance chamber, In group 1, a non-pulsatile centrifugal pump was used as a control (n=6). In group 2 (n=4), a single-pulsatile pump was used. In group 3 (n=6), a single-pulsatile pump equipped with a compliance chamber was used. The experimental model was a partial bypass between the right atrium and the aorta at a pump flow of 1.8∼2 L/min for 2 hours. The observed parameters were focused on hemodynamic changes, intra-circuit pressure, laboratory studies for blood profile, and the effect on blood cell trauma. In hemodynamics, the pulsatile group II & III generated higher arterial pulse pressure (47$\pm$ 10 and 41 $\pm$ 9 mmHg) than the nonpulsatile group 1 (17 $\pm$ 7 mmHg, p<0.001). The intra-circuit pressure at membrane oxygenator were 222 $\pm$ 8 mmHg in group 1, 739 $\pm$ 35 mmHg in group 2, and 470 $\pm$ 17 mmHg in group 3 (p<0.001). At 2 hour bypass, arterial oxygen partial pressures were significantly higher in the pulsatile group 2 & 3 than in the non-pulsatile group 1 (77 $\pm$ 41 mmHg in group 1, 96 $\pm$ 48 mmHg in group 2, and 97 $\pm$ 25 mmHg in group 3: p<0.05). The levels of plasma free hemoglobin which was an indicator of blood cell trauma were lowest in group 1, highest in group 2, and significantly decreased in group 3 (55.7 $\pm$ 43.3, 162.8 $\pm$ 113.6, 82.5 $\pm$ 25.1 mg%, respectively; p<0.05). Other laboratory findings for blood profile were not different. The above results imply that the pulsatile blood pump is beneficial in oxygenation while deleterious in the aspects to high pressure generation in the circuits and blood cell trauma. However, when a pressure-relieving compliance chamber is applied between the pulsatile pump and a membrane oxygenator, it can significantly reduce the high circuit pressure and result in low blood cell trauma.

Electronic Word-of-Mouth in B2C Virtual Communities: An Empirical Study from CTrip.com (B2C허의사구중적전자구비(B2C虚拟社区中的电子口碑): 관우휴정려유망적실증연구(关于携程旅游网的实证研究))

  • Li, Guoxin;Elliot, Statia;Choi, Chris
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.3
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    • pp.262-268
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    • 2010
  • Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, "stickiness," and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are threefold: (i) to empirically test a B2CVC model that integrates measures of beliefs, attitudes, and behaviors; (ii) to better understand the nature of these relationships, specifically through word-of-mouth as a measure of revenue generation; and (iii) to better understand the role of stickiness of B2CVC in CRM marketing. The model incorporates three key elements concerning community members: (i) their beliefs, measured in terms of their involvement assessment; (ii) their attitudes, measured in terms of their satisfaction and trust; and, (iii) their behavior, measured in terms of site stickiness and their word-of-mouth. Involvement is considered the motivation for consumers to participate in a virtual community. For B2CVC members, information searching and posting have been proposed as the main purpose for their involvement. Satisfaction has been reviewed as an important indicator of a member's overall community evaluation, and conceptualized by different levels of member interactions with their VC. The formation and expansion of a VC depends on the willingness of members to share information and services. Researchers have found that trust is a core component facilitating the anonymous interaction in VCs and e-commerce, and therefore trust-building in VCs has been a common research topic. It is clear that the success of a B2CVC depends on the stickiness of its members to enhance purchasing potential. Opinions communicated and information exchanged between members may represent a type of written word-of-mouth. Therefore, word-of-mouth is one of the primary factors driving the diffusion of B2CVCs across the Internet. Figure 1 presents the research model and hypotheses. The model was tested through the implementation of an online survey of CTrip Travel VC members. A total of 243 collected questionnaires was reduced to 204 usable questionnaires through an empirical process of data cleaning. The study's hypotheses examined the extent to which involvement, satisfaction, and trust influence B2CVC stickiness and members' word-of-mouth. Structural Equation Modeling tested the hypotheses in the analysis, and the structural model fit indices were within accepted thresholds: ${\chi}^2^$/df was 2.76, NFI was .904, IFI was .931, CFI was .930, and RMSEA was .017. Results indicated that involvement has a significant influence on satisfaction (p<0.001, ${\beta}$=0.809). The proportion of variance in satisfaction explained by members' involvement was over half (adjusted $R^2$=0.654), reflecting a strong association. The effect of involvement on trust was also statistically significant (p<0.001, ${\beta}$=0.751), with 57 percent of the variance in trust explained by involvement (adjusted $R^2$=0.563). When the construct "stickiness" was treated as a dependent variable, the proportion of variance explained by the variables of trust and satisfaction was relatively low (adjusted $R^2$=0.331). Satisfaction did have a significant influence on stickiness, with ${\beta}$=0.514. However, unexpectedly, the influence of trust was not even significant (p=0.231, t=1.197), rejecting that proposed hypothesis. The importance of stickiness in the model was more significant because of its effect on e-WOM with ${\beta}$=0.920 (p<0.001). Here, the measures of Stickiness explain over eighty of the variance in e-WOM (Adjusted $R^2$=0.846). Overall, the results of the study supported the hypothesized relationships between members' involvement in a B2CVC and their satisfaction with and trust of it. However, trust, as a traditional measure in behavioral models, has no significant influence on stickiness in the B2CVC environment. This study contributes to the growing body of literature on B2CVCs, specifically addressing gaps in the academic research by integrating measures of beliefs, attitudes, and behaviors in one model. The results provide additional insights to behavioral factors in a B2CVC environment, helping to sort out relationships between traditional measures and relatively new measures. For practitioners, the identification of factors, such as member involvement, that strongly influence B2CVC member satisfaction can help focus technological resources in key areas. Global e-marketers can develop marketing strategies directly targeting B2CVC members. In the global tourism business, they can target Chinese members of a B2CVC by providing special discounts for active community members or developing early adopter programs to encourage stickiness in the community. Future studies are called for, and more sophisticated modeling, to expand the measurement of B2CVC member behavior and to conduct experiments across industries, communities, and cultures.

Development of a complex failure prediction system using Hierarchical Attention Network (Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발)

  • Park, Youngchan;An, Sangjun;Kim, Mintae;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.127-148
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    • 2020
  • The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.