Earticle

인간은 언어를 가진 존재로서 고대 그리스에서부터 좋은 것과 나쁜 것, 선과 악, 종교와 비종교에 대한 논의가 가능한 존재이다. 독일 교육 철학자 볼르노는 다양한 학문들과의 대화에서 진리에 접근하기 위한 방법으로서 인간의 선과 악을 종교과학적인 측면에서 새롭게 접근을 시도하였다. 그 는 학문적인 대화에서 개별자가 자기 자신에 대하여 독백적으로만 사고하며, 타인에 대해서 관심을 갖지 않는 곳에서는 학문은 단절된다고 주장한다. 그렇기 때문에 인간 은 파트너간의 대화와 논쟁가운데 다양한 물음과 인식의 성장을 요한다. 이러한 관점 에서 그는 인간이성이 왜 교육과 종교를 필요로 하며 또한 다양한 삶의 형태가 본질 적으로 종교적인 요소를 가지고 있는가 하는 물음들을 제시한다. 볼르노의 철학적 해석학이 특수하게 종교과학에 대한 중요성의 증거는 진리로서 두 가지 근거에 환원되어 지고 있다. 첫째, 볼르노는 종교적인 문제에 대하여 종교과학적인 방법론적 논의를 삶의 철학 적 가치의 중요성과 함께 표명하였다. 둘째, 종교과학은 과거의 압도적인 신학으로부터 해방되어 나오는 과정에서 점점 더 강하게 과학주의적 방법적인 선 판단들에 적용되었다. 종교과학에 대한 볼르노의 논의는 매우 ‘엄밀한’매개 작업과 해석 작업이 중요하 게 부각되어 진다. 이는 종교 과학적 문제 설정들이 철학적인 해석학간의 숨어 있는 관계들을 객관적인 방법이라고 추구하는 ‘자명성’등을 해체한 후에야 비로소 인식되어 질 수 있다. 그의 종교철학은 인간이 가지고 있는 자연과학적인 변화의 근거에 입각하여 측량 할 수 없는 것에 대한 신뢰이다. 이러한 측면에서 인간에 대한 종교를 해석학적인 관 점과 과학적인 관점으로 탐구한 그의 연구는 이 시대에 가치가 있다고 생각되어진다. 따라서 종교에 대한 그의 고찰방법은 현 시대 기독교교육에 대한 이해지평을 우리에 게 새롭게 던져주고 있다.

Bollow, German educational philosopher, tried new approach of human good and evil in religious scientific aspect as the method for approach the truth through various academic conversation. In academic conversation, he asserts that the study is broken if the individual thinks only himself his own self and isn’t interested in other people. So, human needs the growth of various questions and realizations through conversations and disputes with other people. From this viewpoint, he suggests some questions; why does human reason need education and religion? and does various forms of life have the religious elements essentially? Human is the being to communicate with language about the good one and bad one, good and evil, religion and non-religious since ancient Greece. Bollow’s philosophical analytics is specially important in religion-science on the basis of two aspects. First, Bollow discussed the religion-scientific methodology in religious matters as well as the importance of philosophical value in life. Second, religious science applied to good judgment of scientific method increasingly stronger in the process of being free from the past overwhelming theology. In the process of proving Bollow’s usefulness of religious science, the precise mediating and analytical works emerged significantly. This can be realized after dissolving obviousness which pursue hiding relations between region-scientific problem set-up and Bollow’s philosophical analytics as objective method. His religious philosophy is the trust of illimitableness based on human natural scientific change. His study which searches for religion about human from analytical and scientific view is worth in this time. So, his considering method about religion renews the understanding surface of Christian education in the present age.

본 연구는 국내 HR Analytics 연구에서 활용한 데이터와 분석방법을 탐색하여 향후 연구를 위한 기초자료를 제공하고 HR Analytics 연구 현황을 밝히는 것을 목적으로 한다. 이를 위하여 체계적 문헌고찰 방법을 활용하여 국내 KCI 등재 학술지에 수록된 실증연구 논문 78편을 선정하였고 해당 논문을 근로자 생애주기에 따라 분류하여 검토하였다. 문헌고찰 결과 다음과 같은 결과를 얻을 수 있었다. 첫째, 근로자 생애주기에 따른 HR Analytics 연구 동향을 살펴본 결과, 선행연구에서는 구성원의 유지(retention)와 관련한 연구가 가장 많았고 성과 관리에 대한 연구가 그 뒤를 이었다. 둘째, HR Analytics 연구에서 사용한 데이터를 살펴본 결과 각 연구는 해당 연구문제에 따라 다양한 데이터(정형, 비정형)를 활용하고 있었으며 데이터 출처 또한 조직내부 시스템부터 국가 통계 DB까지 매우 다양한 것으로 확인하였다. 셋째, 문헌고찰 결과 국내 HR Analytics 연구는 기술적, 진단적 분석이 가장 많으며, 예측 및 처방과 관련한 연구는 미미한 수준임을 알 수 있었다.

The purpose of this study was to explore the various data and methods employed by HR analytics studies. The researchers selected 78 KCI-indexed empirical articles on HR analytics and categorized them using the Employee Life Cycle framework. This yielded several important findings. First, employee retention has been the most common subject of extant studies, followed by performance management. Second, HR analytics studies have used a variety of data (structured and unstructured) according to their research questions, and the data sources have ranged from organizations' internal systems to national databases. Third, most domestic HR analytics studies have been descriptive and diagnostic, whereas predictive and prescriptive studies have been rare. These results have important theoretical and practical implications for future HR analytics research.

The logistics industry's digital transformation has had a great impact and brought the usage of data-driven predictive analytics for demand forecasting and inventory management almost everywhere. In this paper, a multi-model prediction framework using random forest, XGBoost, and LSTM is proposed. The forecasting process relies on previous demand and stock data together with some contextual characteristics. The research made use of publicly available data, where the demand patterns and inventory movements were analyzed in a very systematic way, and machine learning models were also applied in the area of demand forecasting and inventory management. The results of the experiment indicate that the LSTM model is very prone to dealing with the time-series properties and capturing the long-term dependencies, thereby reaching the demand forecasting accuracy of 95% and having an inventory forecasting error of only 5%. Additionally, as a result of the continuous adjustments to safety stock and order quantity, shortages were reduced by 50% and overstock by 30%, which made a great contribution to logistics efficiency and economic gains. This study not only lays down a theoretical base for Digital Logistics, but it also provides the logistics companies with practical help in optimizing their supply chain operation strategies. Besides, one can expect real-time streaming prediction and multimodal data fusion for managing more complicated logistics scenarios.

Big data is the compilation of enormous data that arrives from diverse sources for instance online transaction details, social media, sensor data, etc. Such assortment of enormous data develop into tough to evaluate by conventional processing relevance’s. By the development and upcoming latent in the healthcare business field, it is essential to analyze a enormous noisy data to get significant information. In healthcare system, the aim of this work is to evaluate the medical database of diabetes patients by a mixture of innovative hierarchical decision attention network, association rules (AR) and multiclass outlier classification with MapReduce framework. The association rule apriori algorithm in a MapReduce framework considers health data to create regulations. This is employed to discover the association among disease and their signs. This examination is made by means of UCI machine learning datasets of diabetes containing 50 attributes. The results of the proposed algorithm are offered by parameters for instance precision, accuracy, recall, and F-score.

.

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

최근 산업기밀보호센터의 통계에 의하면 국내 기밀유출 사고의 경우 전·현직 직원에 의해 기업기밀유출의 약 80%를 차지하고 이러한 내부자에 의한 정보유출 사고의 대다수가 허술한 보안 관리체계와 정보유출 탐지기술의 이유로 발생하고 있다. 내부자의 기밀유출을 차단하는 업무는 기업보안 부문에서 매우 중요한 문제이지만 기존의 많은 연구들은 내부자에 의한 유출위협보다는 외부 위협에 의한 침입에 대응하는데 초점이 맞추어져 있다. 따라서 본 논문에서는 기업 내에서 발생하는 다양한 비정상 행위를 효과적이고 효율적으로 탐지하기 위해 내부정보 유출 시나리오를 설계하고 시나리오에서 도출 된 유출 징후의 핵심 위험지표를 데이터 분석(Data analytics)함 으로써 정교하지만 신속하게 유출행위를 탐지하는 모형을 제시하고자 한다.

According to the recent statistics of the National Industrial Security Center, about 80% of the confidential leak are caused by former and current employees in the case of domestic confidential leak accidents. Most of the information leak incidents by these insiders are due to poor security management system and information leak detection technology. Blocking confidential leak of insiders is a very important issue in the corporate security sector, but many previous researches have focused on responding to intrusions by external threats rather than by insider threats. Therefore, in this research, we design an internal information leak scenario to effectively and efficiently detect various abnormalities occurring in the enterprise, analyze the key indicators of the leak symptoms derived from the scenarios by using data analytics and propose a model that accurately detects leak activities.

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability.

Among various fields at home and abroad, machine learning technology is developing rapidly, centering on deep learning. Active research is being conducted. However, due to the lack of sufficient machine learning data, opportunities such as providing high value-added content and improving service performance are missed. New entity names are registered in various documents and interpreted as different entity names depending on the context. Through prescriptive analysis-based key entity name recognition technology that collects researcher's research field, research propensity, and research activity information and converts the value of data into new phrase-unit information into word-unit qualities by constructing and utilizing general-purpose data, we provide a research information curation service based on prescriptive analysis to solve difficulties of securing learning data for researchers who want to apply machine learning and for obtaining customized research information that corresponds to individualized services for each researcher.

Standardization efforts have led to the emergence of conceptual models in the insurance industry. Simultaneously, the proliferation of digital information poses new challenges for the efficient management and analysis of available data. Based on the property and casualty data model, we propose an OWL ontology to represent insurance processes and to map large data volumes collected in traditional data stores. By the virtue of reasoning, we demonstrate a set of semantic queries using the ontology vocabulary that can simplify analytics and deduce implicit facts from these data. We compare this mapping approach to data in native RDF format, as in a triple store. As proof-of-concept, we use a large anonymized dataset for car policies from an actual insurance company.

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

본 논문은 평균 이상의 조직몰입과 가장 연관이 있는 인사제도를 순서 데로 나타내 보임으로써, 방대한 양의 인사관련 데이터의 통계적 분석을 통해 어떻게 조직이 귀납적으로 조직성과 향상을 위한 유의미한 정보를 추출할 수 있는지에 대한 하나의 예시를 제시한다. 한국의 제조업 분야 특정 기업 1,451명의 종업원을 대상으로 한 서베이 데이터를 R을 이용해 트리분석 (tree analysis)을 한 결과, 종업원이 회사의 성과관리 제도에 대한 만족도가 일정 수준 (5점 만점에 3.08)을 넘어서거나, 그렇지 않으면 그 종업원이 보상제도에 대한 만족도가 일정 수 준 (5점 만점에 2.83)을 넘어서야 그 종업원은 평균 이상의 조직몰입을 가질 것으로 예측할 수 있었다. 이러한 결과는 최소한 이 회사는 조직 몰입 향상을 위해 첫 번째 우선순위로 종업원의 성과관리에 대한 만족도 수준을 관리해야 하고, 이와 더불어 종업원의 보상제도에 대한 만 족도 수준을 짝으로 같이 관리할 때 효과적으로 조직몰입 향상을 이끌어 낼 수 있음을 제시한다. 본 연구가 학문적, 실무적으로 유의미하고 다양한 인적자원 Analytics에 대한 연구 활성화에 기여하기를 기대해 본다.

Which HR practices are more important in predicting employee commitment and how might they best be identified? Using classification tree analysis, a data mining technique, we find the priorities between practices the cut-off scores of each. The most important variable to predict above average-commitment employees is “performance management” and the next is “compensation.” Thus, performance management and compensation is a best fit to predict above average-commitment employees. The proposed approach contributes to the HR analytics and SHRM literature by introducing the notion of hierarchy of HR practices in eliciting higher commitment. Our hope is that the model presented in this paper provides a starting place for research and practice examining the role of data mining in HR, and toward the goal of developing effective organizational mechanisms for improving employee commitment.

본 연구는 인공지능(AI) 기반의 학습 플랫폼 도입 등 급변하는 교육 환경 속에서 초등 교사의 학습분석역량 (Learning Analytics Competency, LAC)을 제고하기 위한 교사 연수의 질적 결정요인을 탐색하는 데 목적이 있 다. 이를 위해 전국의 초등 교사 156명을 대상으로 설문 조사를 실시하였으며, 수집된 데이터는 위계적 회귀분석 을 통해 분석하였다. 연구 결과, 첫째, 연수 이수 시간과 같은 양적 요인은 LAC에 제한적인 영향만을 미쳤으며, 다른 배경 변인이 통제될 경우 그 유의성이 사라졌다. 둘째, 연수의 질적 인식 요인인 '현장 적용성'과 교사의 연 수 후 '자기효능감'은 LAC의 가장 강력한 예측 변인으로 나타났다. 셋째, LAC의 하위 영역별로 영향을 미치는 요 인이 상이함을 확인하였다. 본 연구는 교사 연수의 효과성을 높이기 위해서는 물리적인 연수시간의 확대보다, 교 사가 현장에서 즉시 적용 가능한 실효성, 적용성, 유용성, 효과성이 높은 콘텐츠 제공과 효능감 증진을 위한 정의 적 지원이 필수적임을 시사한다.

This study aims to explore the quality determinants of teacher professional development(PD) designed to enhance elementary teachers' Learning Analytics Competency(LAC) in the context of the rapidly changing educational environment. To achieve this, a survey was conducted with 156 elementary school teachers nationwide, and the collected data were analyzed using hierarchical regression analysis. The results revealed that qualitative factors - specifically 'applicability' and 'self-efficacy' - are significantly stronger predictors of LAC than quantitative factors like training hours. The findings also confirmed that the factors influencing LAC varied across its sub-domains (Interpretation, Implementation, Communication, and Pedagogy). These results suggest that, to effectively enhance teachers' learning analytics competency, PD policies must prioritize practical relevance and affective support to foster self-efficacy rather than merely expanding physical training hours.

광고산업에서 빅데이터와 데이터 어낼리틱스가 중요한 이슈로 부상하면서 광고회사는 상당한 변화에 직면하고 있다. 본 연구는 국내·외 광고 전문가들과의 인터뷰를 통해 현재 빅데이터를 둘러싼 광고업계의 인식과 이슈를 고찰하고 향 후 광고회사의 빅데이터 운영과 분석 역량 강화를 위한 실질적이고 전문적인 제언을 도출하였다. 빅 데이터와 데이터 어낼리틱스에 대한 중요성과 역량 강화의 필요성에 대해서는 충분히 인식하고 있 지만, 데이터 어낼리틱스의 근간이 되는 데이터 확보와 관리에 대한 구체적인 전략은 아직까지 미 흡한 것으로 보인다. 또한 빅데이터의 중요성이 강조될수록 필요한 데이터나 분석역량, 운영 노하우 등이 광고회사 내부 조직 간에 잘 공유되지 않는 사일로 현상이 빈번하다는 것 또한 문제로 지적된 다. 데이터 어낼리틱스의 시장성도 주요 이슈로 제기되고 있다. 빅데이터는 막대한 비용적 리스크를 내포하고 있기 때문에 광고회사가 그에 대한 결정을 하기 전에 빅데이터나 데이터 어낼리틱스가 자 사에 어떤 효용을 제공할 수 있는지 충분히 검토해야 한다. 빅데이터를 다룰 수 있는 인력 확보의 어 려움도 광고회사가 당면한 문제로 인식된다. 향후 광고회사가 데이터 어낼리틱스 역량을 더욱 강화 하기 위해서는 우선적으로 추진하고자 하는 사업 전략을 충분히 고려하여 자사의 데이터 어낼리틱 스 서비스가 지향하는 방향성을 명확히 해야 한다는 것이 전문가의 의견이다. 만약 광고회사 내부 에 데이터 어낼리틱스를 담당하는 부서를 조직하기로 했다면 즉각적인 수익 부서가 아닌 투자 부서 로서 시작하는 것이 바람직할 수 있지만, 계속적으로 비용만 발생하는 조직으로만 머물러서는 성공 하기 어렵다고 전문가들은 인식하고 있다. 더불어, 데이터에 의한 의사결정이 조직문화로 자리 잡아 광고회사의 전체 경쟁력을 높이기 위해서는 조직 리더의 역할이 매우 중요한데, 데이터 어낼리틱스 가 광고회사 내부에 안정적으로 정착되어 역량이 충분히 성장할 때까지 인내하는 경영진의 인식이 뒷받침되어야 한다고 제언하고 있다. 마지막으로, 데이터 어낼리틱스 기술이나 솔루션 자체가 모든 문제를 해결할 수 있다는 생각을 경계해야 한다고 전문가들은 전하고 있다.

As big data and data analytics have become an important issue in ad land, advertising agencies are facing various environmental changes. By conducting in-depth interviews with advertising experts, this study examines the experts’ perceptions about big data used in advertising industry. Then, it suggests the practical and professional implications in order for advertising agencies to strengthen the capability of data analytics. Advertising experts recognize that big data and data analytics are not only the basic and fundamental competence but also the problem of survival for advertising agencies in the age of digital media. They say that the issues faced by advertising agencies are the awareness of the need for obtaining and managing data, data silos and integration challenges, market uncertainty of the field of data analytics, and the difficulties in securing top talents dealing with big data and technologies. Experts suggest that advertising agencies preemptively should come up with a blueprint for data analytics to gain a competitive advantage. They emphasize that developing the organization of data analytics into a profit center within an agency and unrelenting investment in both people and infrastructure with a long term perspective are should be considered. More importantly, advertising agencies have to beware of the omnipotence of the analysis tool. It should be realized that the value of data analytics will be maximized when connecting the data analytics solutions or skills and domain experiences and knowledge of advertising experts.

Increased use of hazardous materials increases overall number of safety accidents such as leaks, fires, and explosions. Also, it causes environmental damage such as water pollution and air pollution, exacerbating extensive and diverse damage. In this study, a wide range of surveys were conducted on the entire groups related to safety management of hazardous materials, including safety managers and carriers responsible for safety management at hazardous materials industrial sites, and safety management agents and tank performance test workers who are specialized of hazardous materials, and civil service personnel and inspectors at fire stations. Because the sample size was large for each group, a multi-faceted survey was analyzed using data analytics such as correlation analysis and regression. The results show the findings in the aspect of safety capabilities and institutional environment to be improved for the enhanced safety management of hazardous materials.

COVID-19로 인해 국내외 조직들은 전례없는 상황을 겪고 있으며 이에 대응하기 위한 리더십 연구 역시 부족한 실정이다. 본 연구는 COVID-19 전과 후의 리더십을 데이터로 규명하고자 했다. 이를 위해 한국 기업 조직에서 COVID-19 전과 후로 위기에 요구되는 리더십 행동 차이를 데이터 마이닝 방법을 통해서 규명했다. COVID-19가 팬데믹(Pandamic) 수준으로 선포 되기 이전에 구성원들은 리더들이 내재적/외재적 보상에 더욱 공을 들이고 수평적 조직문화를 만들어주기를 기대한 반면, COVID-19 시대에는 심리적 안전감, 잦은 소통, 미래에 대한 방향성 제시 등을 요구하고 있었다. 이와 같은 위기 상황에서 부각되는 리더십을 본 연구자들은 변혁적 리더십으로 간주하고 두 번째 연구를 설계하여 구성원들의 태도변수인 회복 탄력성과 관계를 실증했다. 회복 탄력성은 위기 상황에서 구성원들이 갖춰야 할 요소로 자주 언급된다. 변혁적 리더십이 COVID-19 시대에 도 회복 탄력성에 유의미하게 영향을 미치는지 살펴봤다. 분석 결과, 변혁적 리더십은 회복 탄력성과 정(+)의 관계를 가지고, 정서적 몰입은 변혁적 리더십과 회복 탄력성의 관계를 부분적으로 매개하였다. 본 연구는 COVID-19 이후로 요구되는 위기 리더십을 데이터로 규명했다는데 실무적으로 기여점을 가지고 있다. 더불어, 위기 상황에서 종종 강조되는 구성원 태도인 회복 탄력성과 관련성을 실증했다는 이론적인 기여점이 있다.

Many organizations are under crisis due to the pandemic of COVID-19, and they may need to understand different leadership during the crisis. This study consisted of two phases. In the first phase, leadership required after COVID-19 was studied by topical modeling which is one of data mining techniques. In the second phase, the primary purpose was to study the correlation between perceived leadership behaviors and personal performance through empirical analysis. Specifically, we analyzed the change in perceived leadership demanded by members of an organization by comparing before and after the declaration of COVID-19. Prior to the declaration, the members frequently mentioned leadership behaviors related to intrinsic and external rewards. After the declaration, on the other hand, words and phrases related to psychological safety, effective communication, and clear direction to the future were frequently observed. Based on the results of the first study, we determined the transformational leadership as the kind of leadership demanded during a crisis like COVID-19, and the second study was designed to test the relationship between perceived transformational leadership and the individual resilience among the members of the organization. In addition, we hypothesized that affective commitment mediates the relationship between the two variables, and the mediation was tested. As a result, we observed a positive relationship between the perceived transformational leadership and the individual resilience, and it was evident that affective commitment partially mediates the relationship. This research contributes to reveal the leadership behaviors demanded in the crisis of COVID-19 by the data mining technique which is important in HR practices. In addition, this research has a theoretical contribution by investigating the leadership style demanded during the time of a crisis and by relating to the individual attitudes. finally, limitations of this research and directions of future research are discussed.

오늘날 브랜드는 예전과 전혀 다른 상황에 놓여 있다. 대부분의 산업이 성숙기를 넘어 포화상태에 이르렀고, 개별 브랜드를 보증하는 기업 브랜드의 중요성이 날로 강조되고 있는 상황이다. 이에 따라 개별 브랜드를 진단함에 있어 브랜드 자산의 구성요소인 브랜드 인지도와 브랜드 이미지 관리에만 치중했던 기존의 브랜드 진단 모델에 대한 새로운 접근이 요구되고 있다. 본 연구는 관계마케팅 관점, 소비자-브랜드 관계 관점에서 브랜드 신뢰, 브랜드 감정, 브랜드 충성도 등을 핵심개념으로 새로운 브랜드 진단 모델의 개발을 시도하였다. 또한 본 연구는 기업 브랜드와 개별 브랜드 간의 시너지 관점에서 기업 브랜드가 개별 브랜드에 미치는 영향 관계를 포함하는 통합적 브랜드 진단 모델의 개발을 시도하였다. 이동통신, 주유소 등 5개 카테고리에 속한 기업 브랜드와 개별 브랜드를 대상으로 총 3,600명의 표본에 대한 설문조사를 실시하였고, 구조방정식 모형으로 새로운 통합적 브랜드 진단모델인 B-AID (Brand Analytics In Dynamics)를 개발하였다.

Many companies and its brands are now in an extraordinary situation. Almost industries have reached its saturation point and corporate brand which guarantees individual brand is becoming more important. However, previous brand diagnosis model have tried to identify only individual brands in terms of brand awareness and brand image management. Thus, taking a perspective relationship marketing whose main concern with consumer-brand relationships, We have attempted to develop a new brand diagnosis model that has brand trust, brand affect, and brand loyalty as key concepts in it. In addition, we have developed an integrated model of brand diagnosis model with which one can effectively analyze the way in which corporate brand can synergistically affect individual brand. We conducted survey data targeting 3,600 samples with individual brand and corporate brand belong to 5 categories. SK M&C has developed an integrated brand diagnosis model named B-AID(Brand Analytics In Dynamics) with using Structural Equation Methods.

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.