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- E-ISSN2233-5382
- KCI
E-ISSN : 2233-5382
Vol.11 No.7
Abstract
Purpose: The purpose of this study was to identify factors that enhance the effectiveness of bulk text message advertising on consumer attention in the telecommunications industry in Zimbabwe. Research design, data and methodology: The study collected data using structured questionnaires. The study attracted 293 responses from consumer subscribers of the Zimbabwean telecommunications industry. Data was analysed using SPSS and measures of association, direction, strength and significance were used. Results: The study found out that the examined variables of bulk text messaging (Simplicity, Frequency and Informativeness) had a positive significant impact on consumers' attention (β= 0.645; p-value < 0.05). The study examined four bulk text advertising determinants, namely frequency, simplicity, informativeness and credibility. Only credibility was found to be statistically insignificant (p-value > 0.05), whilst frequency had an inverse effect on consumer attention. Simplicity of bulk text advertisements recorded a high positive and significant impact whilst informativeness was also positively, and significantly affecting consumer attention. Conclusions: The study concluded that for bulk text advertising to be effective, text messages should be informative, easy to understand and dispatched less frequently. It was further concluded that bulk text advertising should follow permission marketing where consumers consent before hand to be recipients of commercials.
Abstract
Purpose: In this study, we would like to confirm that the transfer of young workers may be a means of enhancing their internal satisfaction, not to get a better job, by setting work-related characteristics that are highly relevant to job-related factors. Research design, data, and methodology: In this study, preparation for turnover was set as dependent variables to identify factors related to the turnover of young people, and the type of business, employment type, debt status, job satisfaction, job difficulty compared to education level, job difficulty, job degree, job major agreement, debt status, and other demographic social characteristics were selected as independent variables. Results: The characteristics related to personal criteria in job-seeking process were significant in the form of business, employment type, job satisfaction, work difficulty compared to the level of education, work difficulty compared to the level of technology, job major matching, and debt status. Conclusions: This study confirmed that young people's turnover may not simply be a means to get a better job, but to increase satisfaction in the internal aspects of their jobs, and that for young people, a job is an important development process that represents their identity and needs to be approached from a life-cycle perspective.
Abstract
Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.
Abstract
Purpose: The purpose of this study is to recognize the role and necessity of public data visualization through prior research, investigation, and data verification processes. In addition, this study intends to check what factors should be considered in order to visualize data on the mobile web. Through this process, by identifying the cognitive load affecting information visualization by type, as a result, I would like to propose an effective information visualization method to effectively deliver public data related to government policies. Research design, data and methodology: In this study, we analyzed the case of information visualization according to infographics, which has been widely used in the public field among various visualization methods. For this study, a questionnaire survey was conducted for young people in their 20s and 30s with the highest mobile usage rate. Results: Based on the results, IPA (Importance Performance Analysis) was performed to conduct cognitive load test tools for information visualization of public data and confirmed the implications for each type of infographics. Conclusions: As a result of research, in order to efficiently deliver public data on the mobile web, first, it is necessary to construct a visual screen that can be easily identified through clear data. Appropriate graphic elements can be used according to the type to make it easier for users to acquire and understand information. Second, it is necessary to provide useful content in visualizing information. Third, in order to efficiently transmit information and increase understanding of data, it is necessary to visualize information that can induce interest in data and form metaphors. Fourth, it is necessary to visualize information to reduce cognitive load in terms of physical and mental aspects in order to accommodate users' comfortable information. Fifth, in order to effectively deliver public data, it is necessary to compose contents and information that are easy for users to understand. This study examines effective information visualization methods to increase the communication effect of public data in response to changes in the data-based intelligent information society and suggests implications for each type considering cognitive loads to help future public institutions to communicate and accept information.
Abstract
Purpose: Recently, Korean cosmetics distribution market has been reorganized with the H&B store. In the domestic cosmetics distribution market, existing brand road shops are decreasing, and multi-shops are leading the H & B stores, which have greatly improved their experience and content. In these environmental changes, the offline distribution channels are turning into the multi-editing shops that have introduced products of various brands and greatly enhanced experiences and contents. Nevertheless, most studies of factors and measurement items for measuring customer experience in the H&B store use Schmitt (1999)'s Strategic Experience Modules (SEMs). Therefore, the purpose of this study is to propose a measure that is practicable through consideration of the in-store customer experience components of the H&B store. Research design, data and methodology: Based on Schmitt's Strategic Experience Modules (SEMs), which are widely used in customer experience marketing, the metric pool was constructed through customer and literature research on H & B store managers. Since then, 101 preliminary surveys and 211 main surveys have been conducted in order to propose a dimension of customer experience and refine the metrics. Results: As a result of the research, H&B store's customer experience was derived from a measurement model consisting of 19 measurement items in total of five dimensions: environmental experience, intellectual experience, behavioral experience, tech experience, and relationship experience. This study analyzed that compared to the existing Schmitt's Strategic Experience Modules (SEMs), (1) emotional experience expanded to environmental experience, (2) Cognitive and relationship experiences are maintained (3) behavioral experience was subdivided into physical and technical experiences. In particular, the environmental experience has been proposed as a major component is an important point because the H&B store recently opened a large flagship store and is competitive in constructing a differentiated space. Conclusions: Related experience was seen as an important component of customer experience in the offline store, but in the process of refining the scale, interaction items with employees of the H&B store were removed, and rather, participation in the APP or SNS channel of the company, event Participation, interaction with other customers, etc. appear to be important, while suggesting the practical implications.