• Title/Summary/Keyword: empirical modeling

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Movie Choice under Joint Decision: Reassessment of Online WOM Effect

  • Kim, Youngju;Kim, Jaehwan
    • Asia Marketing Journal
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    • v.15 no.1
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    • pp.155-168
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    • 2013
  • This study describes consumers' movie choices in conjunction with other group members and attempts to reassess the effect of the online word of mouth (WOM) source in a joint decision context. The tendency of many people to go to movies in groups has been mentioned in previous literature but there is no modeling research that studies movie choice from the group decision perspective. We found that ignoring the group movie-going perspective can result in a misunderstanding, especially underestimation of genre preference and the impact of the WOM variables. Most of the studies to measure online WOM effects were done at the aggregate level, and the role of online WOM variables(volume vs valence) is mixed in the literature. We postulate that group-level analysis might offer insight to resolve these mixed understanding of WOM effects in the literature. We implemented the study via a random effect model with group-level heterogeneity. Romance, drama, and action were selected as genre variables; valence and volume were selected as online WOM variables. A choice-based conjoint survey was used for data collection and the models was estimated via Bayesian MCMC method. The empirical results show that (i) both genre and online WOM are important variables when consumers choose movies, especially as group, and (ii) the WOM valence effect are amplified more than the volume effect does as individuals are engaged in group decision. This research contributes to the literature in several ways. First, we investigate movie choice from a group movie-going perspective that is more realistic and consistent with the market behavior. Secondly, the study sheds new light on the WOM effect. At group-level, both valence and volume significantly affect movie choices, which adds to the understanding of the role of online WOM in consumers' movie choice.

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Meta Analysis of Trade Insurance Using Text Mining (텍스트 마이닝을 활용한 무역보험분야의 메타분석)

  • Hyun-Hee Park;Sung-Je Cho
    • Korea Trade Review
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    • v.45 no.6
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    • pp.157-179
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    • 2020
  • This study presented the results of meta-analysis through topic modeling among the papers published in the Journal of the International Trade Association for the purpose of presenting academic research trends in the field of trade insurance and future research directions. Among the total 2,010 papers included in the Journal of the Korea International Trade Association, the analyzed paper covers the subject of trade-related insurance. According to detailed topics, 33 marine insurance (42.31%), 16 export insurance (20.51%), 11 hull insurance (14.10%), and 18 others (23.08%), and 4 other products liability insurance. According to the empirical analysis results, Topic 1 was classified as marine insurance, airworthiness, notice obligation, and collateral, and Topic 2 was derived as a representative topic for loading insurance, emergency risk, and immunity as export insurance. And Topic 3 was classified as vessel, sinking and container in relation to ship insurance, and Topic 4 was analyzed as an important topic such as manufacture and British marine insurance. Through the analysis results, we selected the representative topic used for the trade insurance topic and looked at the status of major research. Trade insurance is an area that requires the development of more theoretical and practical research subjects as an optimal risk management means in international trade transactions. To this end, first, support from the Korea International Trade Association is needed to establish a continuous research subject sharing system for the development of research subjects in the field of trade insurance. Second, academic journal operation management must be continuously managed in which academic research papers can be submitted and published.

A Study on the Purchasing Factors of Color Cosmetics Using Big Data: Focusing on Topic Modeling and Concor Analysis (빅데이터를 활용한 색조화장품의 구매 요인에 관한 연구: 토픽모델링과 Concor 분석을 중심으로)

  • Eun-Hee Lee;Seung- Hee Bae
    • Journal of the Korean Applied Science and Technology
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    • v.40 no.4
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    • pp.724-732
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    • 2023
  • In this study, we tried to analyze the characteristics of color cosmetics information search and the major information of interest in the color cosmetics market after COVID-19 shown in the text mining analysis results by collecting data on online interest information of consumers in the color cosmetics market after COVID-19. In the empirical analysis, text mining was performed on all documents such as news, blogs, cafes, and web pages, including the word "color cosmetics". As a result of the analysis, online information searches for color cosmetics after COVID-19 were mainly focused on purchase information, information on skin and mask-related makeup methods, and major topics such as interest brands and event information. As a result, post-COVID-19 color cosmetics buyers will become more sensitive to purchase information such as product value, safety, price benefits, and store information through active online information search, so a response strategy is required.

Real-time private consumption prediction using big data (빅데이터를 이용한 실시간 민간소비 예측)

  • Seung Jun Shin;Beomseok Seo
    • The Korean Journal of Applied Statistics
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    • v.37 no.1
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    • pp.13-38
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    • 2024
  • As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.

An empirical study of the risk-benefit perceptions between the nuclear and non-nuclear groups towards the nuclear power plant in Bangladesh

  • Md Shafiqul Islam;Swapnil Roy;Sadia Lena Alfee;Animesh Pal
    • Nuclear Engineering and Technology
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    • v.55 no.12
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    • pp.4617-4627
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    • 2023
  • Public perception of benefit over risk is the de facto factor in planning, construction, operation, halting, or phase-out of a nuclear power plant in any country. Even if there are multiple pathways of perceiving risk/benefit among different stakeholders, the perception of nuclear and non-nuclear groups needs to be individually tracked to help understand sectoral influence. Related studies were basically performed between the STEM (Science, Technology, Engineering, and Mathematics) and non-STEM groups. However, there are no such studies between the nuclear and non-nuclear groups. This study investigated the risk-benefit perceptions between the nuclear group (N = 102) and the non-nuclear group (N = 467) using survey data to measure their stake and identify the underlying factors by validating the hypotheses, through descriptive analysis, and structural equation modeling (SEM). Results showed that risk perception is significantly high in the non-nuclear group (as the P-value is > 0.001 to <0.01) while the benefit perception is slightly low in the nuclear group (as the P-value is > 0.01 to <0.05). The non-nuclear group was significantly influenced by risk perception due to a lack of involvement in nuclear activities. Notably, the nuclear group is less interactive in disseminating nuclear energy benefits to the non-nuclear group. Surprisingly, misperceptions and lack of confidence about the benefits of nuclear energy also exist in the nuclear group. The study emphasizes debunking nuclear myths in the nuclear and non-nuclear groups through meaningful interactions and demands effective public awareness-building programs by competent authorities for the growth of the nuclear industry.

Machine Learning Framework for Predicting Voids in the Mineral Aggregation in Asphalt Mixtures (아스팔트 혼합물의 골재 간극률 예측을 위한 기계학습 프레임워크)

  • Hyemin Park;Ilho Na;Hyunhwan Kim;Bongjun Ji
    • Journal of the Korean Geosynthetics Society
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    • v.23 no.1
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    • pp.17-25
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    • 2024
  • The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

Analysis of Influential Factors in the Relationship between Innovation Efforts Based on the Company's Environment and Company Performance: Focus on Small and Medium-sized ICT Companies (기업의 환경적 특성에 따른 혁신활동과 기업성과간 영향요인 분석: ICT분야 중소기업을 중심으로)

  • Kim, Eun-jung;Roh, Doo-hwan;Park, Ho-young
    • Journal of Technology Innovation
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    • v.25 no.4
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    • pp.107-143
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    • 2017
  • This study aims to understand the impact of internal and external environments and innovation efforts on a company's performance. First, the relationships and patterns between variables were determined through an exploratory factor analysis. Afterwards, a cluster analysis was conducted, in which the influential factors summarized in the factor analysis were classified. Finally, structural equation modeling was used to carry out an empirical analysis of the structural relationship between innovation efforts and the company's performance in the classified clusters. 7 factors were derived from the exploratory factor analysis of 40 input variables from external and internal environments. 4 clusters (n=1,022) were formed based on the 7 factors. Empirical analysis of the 4 clusters using structural equation modelling showed the following: Only independent technology development had a positive impact on the company's performance for Cluster 1, which is characterized by sensitivity to a technological/competitive environment and innovativeness. Only independent technology development and joint research had positive impacts on the company's performance for Cluster 2, which is characterized by sensitivity to a market environment and internal orientation. Joint research and the mediating variable of government support program utilization had positive impacts, while the introduction of technology had a negative impact on the company's performance for Cluster 3, which is characterized by sensitivity to a competitive environment, innovativeness, and willingness to cooperate with the government and related institutions. Independent technology development as well as the mediating variables of network utilization and government support program utilization had positive impacts on the company's performance for Cluster 4, which is characterized by openness and external cooperation.

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.

A Meta Analysis of Using Structural Equation Model on the Korean MIS Research (국내 MIS 연구에서 구조방정식모형 활용에 관한 메타분석)

  • Kim, Jong-Ki;Jeon, Jin-Hwan
    • Asia pacific journal of information systems
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    • v.19 no.4
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    • pp.47-75
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    • 2009
  • Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

An Empirical Study on the Impact of the Perception of the Monitoring Function on Effective BPMS Adoption (모니터링 기능에 대한 인식이 효과적인 BPMS 도입에 미치는 영향)

  • Chae, Myung-Sin;Park, Jin-Suk;Lee, Byung-Tae
    • Asia pacific journal of information systems
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    • v.17 no.3
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    • pp.105-130
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    • 2007
  • Recently, there is a substantial interest in implementing Business Process Management System(BPMS) among enterprises with the purpose of business process innovation. BPMS redesigns and coordinates business processes in terms of both automated steps and human involvement in order to maximize the value of both involved people and systems. The reason why BPMS is getting attention from top managers is that it has the possibility to optimize the business processes by cycling the process of modeling, execution, monitoring, evaluation, and redesigning work processes. Thus, it has created high expectations about not only productivity improvement but also business process innovation. However. having an innovative nature, which is used for process innovation, BPMS implementation has great potential to stir up employee resistance. The analysis and the discussion about the prevention of the resistance against IS(Information Systems) is important because IS change the way people work and also alter the power structure within the organization, in general. The purpose of this study is to investigate factors that have an impact on the effective adoption of BPMS at the enterprise level. To find out these factors, this study considers two characteristics of BPMS: First. BPMS shares some characteristics with other enterprise-wide IS such as ERP. Second, it has special BPMS-specific characteristics. Due to the lack of previous research on BPMS adoption, interviews were carried out with IT-consultants and CIOs who conducted BPMS projects previously to find out BPMS-specific features that would make BPMS unique when compared to other enterprise-wide IS. As a result, the monitoring function was chosen as the main BPMS-specific factor. Thus, this paper reviewed studies both on enterprise-wide IS adoptions, which applied Technology Acceptance Model (TAM) and secondly on computer based monitoring to find out factors that would influence the employees' perception on the monitoring function of BPMS. Based on the literature review, the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses, out factors that would impact the employees' perception on the monitoring function of BPMS. Based on the literature review the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses. The data analysis results showed that two among three monitoring function related factors - enterprise evaluation system and fairness of the boss - were significant. This implies that employees would worry less about the BPMS implementation as long as they perceive the monitoring results will be used fairly for their performance evaluation. However, employees' high self-efficacy on their job was not a significant factor in their perception of the usefulness of BPMS. This is related to cases that showed employees resisted against the information systems because they automated their works (Markus, 1983). One specific case was an electronic company, where the accounting department workers were requested to redefine their job because their working processes were automated due to BPMS implementation.