• Management & Information Systems Review
• /
• v.32 no.5
• /
• pp.193-231
• /
• 2013

As developed IT, Word-of-Mouth(WOM) used varied terms as buzz marketing and viral marketing, and impressed that importance. Despite introduced new marketing tool on managers and professionals, online word-of-mouth including SNS lack of study on social network what based viral in marketing. In social network, patterns of relationship between individuals influence each other individual behaviors. Therefore this research grouped friendship-network and advice-network by characteristics, studied on trust of information source that antecedents of word-of-mouth in network. This study examined that affect- and cognition based trust affect WOM acceptance as WOM behaviors and examined effect of type of product as moderating variable. Additional this literature studied that WOM acceptance affect WOM recommend. To find the Influence of Trust on Word-of-Mouth Behaviors, a survey has done 206 samples(undergraduate students). The results of this study are as following : First, type of trust different friendship network and advice network. Affect-based trust is outstanding in friendship network than in advice network, while cognition-based trust stands out in advice network than another. Second, affect- and cognition based trust positive affect WOM acceptance. Contrary to expectations, what is preconceived trust in network have a similar effect for WOM acceptance regardless of type of trust. Third, WOM acceptance positive affect WOM recommend. Fourth, affect based trust affect WOM acceptance of hedonic product rather than utilitarian product. Upon especially in friendship network terms, affect-based trust has a more effect on WOM acceptance than cognition-based trust. This study has many implications. First, it is important that trust what have an influence WOM acceptance grouped affect- and cognition based trust. Second, it confirmed that trust is antecedents of positive WOM. Third, it is important that network grouped friendship network and advice-network by trust. Fourth, it gave managerial implications that they have to supply WOM through which network by type of product. We This study classified network and trust based on previous study. Then it examined relations between WOM behaviors. Further research could do enrich various things for example various age group, valence of message, quality of information.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.107-129
• /
• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.