數(shù)據(jù)驅(qū)動(dòng)下的個(gè)體移動(dòng)服務(wù)產(chǎn)品采納行為的擴(kuò)散研究
發(fā)布時(shí)間:2018-07-25 18:56
【摘要】:隨著信息技術(shù)的不斷發(fā)展,個(gè)體間的交互更加便捷,同時(shí)各類網(wǎng)絡(luò)平臺(tái)的不斷興起,使得個(gè)體之間的決策變得越來(lái)越相互依賴。新產(chǎn)品擴(kuò)散的研究自Bass模型起,主要是對(duì)市場(chǎng)整體擴(kuò)散過(guò)程及結(jié)果進(jìn)行描述和預(yù)測(cè),但這些方法均存在一定的局限性,包括完全連接的網(wǎng)絡(luò)結(jié)構(gòu)、未考慮個(gè)體之間的異質(zhì)性等。本研究從社會(huì)網(wǎng)絡(luò)的視角來(lái)研究同伴影響在個(gè)體新產(chǎn)品采納行為中的作用,并通過(guò)社會(huì)網(wǎng)絡(luò)將個(gè)體行為與市場(chǎng)擴(kuò)散過(guò)程相連接。對(duì)于此類研究中難以區(qū)分的同質(zhì)性所帶來(lái)的內(nèi)生性問(wèn)題,本研究通過(guò)PSM模型進(jìn)行了有效的控制。同時(shí),本文使用超過(guò)120萬(wàn)社交網(wǎng)絡(luò)用戶的真實(shí)數(shù)據(jù)集,包含完整的社會(huì)網(wǎng)絡(luò)、個(gè)體屬性及采納行為等信息,通過(guò)數(shù)據(jù)分析、靜態(tài)與動(dòng)態(tài)PSM模型相結(jié)合的方法,證實(shí)了在新產(chǎn)品的擴(kuò)散中同伴影響的作用。此外,研究發(fā)現(xiàn)關(guān)系強(qiáng)度對(duì)同伴影響具有正向的調(diào)節(jié)作用。本文對(duì)后續(xù)從社會(huì)網(wǎng)絡(luò)視角進(jìn)行產(chǎn)品擴(kuò)散的研究提供了方法及依據(jù)。
[Abstract]:With the continuous development of information technology, the interaction between individuals is more convenient. At the same time, with the rising of various network platforms, the decision-making between individuals becomes more and more interdependent. Since the Bass model, the study of new product diffusion mainly describes and predicts the whole market diffusion process and results. However, these methods have some limitations, including fully connected network structure, not considering the heterogeneity between individuals and so on. This study studies the role of peer influence in individual new product adoption behavior from the perspective of social network, and connects individual behavior with market diffusion process through social network. In this paper, the PSM model is used to control the endogenetic problem caused by the indistinguishable homogeneity in this kind of research. At the same time, we use the real data set of more than 1.2 million social network users, including complete social network, individual attributes and adoption behavior, through data analysis, static and dynamic PSM model method. The role of peer influence in the diffusion of new products is confirmed. In addition, the study found that the relationship strength has a positive regulating effect on peer impact. This paper provides a method and basis for further research on product diffusion from the perspective of social network.
【作者單位】: 西安交通大學(xué)管理學(xué)院;
【基金】:國(guó)家自然科學(xué)基金資助項(xiàng)目(71301128,91546119,71331005,71371151)
【分類號(hào)】:C93
,
本文編號(hào):2144763
[Abstract]:With the continuous development of information technology, the interaction between individuals is more convenient. At the same time, with the rising of various network platforms, the decision-making between individuals becomes more and more interdependent. Since the Bass model, the study of new product diffusion mainly describes and predicts the whole market diffusion process and results. However, these methods have some limitations, including fully connected network structure, not considering the heterogeneity between individuals and so on. This study studies the role of peer influence in individual new product adoption behavior from the perspective of social network, and connects individual behavior with market diffusion process through social network. In this paper, the PSM model is used to control the endogenetic problem caused by the indistinguishable homogeneity in this kind of research. At the same time, we use the real data set of more than 1.2 million social network users, including complete social network, individual attributes and adoption behavior, through data analysis, static and dynamic PSM model method. The role of peer influence in the diffusion of new products is confirmed. In addition, the study found that the relationship strength has a positive regulating effect on peer impact. This paper provides a method and basis for further research on product diffusion from the perspective of social network.
【作者單位】: 西安交通大學(xué)管理學(xué)院;
【基金】:國(guó)家自然科學(xué)基金資助項(xiàng)目(71301128,91546119,71331005,71371151)
【分類號(hào)】:C93
,
本文編號(hào):2144763
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