Browsing by Author "Ma, J."
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Item High or Low Season?: Contrasting Launch Timing Considerations for Big-Budget and Low-Budget Entertainment Products(Emerald Group Publishing Ltd.) Ma, J.; Huang, D.; Markovitch, D. G.; Ratchford, Brian T.; 35779481 (Ratchford, BT); Ratchford, Brian T.Purpose: This paper aims to investigate the moderating impacts of seasonality on the effectiveness of new product commercialization strategies in short-lifecycle markets. The authors contextualize their theory in the vast and culturally significant entertainment industry sector and contrast the effects between independent films and big budget movies. Design/methodology/approach: This study uses an econometric modeling approach. Findings: This study finds that unlike new films by well-resourced studios, which must launch in a high season for best performance, independents can generate more revenue in low seasons under certain conditions. The study shows how seasonality moderates the effectiveness of new films’ commercialization strategies and how new product outcomes are different for small independent products than for big-budget productions with regards to distribution duration, advertising expenditure and product characteristics. Research limitations/implications: This research extends the literature on launch timing, which examines various strategic tradeoffs. In contrast with the few extant studies whose concern is sensitizing to the effects of seasonality (Siqueiraet al., 2016), this research treats seasonality as an exploitable opportunity that can be strategically factored into business planning for small producers. Accordingly, this is the first study to theoretically and empirically investigate the moderating relationship between seasonality, marketing decisions, product characteristics and performance. Practical implications: To achieve useful specificity, the study constructs its discussion around the highly seasonal entertainment industry sector. The study shows that seasonality moderates the effectiveness of new films’ commercialization decisions and that the strategic outcomes are different for small independent products than for major studio productions in particular. Originality/value: In contrast with extant research whose concern is sensitizing to the effects of seasonality, our research treats seasonality as an exploitable opportunity that can be strategically factored into business planning. Accordingly, ours is the first study to theoretically and empirically investigate the moderating relationship between seasonality, marketing decisions, product characteristics and performance.Item Marginal Gains to Maximize Content Spread in Social Networks(Institute of Electrical and Electronics Engineers Inc., 2019-05-06) Yang, W.; Ma, J.; Li, Y.; Yan, R.; Yuan, Jing; Wu, Weili; Li, D.; 56851698 (Wu, W); Yuan, Jing; Wu, WeiliThe growing importance of social network for sharing and spreading various contents is leading to the changes in the way of information diffusion. To what extent can social content be diffused highly depends on the size of seed nodes and connectivity of the network. If the seed set is predetermined, then the best way to maximize the content spread is to add connectivities among the users. The existing work shows the content spread maximization problem to be NP-hard. One of the difficulties of designing an effective and efficient algorithm for the content spread maximization problem lies in that the objective function we aim to maximize lacks submodularity. In our work, we formulate the maximize content spread problem from an incremental marginal gain perspective. Although the objective function we derive is not submodular, both submodular lower and upper bounds are constructed and proved. Therefore, we apply the sandwich framework and devise a marginal increment-based algorithm (MIS) that guarantees a data-dependent factor. Furthermore, a novel scalable content spread maximization algorithm influence ranking and fast adjustment (IRFA), which is based on the influence ranking of a single node and fast adjustment with each boosting step in the network, is proposed. Through extensive experiments, we demonstrate that both MIS and IRFA algorithms are effective and outperform other edge selection strategies.