The Janus Face of Cross-Platform Spillover: Who Reap the Benefits?

The booming of online platforms has attracted academia’s increasing interest in cross-platform spillover of product consumption. This study investigates how physicians’ content creation in Tik Tok influences patients’ demands, comments and satisfaction towards the physicians on online health communities (OHCs). Using the difference-in-differences approach, we uncover asymmetric influences of cross-platform spillovers for high- and low-awareness physicians in Tik Tok. Specifically, low-awareness physicians do not enjoy the benefits (i.e., the increased volume of orders and comments on OHC) from content creation in Tik Tok, but their ratings turn to decline due to attention distraction caused by cross-platform activities. Conversely, for high-awareness physicians, we find a positive cross-platform spillover effect for orders and comments on OHC without decreasing their ratings. Despite the existence of attention distraction from cross-platform services for high-awareness physicians, the negative impact on feedbacks is offset by higher ratings from their cross-platform consumers

Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

Network failure: digital technology in sponsored search advertising

The current study advances understanding of sponsored search advertising (SSA) by exploring failures in networks of SSA tools and human actors. SSA represents a novel form of information technology-bound marketing practice that has rapidly proliferated marketing over the last 25 years. The confluence of search technology and advertising has redefined how contemporary marketing is practiced, causing significant redistribution in marketing spent, advertising activity and the emergence of new actors. These shifts have attracted significant interest with rapidly growing number of studies addressing matters around SSA strategy, including various SSA features and functions. In radical departure from mainstream SSA literature, the current study adopts a practice-based view to provide a more nuanced understanding of how the networks of human and technological actors emerge, are stabilised and fail in SSA. By casting SSA as networked practice, the study highlights social construction and the dynamic, multiple and fluid nature of SSA. Actor network theory (ANT) theoretically frames failure in SSA and the networked nature of human and nonhuman actors that contribute to it. The study adopts a qualitative research design, where the data was collected through a 7-month ethnography and the data set includes semi-structured and insitu interviews, day-to day (participant) observations, images, field notes, secondary data and a detailed research diary. The data is anchored on events made up of relations – the principal units of analysis. The findings are presented as a set of ethnographic stories from problematised events. They show how SSA dynamism, fluidity and multiplicity can only be acknowledged accurately enough if human and nonhuman actors in networks are followed in their attempts to build heterogeneous relations. This enables enactment of several new actors, intentions and roles from the Google advertising practice in a specialised SSA agency. The findings provide novel insights that address several gaps in the marketing literature

Modelling fashion microblogs to increase the influence of social media marketing in Ireland and China

With the breakthrough of social media in the 21st century, microblogging has become an influential medium for marketing fashion brands and products online. For this reason, this study explores ten Irish and another ten Chinese fashion microblogging influencers’ microblogs using Text Mining and Netnography. By this comparison, the study finds a current model of how fashion microblogs influence fashion consumption in Ireland and China. With the help of this model, the study proposes a typology of Irish and Chinese fashion microblogging influencers and their basic microblogging strategies. The proposed typology intends to help fashion marketers to model their fashion microblogs in order to increase the influence of social media marketing in Ireland and China. Furthermore, the proposed typology is applied to develop a digital artefact that not only can deal with Irish and Chinese fashion microblogs at the same time but also show the results employing text visualisation. This bilingual digital website tries to make up for the lack of attention to text analysis on fashion-related words in the development of text mining tools. Finally, the methodological combination of Text Mining and Netnography employs digital tools and computer programming to conduct studies in the field of arts and humanities. The success of methodological combination in the study opens up a bright prospect for interdisciplinary research methodology