1,721,165 research outputs found
Online market entry: the motivations for imitation across retailer types
Get PDFThis study examines the motivations for imitation in retailers’ online channel entry. Extant literature suggests that legitimacy and efficiency are the primary motivators for firms to imitate. We develop hypotheses which center on the belief that not all firm types would use the same motivator for deciding to imitate and enter the online market;
legitimacy would be the driving force for some retailer types while efficiency would be the motivator for others. We test our hypotheses on a unique data collected from multiple sources. Our findings confirm that the motivators for imitation vary across retailer types.
Bhatnagar, Amit and Nikolaeva, Ralitza and Ghose, Sanjoy, Online Market Entry: The Motivations for Imitation Across Retailer Types (November 2014). Managerial and Decision Economics, Forthcoming. Available at SSRN: http://ssrn.com/abstract=252208
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
THREE ESSAYS ON AN LLM-BASED APPROACH AND ITS APPLICATIONS IN MARKETING
No full textOne rich source of data, especially as consumers grow increasingly comfortable with the online platform, is text data, continuously generated at many consumer touchpoints such as online reviews, social media posts, crowdfunding campaigns, promotional emails, product descriptions, etc. Online reviews on Yelp, for example, experienced a 637% growth from 2012 to 2022 (Dixon 2023). Furthermore, the number of social media users is expected to grow from 4.9 billion in 2023 to 5.85 billion by 2027 (Wong 2023). This free-flowing, unstructured text data may well offer marketers an opportunity to identify current emerging trends and consumer perception (Berger et al. 2020, Du et al. 2021). The most common tool used by researchers to analyze text data is the set of topic models, such as Latent Dirichlet Allocation (LDA) (Blei, Ng, and Jordan 2003), which fundamentally uncovers a set of latent themes within a collection of text documents. Researchers have used such topic models in the past to study online reviews (Wang and Chaudhry 2018), paid search ads (Rutz, Sonnier, and Trusov 2017), and loan requests (Netzer, Lemaire, and Herzenstein 2019). While the use of these models could theoretically be extended to identify emerging trends (by detecting topics increasing in popularity/frequency with time), or studying consumer perception, extant topic models suffer from two major weaknesses that limit their ability to identify such trends. First, most topic models do not consider the context in which a word is used. They treat text data as a “bag of words” - independently occurring words amassed together for analysis. This treatment overlooks the contextual features of text, i.e., the order and grammatical roles of words and the semantic relationships between them. While there is a rich body of research which addresses this limitation, we offer a more comprehensive treatment utilizing the latest advances in text analytics. Second, and probably the more critical issue is that extant topic models are based on statistics and therefore cannot detect topics with low frequency of occurrence (Puranam, Kadiyali, and Narayan 2021). Rare or emerging topics would clearly appear in fewer online discussions making them harder to detect using frequentist approaches. Computer scientists have recently developed Large Language Models (LLMs) to analyze text while factoring in the context in which a word is used. Marketers have used LLM models extensively to study sentiment analysis (Hartmann et al. 2023), service quality attributes (Puranam, Kadiyali, and Narayan 2021), social media moods (Wang and Lau 2024), polarity of online reviews (Lutz, Pröllochs, and Neumann 2022), consumer complaint types (Grosz and Raval 2025), marketing research (Arora, Chakraborty, and Nishimura 2025), human preferences (Goli and Singh 2024), and automated perceptual analysis (Li et al. 2024). This dissertation uses LLMs and designs a novel algorithm to extend the topic modeling approach in order to incorporate the contextual nuances from text data. Two broad factors distinguish this work from current literature. This dissertation is the first study in marketing (to the best of our knowledge) to propose a topic model based on Large Language Models (as opposed to statistical models). Since LLMs are not based on statistics, they can capture topics that occur with low frequency (namely, emerging trends). Second, to interpret these topics, as opposed to current frequentist approaches, this dissertation designs a novel algorithm, the Contextual Topic Interpretation (CTI) algorithm, that considers context while assigning topics to documents and words to topics.2027-05-2
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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