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Chatbots are website artificial intelligence-based and automated customer support tools to improve the customer experience, to reduce costs, and to improve service quality. This study aims to understand and analyze the user-technology interaction and technology-engagement success measures to assess online customer engagement with chatbots and the impact on repurchase intention, within e-commerce websites. The sample data consists of 227 online consumer responses collected through an electronic survey. Only 165 respondents, which have used a chatbot to assist the online purchase process, are included in the effective sample. This research contributes to the digital marketing literature by complementing existing research exploring human-technology interactions, assessing how consumers interact with chatbot technology and how it affects customer engagement and behavioral outcomes within e-retail contexts. The study findings provide several challenges for managers. Finally, it discusses emerging trends in the digital marketing field, offering insights for future research avenues.

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Social media has become a crucial point for brands to establish a connection with their consumers and potential consumers, being many times responsible for developing the need and converting it into a purchase. Thus, it is worth highlighting the role of influencers in social media that affect fashion purchase. Given the growth of sustainable fashion, it is necessary to verify the relationship between influencers and social media and the intention to purchase sustainable fashion. A conceptual model that aims to understand the effect of influencers' characteristics in the intention to purchase sustainable fashion is presented. The results show that consumer knowledge and willingness to pay more are the only factors that positively affect the purchase intention of sustainable fashion. Furthermore, the authors highlight that consumer knowledge is the construct that has a distinctly greater impact on the intention to purchase sustainable fashion.

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There is a literature gap regarding the period representativeness bias associated with sample selection in longitudinal bibliometric studies. The purpose of this paper is to analyse and compare, in terms of period representativeness, the common methods used for selecting a sample of the highly impactful papers in a field/ journal. Using 92 593 papers (Information Science & Library Science area, 1977-2016), we compared, in terms of the number of papers/year, samples of the 100 most impactful papers, obtained with different selection options. We repeated the analysis also for Top500, Top2000, and Top20000. This study shows that the frequently used metrics to compare the impact of papers and to select a sample of "most impactful papers"published in each year and each field may privilege specific periods while neglecting others. The main result of our study is that the percentile citation-based method reduces this "year of publication"representativeness bias. This paper draws attention to the importance of the sample selection, in bibliometric studies, and to the period representativeness bias associated with different choices to select the "most impactful papers". © 2021 Elsevier Ltd.

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