Posting Lists Research Articles

Analysis of the current state of the issue of greenhouse gas emission monitoring

The question of the theory of global warming is being raised more and more every year. There are disputes, dialogues, and various conferences on this problem. Carbon grounds are being created everywhere for research. The world community is increasingly asking questions about decarbonization and carbon trace compensation, but the main problem is that there is no unified standardized methodology for accounting of production and absorption of carbon dioxide, there is no comprehensive monitoring system approved that allows us to receive real-time data from large territories at present. This review article examines the purpose of carbon grounds, the impact of greenhouse gases, the concept of a carbon trace, the goals of decarbonization and the existing monitoring problems. The actively used environmental monitoring posts are considered, and it is also proposed to supplement the existing list of posts with a mobile flying post (using an unmanned aerial vehicle). The planned structure of the information collection system and the block diagram of the unmanned aerial module are proposed. The article also notes that the more reliable the initial data will be in the calculation and forecasting, the more accurate the final result will be. Further necessary improvement of accuracy is possible only thanks to a deep integrated approach to monitoring the absorption and emission of greenhouse gases.

EXPLORING THE DARK SIDE OF CRYPTOCURRENCIES ON FACEBOOK AND TELEGRAM: UNCOVERING MEDIA MANIPULATION AND “GET-RICH-QUICK” DECEPTIVE SCHEMES

The rise of blockchain and cryptocurrencies come with the promise of decentralizing transactions and disrupting the power of market intermediaries. Despite these promises, scholars argue that the risks associated with cryptocurrencies are still unclear. Some preliminary works investigate the manipulative role played by the circulation of problematic content on platforms such as Reddit, Twitter, Discord, or Telegram. Nonetheless, the research still lacks a clear and comprehensive picture of how widespread the phenomenon is across the whole ecosystem. Despite its prominence, Facebook is understudied in this context. To fill this gap, this work focuses on the role played by Meta’s main platform, as a venue employed to reach a wide audience prone to potential manipulative practices or scams related to cryptocurrencies. As part of a broader investigation on coordinated disinformation in Africa, we have come across a cluster of Facebook groups sharing content related to cryptocurrencies. The links shared by these networks revealed a very prolific cluster of 152 groups dedicated to Airdrop and Bounty initiatives for new cryptocurrencies. We collected a list of recent posts created by these groups between November 2021 and January 2022 (378,513 URLs). A preliminary analysis of these URLs pointed out an overwhelming presence of links to Telegram (47%) that highlights the central role played by this platform in this specific ecosystem. This paper explores the overlap between the cryptocurrency community and social media, analyzing how crypto-related projects are disseminated as a new type of problematic content on Facebook and Telegram.

Privacy-aware document retrieval with two-level inverted indexing

Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverted index structure. In this scheme, posting records are grouped with bucket tags and runtime query processing produces query-specific tags in order to gather encoded features of matched documents with a privacy protection during index traversal. To thwart leakage-abuse attacks, our design minimizes the chance that a server processes unauthorized queries or identifies document sharing across posting lists through index inspection or across-query association. This paper presents the evaluation and analytic results of the proposed scheme to demonstrate the tradeoffs in its design considerations for privacy, efficiency, and relevance.

Orthodontics in an online community: A computational analysis of r/Braces subreddit

This study aimed to analyze the semantic network and content analyses of the posts published in a subreddit related to orthodontic treatment on Reddit (Advance Publications, Inc., San Francisco, California). The eight threads in the r/Braces subreddit were divided into two categories: 1) "treatment process" (Braces are off!!!, Braces progress, Before and after!, and Day 1!) and 2) "question/problem" (Question, Discussion!, Need advice! and Rant!). For both categories, a semantic network analysis was performed using the Leximancer software (Leximancer Pty Ltd., Brisbane, Australia). In addition, the quality of the posts published in the "question" thread and the usefulness of the replies provided to these questions (useful, misleading, or neutral) were analyzed. Seven themes (braces, teeth, months, day, worth, started, and result) that mostly emphasized orthodontic treatment and treatment duration were elicited from the "treatment process" category, and seven themes (teeth, orthodontist, braces, week, bands, brush, and extractions) that mostly emphasized orthodontic treatment, orthodontists, and time were elicited from the "question/problem" category. It was also revealed that users voted on the posts related to the "treatment process" category and moved the posts to the list of top posts on the platform. In the "question" thread, 47.79% of the posts asked for advice and 21.11% of them were related to failures. In addition, 69% of the replies were categorized as "useful." Reddit is a successful data mining platform, and the users provide highly useful replies to the questions posted on Reddit regarding orthodontic treatment.

Gender labor inequalities in the public sector: the interplay between policy and micro-politics

PurposeThis paper aims to describe and explain women's labor participation in the public sector, particularly at the local level. The paper analyses the representation of women employees in the public sector through a case study of a city council in a mid-sized Spanish city. The authors delve into the extent of gender labor discrimination in public administration, exploring a diversity of situations, experiences, and perceptions of women workers in female, neutral, and male-dominated areas in the local administration.Design/methodology/approachThe authors have applied a combined methodology of quantitative analysis based on an exhaustive analysis of the list of job posts, and qualitative analysis from the narratives of women workers in biographical interviews, in women-dominated, neutral and male-dominated areas.FindingsThe authors conclude by providing a clear description of women's representation in local administration. Despite the institutional efforts in applying gender equality norms and public policies in administration, employment and labor market, this article shows the persistent inequality in employment within the administration. The paper demonstrates that public administrations can be seen as gender regimes that tend to reproduce inequality by formal and informal dynamics. This inequality gender reproduction in a supposedly gender-neutral administration reflects discrimination in a labor market. The paper details phenomena relating to horizontal occupational segregation, glass ceilings, sticky floors, and the undervaluing of women's work, among other phenomena.Practical implicationsThe administration should consider two essential factors that endanger gender equality: (1) the demonstrated regression of gender mainstreaming and the effects on women's employment as a consequence of the crisis, and (2) neoliberal governments and extreme right-wing parties (or neoliberal governments and extreme right-wing parties' support, as is the case with the current Andalusian regional government), whose agenda includes the fight against what neoliberal governments and extreme right-wing parties call “gender ideology”.Social implicationsThe gap between the effectiveness of gender legislation and actual working practices within the administration has been highlighted. This fact should be a wake-up call for the administrations to strictly comply with gender legislation, given that local administrations are the closest to the citizens. Future research should focus on changes to detect any regression and to prevent losing the improvements already achieved, which can still be very much strengthened.Originality/valueThis article helps to fill the gap in the literature on gender discrimination in the labor market, which often omits the public sector, especially in local administration, which is the closest administrative structure to citizenship respecting public policies. The article contributes to highlighting the need for an egalitarian labor market in order to achieve optimal performance, commitment and efficiency in egalitarian labor relations in local administration.

#Brachytherapy: Physicians As Influencers on Instagram.

PurposeWe aimed to evaluate brachytherapy-related posts on Instagram by identifying patient concerns, the content of related posts, and user outreach.Methods and materialsA list of top posts from searching #brachytherapy on May 7, 2021, were generated on a mobile device and all data are representative as of May 7, 2021. Searching for #brachytherapy resulted in 1010 posts which were analyzed using Instagram. The content was categorized by source (physician, patient, hospital, or not otherwise specified), type (education or experiences), disease site (cervical, endometrial, other), and user influence (number of posts, number of followers). Patient posts were specifically analyzed and all captions and hashtags were reviewed.ResultsThe distribution of users with brachytherapy-related posts was as follows: 23% patients, 15% physicians, 9% hospitals, 53% not otherwise specified. Physicians only made up 11% of posts analyzed while the majority, 79%, were from patients and other Instagram users. From the accounts linked to patients, 99% of them were experience-based and 1% were educational. Posts made by physicians were educational in content 66% of the time, with 34% of posts being experiences. The median number of followers from least to greatest were not otherwise specified (NOS) 450.5, patients (501), hospital-affiliated (527), and physicians (608). In gynecological cancer patients, the reported side effects were as follows: fatigue 31%, gastrointestinal (GI) 16%, genitourinary (GU) 16%, pain 28%, and anxiety 50%.Conclusion:This study shows the influential power physicians have on social media and the need for increased brachytherapy awareness on platforms such as Instagram. Patients have voiced apprehension to pursue radiation due to lack of information provided and fear of the unknown. With this concern in mind, physicians are responsible to increase the availability of knowledge to patients in a more relaxed environment than the clinic. With increased physician social media presence, patients will have another avenue for support and reliable source of treatment information.

METHODS OF ANALYTICS OF BIG DATA OF POPULAR ELECTRONIC NEWSPAPERS ON FACEBOOK

Due to the popularity of social networks, all famous brands use them to promote and support their product. For example, well-known foreign newspapers such as: Washington Post, New York Times, Time, Reuters, Forbes duplicate information about news on the Facebook social network for greater readership. Using methods of automated data collection from web pages, a list of posts is formed based on which data analysis is performed. Statistical results of frequency, popularity of certain articles, audience reach and people's reaction to posts are obtained from a large volume of data. In the Java programming language and using additional Selenium, JavaFX libraries, all processes for data normalization is developed and data visualization is used. In addition, the dependence of the post coverage of newspaper editions on the number of posts published during the day is investigated in Facebook social network. The work also examines the most popular posts and their topics. The relationship between keywords and real events is analyzed. Keywords : big data, social networks, data analytics, automated data collection, data processing.

Социальные сети как инструмент влияния российских СМИ на региональную аудиторию

In the context of competition between the traditional media among themselves and with new media, it is important to improve the methods of influence on the regional audience for the achievement of the goals and survival on media markets of federal entities of the Russian Federation. The study analyzes how effectively traditional media use all the opportunities provided by social networks, and suggests a way to improve media adaptation to new formats of interaction with regional audience. The article analyzes the approaches to the definition and distinctive characteristics of social networks in the social media system, describes the possibilities of social networks in the influence on the audience, identifies the specificity of social media impact on the regional audience in media communities. A content analysis of six media communities was carried out. An analysis of the effectiveness of their impact on the regional audience of the social network was also made. On the basis of the study, a model of effective impact on the regional audience of social networks in media communities is suggested. The model takes into account the content strategy, content list and picturing of posts, interaction with the audience, manipulation, community monetization, visual design and assessment of the community effectiveness. With the help of the proposed model of influence on the regional audience of Russian media, it is possible to improve qualitatively the indicators of the community. It is assumed that the described model will allow establishing friendly relations with the audience and increase the efficiency of achieving the commercial goals of publications through the monetization of the community. The suggested model can be used at the planning stage of creation of a new community, which is suitable for both regional publishing houses and is based on the preferences of the audience of the social network which is identified in the content analysis.

Prospective Bibliography

The prospective bibliography, i.e., the list of referring publications— is a dynamically drawn-up list of works published after the publication under consideration and contained a reference to it. Leading foreign publishers of scientific journals, as a rule, post lists of referring publications on the web pages of their articles. In our country, the posting lists of referring publications has not yet become the norm, but it is gradually becoming widespread. The implementations of the list of referring publications in a number of characteristic projects are considered. The terminology used is discussed.

THE INFLUENCE OF GROUP AFFILIATION ON IPOS PERFORMANCE: AN EMPIRICAL ANALYSIS OF INDIAN IPOS

Securities and Exchange Board of India (SEBI) keeps on improving the securities market regulations to improve investors' protection. To revive the primary market, in 2009, SEBI ICDR (Issue of Capital and Disclosure Requirements) Regulations, 2009 was amended with revised guidelines. The advantage of group affiliation and reputation on IPO performance differs with time and varied business groups. Grouped IPOs evidenced mixed outcomes based on theories developed under tunnelling, propping, network and certification hypothesis. We examine how the business group affiliation impact the underpricing, liquidity and volatility performance of IPOs. Using database of 259 IPOs issued in Indian market from 2009 to 2019, we find less underpricing, liquidity and volatility for group affiliated IPOs. The present study supports certification, network and propping hypothesis. We find underpricing extent is reduced than earlier period, average security return 12 percent while underpricing 11.79 percent is evidenced. Liquidity and volatility reported a declining trend from listing day to 1-month post listing.

A New Modus Operandi for Determining Post - IPO Pricing : Analysis of Indian IPOs using Artificial Neural Networks

The objective of this study was to identify different factors useful in determining post-IPO pricing and test their relative significance by comparing stock performance across 3-, 6-, and 12-months post listing. To do so, the study analyzed data from 299 non-financial companies that had their IPOs listed on the Bombay Stock Exchange from 2005–2018 in India. The data collected were used to train a neural network, called the multilayer perceptron model. The study grouped all factors into four categories viz-a-viz macroeconomic, issue-specific, technical, and fundamental. Analysis of the results generated from 20 iterative constructions of the neural network revealed that the highest relative relevance in prediction was attributed to technical factors. It was also observed that the importance of fundamental factors increased with the investment horizon. The results are country-specific and found that the importance of “underpricing” and “listing gains” as factors reduced within a year post-listing and thus, provide a helpful addition to the present knowledge of financial gains resulting to investors from IPOs.

PDB63 Reimbursement Strategies of First-in-Class Drugs for Type 2 Diabetes Mellitus in Health Technology Assessment in Australia: A Qualitative Review

To gain insights into reimbursement strategies of first-in-class anti-diabetic drugs reflected in public summary documents (PSDs) by the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia. A qualitative review was conducted on PSDs for exenatide, sitagliptin and dapagliflozin. Both initial and re-submissions were reviewed. No further reviews were performed once the indications proposed in the initial submission were recommended. Key information was extracted using a standardized data extraction form and summarized by narrative synthesis. All retrieved submissions were focused on combination therapies. All three drugs in combination with metformin (MET) and/or sulfonylurea (SU) were recommended eventually following an average of 2.3 attempts. Indirect comparisons were generally adopted to assess the comparative effectiveness and safety. In initial submissions, thiazolidinediones was nominated as the main comparator for sitagliptin and exenatide, whereas sitagliptin being the comparator for dapagliflozin. HbA1c reduction was the primary endpoint across all comparisons with a non-inferiority margin of 0.3% - 0.4% agreed by the PBAC. Cost-minimization analyses (CMA) were mainly employed except that cost-utility analyses were applied in the first two submissions for exenatide. All initial submissions were deemed to have high uncertainties with respect to comparative effectiveness, and exenatide was further characterized with high uncertainties in cost-effectiveness and safety concerns. These uncertainties were addressed in resubmissions by providing additional clinical evidence and adopting the comparator and the CMA approach suggested by the PBAC. All submissions estimated a net cost to the government of less than $10 million in Year 5 post listing. Furthermore, PBAC suggested a risk-sharing agreement to manage the potential usage beyond the listing of dapagliflozin to assure its nil net cost to the government. The selection of comparator(s) and the robustness of clinical and economic evaluation should be emphasized by the sponsors. Cost neutral and appropriate risk-sharing agreements may facilitate positive recommendations.

Measuring semantic similarity of documents with weighted cosine and fuzzy logic

Currently, the semantic analysis is used by different fields, such as information retrieval, the biomedical domain, and natural language processing. The primary focus of this research work is on using semantic methods, the cosine similarity algorithm, and fuzzy logic to improve the matching of documents. The algorithms were applied to plain texts in this case CVs (resumes) and job descriptions. Synsets of WordNet were used to enrich the semantic similarity methods such as the Wu-Palmer Similarity (WUP), Leacock-Chodorow similarity (LCH), and path similarity (hypernym/hyponym). Additionally, keyword extraction was used to create a postings list where keywords were weighted. The task of recruiting new personnel in the companies that publish job descriptions and reciprocally finding a company when workers publish their resumes is discussed in this research work. The creation of a new gold standard was required to achieve a comparison of the proposed methods. A web application was designed to match the documents manually, creating the new gold standard. Thereby the new gold standard confirming benefits of enriching the cosine algorithm semantically. Finally, the results were compared with the new gold standard to check the efficiency of the new methods proposed. The measures used for the analysis were precision, recall, and f-measure, concluding that the cosine similarity weighted semantically can be used to get better similarity scores.

Environmental disclosure and sentiment analysis: state of the art and opportunities for public-sector organisations

PurposeBecause of the expansion of the internet and Web 2.0 phenomenon, new challenges are emerging in the disclosure practises adopted by organisations in the public-sector. This study aims to examine local governments’ (LGOs) use of social media (SM) in disclosing environmental actions/plans/information as a new way to improve accountability to citizens to obtain organisational legitimacy and the related sentiment of citizens’ judgements.Design/methodology/approachThis paper analyses the content of 39 Italian LGOs’ public pages on Facebook. After the distinction between five classes of environmental issues (air, water, energy, waste and territory), an initial study is performed to detect possible sub-topics applying latent Dirichlet allocation. Having a list of posts related to specific environmental themes, the researchers computed the sentiment of citizens’ comments. To measure sentiment, two different approaches were implemented: one based on a lexicon dictionary and the other based on convolutional neural networks.FindingsFacebook is used by LGOs to disclose environmental issues, focussing on their main interest in obtaining organisational legitimacy, and the analysis shows an increasing impact of Web 2.0 in the direct interaction of LGOs with citizens. On the other hand, there is a clear divergence of interest on environmental topics between LGOs and citizens in a dialogic accountability framework.Practical implicationsSentiment analysis (SA) could be used by politicians, but also by managers/entrepreneurs in the business sector, to analyse stakeholders’ judgements of their communications/actions and plans on corporate social responsibility. This tool gives a result on time (i.e. not months or years after, as for the reporting system). It is cheaper than a survey and allows a first “photograph” of stakeholders’ sentiment. It can also be a useful tool for supporting, developing and improving environmental reporting.Originality/valueTo the best of the authors’ knowledge, this paper is one of the first to apply SA to environmental disclosure via SM in the public sphere. The study links modern techniques in natural language processing and machine learning with the important aspects of environmental communication between LGOs and citizens.

Content-based Union and Complement Metrics for Dataset Search over RDF Knowledge Graphs

RDF Knowledge Graphs (or Datasets) contain valuable information that can be exploited for a variety of real-world tasks. However, due to the enormous size of the available RDF datasets, it is difficult to discover the most valuable datasets for a given task. For improving dataset Discoverability , Interlinking , and Reusability , there is a trend for Dataset Search systems. Such systems are mainly based on metadata and ignore the contents; however, in tasks related to data integration and enrichment, the contents of datasets have to be considered. This is important for data integration but also for data enrichment, for instance, quite often datasets’ owners want to enrich the content of their dataset, by selecting datasets that provide complementary information for their dataset. The above tasks require content-based union and complement metrics between any subset of datasets; however, there is a lack of such approaches. For making feasible the computation of such metrics at very large scale, we propose an approach relying on (a) a set of pre-constructed (and periodically refreshed) semantics-aware indexes, and (b) “lattice-based” incremental algorithms that exploit the posting lists of such indexes, as well as set theory properties, for enabling efficient responses at query time. Finally, we discuss the efficiency of the proposed methods by presenting comparative results, and we report measurements for 400 real RDF datasets (containing over 2 billion triples), by exploiting the proposed metrics.

Внедрение профессиональных стандартов в системе управления персоналом организации

This article discusses the implementation of professional standards in an organization. The author studies the prerequisites for the implementation of professional standards, the legislative framework to be followed, the methodological steps and the results of implementation. It is described that the implementation of professional standards in the organization requires the following steps: determining the list of posts, familiarizing employees, analyzing internal documents (labor contracts, job descriptions), posting information on a corporate website, creating personal training programs, conducting qualifi cation assessments and adjusting programs advanced training of employees. One of the results of the implementation of professional standards is the improvement of the quality of knowledge, skills and abilities of employees, as well as their eff ective placement in the organization. In addition, the article systematizes a plan for further work with professional standards.

Оцінювання оперативно-тактичної важливості пунктів управління угруповання військ

Важливість пунктів управління угруповання військ оцінюється при підготовці операції (бою) з метою визначення необхідності й порядку проведення заходів із забезпечення їх надійного функціонування під час ведення бойових дій.Важливість пунктів управління насамперед визначається завданнями, що повинні виконуватись ними і підлеглими військовими формуваннями. Залежність важливості пунктів управління від багатьох факторів, їх велика кількість в угрупованні військ обумовила застосування для ранжирування пунктів управління методики, яка ґрунтується на використанні методу аналізу ієрархій. За допомогою методики здійснюється розрахунок коефіцієнтів оперативно-тактичної важливості пунктів управління, під якими розуміються відносні кількісні міри значущості їх функціонування у системі управління військ для виконання завдань.Для визначення важливості пункти управління відповідно до рівнів управління та призначення поділяються на групи. Методикою передбачається оцінювання важливості пунктів управління, яку здійснюється у три етапи: на першому етапі — усередині групи; на другому етапі — груп пунктів управління; на третьому етапі — у системі управління угрупування військ.Під час формування ієрархічного зображення проблеми насамперед визначаються: перелік пунктів управління (альтернатив), що порівнюються; властивості, на які впливає функціонування пунктів управління; показники, які характеризують вплив функціонування пунктів управління на визначені властивості. Як властивості, на які впливає функціонування пунктів управління, можна прийняти: управління угрупованням військ (військовими формуваннями); можливості щодо виконання бойових завдань; можливості щодо забезпечення бойових дій.Для визначення важливості пунктів управління усередині груп і важливості цих груп будується чотирирівнева домінантна ієрархія розв’язання задачі. Коефіцієнти оперативно-тактичної важливості пунктів управління у системі управління розраховуються з використанням значень важливості цих груп, що оцінюються на другому етапі методики.У статті наведено приклад оцінювання важливості пунктів управління угрупування військ в оборонній операції. Запропонована методика може використовуватися органами військового управління при підготовці операції (бою).

Performance evaluation of single vs. batch of queries on GPUs

SummaryThe WAND processing strategy is a dynamic pruning algorithm designed for large scale Web search engines where fast response to queries is a critical service. The WAND is used to reduce the amount of computation by scoring only documents that may become part of the top‐k document results. In this paper, we present two parallel strategies for the WAND algorithm and compare their performance on GPUs. In our first strategy (named size‐based), the posting lists are evenly partitioned among thread blocks. Our second strategy (named range‐based) partitions the posting lists according to document identifier intervals; thus, partitions may have different sizes. We also propose three threshold sharing policies, named Local, Safe‐R, and Safe‐WR, which emulate the WAND algorithm global pruning technique. We evaluated our proposals with different amounts of work, from short to extra‐large queries, using single query processing and batch of queries. Results show that the size‐based strategy reports the highest speedups but at the cost of low quality of results. The range‐based algorithm retrievals the exact top‐k documents and maintains a good speedup. Moreover, both strategies are capable of scaling as the amount of work is increased. In addition, there is no significant difference in the performance of the three threshold sharing policies.

Cache-aware load balancing of data center applications

Our deployment of cache-aware load balancing in the Google web search backend reduced cache misses by ~0.5x, contributing to a double-digit percentage increase in the throughput of our serving clusters by relieving a bottleneck. This innovation has benefited all production workloads since 2015, serving billions of queries daily. A load balancer forwards each query to one of several identical serving replicas. The replica pulls each term's postings list into RAM from flash, either locally or over the network. Flash bandwidth is a critical bottleneck, motivating an application-directed RAM cache on each replica. Sending the same term reliably to the same replica would increase the chance it hits cache, and avoid polluting the other replicas' caches. However, most queries contain multiple terms and we have to send the whole query to one replica, so it is not possible to achieve a perfect partitioning of terms to replicas. We solve this via a voting scheme, whereby the load balancer conducts a weighted vote by the terms in each query, and sends the query to the winning replica. We develop a multi-stage scalable algorithm to learn these weights. We first construct a large-scale term-query graph from logs and apply a distributed balanced graph partitioning algorithm to cluster each term to a preferred replica. This yields a good but simplistic initial voting table, which we then iteratively refine via cache simulation to capture feedback effects.

Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics

The advertising industry has recently witnessed proliferation in native ads, which are inserted into a web stream (e.g., a list of news articles or social media posts) and look like the surrounding nonsponsored contents. This study is among the first to examine native ads and unveil how their effectiveness changes across serial positions by analyzing a large-scale data set with 120 ads. For each ad, the authors use separate “natural experiment” studies to compare the ad’s performance as its serial position varies. Subsequently, they conduct a meta-analysis to generalize the results across all studies. The results reveal vastly asymmetric effects of native ad serial position on publishers’ metrics (click-based) versus advertisers’ metrics (conversion-based). As serial position lowers (i.e., from rank 1 to a lower rank), there are only modest changes in publishers’ metrics, but drastic reductions in advertisers’. This pattern is unique to native ads and has not been indicated by prior research on ad serial position. Moreover, the authors show the moderating effects of audience gender and age. The findings provide new and timely implications for researchers and marketers.