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This paper illuminates the distinction between individual and organizational actors in business-to-business markets as well as the coexistence of formal and informal mechanisms of coordination in multinational corporations. The main questions addressed include the following. (1) What factors influence the occurrence of personal contacts of foreign subsidiary managers in industrial multinational corporations? (2) How such personal contacts enable coordination in industrial markets and within multinational firms? The theoretical context of the paper is based on: (1) the interaction approach to industrial markets, (2) the network approach to industrial markets, and (3) the process approach to multinational management. The unit of analysis is the foreign subsidiary manager as the focal actor of a contact network. The paper is empirically focused on Portuguese sales subsidiaries of Finnish multinational corporations, which are managed by either a parent country national (Finnish), a host country national (Portuguese) or a third country national. The paper suggests eight scenarios of individual dependence and uncertainty, which are determined by individual, organizational, and/or market factors. Such scenarios are, in turn, thought to require personal contacts with specific functions. The paper suggests eight interpersonal roles of foreign subsidiary managers, by which the functions of their personal contacts enable inter-firm coordination in industrial markets. In addition, the paper suggests eight propositions on how the functions of their personal contacts enable centralization, formalization, socialization and horizontal communication in multinational corporations.

The purpose of this study is to apply altmetrics and bibliometric indicators on the top 100 most mentioned articles published related to the sustainable development goal (SDG)-13, Climate Action.

The authors used the Altmetric Explorer’s SDGs filter to extract the most mentioned articles belonging to Climate Action and their other characteristics, such as DOI, titles, tools mentioning them and their demographic descriptions. The same set of papers was searched in the Dimensions database to extract them in the format importable in R-studio to check the distribution of papers across various journals and identify their subject category, countries and institutions publishing these papers. Further, SPSS was used to check the correlation between altmetric attention score (AAS) and citations.

The results of the paper showed the mean of AAS and the citations received by the articles was 3,556.35 and 304.04, respectively. Twitter has been the most used social media platform for mentioning the research related to climate action, covering 88.1% of the total mentions. The Twitter and the News mention demographics show the USA contributing the most tweet mentions (15.2%) as well as news mentions (57.65%) to the papers. Also, the USA has solely published 49 papers from the total papers selected for the study. The papers were published in 31 journals most of them belonging to the quartile first (Q1) category and primarily belonged to the subject category “Earth Sciences.” Pearson’s correlational method showed a significant but low positive correlation between AAS and citation counts (r = 0.365, p = <0.001) and a strong positive correlation between the citations and Mendeley readership counts (r = 0.907).

The research is original in nature and discovered very interesting results about climate action using altmetric and bibliometric techniques.

Purpose: Mentioning a criminal’s country of origin in crime news is a divisive and much-discussed issue among both journalists and members of society. Scholars assume that mentioning a criminal’s foreign origin could develop and maintain prejudices against individuals with a migrant background among news recipients. However, until now, no attention has been paid to what increases the likelihood that a journalist does or does not mention a criminal’s country of origin when reporting on crimes. Methodology/approach: One possible explanation is that the frequency and intensity of specific news factors could lead to mentioning a criminal’s origin, since increased importance of a news story is usually assigned when many high-intensity news factors occur. Even though numerous studies have determined the frequency of specific news factors in (crime) news, the explanation hypothesized in this chapter has not yet been examined. To investigate this supposition empirically, a quantitative content analysis of four German prime newscasts (n = 290), including public and private broadcasts, was conducted in the current study. Findings: The findings indicate that mentioning criminals’ origins is still common practice in journalism; furthermore, criminals with foreign origins are explicitly represented as foreign almost ten times more often than German-origin criminals are explicitly mentioned as German. News factors such as personalization, location, and influence show some effects of positively predicting journalistic mentioning of a criminal’s country of origin.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

The study showcases the social impact (online attention) of funded research compared to nonfunded for the BRICS nations. The key themes achieving online attention across the funded and nonfunded publications have also been identified.

A total of 1,507,931 articles published across the BRICS nations for a period of three (03) years were downloaded from the Clarivate Analytics' InCites database of Web of Science (WoS). “Funding Acknowledgement Analysis (FAA)” was used to identify the funded and nonfunded publications. The altmetric score of the top highly cited (1%) publications was gauged from the largest altmetric data provider, “Altmetric.com”, using the DOI of each publication. One-way ANOVA test was used to know the impact of funding on the mentions (altmetrics) across different data sources covered by Altmetric.com. The highly predominant keywords (hotspots) have been mapped using bibliometric software, “VOSviewer”.

The mentions across all the altmetric sources for funded research are higher compared to nonfunded research for all nations. It indicates the altmetric advantage for funded research, as funded publications are more discussed, tweeted, shared and have more readers and citations; thus, acquiring more social impact/online attention compared to nonfunded publications. The difference in means for funded and nonfunded publications varies across various altmetric sources and nations. Further, the authors’ keyword analysis reveals the prominence of the respective nation names in publications of the BRICS.

The study showcases the utility of indexing the funding information and whether research funding increases social impact return (online attention). It presents altmetrics as an important impact assessment and evaluation framework indicator, adding one more dimension to the research performance. The linking of funding information with the altmetric score can be used to assess the online attention and multi-flavoured impact of a particular funding programme and source/agency of a nation so that necessary strategies would be framed to improve the reach and impact of funded research. It identifies countries that achieve significant online attention for their funded publications compared to nonfunded ones, along with the key themes that can be utilised to frame research and investment plans.

The study represents the social impact of funded research compared to nonfunded across the BRICS nations.

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products.

The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations.

The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation.

The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation.

A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

By analyzing journal articles with high citation counts but low Twitter mentions and vice versa, the purpose of this paper is to provide an overall picture of differences between citation counts and Twitter mentions of academic articles.

Citation counts from the Web of Science and Twitter mentions of psychological articles under the Social Science Citation Index collection were collected for data analysis. An approach combining both statistical and simple content analysis was adopted to examine important factors contributing to citation counts and Twitter mentions, as well as the patterns of tweets mentioning academic articles.

Compared to citation counts, Twitter mentions have stronger affiliations with readability and accessibility of academic papers. Readability here was defined as the content size of articles and the usage of jargon and scientific expressions. In addition, Twitter activities, such as the use of hashtags and user mentions, could better facilitate the sharing of articles. Even though discussions of articles or related social phenomena were spotted in the contents of tweets, simple counts of Twitter mentions may not be reliable enough for research evaluations due to issues such as Twitter bots and a deficient understanding of Twitter users’ motivations for mentioning academic articles on Twitter.

This study has elaborated on the differences between Twitter mentions and citation counts by comparing the characteristics of Twitter-inclined and citation-inclined articles. It provides useful information for interested parties who would like to adopt social web metrics such as Twitter mentions as traces of broader engagement with academic literature and potential suggestions to increase the reliability of Twitter metrics. In addition, it gives specific tips for researchers to increase research visibility and get attention from the general public on Twitter.

The purpose of this paper is to assess the educational value of prestigious and productive Spanish scholarly publishers based on mentions of their books in online scholarly syllabi.

Syllabus mentions of 15,117 books from 27 publishers were searched for, manually checked and compared with Microsoft Academic (MA) citations.

Most books published by Ariel, Síntesis, Tecnos and Cátedra have been mentioned in at least one online syllabus, indicating that their books have consistently high educational value. In contrast, few books published by the most productive publishers were mentioned in online syllabi. Prestigious publishers have both the highest educational impact based on syllabus mentions and the highest research impact based on MA citations.

The results might be different for other publishers. The online syllabus mentions found may be a small fraction of the syllabus mentions of the sampled books.

Authors of Spanish-language social sciences and humanities books should consider general prestige when selecting a publisher if they want educational uptake for their work.

This is the first study assessing book publishers based on syllabus mentions.

Title and URL mentions have recently been proposed as web visibility indicators instead of inlink counts. The objective of this study is to determine the accuracy of these alternative web mention indicators in the Spanish academic system, taking into account their complexity (multi-domains) and diversity (different official languages).

Inlinks, title and URL mentions from 76 Spanish universities were manually extracted from the main search engines (Google, Google Scholar, Yahoo!, Bing and Exalead). Several statistical methods, such as correlation, difference tests and regression models, were used.

Web mentions, despite some limitations, can be used as substitutes for inlinks in the Spanish academic system, although these indicators are more likely to be influenced by the environment (language, web domain policy, etc.) than inlinks.

Title mentions provide unstable results caused by the multiple name variants which an institution can present (such as acronyms and other language versions). URL mentions are more stable, but they may present atypical points due to some shortcomings, the effect of which is that URL mentions do not have the same meaning as inlinks.

Web mentions should be used with caution and after a cleaning-up process. Moreover, these counts do not necessarily signify connectivity, so their use in global web analysis should be limited.

Web mentions have previously been used in some specific academic systems (US, UK and China), but this study analyses, in depth and for the first time, an entire non-English speaking European country (Spain), with complex academic web behaviour, which helps to better explain previous web mention results.

The first purpose of the present study is to investigate the coverage of journal articles in Physics in various sources of altmetrics. Secondly, the study investigates the relationship between altmetrics and citations. Finally, the study also investigates whether the relationship between citations and altmetrics was stronger or weaker for those articles that had been mentioned at least once in the sources of altmetrics.

The journal articles in Physics having at least one author from an Indian Institution and published during 2014–2018 in sources of altmetrics have been investigated. Altmetric.com was used for collecting altmetrics data. Spearman’s rank correlation coefficient (ρ) has been used as the data found to be skewed.

The highest coverage was found on Twitter (22.68%), followed by Facebook (3.62%) and blogs (2.18%). The coverage in the rest of the sources was less than 1%. The average Twitter mentions for journal articles tweeted at least once was found to be 4 (3.99) and for Facebook mentions, it was found to be 1.48. Correlations between Twitter mentions–citations and Facebook mentions–citation were found to be statistically significant but low to weak positive.

The study concludes that due to the low coverage of journal articles, altmetrics should be used cautiously for research evaluation keeping in mind the disciplinary differences. The study also suggests that altmetrics can function as complementary to citation-based metrics.

The study is one of the first large scale altmetrics studies dealing with research in Physics. Also, Indian research has not been attended to in the altmetrics literature and the present study shall fill that void.