Tag Archives: web2.0

August 15, 2015 · 4:52 pm

Unleashing Corporate Social Responsibility through Digital Media

csr

Companies are increasingly focusing their attention on content and inbound marketing. In a nutshell, content marketing necessitates an integrated marketing communications approach involving different media (1). Content strategists and marketers who care about their online reputation are realising that they have to continuously come up with fresh, engaging content with a growing number of quality links. They have to make sure that their websites offer great content for different search engines. Consistent high quality content ought to be meaningful and purposeful for target audiences (2).

Successful marketers are capable of enhancing customer loyalty, particularly if their businesses are delivering ongoing value propositions to promising prospects (on their website). Such businesses are continuously coming up with informative yet interesting content through digital channels, including blogs, podcasts, social media networking and e-newsletters. Online content often include refreshing information which tell stakeholders how to connect the dots. It may appear that many companies are becoming quite knowledgeable in using social media channels to protect their reputation from bad publicity or misinformation.

Several online businesses often tell insightful stories to their customers or inspire them with sustainable ideas and innovations. Corporate web sites could even contain their latest news, elements of the marketing-mix endeavours as well as digital marketing fads.
Most social media networks are effective monitoring tools as they could feature early warning signals of trending topics (3). These networks may help business communicators and marketers identify and follow the latest sustainability issues. Notwithstanding, CSR influencers are easily identified on particular subject matters or expertise. For example, businesses and customers alike have also learned how to use the hashtag (#) to enhance the visibility of their shareable content (4). Some of the most popular hashtags comprise: #CSR #StrategicCSR, #sustainability, #susty, #CSRTalk, #Davos2015, #KyotoProtocol, #SharedValue et cetera. Hashtags could possibly result in financial support to charity, philanthropic or stewardship principles. They may even help to raise awareness of the overall CSR communications. Hence, there are numerous opportunities for businesses to leverage themselves through social networks as they engage with influencers and media.

  • The ubiquity of Facebook and Google Plus over the past years has made them familiar channels for many individuals around the globe. These networks have become very popular communication outlets for brands, companies and activists alike. These social media empower their users to engage with business on a myriad of issues. They also enable individual professionals or groups to promote themselves and their CSR credentials in different markets and segments.
  • Moreover, Linkedin is yet another effective tool, particularly for personal branding. However, this social network helps users identify and engage with influencers. Companies can use this site to create or join their favourite groups on LinkedIn (e.g. GRI, FSG, Shared Value Initiative among others). They may also use this channel for CSR communication as they promote key initiatives and share sustainability ideas. Therefore, LinkedIn connects individuals and groups as they engage in conversations with both academia and CSR practitioners.
  • In addition, Pinterest and Instagram enable their users to share images, ideas with their networks. These social media could also be relevant in the context of the sustainability agenda. Businesses could illustrate their CSR communication to stakeholders through visual content. Evidently, these innovative social networks provide sharable imagery, infographics or videos to groups who may be passionate on certain issues, including CSR.
  • Moreover, digital marketers are increasingly uploading short, fun videos which often turn viral on internet (5). Youtube, Vimeo and Vine seem to have positioned themselves as important social media channels for many consumers, particularly among millennials. These sites offer an excellent way to humanise or animate  SR communication through video content. These digital media also allow their users to share their video content across multiple networks. For instance, videos featuring university resources may comprise lectures, documentaries, case studies and the like.

CSR practices may provide a good opportunity for businesses to raise their profile in the communities around them.  Genuine businesses communicate their motives and rationales behind their CSR programmes. In this case, there are numerous media outlets where businesses can obtain decent coverage of their CSR initiatives, especially on the web (e.g. CSRwire and Triple Pundit among others). Although, there are instances  where consumers themselves, out of their own volition are becoming ambassadors of trustworthy businesses; at the same time certain stakeholders are becoming increasingly acquainted and skeptical on certain posturing behaviours and greenwashing (6).

Generally, digital communications will help to improve the corporate image of firms. Positive publicity can lead to reputational benefits and long lasting relationships with stakeholders (7). Online content and inbound marketing can be successfully employed for CSR communication1. Corporate sites should be as easy as possible, with user-centred design that enables interactive information sharing on CSR activities. Inter-operability and collaboration across different social media can help businesses to connect with stakeholders (1). 

Marketers can create a forum where prospects or web visitors can engage with the business in real time. These days, marketing is all about keeping and maintaining a two-way relationship with consumers. Digital marketing is an effective tool for consumer engagement.

A growing number of businesses are learning how to collaborate with consumers about product development, service enhancement and promotion. These companies are increasingly involving customers in all aspects of marketing. They listen to and join online conversations as they value their stakeholders’ opinions and perceptions.

Today, pervasive social media networks are being used by millions of customers every day. In a sense, it may appear that digital marketing tools have reinforced the role of public relations. These promotional strategies complement well with CSR communication and sustainability reporting.

This contribution encourages businesses to use digital media to raise awareness of their societal engagement and environmentally sustainable practices. Further research may possibly identify how successful businesses are using digital channels to forge genuine relationships with their stakeholders.

References

  1. Camilleri, M.A. “Unleashing Shared Value Through Content Marketing.” Triple Pundit, 10th February 2014. http://www.triplepundit.com/2014/02/unleashing-shared-value-content-marketing/
  2. Camilleri, M.A. “A Search Engine Optimization Strategy for Content Marketing Success.” Social Media Today 28th May, 2014. http://www.socialmediatoday.com/content/search-engine-optimization-strategy-content-marketing-success
  3. Kietzmann, Jan H., Kristopher Hermkens, Ian P. McCarthy, and Bruno S. Silvestre. “Social media? Get serious! Understanding the functional building blocks of social media.” Business horizons 54, no. 3 (2011): 241-251.
  4. Small, Tamara A. “What the hashtag? A content analysis of Canadian politics on Twitter.” Information, Communication & Society 14, no. 6 (2011): 872-895.
  5. Guadagno, Rosanna E., Daniel M. Rempala, Shannon Murphy, and Bradley M. Okdie. “What makes a video go viral? An analysis of emotional contagion and Internet memes.” Computers in Human Behavior 29, no. 6 (2013): 2312-2319.
  6. Laufer, William S. “Social accountability and corporate greenwashing.” Journal of Business Ethics 43, no. 3 (2003): 253-261.
  7. Camilleri, M.A. “The Business Case for Corporate Social Responsibility” (paper presented at the American Marketing Association in collaboration with the University of Wyoming, Oklahoma State University and Villanova University: Marketing & Public Policy as a Force for Social Change Conference. Washington D.C., 5th June 2014): 8-14, Accessed June 26, 2015. https://www.ama.org/events-training/Conferences/Documents/2015-AMA-Marketing-Public-Policy-Proceedings.pdf
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July 17, 2015 · 7:32 pm

Crunching Big Data and Analytics from Web2.0

social media

The use of data and its analyses are becoming ubiquitous practices. As a result, there has been a dramatic surge in the use of business intelligence and analytics. These developments have inevitably led to endless opportunities for marketers to leverage themselves and gain a competitive advantage by untangling big data. Relevant data could help businesses to better serve customers as they would better know what they need, want and desire. This knowledge will lead to customer satisfaction and long lasting relationships.

Businesses are increasingly collecting and analysing data from many sources for many purposes. Much of the value of data is derived from secondary uses that were not intended in the first place. Very often datasets can possess intrinsic, hidden, not-yet-unearthed value. According to a research from IBM and the Saïd Business School at the University of Oxford; nearly nine in 10 companies were using transactional data, and three-quarters were collecting log data in 2012. This study suggested that business practitioners also gathered data from events, emails and social data (eMarketer, 2012).

This data is being collected and stored in massive amounts by search engines including Google, Bing and Yahoo as well as by e-commerce conglomerates such as eBay and Amazon. For instance, Security First boosted its productivity and customer satisfaction by using content analytics to bridge social media and the claims process. Similarly, Banco Bilbao Vizcaya Argentaria has improved its online reputation with analytics that quickly responded to online feedback (IBM, 2015).

In addition, users can easily access multiple sources of digital data that is readily available through websites, social networks, blogs, as well as from mobile devices, including smart phones and tablets. Big data is being gathered from social media content and video data from Facebook, Twitter, LinkedIn and Google Plus among others. These modern digital marketing tools are helping business to engage in social conversations with consumers. Social networks have surely amplified the marketers’ messages as they support promotional efforts. Here are some of the unique pieces of data each social network is collecting:

  • “Facebook’s interest/social graph: The world’s largest online community collects more data via its API than any other social network. Facebook’s “like” button is pressed 2.7 billion times every day across the web, revealing what people care about.
  • Google+’s relevance graph: The number of “+1s” and other Google+ data are now a top factor in determining how a Web page ranks in Google search results.
  • LinkedIn’s talent graph: At least 22% of LinkedIn users have between 500-999 first-degree connections on the social network, and 19% have between 301-499.The rich professional data is helping LinkedIn build a “talent graph.”
  • Twitter’s news graph: At its peak late last year the social network was processing 143,199 tweets per second globally. This firehose of tweets provide a real-time window into the news and information that people care about. Fifty-two percent of Twitter users in the U.S. consume news on the site (more than the percent who do so on Facebook), according to Pew.
  • Pinterest’s commerce graph: More than 17% of all pinboards are categorized under “Home,” while roughly 12% fall under style or fashion, these are windows into people’s tastes and fashion trends.
  • YouTube’s entertainment graph: What music, shows, and celebrities do we like? YouTube reaches more U.S. adults aged 18 to 34 than any single cable network, according to Nielsen. YouTube knows what they like to watch.
  • Yelp’s and Foursquare’s location graphs: These apps know where we’ve been and where we’ll go. Foursquare has over 45 million users and 5 billion location check-ins” (Business Insider, 2014).

Big data is fundamentally shifting how marketers collect, analyse and utilise data to reach out to customers. Business intelligence and analytics are helping companies to get new insights into how consumers behave. It is envisaged that the IT architecture will shortly develop into an information eco-system: a network of internal and external services where information is shared among users. Big data can support business in their decision making. It could be used to communicate meaningful results and to generate insights for an effective organisational performance. New marketing decision-making ought to harness big data for increased targeting and re-targeting of individuals and online communities. On-demand, direct marketing through digital platforms has already become more personalised than ever. The challenge for marketers is to recognise the value of big data as a tool that drives consumer in-sights.

Every customer contact with a brand is a moment of truth, in real-time. Businesses who are not responding with seamless externally-facing solutions will inevitably lose their customers to rivals. This contribution posits that a strategic approach to data management could drive consumer preferences. An evolving analytics ecosystem that is also integrated with web2.0 instruments could lead to better customer service and consumer engagement.

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May 30, 2015 · 4:11 pm

The future of marketing is mobile…

mobile

An IBM (2012) technology trends survey indicated that mobile devices could increase the productivities and efficiencies of organisations. This study showed that mobile software was the second most “in demand” area for research and development. In addition, Gartner BI Hype Cycle (2012) also anticipated that mobile analytics was one of the latest technologies that may potentially disrupt the business intelligence market. At the same time, the market for mobile advertising is escalating at a very fast pace. Interestingly, eMarketer (2012) had predicted that mobile advertising shall experience a surge from an estimated $2.6 billion in 2012 to more than $10.8 billion in 2016. Evidently, there are niche areas for professional growth, particularly in this specialised field; as more and more individuals are increasingly creating new applications for mobile operating systems.

Recent advances in mobile communication and geo-positioning technologies have presented marketers with a new way how to target consumers based on their location. Location-targeted mobile advertising involves the provision of ad messages to cellular subscribers based on their geographic locations. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising seems to offer tremendous marketing benefits.

In addition, many businesses are commonly utilising applications, including browser cookies that track consumers through their mobile devices as they move out and about. Once these users leave these sites, the products or services that they had viewed online will be shown to them again in advertisements, across different websites. Hence, businesses are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. Therefore, savvy brands are becoming increasingly proficient in personalising their offerings as they collect, classify and use large data volumes on their consumers’ behaviours. As more consumers carry smartphones with them, they are (or may be) receiving compelling offers that instantaneously pop up on their mobile devices.

For instance, consumers are continuously using social networks and indicating their geo location as they use mobile apps. This same data can be used to identify where people tend to gather — information that could be useful in predicting real estate prices et cetera. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. Businesses are using mobile devices and networks to capture important consumer data. Smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more brands shall be using mobile devices and networks as a sort of sensor data – to acquire relevant information on their consumers’ digital behaviours and physical movements. These businesses have become increasingly interactive through the proliferation of near-field communication (NFC). Basically, embedded chips in the customers’ mobile phones are exchanging data with retailers’ items possessing the NFC tags. It is envisaged that mobile wallet transactions using NFC technologies are expected to reach $110 billion, by the year 2017. The latest Android and Microsoft smartphones have already include these NFC capabilities. Moreover, a recent patent application by Apple has revealed its plans to include NFC capabilities in their next products. This will inevitably lead to an increase in the use of mobile wallets (GSMA, 2015). Undoubtedly, the growth of such data-driven, digital technologies is adding value to customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

Apparently, there are promising revenue streams in the mobile app market. Both Apple and Android are offering paid or free ad-supported apps in many categories. There are also companies that have developed apps for business intelligence. For example, enterprise / industry-specific apps, e-commerce apps and social apps. Evidently, the lightweight programming models of the current web services (e.g., HTML, XML, CSS, Ajax, Flash, J2E) as well as the maturing mobile development platforms such as Android and iOS have also contributed to the rapid proliferation of mobile applications (Chen et al., 2012). Moreover, researchers are increasingly exploring mobile sensing apps that are location-aware and activity-sensitive.

Possible future research avenues include mobile social innovation for m-learning; (Sharples, Taylor and Vavoula, 2010; Motiwalla, 2007), mobile social networking and crowd-sourcing (Lane et al., 2010), mobile visualisation (Corchado and Herrero, 2011), personalisation and behavioural modelling for mobile apps in gamification (Ha et al., 2007), mobile advertising and social media marketing (Bart et al., 2014; Yang et al., 2013). Google’s (2015) current projects include gesture and touch interaction; activity-based and context-aware computing; recommendation of social and activity streams; analytics of social media engagements, and end-user programming (Dai, Rzeszotarski, Paritosh and Chi, 2015;  Fowler, Partridge, Chelba, Bi, Ouyang and Zhai, 2015; Zhong, Weber, Burkhardt, Weaver and Bigham, 2015; Brzozowski, Adams and Chi, 2015).

 

References:

Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51(3), 270-285.

Brzozowski, M. J., Adams, P., & Chi, E. H. (2015, April). Google+ Communities as Plazas and Topic Boards. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3779-3788). ACM. Retrieved May 22nd, 2015, from http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43453.pdf

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Corchado, E., & Herrero, Á. (2011). Neural visualization of network traffic data for intrusion detection. Applied Soft Computing, 11(2), 2042-2056.

Dai, P., Rzeszotarski, J. M., Paritosh, P., & Chi, E. H. (2015). And Now for Something Completely Different: Improving Crowdsourcing Workflows with Micro-Diversions. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 628-638). ACM. Retrieved May 17th, 2015, from http://dl.acm.org/citation.cfm?id=2675260

eMarketer (2012). eMarketer in the News: June 1, 2012 Retrieved January 28th, 2015, from http://www.emarketer.com/newsroom/index.php/emarketer-news-june-1-2012/

Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S. (2015, April). Effects of Language Modeling and its Personalization on Touchscreen Typing Performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 649-658). ACM. Retrieved May15th, 2015, from http://cslu.ohsu.edu/~fowlera/Fowler_CHI2015.pdf

Gartner (2012). Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016. Retrieved January 20th, 2015, from https://www.gartner.com/doc/2195915/big-data-drives-rapid-changes

Google (2015). Human-Computer Interaction and Visualization Research at Google. Retrieved May 20th, 2015, from http://research.google.com/pubs/Human-ComputerInteractionandVisualization.html

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.

IBM (2012) Tech Trends Report. Fast track to the future. Retrieved May15th, 2015, from http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=XB&infotype=PM&appname=CHQE_XI_XI_USEN&htmlfid=XIE12346USEN&attachment=XIE12346USEN.PDF#loaded

Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9), 140-150.

Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581-596.

Sharples, M., Taylor, J., & Vavoula, G. (2010). A theory of learning for the mobile age. In Medienbildung in neuen Kulturräumen (pp. 87-99). VS Verlag für Sozialwissenschaften.

Yang, B., Kim, Y., & Yoo, C. (2013). The integrated mobile advertising model: The effects of technology-and emotion-based evaluations. Journal of Business Research, 66(9), 1345-1352.

Zhong, Y., Weber, A., Burkhardt, C., Weaver, P., & Bigham, J. P. (2015). Enhancing Android accessibility for users with hand tremor by reducing fine pointing and steady tapping. In Proceedings of the 12th Web for All Conference (p. 29). ACM. Retrieved Ma7 20th, 2015, from http://dl.acm.org/citation.cfm?id=2747277

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March 29, 2015 · 11:07 am

Using Content Marketing Metrics for Academic Impact

Academic contributions start from concepts and ideas. When their content is relevant and of a high quality they can be published in renowned, peer-reviewed journals. Researchers are increasingly using online full text databases from institutional repositories or online open access journals to disseminate their findings. The web has surely helped to enhance fruitful collaborative relationships among academia. The internet has brought increased engagement among peers, over email or video. In addition, they may share their knowledge with colleagues as they present their papers in seminars and conferences. After publication, their contributions may be cited by other scholars.

The researchers’ visibility does not only rely on the number of publications. Both academic researchers and their institutions are continuously being rated and classified. Their citations may result from highly reputable journals or well-linked homepages providing scientific content. Publications are usually ranked through metrics that will assess individual researchers and their organisational performance. Bibliometrics and citations may be considered as part of the academic reward system. Highly cited authors are usually endorsed by their peers for their significant contribution to knowledge by society. As a matter of fact, citations are at the core of scientometric methods as they have been used to measure the visibility and impact of scholarly work (Moed, 2006; Borgman, 2000). This contribution explores extant literature that explain how the visibility of individual researchers’content may be related to their academic clout. Therefore, it examines the communication structures and processes of scholarly communications (Kousha and Thelwall, 2007; Borgmann and Furner 2002). It presents relevant theoretical underpinnings on bibliometric studies and considers different methods that can analyse the individual researchers’ or their academic publications’ impact (Wilson, 1999; Tague-Sutcliffe, 1992).

 

Citation Analysis
The symbolic role of citation in representing the content of a document is an extensive dimension of information retrieval. Citation analysis expands the scope of information seeking by retrieving publications that have been cited in previous works. This methodology offers enormous possibilities for tracing trends and developments in different research areas. Citation analysis has become the de-facto standard in the evaluation of research. In fact, previous publications can be simply evaluated on the number of citations and the relatively good availability of citation data for such purposes (Knoth and Herrmannova, 2014). However, citations are merely one of the attributes of publications. By themselves, they do not provide adequate and sufficient evidence of impact, quality and research contribution. This may be due to a wide range of characteristics they exhibit; including the semantics of the citation (Knoth and Herrmannova, 2014), the motives for citing (Nicolaisen, 2007), the variations in sentiment (Athar, 2014), the context of the citation (He, Pei, Kifer, Mitra and Giles, 2010), the popularity of topics, the size of research communities (Brumback, 2009; Seglen, 1997), the time delay for citations to show up (Priem and Hemminger, 2010), the skewness of their distribution (Seglen, 1992), the difference in the types of research papers (Seglen, 1997) and finally the ability to game / manipulate citations (Arnold and Fowler, 2010).

Impact Factors (IFs)
Scholarly impact is measure of frequency in which an “average article” has been cited over a defined time period in a journal (Glanzel and Moed, 2002). Journal citations reports are published in June, every year by Thomson-Reuters’ Institute of Scientific Information (ISI). These reports also feature data for ranking the Immediacy Index of articles, which measure the number of times an article appeared in academic citations (Harter, 1996). Publishers of core scientific journals consider IF indicators in their evaluations of prospective contributions. In Despite there are severe limitations in the IF’s methodology, it is still the most common instrument that ranks international journals in any given field of study. Yet, impact factors have often been subject to ongoing criticism by researchers for their methodological and procedural imperfections. Commentators often debate about how IFs should be used. Whilst a higher impact factor may indicate journals that are considered to be more prestigious, it does not necessarily reflect the quality or impact of an individual article or researcher. This may be attributable to the large number of journals, the volume of research contributions, and also the rapidly changing nature of certain research fields and the increasing representation of researchers. Hence, other metrics have been developed to provide alternative measures to impact factors.

h-index
The h-index attempts to calculate the citation impact of the academic publications of researchers. Therefore, this index measures the scholars productivity by taking into account their most cited papers and the number of citations that they received in other publications. This index can also be applied to measure the impact and productivity of a scholarly journal, as well as a group of scientists, such as a department or university or country (Jones, Huggett and Kamalski, 2011). The (Hirsch) h-index was originally developed in 2005 to estimate the importance, significance and the broad impact of an academic’s researcher’s cumulative research contributions. Initially, the h-index was designed to overcome the limitations of other measures of quality and productivity of researchers. It consists of a single number that reports on an author’s academic contributions that have at least the equivalent number of citations. For instance, an h-index of 3 would indicate that the author has published at least three papers that have been cited three times or more. Therefore, the most productive researcher may possibly obtaining a high h-index. Moreover, the best papers in terms of quality will be mostly cited. Interestingly, this issue is driving more researchers to publish in open access journals.

 

Webometrics
The science of webometrics (also cybermetrics) is still in an experimental phase. Björneborn and Ingwersen (2004) indicated that webometrics involves an assessment of different types of hyperlinks. They argued that relevant links may help to improve the impact of academic publications. Therefore, webometrics refer to the quantitative analysis of activity on the world wide web, such as downloads (Davidson, Newton, Ferguson, Daly, Elliott, Homer, Duffield and Jackson, 2014). Webometrics recognise that the internet is a repository for a massive number of documents. It disseminates knowledge to wide audiences. The webometric ranking involves the measurement of volume, visibility, and the impact of web pages. Webometrics emphasise on scientific output including peer-reviewed papers, conference presentations, preprints, monographs, theses, and reports. However, these kind of electronic metrics also analyse other academic material (including courseware, seminar documentation, digital libraries, databases, multimedia, personal pages and blogs among others). Moreover, webometrics consider online information on the educational institution, its departments, research groups, supporting services, and the level of students attending courses.

Web 2.0 and Social Media
Internet users are increasingly creating and publishing their content online. Never before has it been so easy for academics to engage with their peers on both current affairs and scientific findings. The influence of social media has changed the academic publishing scenario. As a matter of fact, recently there has been an increased recognition for measures of scholarly impact to be drawn from Web 2.0 data (Priem and Hemminger, 2010).

The web has not only revolutionised how data is gathered, stored and shared but also provided a mechanism of measuring access to information. Moreover, academics are also using personal web sites and blogs to enhance the visibility of their publications. This medium improves their content marketing in addition to traditional bibliometrics. Social media networks are providing blogging platforms that allows users to communicate to anyone with online access. For instance, Twitter is rapidly becoming used for work related purposes, particularly scholarly communication, as a method of sharing and disseminating information which is central to the work of an academic (Java, Song, Finin and Tseng B, 2007). Recently, there has been rapid growth in the uptake of Twitter by academics to network, share ideas and common interests, and promote their scientific findings (Davidson et al., 2014).

Conclusions and Implications

There are various sources of bibliometric data, each possess their own strengths and limitations. Evidently, there is no single bibliometric measure that is perfect. Multiple approaches to evaluation are highly recommended. Moreover, bibliometric approaches should not be the only measures upon which academic and scholarly performance ought to be evaluated. Sometimes, it may appear that bibliometrics can reduce the publications’ impact to a quantitative, numerical score. Many commentators have argued that when viewed in isolation these metrics may not necessarily be representative of a researcher’s performance or capacity. In taking this view, one would consider bibliometric measures as only one aspect of performance upon which research can be judged. Nonetheless, this chapter indicated that bibliometrics still have their high utility in academia. It is very likely that metrics will to continue to be in use because they represent a relatively simple and accurate data source. For the time being, bibliometrics are an essential aspect of measuring academic clout and organisational performance. A number of systematic ways of assessment have been identified in this regard; including citation analysis, impact factor, h-index and webometrics among others. Notwithstanding, the changes in academic behaviours and their use of content marketing on internet have challenged traditional metrics. Evidently, the measurement of impact beyond citation metrics is an increasing focus among researchers, with social media networks representing the most contemporary way of establishing performance and impact. In conclusion, this contribution suggests that these bibliometrics as well as recognition by peers can help to boost the researchers’, research groups’ and universities’ productivity and their quality of research.

References
Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the AMS, 58(3), 434-437.

Athar, A. (2014). Sentiment analysis of scientific citations. University of Cambridge, Computer Laboratory, Technical Report, (UCAM-CL-TR-856).

Borgman, C. L. (2000). Digital libraries and the continuum of scholarly communication. Journal of documentation, 56(4), 412-430.

Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics.

Bornmann, L., & Daniel, H. D. (2005). Does the h-index for ranking of scientists really work?. Scientometrics, 65(3), 391-392.

Bornmann, L., & Daniel, H. D. (2007). What do we know about the h index?. Journal of the American Society for Information Science and technology, 58(9), 1381-1385.

Björneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216-1227.

Glänzel, W., & Moed, H. F. (2002). Journal impact measures in bibliometric research. Scientometrics, 53(2), 171-193.

Harter, S. (1996). Historical roots of contemporary issues involving self-concept.

He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010, April). Context-aware citation recommendation. In Proceedings of the 19th international conference on World wide web (pp. 421-430). ACM.

Java, A., Song, X., Finin, T., & Tseng, B. (2007, August). Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56-65). ACM. http://scholar.google.com/scholar?q=http://dx.doi.org/10.1145/1348549.1348556

Knoth, P., & Herrmannova, D. (2014). Towards Semantometrics: A New Semantic Similarity Based Measure for Assessing a Research Publication’s Contribution. D-Lib Magazine, 20(11), 8.

Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google Web/URL citations: A multi‐discipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065.

Moed, H. F. (2006). Citation analysis in research evaluation (Vol. 9). Springer Science & Business Media.

Nicolaisen, J. (2007). Citation analysis. Annual review of information science and technology, 41(1), 609-641.

Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday, 15(7).

Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628-638.

Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. Bmj, 314(7079), 497.

Tague-Sutcliffe, J. (1992). An introduction to informetrics. Information processing & management, 28(1), 1-3.

Wilson, C. S. (1999). Informetrics. Annual Review of Information Science and Technology (ARIST), 34, 107-247.

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