Tag Archives: academia

November 11, 2019 · 1:00 pm

The online users’ engagement with e-Government services

This is an excerpt from my latest academic contribution.

How to Cite: Camilleri, M.A. (2019). The online users’ perceptions toward electronic government services. Journal of Information, Communication & Ethics in Society. 10.1108/JICES-09-2019-0102

tech

Several governments around the globe are utilizing the digital and mobile technologies to enhance the provision of their public services (EuroParl, 2015; Zuiderwijk Janssen & Dwivedi. 2015). Digital and mobile services are the facilitating instruments that are enabling all levels of the governments’ operations, to better service their citizens, big businesses, small enterprises and non-profit organizations (Wirtz & Birkmeyer, 2018; Rana & Dwivedi, 2015; Evans & Campos, 2013). The-governments are increasingly relying on ICT, including computers, websites and business process re-engineering (BPR) to engage with online users (Isaías, Pífano & Miranda, 2012; Weerakkody, Janssen & Dwivedi, 2011). Hence, the delivery of e-government and m-government services may usually demand the public service to implement specific transformational processes and procedures that are ultimately intended to add value to customers (Pereira, Macadar, Luciano & Testa, 2017).  Previously, the-governments’ consumers relied on face-to-face interactions or on telephone communications to engage with their consumers. Gradually, many governments had introduced interactive communications as departments and their officials started using the emails to engage with online users. Today, citizens and businesses can communicate and interact with the-government departments and agencies in real-time, through virtual call centers, via instant-messaging (IM), graphical user interfaces (GUI) and audio/video presentations.

In the past, the-governments’ services were operated in administrative silos of information (EuroParl, 2017). However, the electronic governance involves the data exchange between the-government and its stakeholders, including the businesses as well as the general public (Pereira et al., 2017; Rana & Dwivedi, 2015; Chun et al., 2010). The advances in interactive technologies have brought significant improvements in the delivery of service quality to online users of the Internet (Sá, Rocha & Cota, 2016; Isaías et al., 2012). As a result, the e-government and m-government services have become refined and sophisticated. Thus, the provision of online services is more efficient and less costly when compared to the offline services.

However, there are still many citizens and businesses who for various reasons may not want to engage with the-governments’ electronic and/or mobile services (Shareef, Kumar, Dwivedi & Kumar, 2016; 2014). This argumentation is conspicuous with the digital divide in society as not everyone is benefiting from an equitable access and democratic participation in the Internet or from the e-government systems (Ebbers, Jansen & van Deursen, 2016; Friemel, 2016; Luna-Reyes, Gil-Garcia & Romero, 2012; Isaías, Miranda & Pífano, 2009). The low usage of e-government systems impedes the ability of many governments to connect to citizens (Danila & Abdullah, 2014). Mensah (2018) held that the government authorities should promote the utilization of user-friendly mobile applications as the majority of citizens are increasingly engaging with their smartphones for different purposes, including to access information and services. Many countries around the world have introduced online government portals can be accessed through desktop computers as well as via mobile-friendly designs (Camilleri, 2019a; Ndou, 2004). Massey et al. (2019) posited that the government’s electronic services can be integrated among different devices in order to ensure an effective service delivery. These authors also maintained that the citizens are increasingly relying on the features of the mobile technologies as they are always connected to wireless networks. Their portable, mobile devices can provide access to a wide array of public information at any time and in any place (Camilleri & Camilleri, 2019; Wirtz & Birkmeyer, 2018; Sareen, Punia, & Chanana, 2013).

In a similar vein, many citizens may easily access their respective government’s online portal via virtual, open networks. They can also receive instantaneous messages and responses from the governments’ public service systems in their mobile devices, including smart phones or tablets (Shareef et al., 2016). Therefore, m-governance can possibly enhance the quality of the public services in terms of improved efficiency and cost savings (Madden, Bohlin, Oniki, & Tran, 2013). Notwithstanding, in the near future, the government’s electronic systems will be in a better position to exceed their citizens’  expectations, in terms of quality of service (Li & Shang, 2019). The advances in technology, including the increased massive wireless data traffic from different application scenarios, as well as the efficient resource allocation schemes will be better exploited to improve the capacity of online and mobile networks (Zhang, Liu, Chu, Long, Aghvami & Leung, 2017). For instance, the fifth generation (5G) of mobile communication systems is expected to enhance  the citizens’ service quality as they may offer higher mobile connection speeds, capacities and reduced latencies (Osseiran, Boccardi, Braun, Kusume, Marsch, Maternia & Tullberg, 2014; Zhang et al., 2017).

Nevertheless, despite these technological breakthroughs, there are many citizens who are still reluctant to use the-governments’ electronic and/or mobile services as they hold negative perceptions toward public administration (Wirtz & Birkmeyer, 2018; Shareef, Dwivedi, Stamati, & Williams, 2014). These individuals are not comfortable to share their personal information online (Van Deursen & Van Dijk, 2014). They may perceive that e-government and/or m-government platforms are risky and unsecure (Conradie & Choenni, 2014; Bélanger & Carter, 2008). Consequentially, they will decide not to upload their data as they suspect that it can be used by third parties (Picazo-Vela et al., 2012; Bélanger & Carter, 2008).

References (these are all the references that appeared in the bibliography section of the full paper).

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Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, pp. 179-211.

Bélanger, F. and Carter, L. (2008), “Trust and risk in e-government adoption”, The Journal of Strategic Information Systems, Vol. 17, No. 2, pp. 165-176.

Camilleri, M. A. and Camilleri, A.C. (2017a), “The technology acceptance of mobile applications in education”, In 13th International Conference on Mobile Learning (Budapest, April 10th). Proceedings, International Association for Development of the Information Society.

Camilleri, M.A., and Camilleri, A.C. (2017b), “Digital learning resources and ubiquitous technologies in education”, Technology, Knowledge and Learning, Vol. 22, No. 1, pp. 65-82.

Camilleri, M. A. (2019a), “Exploring the Behavioral Intention to Use e-Government Services: Validating the Unified Theory of Acceptance and Use of Technology”. 9th International Conference on Internet Technologies & Society, Lingnan University, Hong Kong. IADIS.

Camilleri, M. (2019b), “The SMEs’ technology acceptance of digital media for stakeholder engagement”, Journal of Small Business and Enterprise Development, Vol. 26 No. 4, pp. 504-521.

Camilleri, M.A. and Camilleri, A.C. (2019), “The Students’ Readiness to Engage with Mobile Learning Apps”, Interactive Technology and Smart Education”, available at: DOI: 10.1108/ITSE-06-2019-0027 (accessed 5 September 2019).

Carter, L. and Bélanger, F. (2005), “The utilization of e‐government services: citizen trust, innovation and acceptance factors”, Information Systems Journal, Vol. 15, No. 1, pp. 5-25.

Chun, S., Shulman, S., Sandoval, R. and Hovy, E. (2010), “Government 2.0: Making connections between citizens, data and government”, Information Polity, Vol. 15, Nos. (1, 2), pp. 1-9.

Conradie, P. and Choenni, S. (2014), “On the barriers for local government releasing open data”, Government Information Quarterly, Vol. 31, pp. S10-S17.

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Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35, No. 8, pp. 982-1003.

Davis, F. D., Bagozzi, R.P. and Warshaw, P.R. (1992), “Extrinsic and intrinsic motivation to use computers in the workplace”, Journal of Applied Social Psychology, Vol. 22, No. 14, pp. 1111-1132.

Ebbers, W. E., Jansen, M. G. and van Deursen, A. J. (2016), “Impact of the digital divide on e-government: Expanding from channel choice to channel usage”, Government Information Quarterly, Vol. 33, No. 4, pp. 685-692.

EU (2018), “EU Data Protection Rules”, https://ec.europa.eu/commission/priorities/justice-and-fundamental-rights/data-protection/2018-reform-eu-data-protection-rules/eu-data-protection-rules_en

EuroParl (2015), “e-government: Using technology to improve public services and democratic participation”, available at: http://www.europarl.europa.eu/RegData/etudes/IDAN/2015/565890/EPRS_IDA(2015)565890_EN.pdf (accessed 12 August 2019).

EuroParl (2017), “The role of e-government in deepening the single market”, available at: http://www.europarl.europa.eu/RegData/etudes/BRIE/2017/608706/EPRS_BRI(2017)608706_EN.pdf (accessed 12 August 2019).

Evans, A. M. and Campos, A. (2013), “Open government initiatives: Challenges of citizen participation”, Journal of Policy Analysis and Management, Vol. 32, No. 1, pp. 172-185.

Fishbein, M. and Ajzen, I. (1975), “Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research”, Reading, MA, USA: Addison-Wesley.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 48, pp. 39-50.

Friemel, T. N. (2016), “The digital divide has grown old: Determinants of a digital divide among seniors”, New Media & Society, Vol. 18, No. 2, pp. 313-331.

Isaías, P., Miranda, P. and Pífano, S. (2009), “Critical success factors for web 2.0–A reference framework”, In International Conference on Online Communities and Social Computing (pp. 354-363). Berlin,Germany: Springer.

Isaías, P., Pífano, S. and Miranda, P. (2012), “Web 2.0: Harnessing democracy’s potential”, In Public Service, Governance and Web 2.0 Technologies: Future Trends in Social Media (pp. 223-236). Hershey, USA: IGI Global.

Jaeger, P. and Matteson, M. (2009), “e-Government and Technology Acceptance: The Case of the Implementation of Section 508 Guidelines for Websites”, Electronic Journal of E-Government, Vol. 7, No. 1, pp. 87-98.

Kline, R.B. (2005), “Principles and practice of structural equation modeling” (2nd ed.). New York, USA: Guilford Press.

Layne, K. and Lee, J. (2001), “Developing fully functional E-government: A four stage model”, Government Information Quarterly, Vol. 18, No. 2, pp. 122-136.

Lee, J. B. and Porumbescu, G. A. (2019), “Engendering inclusive e-government use through citizen IT training programs”, Government Information Quarterly, Vol. 36, No. 1, pp. 69-76.

Li, Y. and Shang, H. (2019), “Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China”, Information & Management, https://www.sciencedirect.com/science/article/pii/S0378720617306912

Luna-Reyes, L. F., Gil-Garcia, J. R. and Romero, G. (2012), “Towards a multidimensional model for evaluating electronic government: Proposing a more comprehensive and integrative perspective”, Government Information Quarterly, Vol. 29, No. 3, pp. 324-334.

Madden, G., Bohlin, E., Oniki, H. and Tran, T. (2013), “Potential demand for m-government services in Japan”, Applied Economics Letters, Vol. 20, No. 8, pp. 732-736.

Mensah, I. K. (2018), “Citizens’ Readiness to adopt and use e-government services in the city of Harbin, China”, International Journal of Public Administration, Vol. 41, No. 4, pp. 297-307.

Mossey, S., Bromberg, D. and Manoharan, A. P. (2019), “Harnessing the power of mobile technology to bridge the digital divide: a look at US cities’ mobile-government capability”, Journal of Information Technology & Politics, Vol. 16, No. 1, pp. 52-65.

Ndou, V. (2004), “E–Government for developing countries: opportunities and challenges”, The electronic journal of information systems in developing countries, Vol 18, No. 1, pp. 1-24.

Osseiran, A., Boccardi, F., Braun, V., Kusume, K., Marsch, P., Maternia, M. and Tullberg, H. (2014), “Scenarios for 5G mobile and wireless communications: the vision of the METIS project”, IEEE Communications Magazine, Vol. 52, No. 5, pp. 26-35.

Park, S.Y., Nam, M.W. and Cha, S. B. (2012), “University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model”, British Journal of Educational Technology, Vol. 43, No. 4, pp. 592-605.

Pereira, G. V., Macadar, M. A., Luciano, E. M. and Testa, M. G. (2017), “Delivering public value through open government data initiatives in a Smart City context”, Information Systems Frontiers, Vol. 19, No. 2, pp. 213-229.

Picazo-Vela, S., Gutiérrez-Martínez, I. and Luna-Reyes, L. F. (2012), “Understanding risks, benefits, and strategic alternatives of social media applications in the public sector”, Government Information Quarterly, Vol. 29, No. 4, pp. 504-511.

Rana, N. P., Dwivedi, Y. K. and Williams, M. D. (2013), “Analysing challenges, barriers and CSF of e gov adoption”, Transforming Government: People, Process and Policy, Vol. 7, No. 2, pp. 177-198.

Rana, N. P. and Dwivedi, Y.K. (2015), “Citizen’s adoption of an e-government system: Validating extended social cognitive theory (SCT)”, Government Information Quarterly, Vol. 32, No. 2, pp. 172-181.

Sá, F., Rocha, Á. and Cota, M. P. (2016), “From the quality of traditional services to the quality of local e-Government online services: A literature review”, Government Information Quarterly, Vol. 33, No. 1, pp. 149-160.

Scott, M., DeLone, W. and Golden, W. (2016), “Measuring e-government success: a public value approach”, European Journal of Information Systems, Vol. 25, No. 3, pp. 187-208.

Shareef, M. A., Dwivedi, Y. K., Stamati, T. and Williams, M. D. (2014), “SQ m gov: a comprehensive service-quality paradigm for mobile-government”, Information Systems Management, Vol. 31, No. 2, pp. 126-142.

Shareef, M. A., Kumar, V., Dwivedi, Y. K. and Kumar, U. (2016), “Service delivery through mobile-government (m gov): Driving factors and cultural impacts”, Information Systems Frontiers, Vol. 18, No. 2, pp. 315-332.

Sharma, R., Yetton, P. and Crawford, J. (2009), “Estimating the effect of common method variance: The method—method pair technique with an illustration from TAM Research”, MIS Quarterly, Vol. 33, No. 3, pp. 473-490.

Van Deursen, A. and Van Dijk, J. (2011), “Internet skills and the digital divide”, New Media & Society”, Vol. 13 No. 6, pp. 893-911.

Van Deursen, A. J., & Van Dijk, J. A. (2014), “The digital divide shifts to differences in usage”, New Media & Aociety, Vol. 16 No. 3, pp. 507-526.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of information technology: Toward a unified view”, MIS Quarterly, pp. 425-478.

Venkatesh, V., Thong, J.Y. and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, pp. 157-178.

Wang, Y.S. and Shih, Y.W. (2009), “Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology”, Government Information Quarterly, Vol. 26, No. 1, pp. 158-165.

Weerakkody, V., Janssen, M. and Dwivedi, Y.K. (2011), “Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector”, Government Information Quarterly, Vol. 28, No. 3, pp. 320-328.

Wirtz, B. W. and Birkmeyer, S. (2018), “Mobile-government Services: An Empirical Analysis of Mobile-government Attractiveness”, International Journal of Public Administration, Vol. 41, No. 16, pp. 1385-1395.

Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A. H., & Leung, V. C. (2017). Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE Communications Magazine, 55(8), 138-145.

Zuiderwijk, A., Janssen, M. and Dwivedi, Y.K. (2015), “Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology”, Government Information Quarterly, Vol. 32, No. 4, pp. 429-440.

 

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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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