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Mr. Ioannis Drivas
University of West Attica. Dept of Archival, Library Science and Information Studies

Basic Info


Research Keywords & Expertise

0 Digital Marketing
0 Predictive Analytics
0 Predictive Modeling
0 Web Analytics
0 Web Mining

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Digital Marketing
Web Analytics
Web Mining

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Short Biography

Ioannis (or Giannis) is PhD candidate at the Information Management Research Lab of the Department of Archival, Library and Information Studies. He received his B.Sc. in Library Science & Information Systems in December 2014. In June 2017, Giannis received his Master of Philosophy (M.Phil) in Information Systems from Linnaeus University. His current professional experience focuses on the Web Mining & Data Analytics sector. From February 2020, Ioannis is a member of the Special Interest Group in Computer-Human Interaction in the Association of Computing Machinery ACM-SIGCHI and Reviewer in MDPI Open Access Journals Database.

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Journal article
Published: 24 June 2021 in Information
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While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

ACS Style

Ioannis Drivas; Dimitrios Kouis; Daphne Kyriaki-Manessi; Georgios Giannakopoulos. Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology. Information 2021, 12, 259 .

AMA Style

Ioannis Drivas, Dimitrios Kouis, Daphne Kyriaki-Manessi, Georgios Giannakopoulos. Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology. Information. 2021; 12 (7):259.

Chicago/Turabian Style

Ioannis Drivas; Dimitrios Kouis; Daphne Kyriaki-Manessi; Georgios Giannakopoulos. 2021. "Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology." Information 12, no. 7: 259.

Conference paper
Published: 01 February 2021 in Sustainable Transport Development, Innovation and Technology
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Display advertising constitutes one of the most efficient digital marketing strategies for the development of organizations’ brand awareness. Proper targeting of display ads campaigns potentially leads to the improvement of web users’ consideration and engagement about products and services that organizations offer through their websites. As prior studies indicate, this kind of consideration and engagement, which resulted through display ads, leads web users to type the name of the brand in search engines. The submitted search terms that contain the brand name of the organizations are called branded keywords, and the traffic that comes from them as branded search traffic. In this paper, the authors propose a computational data-driven methodology for the estimation and prediction of display advertising effectiveness in terms of optimizing brand popularity in search engines. One step further, preliminary research efforts of the authors indicate that branded search traffic visitors show higher interaction with the content of the websites regarding the time they spend and the number of pageviews they are browsing. In this respect, if display advertising campaigns increase the number of branded keywords and hence, the volume of branded search traffic, then this raises opportunities to optimize users’ engagement inside websites. Against this research gap, the authors proceed into a data-driven methodological process that is expanded in three major stages. In the first stage, the web mining process of extracting several web behavioral analytics metrics takes place for 125 continuous days at 7 courseware websites. At the second stage, analysis and interpretation of possible intercorrelations between the web analytics metrics take place with the purpose to integrate a computational model that relies on web behavioral data harvesting and their statistical analysis. Subsequently, in the third stage, the authors develop a data-driven computational model based on the agent-based modeling approach for estimating and predicting the optimal interaction rates of branded search traffic visitors of the examined websites. The results of the study constitute a practical toolbox for digital marketing practitioners in order to understand their display advertising effectiveness in terms of brand popularity and branded search traffic improvement for their websites.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos. Display Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitors. Sustainable Transport Development, Innovation and Technology 2021, 3 -15.

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos. Display Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitors. Sustainable Transport Development, Innovation and Technology. 2021; ():3-15.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos. 2021. "Display Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitors." Sustainable Transport Development, Innovation and Technology , no. : 3-15.

Conference paper
Published: 01 February 2021 in Sustainable Transport Development, Innovation and Technology
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Optimized paid search advertising campaigns composed of multiple data analytics insights and prior experiences of search engine marketing performances. However, when marketers compete in the battle of paid search ads’ rankings, complexity in optimization is increased. The higher the search ads’ ranking position, the greater the chance that users of search engines will click the ads. Despite the existing knowledge of the factors that contribute to the higher ranking position in search ads, such as proper relevancy among users’ search terms and text ads, or landing pages content, little is known about search engine users’ behavior after ads clicking. Low interaction or immediate abandonments from the landing pages potentially lead to a waste of budget spent on each paid advertising campaign. In this regard, marketers should pay much more attention to the interaction of paid traffic visitors after clicking on search ads, and not only to search engine rankings and user impression share rates. In this paper, the authors develop a computational data-driven methodology with a purpose to estimate and predict paid traffic visitors’ engagement in seven courseware websites after clicking on the search ads. The higher the engagement with the landing page, the higher will be the probability for conversions. At the first stage, web behavioral analytics are retrieved for 120 consecutive days in certain web metrics. These are the volume of paid traffic visitors, the average pages per session, the average session duration, and the bounce rate. Statistical analysis of the extracted web behavioral datasets takes place for understanding the cohesion, validity, and intercorrelations between the web metrics. KMO and Bartlett’s test of sphericity and Pearson coefficient of correlation are adopted. One step further, agent-based modeling and simulation is adopted as a methodology for abstracting and calibrating paid traffic visitors’ behavior inside the examined websites. Poisson distributions are implemented for predicting the potential engagement of paid traffic visitors in specific date ranges. Through this, the paper highlights its practical contribution to marketers with the purpose to develop search engine marketing campaigns composed of search ads relevant to the users and sufficient content engagement after ads clicking.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. Optimization of Paid Search Traffic Effectiveness and Users’ Engagement Within Websites. Sustainable Transport Development, Innovation and Technology 2021, 17 -30.

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos, Daphne Kyriaki-Manessi. Optimization of Paid Search Traffic Effectiveness and Users’ Engagement Within Websites. Sustainable Transport Development, Innovation and Technology. 2021; ():17-30.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. 2021. "Optimization of Paid Search Traffic Effectiveness and Users’ Engagement Within Websites." Sustainable Transport Development, Innovation and Technology , no. : 17-30.

Conference paper
Published: 01 February 2021 in Sustainable Transport Development, Innovation and Technology
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In the new era of marketing, being at the top results of search engines constitutes one of the most competitive advantages to the organizations’ overall online advertising strategy. In search engines, users type their search terms to cover their informational or purchasing needs and subsequently, search engines rank websites to the relevance of users’ search terms. The higher are the rankings of the websites, the more is the percentage of visitors who explicitly come from search engines. Nevertheless this obvious one marketing advantage, there is no prior research evidence as regards the level of engagement between users and content, after they visit the websites from search engines’ results. That is, users probably visit a website that comes at the top of search engines’ results, however, they do not spend an amount of time, or they do not browse in several webpages inside of it and vice versa. Against this backdrop, the authors proceed into the construction of a methodology composed of the retrieval of web analytics datasets and the development of computational models with the purpose to evaluate users’ engagement and content use within the websites. At the first stage, the authors proceed into the retrieval of web behavioral analytics at certain metrics for 125 sequential days as regards the time users are spending, the number of pageviews they are browsing, the percentage of immediate abandonments, and the percentage of traffic that explicitly comes from search engines. Following a data-driven methodological approach for the development of computational models, the fuzzy cognitive mapping at the descriptive modeling stage is adopted with the purpose to indicate the possible correlations between web analytics metrics. One step further, a corroborative and predictive model is proposed through the agent-based modeling method in order to compute the date ranges that resulted in the highest and the lowest engagements of users as regards the content of seven examined courseware websites. The proposed methodology and the results of this study work as a practical toolbox for decision makers while computing and evaluating through a data-driven way the level of engagement between visitors and the content they receive for online presence optimization on the web.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content. Sustainable Transport Development, Innovation and Technology 2021, 31 -45.

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos, Daphne Kyriaki-Manessi. Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content. Sustainable Transport Development, Innovation and Technology. 2021; ():31-45.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. 2021. "Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content." Sustainable Transport Development, Innovation and Technology , no. : 31-45.

Journal article
Published: 17 November 2020 in Entropy
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Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

ACS Style

Ioannis Triantafyllou; Ioannis Drivas; Georgios Giannakopoulos. How to Utilize my App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes. Entropy 2020, 22, 1310 .

AMA Style

Ioannis Triantafyllou, Ioannis Drivas, Georgios Giannakopoulos. How to Utilize my App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes. Entropy. 2020; 22 (11):1310.

Chicago/Turabian Style

Ioannis Triantafyllou; Ioannis Drivas; Georgios Giannakopoulos. 2020. "How to Utilize my App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes." Entropy 22, no. 11: 1310.

Conference paper
Published: 03 June 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The untamed big data era raises opportunities in learning analytics sector for the provision of enhanced educational material to learners. Nevertheless, big data analytics, brings big troubles in exploration, validation and predictive model development. In this paper, the authors present a data-driven methodology for greater utilization of learning analytics datasets, with the purpose to improve the knowledge of instructors about learners performance and provide better personalization with optimized intelligent tutoring systems. The proposed methodology is unfolded in three stages. First, the learning analytics summarization for initial exploratory purposes of learners experience and their behavior in e-learning environments. Subsequently, the exploration of possible interrelationships between metrics and the validation of the proposed learning analytics schemas, takes place. Lastly, the development of predictive models and simulation both on an aggregated and micro-level perspective through agent-based modeling is recommended, with the purpose to reinforce the feedback for instructors and intelligent tutoring systems. The study contributes to the knowledge expansion both for researchers and practitioners with the purpose to optimize the provided online learning experience.

ACS Style

Ioannis C. Drivas; Georgios A. Giannakopoulos; Damianos P. Sakas. Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models Development. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 407 -410.

AMA Style

Ioannis C. Drivas, Georgios A. Giannakopoulos, Damianos P. Sakas. Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models Development. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():407-410.

Chicago/Turabian Style

Ioannis C. Drivas; Georgios A. Giannakopoulos; Damianos P. Sakas. 2020. "Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models Development." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 407-410.

Journal article
Published: 02 April 2020 in Big Data and Cognitive Computing
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In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. Big Data Analytics for Search Engine Optimization. Big Data and Cognitive Computing 2020, 4, 5 .

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos, Daphne Kyriaki-Manessi. Big Data Analytics for Search Engine Optimization. Big Data and Cognitive Computing. 2020; 4 (2):5.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos; Daphne Kyriaki-Manessi. 2020. "Big Data Analytics for Search Engine Optimization." Big Data and Cognitive Computing 4, no. 2: 5.

Conference paper
Published: 26 May 2019 in Sustainable Transport Development, Innovation and Technology
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Attracting visitors to a website is a complex and multidimensional task for each decision maker in the digital marketing sector. Even an organization in relation with its competitors holds the reins in the provision of the most qualitative products and services rather than others, the hard reality though, depicts that if the online users are not able to navigate easily in the organization’s website, they will jump to another. This fact also brings low visibility and traffic metrics in the organization’s website, which unintentionally leads to poor communicational promotion of products and services. In this paper, the authors combine the fragmented pieces of the usability and the levels of traffic that a website has, based on the utility of Search Engine Optimization process for improving the website’s usability and traffic as well. To this respect, the SEO process addresses and examines the website’s usability in design, architecture, and content, for improving greater volume and quality of online users’ visits to the website through search engines. Following a user-centered digital marketing approach, the authors examine, if the level of traffic of a website, related with its level of usability that express, based exclusively on its user’s perceptions and suggestions about that under examined website. Implementing all user’s suggestions and thereafter, adopting Google Analytics as a web usage mining tool for measuring the optimization, the results indicate that following the website’s user’s perceptions and suggestions about it for improving its usability, the total pageviews, the organic traffic, and also the referral traffic of the website rose significantly. To this end, highlighting the utility and practicality of this paper, it is useful to refer that it could be used as a practical toolbox for each digital marketing team, in order to estimate in a well-organized and descriptive manner, the users’ perceptions as regards to a website in order to improve its usability levels and thus its traffic.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Panagiotis Reklitis. Improving Website Usability and Traffic Based on Users Perceptions and Suggestions––A User-Centered Digital Marketing Approach. Sustainable Transport Development, Innovation and Technology 2019, 255 -266.

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Panagiotis Reklitis. Improving Website Usability and Traffic Based on Users Perceptions and Suggestions––A User-Centered Digital Marketing Approach. Sustainable Transport Development, Innovation and Technology. 2019; ():255-266.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Panagiotis Reklitis. 2019. "Improving Website Usability and Traffic Based on Users Perceptions and Suggestions––A User-Centered Digital Marketing Approach." Sustainable Transport Development, Innovation and Technology , no. : 255-266.

Conference paper
Published: 03 June 2017 in Sustainable Transport Development, Innovation and Technology
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In this research paper the authors highlight the importance of Search Engine Optimization of a company’s website in order to improve its visibility in the global ranking of websites. First, the authors implement an SEO analyzing tool for the identification of rectifications that need to be done for the augmentation of website’s visibility. In the next step the recommendations that SEO analyzer indicated implemented and completed improving in this way the overall SEO rating. Thereafter, a Dynamic Simulation Modeling process takes place for the estimation of the proper time and way of spending company’s resources for the augmentation of website’s visibility. The model predicted and estimated that the total satisfaction of a decision-maker regarding this return on investment is gradually increased as each one of these recommendations implemented in a specific way of resources’ distribution, strengthening the final decision in order to adopt such a digital marketing tool in decision-maker’s quiver.

ACS Style

A. S. Sarlis; Ioannis C. Drivas; D. P. Sakas. Implementation and Dynamic Simulation Modeling of Search Engine Optimization Processes. Improvement of Website Ranking. Sustainable Transport Development, Innovation and Technology 2017, 437 -443.

AMA Style

A. S. Sarlis, Ioannis C. Drivas, D. P. Sakas. Implementation and Dynamic Simulation Modeling of Search Engine Optimization Processes. Improvement of Website Ranking. Sustainable Transport Development, Innovation and Technology. 2017; ():437-443.

Chicago/Turabian Style

A. S. Sarlis; Ioannis C. Drivas; D. P. Sakas. 2017. "Implementation and Dynamic Simulation Modeling of Search Engine Optimization Processes. Improvement of Website Ranking." Sustainable Transport Development, Innovation and Technology , no. : 437-443.

Conference paper
Published: 03 June 2017 in Sustainable Transport Development, Innovation and Technology
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In this study, the authors highlight the importance of Keywords in the process of Search Engine Optimization in an effort to increase the global ranking of websites in search engines. Through the usage of tools and indicators, the authors proceed into the extraction of appropriate keywords that can be used in websites for the construction of text and content. In addition a dynamic simulation modeling process takes place in order to calculate and estimate the proper distribution of a company’s resources which intends to invest in the optimization of its website for the improvement of the current presence in the digital marketing world.

ACS Style

Ioannis C. Drivas; Apostolos S. Sarlis; Damianos P. Sakas; Alexandros Varveris. Stuffing Keyword Regulation in Search Engine Optimization for Scientific Marketing Conferences. Sustainable Transport Development, Innovation and Technology 2017, 117 -123.

AMA Style

Ioannis C. Drivas, Apostolos S. Sarlis, Damianos P. Sakas, Alexandros Varveris. Stuffing Keyword Regulation in Search Engine Optimization for Scientific Marketing Conferences. Sustainable Transport Development, Innovation and Technology. 2017; ():117-123.

Chicago/Turabian Style

Ioannis C. Drivas; Apostolos S. Sarlis; Damianos P. Sakas; Alexandros Varveris. 2017. "Stuffing Keyword Regulation in Search Engine Optimization for Scientific Marketing Conferences." Sustainable Transport Development, Innovation and Technology , no. : 117-123.

Journal article
Published: 04 April 2016 in Library Review
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Purpose – This paper aims to examine the Self-Other Agreement between leaders and employees in the sector of Libraries and Information Services (LIS) to construct a sustainable and strategic communicational process among library directors and staff. Design/methodology/approach – A sample of 135 leaders-employees of 17 organisations of LIS in more than five countries answered on a quantitative methodological research instrument in a multiplicity of variables. Statistical analysis of independent samples t-test was used to testify our research hypotheses. Findings – Results indicated that there is a difference in means between the two independent samples (leaders-employees). There are library leaders who rate themselves quite high, and there are employees who rate their leaders with lower evaluations. Research limitations/implications – This research extends and improves the matter of Self-Other Agreement in the sector of LIS through the collection of data that indicated a possible gap of communication and trustworthiness between leaders and employees. Practical implications – Regardless of the difference or the consensus of ratings among leaders and employees, the results of this research could be served as a stimulus plus as a starting point for library leaders by correcting or developing relations of communication and trustworthiness between them and their followers. Originality/value – Self-Other Agreement is one of the major factors that positively or negatively affect the overall operation of the organization in the way a leader could perceive the additional feedback. In the sector of LIS, the study of Self-Other Agreement is a rich and unexplored research area which deserves further analysis.

ACS Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos. Self-other agreement for improving communication in libraries and information services. Library Review 2016, 65, 206 -223.

AMA Style

Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos. Self-other agreement for improving communication in libraries and information services. Library Review. 2016; 65 (3):206-223.

Chicago/Turabian Style

Ioannis C. Drivas; Damianos P. Sakas; Georgios A. Giannakopoulos. 2016. "Self-other agreement for improving communication in libraries and information services." Library Review 65, no. 3: 206-223.

Conference paper
Published: 01 January 2015 in INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings of the 4th International Conference on Integrated Information
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While attempting decision-making in leadership, one of the criteria that should be considered is the actual behavior of the leader towards the work group. The object of this study is the prevalence of two key leadership models, the Task Behavior and the Relationship Behavior. Firstly, the leader’s behavior is focused on completing the project combined with a leadership style which is more interventionist and less conciliatory. The second leadership model focuses on working relationships, developed between leaders and employees. In this case, the character of the model is associated with a leadership behavior more supportive, motivating and participative. This article refers to the characteristics of the two models and the mechanisms and strategies that can be adopted in both leadership behaviors. Furthermore, through the research which took place, we have come to conclusions regarding the public’s views about the different leadership behaviors and, also, the possibility of combining the two styles of leadership.

ACS Style

Aikaterini I. Damaskinou; Ioannis C. Drivas; Damianos P. Sakas. Leadership models with an emphasis on relationships and the completion of the project in information science. INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings of the 4th International Conference on Integrated Information 2015, 1644, 271 -278.

AMA Style

Aikaterini I. Damaskinou, Ioannis C. Drivas, Damianos P. Sakas. Leadership models with an emphasis on relationships and the completion of the project in information science. INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings of the 4th International Conference on Integrated Information. 2015; 1644 ():271-278.

Chicago/Turabian Style

Aikaterini I. Damaskinou; Ioannis C. Drivas; Damianos P. Sakas. 2015. "Leadership models with an emphasis on relationships and the completion of the project in information science." INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings of the 4th International Conference on Integrated Information 1644, no. : 271-278.