The research interests of Ioannis focus on Search Engines Results Optimization, Web Mining, Web Analytics and Predictive Modeling. The main purpose is to accurately predict the smooth adoption and the successful implementation of optimal strategies for the improvement of the visibility range and usability level of information organizations digital promotional methods. Ioannis has also previously published several papers focused on managing communicational issues in Information Organizations sector.
In the ever-evolving social media landscape, Instagram has transcended from a mere image-sharing platform to a dynamic space for academic libraries to engage with their communities. Following the increased utilization of this platform, several studies have tried to unravel the interplay between nuanced content aspects and follower engagement, but the results are cursory and contradicting. Aiming to address these shortcomings, we conducted an in-depth analysis of 1681 posts from 120 academic libraries' Instagram profiles worldwide to explore the following: content volume and posting frequency; qualitative content aspects such as post categories, characters length, hashtags usage, emojis frequency, and post types; and possible correlations between these content aspects and follower post interaction rates. Our findings uncovered notable disparities in interaction rates among 14 distinct post categories, with content structure characteristics showing minimal influence on these rates. By shedding light on the association between aspects of content structure and follower interaction, the study contributes to the development and optimization of academic libraries' social media strategy, policy redefinition, staff knowledge and practical skills improvement to manage social media, while also opening new research avenues in Instagram utilization in the academic library context.
Keywords: instagram; analytics; academic libraries; content strategy; social media; engagement; interaction
Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits’ metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors’ behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations.
Keywords: museums; visitor experience; detection tracking; analytics
Website loading speed time matters when it comes to users’ engagement and conversion rate optimization. The websites of libraries, archives, and museums (LAMs) are not an exception to this assumption. In this research paper, we propose a methodological assessment schema to evaluate the LAMs webpages’ speed performance for a greater usability and navigability. The proposed methodology is composed of three different stages. First, the retrieval of the LAMs webpages’ speed data is taking place. A sample of 121 cases of LAMs worldwide has been collected using the PageSpeed Insights tool of Google for their mobile and desktop performance. In the second stage, a statistical reliability and validity analysis takes place to propose a speed performance measurement system whose metrics express an internal cohesion and consistency. One step further, in the third stage, several predictive regression models are developed to discover which of the involved metrics impact mostly the total speed score of mobile or desktop versions of the examined webpages. The proposed methodology and the study’s results could be helpful for LAMs administrators to set a data-driven framework of prioritization regarding the rectifications that need to be implemented for the optimized loading speed time of the webpages.
Keywords: website; webpage; loading time; speed; optimization; PageSpeed insights; libraries; archives; museums; LAM
Social media platforms can be used as a tool to expand awareness and the consideration of cultural heritage organizations and their activities in the digital world. These platforms produce daily behavioral analytical data that could be exploited by the administrators of libraries, archives and museums (LAMs) to improve users’ engagement with the provided published content. There are multiple papers regarding social media utilization for improving LAMs’ visibility of their activities on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’ engagement with the content that LAMs post to social network platforms. In this paper, we propose a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, (b) examining a more extended set of LAMs social media pages compared to other prior investigations with limited samples as case studies, and (c) understanding which are the administrators’ actions that increase the engagement of users. The results of this study constitute a solid stepping-stone both for practitioners and researchers, as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs services on the Social Web.
Keywords: social media platforms; Facebook; social media networks; social media data; analytics; metrics; libraries; archives; museums; users’ engagement
In Greece, interest in university archives began in the 1990s and, despite efforts that have taken place since then, only a few universities have collected archives classified by date. The University of West Attica was founded in March 2018 by the National Law 4521. The foundation of the newly established University came from the merging process of the former Technological Educational Institute of Athens (founded 1970) and Piraeus University of Applied Sciences (founded 1983). In 2019, the National School of Public Health (founded 1929) joined the newly established university. It is, therefore, a historically important academic institution with several unique features. At this stage, in the context of the administrative changes and the establishment of the new institution, it is crucial to pay more attention to its archival material and its history. The various administrative changes, the changes in the premises and the relocation of departments and administrative services, but also the emerging framework of the organization in relation to the archives in a single framework provide an opportunity to substantiate new possibilities and proposals that will improve the administrative function of the University and will utilize its cultural heritage and memory. The history of archive management in public administration in Greece has demonstrated that such administrative changes have led to the loss or destruction of archival material that is important for scientific, historical, and administrative reasons. Our research project focuses on the development of a strategic plan for the systematic preservation and classification of historical archival material, and the appropriate management of records at the University of West Attica (UNIWA). This paper will discuss the problems and the questions to be addressed during the elaboration of a strategy on ethics questions in relation to the collection and the access to the archival material.
Keywords: university archives; archives management;
Purpose: University archives (UA) are the bridge between the past and the present and serve as a beacon for highlighting the contribution of academic institutions to society. Although the UA topic was introduced and formalized in the 1950s, the scientific research interest has increased significantly in the past two decades. This paper aims to provide insights into the UA research topic during the previous 15 years. Design/methodology/approach: The combination of two well-established methods for performing literature review was deployed, aiming to identify, select and assess the research documents. Based on the selection criteria, 49 documents presenting research efforts around the UA topic were finally examined from the Scopus citation index. The selected studies have been classified into three main topics: strategic management of UA and the derived challenges, the educational contribution of UA and the strategic information systems for UA. Findings:Some of the main findings are the lack of well-defined administrative policies, the low level of awareness and archival consciousness within the universities, the inadequacy of university archivists’ educational and training background, the need to use UA for building relationships with alumni and society, and finally, the need for metadata standardization by the UA management systems. Originality/value:As a literature review around UA has not been conducted before, the reader will gain insights into the methods and research designs that other scholars had already applied to designate useful findings and results.
Keywords: university archives, case-studies, archival information systems, literature review, archives management
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
Keywords: website seo; data driven seo; website performance; libraries; archives; museums; content management systems; cms comparison; drupal; wordpress
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.
Keywords: app reviews; topics extraction; reviews classification; feature extraction methods; machine learning methods; text classification; text analysis; app business strategy
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.
Keywords: learning analytics; big data; methods; e-learning; intelligent tutoring systems; online learning platforms; learning management systems.
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.
Keywords: cultural analytics; cultural data; search engine optimization; SEO strategy; SEO factors; big data; websites visibility; predictive modeling; website security; website load speed; user behavior
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 that 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 spending, the number of pageviews they 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.
Keywords: data-driven marketing, search engine marketing, websites content engagement, websites traffic evaluation, web analytics, behavioral analytics, agent-based models in marketing
Optimized paid search advertising campaigns composed of multiple data analytics insights and prior experiences of search engine marketing performances. However, when advertisers compete in the battle of paid search ads rankings in search engines, complexity in optimization is increased. The higher the ranking position of search ads are, the higher the probability to be clicked by the search engines users. Despite the existing knowledge of the factors that contribute to the higher ranking position in search ads, such as proper relevancy amongst users’ search terms, text ads and landing pages content, little is known about search engines users’ behavior after ads clicking. Low interaction or immediate abandonments from the landing pages potentially leads to a waste of budget spent on each paid advertising campaign. In this respect, advertisers should pay much more attention to the engagement of paid traffic visitors after clicking on search ads, and not only on ads rankings and rates of search impression shares in search engines. 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 is, 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 such as 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. It is noted that, in an overall point of view, results indicate low rates of engagement after search ads clicking. 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 decision makers with the purpose to develop search engine marketing campaigns composed of relevant to the users search ads and sufficient content engagement after ads clicking.
Keywords: Digital Marketing, Search Engine Marketing, Search Advertising Optimization, Web Analytics, Behavioral Analytics, Data-Driven Modeling, Agent-Based Models in Marketing, Modeling and Simulation in Marketing
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 that relies on the agent-based modeling approach for estimating and predicting the optimal interaction rates of branded search traffic visitors of the examined websites. Agent-based models are suitable for describing, estimating and predicting the complexity within the systems and how their users behave and interact inside them. The results of the study constitute a practical toolbox for advertisers and decision makers in order to understand their display advertising effectiveness in terms of brand popularity and branded search traffic improvement for their websites.
Keywords: Display Advertising, Brand Awareness, Branded Search Traffic, Branded Keywords, Data-driven Marketing, Agent-based Models in Marketing, Web Analytics, Websites Traffic
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.
Keywords: Website traffic Website usability Website visits improvement Website usability improvement Search engine optimization Digital marketing strategies User-centered website design
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.
Keywords: Search engine optimization, SEO, Dynamic simulation models, DMS, Website ranking, Decision making tools, Digital marketing
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.
Keywords: Search engine optimization, Keywords in SEO, Keywords stuffing in SEO, Keywords overuse in SEO, Dynamic simulation modeling, Decision-making tools, DMS
In the era of digital marketing competitiveness Search Engine Optimization process holds the reins for a strategically sustainable web positioning and presence for each website which is under the paternity of a company. However, the reduced financial flexibility entails risks in the way of disseminating company’s resources for improving of website visibility. In this research paper the authors proceed into sequential steps in order to indicate not only a proper dissemination of resources for augmenting website visibility, but also to estimate a return on investment which a company potentially has via using an actuarial dynamic simulation modeling approach. In this work, the authors adopt several SEO recommended rectifications that the literature review indicates in order to be practically implemented into the under examination websites correlated with the promotion of scientific journals into the field of marketing. Thereafter, a dynamic simulation procedure takes place in order to specify a proper distribution of company’s resources in precision with an improvement of websites’ organic reach, thus the higher visibility of them.
Keywords: Search engine optimization Dynamic simulation modeling Decision-making tools Company resources dissemination Website visibility improvement
The World Wide Web provides a well standing environment in any kind of organizations for exposing online products and services. However, no one ensures that web products or services which provided by organizations or enterprises, would receive the proper visibility and accessibility by the internet users. The process of Search Engine Optimization examines usability in design, architecture and content that an internet-based system has, for improving its visibility and accessibility in the web. Successful SEO process in an internet-based system, which is set under the paternity of an organization, ensures higher recognition, visibility and accessibility for the web services that the system provides to internet users.
The aim of this study characterized with a trinity of axes. In the first axe, an internet-based system and the web services that provides is examined in order to understand its initial situation regarding its visibility and accessibility in the web. In the second axe, the study follows a user-centric approach on how and in what way the examined system could be improved based on its users’ needs and desires.
After the encapsulation of needs and desires that the users expressed as regards the usability of the system in design, architecture and content, the third axe takes place. In the third axe, the extracted needs and desires of users are implemented in the under-examined system, in order to understand if its visibility and accessibility has improved in the World Wide Web.For the completion of this trinity of axes, the Soft Systems Methodology approach is adopted.
SSM is an action-oriented process of inquiry which deals with a problematic situation from the Finding Out about the situation through the Taking Action to improve it. Following an interpretative research approach, ten semi-structured interviews take place in order to capture all the participants’ perceptions and different worldviews regarding of what are the changes that they need and desire from the examined system. Moreover, in this study, the conduction of three Workshops, constitute a cornerstone for implementing systemically desirable and culturally feasible changes where all participants can live with, in order to improve system’s visibility and accessibility in the internet world.
The results indicate that the adoption of participants’ needs and desires, improved the levels of usability, visibility and accessibility of the under examined internet-based system. Overall, this study firstly contributes to expand the knowledge as regards the process of improving the visibility and accessibility of internet-based systems and their web services in the internet world, based on a user-centric approach. Secondly, this study works as a practical toolbox for any kind of organization which intends to improve the visibility and accessibility of its current or potential web services in the World Wide Web.
Keywords: Soft Systems Methodology, Search Engine Optimization, Search Engine Optimization for Organizations, Search Engines, Improvement of the Visibility on the WWW, Improvement of the Accessibility on the WWW, Web Services, Internet-based Systems, Usability, User-Centric Approach
This research seeks to demonstrate the effectiveness of communication strategies and practices from decision makers to employees in the Information and Communication Technologies organizations. The purpose is the creation and eventual promotion of a communicational model that embraces best practice relative to overall team effectiveness. By placing decision makers and employees in the center of attention, we examine relations among several communication and team effectiveness practices in ICTs sector. Employees’ recognition, the motivation of exchange and sharing information, clear vision of goals and objectives for employees, and the troubleshooting effectiveness of team, are some of the major factors that indicate the relationship between Communication Strategies and Team Effectiveness. Via the application of a cross-cultural quantitative methodological tool in more than ten countries with a response rate more than 69%, we evaluate communicational strategies that decision makers adopt and their impact in Teamwork Effectiveness in the sensitive sector of ICTs.
Keywords: Strategic communication Team effectiveness ICTs organizations Strategic leadership B2B communication Communication strategies
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.
Keywords: Library management, Transformational leadership, Information science, Leader-member exchange, Strategic communication, Self-other agreement
The commercial success of customer behavior and characteristics must be outweighed by the appropriate strategic planning. One of the most important criteria for the sustainability of a company is to minimize possibilities for making an incorrect decision. In addition, high importance shows the correct quantitatively dissemination of company resources with a vision of potential development. In this research approach there is an effort to design a dynamic simulation model. This model has been designed to minimize chances for receiving an incorrect decision, as well as the determination of channelling company resources at the right time in the right quantity creating in this way the proportional feedback resources for an organisation.
Keywords: Customer BehaviorCustomer CharacteristicsDynamic Simulation Models