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The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.

First, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.

The novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.

The rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.

The algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

The purposes of this study are fourfold: (1) to explore the state of ICT competency framework for teachers in higher education, (2) to identify general themes and trends in publications, key publication resources, authors, affiliations and countries in the domain of teachers’ ICT competence in higher education, (3) to investigate the relationships among author keywords to understand the interconnectedness within the research on teachers’ ICT competence in higher education and (4) to investigate the thematic evolution of author keywords to better understand the current situation and future research trends.

This study details the bibliometric methodology used in terms of performance analysis and thematic evolution of author keywords and reports the results of systematically analyzing 275 Scopus-indexed publications on teachers’ ICT competence and ICT competency framework for teachers in higher education from July 25th, 2008 to June 7th, 2024.

The results of this bibliometric review paper show that the synthesized teachers’ ICT competency framework offers greater flexibility and enables other HEIs (higher education institutes) to contextualize these standards for developing teachers’ ICT competence based on their specific needs. Moreover, the rigorous bibliometric analysis reveals increasing trends in exploring higher education teachers’ ICT competence, driven by the growing integration of ICT into educational practices. Additionally, the thematic evolution of author keywords illuminates emerging research themes such as ICT competence and distance learning, which provides valuable and novel insights into the current and future directions of the field.

The ICT competency framework for higher education teachers can be beneficial for improving their ICT competence and further promoting students’ achievements and HEIs’ performance. Simultaneously, the findings of our thematic analysis of author keywords suggest that these thematic clusters can be valuable resources for informing institutional policy and providing novel research directions in this field.

This study addresses current research gaps in the field of ICT competency framework for teachers as well as higher education teachers’ ICT competence by utilizing advanced bibliometric analyses such as thematic mapping to systematically review higher education teachers’ ICT competence. Based on the results obtained through thematic analysis of authors’ keywords from July 25th, 2008 to June 7th, 2024, it proposes new research directions, laying the foundation for future investigations, thereby aiding researchers in developing this area further.

This paper aims to provide a social network site influence (SNSI) profile of early adopters. This study explores the relationship between personality traits of early adopters of social network sites (SNS), their propensity to share information and rumors and their general SNSI.

An online survey was sent to the first users of Twitter (n = 200) and Google+ (n = 130) to assess their personality traits. Answers of each respondent were matched to their SNSI scores from Klout and PeerIndex, the industry standard for measuring SNSI.

Early adopters of SNS, in comparison to market mavens, are more likely to exert influence on one particular topic related to their profession: technology and the internet. Their levels of extraversion, openness and conscientiousness have a positive and significant impact on information sharing, and a negative impact on rumor sharing. Both, information sharing and rumor sharing have a positive and significant impact on the general SNSI of early adopters.

Firms struggle to decide whether to invest early in the life of newly created SNS as they are unsure about the characteristics of early adopters of such networks, and, more importantly, whether these sites are effective initial vectors for word-of-mouth. The findings demonstrate that early adopters’ influence (SNSI score) is on par with that of the rest of SNS users, suggesting their influence may be somewhat limited. The study also shows that the opinion leadership impact of the more influential early adopters is monomorphic in nature, being mainly confined to the related technology and internet domains.

The purpose of this study is to understand and compare how business-to-business (B2B) and business-to-consumer (B2C) companies evaluate the return on investment (ROI) on their social media marketing (SMM) programmes and how the investment is handled in these type of marketing programmes.

A mixed-methods approach involving multiple cases and a survey was used. Data were collected from personal interviews with eight professionals responsible for SMM management, from four B2B and four B2C companies, complemented with responses to a web-based survey by the other 28 companies’ marketing managers.

The results show that there are some differences between B2B and B2C companies regarding SMM evaluation and investment but in general marketing managers for both types of firms use simple metrics to evaluate their SMM programmes. The main measures used relate to awareness, engagement and reach and most of the metrics identified are interaction-related.

Given the complex and sensitive nature of the subject, more research is needed focussed on providing additional evidence from a larger sample of B2B and B2C organizations to allow the extension of the finding to the population as the non-probabilistic nature and size of the current sample impose that the findings should be interpreted carefully. Future research should focus on understanding what the firm’s characteristics predict the importance and level of effort placed in SMM and the barriers to ROI measurement in SMM programmes, especially in B2B firms.

The current findings confirm that the topic of SMM ROI evaluation is not a priority for B2C or B2B companies. There is a need for an update of their online marketing strategy, namely, on budget definition and allocation. Furthermore, companies should increase the autonomy of SM managers, as they are dependent from marketing managers and hire specialized professionals devoted to SMM in both B2C and B2B companies.

The findings of this study contribute to improve the understanding of the evaluation of SMM and to extend the literature on the subject. It also provides a relevant advance into the assessment and understanding on the measures used to evaluate the effectiveness of SMM programmes by offering a comparison on how B2B and B2C use metrics and allocate resources to the SMM management.

The purpose of this paper is to assess the state of the art in social media and pharmaceutical marketing through empirical analysis of online consumer conversations. Proliferation of social media has significantly changed traditional one-way, marketing-controlled communications. Balance of power has shifted to consumers, who use social networking sites, blogs and forums to obtain extensive brand and product information, often from each other. This prompts companies towards more intimate, transparent and constant two-way consumer engagement. Pharmaceutical marketing and direct to consumer advertising (DTCA) are not immune to this pervasive, disruptive cultural/technological phenomenon, which poses particular challenges given regulatory, legal and ethical constraints on their marketing.

This research uses “netnographic” data collection of online conversations occurring in social media and develops an explanatory framework using grounded theory analytical methods.

This research shows that significantly impactful and pervasive bonding among consumers, bloggers and unofficial “experts” about pharmaceutical offerings is widespread, and occurs regardless (and perhaps in spite of) pharmaceutical companies’ involvement.

Considering the structure and nature of online consumer bonding, a way forward is proposed for pharmaceutical companies to implement social media strategies as part of their pharmaceutical marketing and DTCA efforts through an intermediary and interactive online presence arising from disease and health care education.