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The purpose of this study is to reveal the role of individual–social–technology fit in online social network (OSN) value development. The social software features (e.g. communication and interaction), social features (e.g. privacy and trust) and individual features (e.g. sense of belonging and self-disclosure) are considered fitting forms to describe the OSN value. Implications and suggestions are addressed.

The literature review on social software, the social and individual characteristics and the research gap with respect to OSN value is presented. The research arguments are then hypothesized, and research model used to describe the proposed role is examined empirically. The research targeted mobile phone users as the subjects, and the extent of the activities of these users on OSN for both work and studies. A salient investigation explores the moderation effect of gender. The research results are obtained, and the findings are revealed on the basis of 468 social software users.

The significant effect of individual–social–technology fit on OSN value development is presented through the satisfaction of both participation and sharing information, and knowledge about this fit is verified. The interplay of social software, social and individual features contributes significantly to individual–social–technology fit development, implying that OSN value development is not a single issue. OSN value development should be considered concurrently with technological, personal and social issues.

The empirical study confirms that fitness analysis produces a systematic outcome, in which all elements (e.g. social, technology and individual) are required to cooperate with one another to maximize the OSN value. An individual adopts online channels to communicate with others; thus, the benefits may be a multidimensional issue instead of only a single information service issue. They also consider building an equal social relationship to be important, as it enables diverse propositions, maintains acceptable privacy and behaves on faith to enhance the fit of technology features and individual features to value development. The subjects also likely accepted the fact that emotion generation is important for the advantage of fit of technology features and social features, thereby likely benefitting OSN value development.

The OSN does not only add new values to the society but also brings new effects on social development, especially in terms of social cognition from virtual community formation, development and creation. Although existing studies in the literature present the important aspects and antecedents linked significantly to OSN value development, these studies also insufficiently discuss the effect of fit of these facets on OSN value development. This exploratory study mainly aims to propose and examine the individual–social–technology fit model through an empirical investigation. The main argument of the study is that when a positive and healthy virtual society is developed through social software, the individual and social characteristics, as well as the social software features, should be defined with a suitable fit to promote the social networking value.

The purpose of this paper is threefold: first, to present a framework of five “facets,” i.e., distinct but complementary ways in which the observed appeal of a consumer shopping site’s features can potentially be generalized across product/service domains (the authors call this framework the feature appeal generalization perspective); second, to determine if and how observed feature preferences for consumer electronics, bookstores, and sites “in general” generalize across domains; third, to test hypotheses about the impact of frequency of domain usage upon feature generalizability.

Via an online survey administered in a controlled laboratory setting, 313 respondents evaluated 26 website features in three domains (books, electronics, general) for a total of 24,414 preference judgments.

Two facets, individual feature values and within domain evaluative dimensions, revealed minimal generalizability, while there was moderate comparability across all domains in between domain feature correspondence. Personal preference elevation could be generalized between books and general, but not between these two and electronics. Differentiating dimensions showed that preferences were not generalizable from electronics to books and general because consumers wanted electronics features to provide “flashy sizzle” and books/general features to give “comfortable safety.” As hypothesized, patterns of generalizability coincided with frequency of domain usage.

Practitioners should not apply published studies of feature appeal to their domain of interest unless those studies directly analyzed that domain. Scientists should incorporate all five facets in modeling what attracts consumers to commercial websites.

This is the first multidimensional analysis of the generalizability of site feature appeal across business-to-consumer product/service domains, and the first to propose this integrated evaluative framework with its unique facets.

The study seeks to investigate the mechanisms for the effects of environmental design features of a green building on occupants' environmental awareness (EA) and organizational image (OI).

One mechanism investigated the direct effects of environmental design features of a green building on occupants' EA and OI. The other mechanism investigated the indirect effects on occupants' EA and OI resulting from the direct effects of environmental design features on occupants' workplace satisfaction. The data were collected from 175 occupants of the Leadership in Energy and Environmental Design (LEED)‐certified green building using a questionnaire instrument.

Based on frequency, correlational, and regression analyses of the data, the study found no evidence for direct effects of environmental design features on occupants' EA and OI. The study, however, found some evidence for indirect effects, indicating that individual workspace and departmental space features affected occupants' satisfaction with individual workspaces and the building, which then affected occupants' EA and OI.

The study involved only the employees of an organization who occupy a single LEED‐certified green building. Future studies should involve a larger sample of green buildings. Future studies should also involve other stakeholders of these buildings.

The study is important for the long‐term market growth of green buildings, because it provides supporting evidence for the organizational leaders who want to use green buildings to enhance organizational values and benefits.

The study makes an original contribution to the field, because studies focusing on the potential links between green buildings and organizational benefits and values, and the mechanisms that may help explain these links are still rare.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

This paper aims to provide a review of different types of non-wearable human identification sensors which can be applied for smart home environment.

The authors performed a systematic review to assess and compare different types of non-wearable and non-intrusive human identification sensors used in smart home environment. The literature research adds up to 5,567 records from 2000 to 2016, out of which 40 articles were screened and selected for this review.

In this review, the authors classified non-wearable human identification technologies into four main groups, namely, object-based, footstep-based, body shape-based and gait-based identification technologies. Assessing these four group of identification technologies showed that the maturity of non-wearable identification is not high and most of these technologies are verified in a lab environment. Additionally, footstep-based identification is the most popular identification approach listed in the literature.

This study contributes to the literature on human identification technologies in several ways. This paper identifies the state-of-the-art regarding non-wearable technologies which can be used in smart home environment. Moreover, the results of this paper can provide a better understanding of advantages and disadvantages of the non-wearable identification technologies.

The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user.

In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020.

Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums.

This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.

The purpose of this paper is to assess the impact of seven variables that emerge from forensic research on facial-composite construction and naming using contemporary police systems: EvoFIT, Feature and Sketch.

The paper involves regression- and meta-analyses on composite-naming data from 23 studies that have followed procedures used by police practitioners for forensic face construction. The corpus for analyses contains 6,464 individual naming responses from 1,069 participants in 41 experimental conditions.

The analyses reveal that composites constructed from the holistic EvoFIT system were over four-times more identifiable than composites from “Feature” (E-FIT and PRO-fit) and Sketch systems; Sketch was somewhat more effective than Feature systems. EvoFIT was more effective when internal features were created before rather than after selecting hair and the other (blurred) external features. Adding questions about the global appearance of the face (as part of the holistic-cognitive interview (H-CI)) gives a valuable improvement in naming over the standard face-recall cognitive interview (CI) for all three system types tested. The analysis also confirmed that composites were considerably less effective when constructed from a long (one to two days) compared with a short (0-3.5 hours) retention interval.

Variables were assessed that are of importance to forensic practitioners who construct composites with witnesses and victims of crime.

Using a large corpus of forensically-relevant data, the main result is that EvoFIT using the internal-features method of construction is superior; an H-CI administered prior to face construction is also advantageous (cf. face-recall CI) for EvoFIT as well as for two further contrasting production systems.

Identifying the protected characteristics under the Equality Act of the UK, the purpose of this paper is to discover the extent to which the protected characteristics are featured in British newspaper advertisements, as evidence of diversity and equality in the country.

Content analysis of advertisements obtained from nine national newspapers of the UK collected over 12 months. The criteria used to select the newspapers were category, popularity (circulation figures) and the readership demographics (range and variety of the audience).

Disabled individuals are under-represented in print advertisements, and so are close relationships between individuals of the same sex signifying a civil partnership (or sexual orientation). There seems to be an equal level of portrayal of males and females, though men still feature more in a business setting while women are seen more in home settings.

The findings suggest opportunities for advertisers to integrate disabled individuals into their marketing campaigns, not just as a business strategy for targeted markets but as individuals in a diversified community. Lesbian, gay, bisexual and transgender (LGBT) people could also be featured in advertisements for products and services that couples usually buy together, for example, holidays and mortgages.

This study expands on the existing study on the portrayal in advertisements of stereotypes of genders, different age-groups and ethnic minorities. The portrayal of disability, sexuality and religious beliefs were considered within newspapers in UK, bridging some crucial gaps and providing outcomes relevant to numerous types of stakeholders, including the brands, advertising industry and academic researchers.

As the number of citation management tools including Refworks and EndNote increased in recent years, academic libraries struggle to remain on top of new developments and support all of the tools used by their users. This paper seeks to address these issues.

A librarian at the University of Illinois surveyed graduate students and faculty about which tools they use, reasons for tool adoption, features that influence adoption, and support they expect from their library.

The results highlight that many users still use older tools including EndNote and RefWorks, but do have interests for and reasons to use new tools including Zotero and Mendeley and may not need as much library support as librarians believe.

This is the first research paper on citation management use and can influence what products libraries use and the support they offer.