Search results

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 purpose of this paper is to explore the type of personalisation services satisfying the needs of cancer websites’ target users, and the influence of their emotional states on website usage intentions.

Three data collection methods were employed. Survey questionnaires were distributed to online health users. Interviews with representatives of the cancer-affected population further explored emotions as stimuli for online cancer-related activities. Finally, availability of personalisation features was evaluated on existing health websites in Bosnia and Herzegovina and the UK.

A clear preference emerged for personalisation on cancer-related websites. There are specific personalisation features the cancer-affected population desires. Interestingly, certain emotions were found to stimulate visits to health websites.

Fighting cancer implies constant support, including from cancer-related websites. It is thus vital to understand the required personalisation, stemming from target users’ actual needs, including the neglected user characteristics, as are emotions for cancer-affected people. This supports emotion-based personalisation.

The paper focusses on the cancer-affected population, and developing a comprehensive understanding of their personalisation needs in online health services. It further shows which emotions influence intentions to use cancer websites. The three concepts combined have not yet been studied, to the best of the authors’ knowledge.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

As a result of the so-called information explosion, it is very important for researchers, faculty members and students to access scientific and research information, which highlights the importance of designing university websites. The purpose of this paper was to evaluate Iranian state university websites using the Web quality evaluation method (WebQEM).

The research method was a combination of a descriptive survey and Delphi technique. The research population included 100 Iranian state university websites. Data collection was done using the checklists prepared by WebQEM. Descriptive statistics (frequency, mean and standard deviation) and analysis statistics (Spearman rank-difference correlation coefficient) were used for data analysis.

The results indicated that Iranian state university websites met the four main criteria considered in WebQEM; reliability (mean = 0.67), efficiency (mean = 0.66) and functionality (mean = 0.62) were in a “good” condition, and usability was in a “middle” condition (mean = 0.59). Also, the findings showed that 60 per cent of the websites were in a good condition and 37 per cent were in a middle condition. In conclusion, Iranian state university websites were found to be in a “good” condition (mean = 0.63). Also, Ferdowsi University of Mashhad was ranked in the first place (score = 0.822). The hypothesis that there was a very weak correlation between Iranian state university ranking and Iranian state university websites ranking was confirmed (with the correlation of 0.22).

The paper includes implications for the development of user interface of academic websites. This paper fills a part of the gap in terms of an urgent need for research on how university websites can be standardized. If university websites have significant and necessary standard factors (i.e. ISO 9126-1), students may succeed in academic information retrieval. Using the results of this research can help university website designers to fix weaknesses for active participation in these websites.

This study has evaluated Iranian state university websites using WebQEM.

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is to capture characteristics of different book reviews on aspect preferences by opinion mining techniques.

The authors employ two indicators for identifying aspect preferences, and propose a method for quantifying overall differences of reviews on aspect preferences through three dimensions: aspect awareness, aspect satisfaction and comprehensive value.

The results show that book reviews on e-commerce websites contain information about external aspects of a book (e.g. hardcover), while those on social network websites pay more attention to content-related aspects of the book (e.g. stories). These results indicate that aspect preferences of reviews vary from platforms and make it hard to evaluate book comprehensively based on single-source data. Online book reviews from a wide range of sources can assess book impact from multiple perspectives and dimensions.

In order to illustrate the value of the authors’ method, the authors show book impact assessment based on multi-source data as an application of these difference analyses. Furthermore, the authors present an example of a book promotion to provide customized marketing services for different user clusters.

This study investigates the influence of different data sources on book evaluation from the content of book reviews. The authors also showcase potential applications of these analyses in book impact assessment.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

The online shopping behavior is the outcome of the variety of attribution from product/ service offering to internet experience. The present study attempts to develop a complete product/service offering by exploring and examining the different combinations of online shopping attributes to provide the customized experience. Therefore, this study aims to fill the gap of customer desired experience and present scenario in online shopping behavior.

The exploration of attributes pertaining to online shopping behavior was done by seeking theoretical support from different technology adoption theories/models and the Delphi technique, exercised with active participants of online and offline shopping. The theoretical and experience shared attributes were devised and social desirability scale (SDS) was used for eliminating the social desirability bias. Further, the questionnaire was administered online and offline during mall intercept. The Conjoint analysis was used to investigate the relative importance and utilities of the attributes and its levels individually and compositely at different levels.

In the context, brand loyalty, online reputation management and Web interactivity were found most relavant followed by e-WOM, perceived risk and price. The specific levels of attributes such as taking consumer advice, search engine optimization (SEO), perception-based interactivity, consumer message boards, product risk and discount pricing were the crucial in motivating the customers for online shopping. This research affords the avenue for the marketers to motivate and delight consumers to retribalize by the way of “e-tribalizing.”

The current study was conducted in confined geographical locations and limited in sample size; thus, the issue of generalization may prevail, but forthcoming researchers may exercise the techniques with better probabilistic sampling technique. The mass customization of the website features by comparing attribute orientation of customers around websites was recommended with the third-party certification to reduce the consumers’ perceived risk during online shopping. Finally, the different levels, such as Facebook fan page in ORM and Everyday Low Price (EDLP) in pricing may be considered for the future research work.

The research studies on online shopping behavior with Web interactivity, e-WOM, perceived risk, brand loyalty, ORM and price using a decompositional technique are scant. This study persuades the customers to go for online shopping by putting them in the almost real-time purchasing scenario. The study confirmed the need of people to retribalize through e-tribalization by the way of customization for the masses in the context of online shopping.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

The purpose of this paper is to investigate Chinese and American financial companies’ distinct brand personality indicators shown through culturally based linguistic features online. The potential correlation between culturally oriented brand personalities and companies’ financial performance is also examined.

This study employs computerized content analyses to examine the cross-cultural differences among 28 American and Chinese financial companies’ online communication based on Aaker’s brand personality framework.

The findings reveal that despite some similarities, there are significant differences between the frequencies and patterns of brand personality indicators on American and Chinese websites, which demonstrate the connection between the companies’ linguistic preferences with their different cultural backgrounds. It also proves that there could be significant relationship between financial companies’ corporate brand (CB) personality expressions and their financial performance, and US financial companies’ revenues are more closely correlated with brand personality dimensions than Chinese companies’.

The necessity for cross-cultural adaptation of CB personality is verified in this study. Chinese international companies may have a big room to improve their online corporate communication. Similarly, foreign companies who intend to enter into Chinese market may think about laying emphasis on their personality indicators of competence in their online corporate communication.

This research is among the first to utilize a corpus-based analytical tool to conduct content analyses of financial companies’ online brand personalities, in addition to empirically validate the correlations between companies’ brand personality indicators and financial performance. The study enriches the literature on online marketing communication, draws attention to the connection between cultural differences and linguistic preferences in CB personality construction and emphasizes the importance of making appropriate cross-cultural adaptation in online corporate communication.

The study aims to identify gender differences and similarities in the awareness of legal information resources and problems faced by legal professionals. Further, the study identifies the differences on the basis of gender, regarding the requirements in developing an online legal information system (OLIS) for the Indian environment.

The study was carried out in eight law libraries in Delhi using a structured questionnaire. Data were collected through the questionnaire having 27 questions including dichotomous (Yes/No), multiple-choice questions, rating and opinion questions. A need assessment survey was conducted using the structured questionnaire circulated among 750 respondents of eight institutions in Delhi. In total, 397 filled in questionnaires were received back. A total of 246 of the respondents were males and 151 females. The design and development of an OLIS went through five phases, i.e. preliminary preparation, designing and planning phase, development of OLIS covering preparation of software, data structures, metadata, search form, testing and implementation phase and maintenance.

The study found that 100 per cent of the female respondents were aware of online legal information resources. Maximum 28.4 per cent female respondents rated legal e-resources “very good”, whereas only 19.9 per cent male ranked them “very good”. Female respondents were found less aware about open access resources. In addition, of 246 male respondents, 213 (86.6 per cent) responded “Yes” about awareness of open access resources and 33 (13.4 per cent) marked “No”. In comparison, 116 (76.8 per cent) female respondents revealed they are aware of open access resources; 35 (23.2 per cent) were not aware of open access resources. Fifty-eight (23.6 per cent) male respondents were very dissatisfied, and 60 (24.4 per cent) completely dissatisfied. However, in contrast, 29 (19.2 per cent) female respondents were very dissatisfied and 24 (15.9 per cent) completely dissatisfied in using open access resources.

The study covers only eight institutions in Delhi, India. High courts and law universities in other parts of the country are not covered. In addition, OLIS contains a sample collection.

The study explores the inhibitions faced by female and male legal professionals. A suitable legal information system is developed to match the requirements of female legal professionals, research scholars and faculty members. The study is expected to address problems faced by female legal professionals in accessing the desired judicial and legislative information.

OLIS (www.olisindia.in) has been developed on the basis of a need assessment survey conducted on male and female legal professionals in India. No study has been conducted so far to compare the viewpoints of male and female legal professionals in India for developing an OLIS.