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Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

The purpose of this paper is to investigate the influence of user demographic characteristics on the impact of usage of search engines, particularly Google, with special regard to OPAC in the context of an Indian university setting.

This study is based on a survey where the tool of investigation was a close‐ended questionnaire that was distributed among respondents, was self‐filled and returned. The data, thus collected, were processed on MS‐Excel and subsequently on the SPSS statistical package which was used specifically for the analysis of results and also for producing cross‐tables. The Chi‐square test was applied to determine the comparability or the existence of any relationship between the impact of web searching on OPAC use and demographic characteristics of individual searchers.

The study found significant differences between OPAC usage patterns and demographic characteristics of user categories and age groups. Significant variations were noticed among user age groups for awareness about differences between the inner‐workings of the OPAC and web search engines. A significant relationship was found between male and female users vis‐à‐vis their viewpoint after unsuccessful searches. Certain variations were also observed among academic majors with regard to perceptions of users after failed searches. Interestingly, no significant differences were observed between actions of users after unsuccessful searches and the parameters pertaining to their age groups, categories, academic majors and gender.

This is in all probability one of the first empirical studies to be published on the influence of demographic characteristics of users on the impact of web searching with respect to OPAC use. These characteristics have been observed to definitively impact upon the influence of web searching in some limited activities.

The main aim of this paper is to identify whether user demographic characteristics, computer skills, user education, user assistance and user-friendly OPAC (online public access catalog) have an impact on their satisfaction in the context of users in an Indian university setting.

The survey method was utilized and a questionnaire was designed to collect data. The study is based on a sample of the 384 questionnaires from the three universities located in the Union Territory of Chandigarh and Punjab State. To analyze the data collected, SPSS package (version 14.0) was used, and also to produce requisite cross-tabulations. Chi-square testing was performed to determine the association of user satisfaction with the variables covered in the study.

The findings highlighted that overall there was a low degree of satisfaction among the users. With the exception of academic majors, in general, there were no significant differences between satisfaction and user demographic characteristics. Overall satisfaction was significantly higher for those who were possessed with adequate knowledge on OPAC, and had received staff assistance, as well as necessary education on OPAC. The study showed that satisfaction with ease of usage of OPAC was higher. It was also found that users were well-equipped with computers and had attained skills while frequently searching the web, however, it was also revealed that mere possession of computer skills was not sufficient for efficient use of OPAC, resulting in the attainment of a high level of satisfaction.

The paper brings out the fact of how user demographic characteristics, computer skills, user education, user assistance, and user-friendly OPAC, influence user satisfaction in a university educational set up. The findings will be beneficial for increasing user satisfaction levels in order to retain existing OPAC users because OPAC will continue to be a necessary tool for accessing quality academic information available in both print and electronic format.

An increasing number of users join and become immersed in WeChat official accounts, but many users quit using these services as well. Nevertheless, most of the previous studies mainly focussed on the usage behavior. The purpose of this paper is to fill the gap by examining factors affecting users’ unfollow intentions for WeChat subscriptions in a Chinese context.

Structural equation modeling is used in our study. A field survey with 260 WeChat users is conducted to test the research model and hypotheses.

The results show the following interesting key findings: first, the effect of actual cost on users’ unfollow intentions is larger than the effect of opportunity cost; second, users’ unfollow intentions will decrease with the increase of users’ perceptions of information usefulness; third, the results of the control variables showed that only landing frequency has a negative effect on users’ unfollow intentions; and fourth, users’ demographic differences are also examined in regard to how they may affect users’ unfollow intentions.

First, this paper studies factors influencing users’ unfollow intentions for WeChat subscriptions from a social exchange theory perspective; the authors considered both extraneous factors and users’ internal perception factors potentially affecting users’ unfollow intentions, which has rarely been researched. Furthermore, the authors examined significant differences among users’ demographic characteristics in affecting users’ unfollow intentions. The results of the study provide a more comprehensive understanding of the influencing factors of users’ unfollow intentions.

Considering the ubiquity of FinTech services, the study proposes a research framework to examine FinTech adoption and use from the technology acceptance perspective by adding sub-constructs of technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), ServPerf and WebQual 4.0. This study broadly classified these sub-constructs in three dimensions: adoption, behavior and technological and explores the relationship between these attributes. It also proposes that digital behavior (Internet experience and level of awareness) and demographic characteristics (age and gender) moderate the main relationships.

The measurement scale for the study is developed through iterative discussion with domain experts. The data are collected from 439 active Internet users though a digital survey and analysis were done by applying structural equation modeling and multi-group analysis.

Perceived usefulness and social influence are found to be the key determinant for behavior intention to use FinTech services, with social influence having significant negative influence. Actual use is significantly influenced by ease of use and social influence but is not determined by behavior intention and perceived usefulness. Behavioral attributes are significantly impacted by technological attributes and digital behavior. Also, age significantly affects the perception of security among older users.

This study will help FinTech service providers to design FinTech services considering a wide spectrum of users. More consideration should be on enhancing the usefulness and security features to create social affirmations for the use of FinTech services. This will entice users for frequent use and attract nonusers to do their first online financial transaction.

The study adds to the technology acceptance literature by incorporating relevant technological and behavioral attributes and investigating the moderating effect of digital behavior and demographic characteristics. It contributes to the understanding of user beliefs and perceptions about actual use of FinTech services.

This study aims to explore differences in the usability of the Google Drive application based on demographic characteristics, computer skills and use frequency of Google Drive among public library users of Lahore.

The study adopted a quantitative approach and survey-based research method to achieve the study's objectives. The cloud usability model (user perspective) has been used as a theoretical lens to guide the study objectives. It comprises five dimensions of usability – capable, personal, reliable, valuable and secure.

The findings of the study revealed that the usability of Google Drive varied statistically significantly on the basis of depending on the respondents gender, age, academic qualification, computer skills and Google Drive use frequency.

It is a valuable study since it and adds knowledge to existing literature and has implications for practice.

The findings might be helpful for cloud support teams including Google Drive as they can notice the demographic and other differences among users' perceived usability of Google Drive and can enhance certain features of usability which leads attributes to increase its usage among users.

This paper aims to report the findings of an empirical research focusing on Hong Kong online users’ intention to book online tourism products with latest figures. Focusing on the technology acceptance model (TAM), this case study extends the recent research with providing insight regarding the online users’ perceptions of TAM dimensions and how these dimensions are perceived among different demographic groups and Internet usage characteristics with latest figures in Hong Kong, a major tourism destination in Asia with many world-class hotels.

A total of 213 Hong Kong online users were surveyed in March and April 2013 in Hong Kong business districts.

Findings reveal that most respondents who use the Internet for booking online travel products are young, and people above a certain age are not likely to favor booking tourism products online. They are more likely to stick to traditional personal service. Perceived usefulness of the Internet is found more influential than its ease of use in forming a usage intention, and ease of use is perceived more important by jobseekers, student and employees than the other profession groups. In addition, respondents who use the Internet every day and did purchase online tourism product perceived usefulness more important than ease of use.

The generalization of this research is limited by its sample size and number of questions.

The study gives a new perspective by linking TAM with intention to book online in Hong Kong. The paper would be of interest to the Web site planners or online tourism practitioners to consider Web site usefulness as much as its ease of its use, as both usefulness and ease of use of tourism Web sites are strong predictors of intention to book online.

The purpose of this paper is to identify which factors influence mobile banking adoption and examine those factors for segmentation, using a sample of Indian consumers.

In total, 59 statements were identified based on a literature review, focus group discussions and personal interviews. Exploratory factor analysis was conducted to identify the relevant factors. An online survey of 367 mobile phone users in India was conducted. Confirmatory factor analysis was conducted using structural equation modeling. Appropriate statistical techniques (hierarchical cluster analysis, k-means cluster analysis) were used to segment the users. A profile of each segment was developed based on demographics, mobile banking services used, and attitude and intentions toward mobile banking. Further, a post hoc test was used to test the variation between the obtained clusters and user attitudes and intentions toward mobile banking. The demographic characteristics of users within each cluster were also examined.

Mobile users were segmented into three clusters based on their perceptions of various factors influencing mobile banking. These segments were labeled as technology adoption (TA) leaders, TA followers and TA laggards. The results show that both attitude and intentions toward mobile banking significantly differs across the three segments. In terms of relative positioning, TA leaders have the most favorable attitudes and intentions followed by TA followers, and TA laggards. Age was found to significantly influence TA and usage.

The findings of the study are based on responses from young, educated and salaried Indian consumers from large metro cities. Therefore, it is important to include respondents from smaller cities and towns to be able to generalize the findings. The sample is skewed toward users having accounts with private banks and hence, a balanced representation of respondents from public and private sector banks would help in identifying gaps pertaining to each sector. In future research, attempting to compare the results with other developing and developed countries may be beneficial.

The results offer service providers better knowledge about typical mobile banking user segments, providing banks with ideas for customizing their services to meet customer expectations.

This paper provides insights into factors that influence mobile banking adoption in India, which has not been investigated. In contrast to earlier studies conducted on internet banking, this study attempts to examine the perceptions, attitudes and intentions of mobile users. Although traditional TA models and theories of technology diffusion have been used, this study attempts to tailor the model specifically for mobile banking.

The aim of this study is to investigate the market status for online/mobile banking in China. With the recent and forecasted high growth of Chinese electronic banking, it has the potential to develop into a world‐scale internet economy and requires examination.

The demographic, attitudinal and behavioural characteristics of online and mobile bank users were examined. Respondents from six major Chinese cities participated in the consumer survey.

The results showed Chinese online and mobile bank users were predominantly males, not necessarily young and highly educated, in contrast with the electronic bank users in the West. The issue of security was found to be the most important factor that motivated Chinese consumer adoption of online banking. Main barriers to online banking were the perception of risks, computer and technological skills and Chinese traditional cash‐carry banking culture. The barriers to mobile banking adoption were lack of awareness and understanding of the benefits provided by mobile banking.

This study offers an insight into online/mobile banking in China, which has not previously been investigated. Distinct differences and common trends between Chinese and other countries were observed with clear indication of marketing strategy to be deployed by the service providers.