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Understanding factors that influence online shopping and managing consumer relationships is not a trivial task for firms, considering the many pertinent factors that influence behavior, including the product being shopped (i.e., the \"what\") and the context of the website itself (i.e., the \"where\"). This study investigates the impact of these characteristics on an online transaction's basket value, after incorporating the role of other aspects of the browsing process including page views and visit duration. The authors estimate a multivariate mixed-effects Type II Tobit model with a system of equations to explain variation in shopping basket value, using data involving 773,262 browsing sessions resulting in 9,664 transactions across 43 product categories from 385 unique websites. The results support the assertions that contextual factors are associated with online browsing. For example, a website's scope in terms of product variety is associated positively with visit durations and basket values but negatively with page views. Furthermore, a website's communication functionality is positively associated with basket value for hedonic products. Insights suggest managerial implications involving product and website strategies for online retailers.

This study examined determinants of library and information science undergraduate students’ first impression with the university library websites. A total enumeration method was used to involve 54 year 4 undergraduate students of Library and Information Science from two selected universities. Undergraduate Students’ Determinants of First Impression with University Library Websites Questionnaire was used to gather data. The results obtained demonstrate that there is significant correlation between LIS students’ Perception of Website quality, Website interactivity, Website aesthetic perception, Website prototypicality, and Website satisfaction with first impression toward the university library website. The five independent variables (Website quality, Website interactivity, Website aesthetic perception, Website prototypicality, and Website satisfaction) jointly (as indicated by the R-square value) explained or predicted 66% of the variation in LIS students’ first impression towards university library website. Aesthetic perception of library website contributed most to the prediction of LIS students’ first impression towards library website, followed in declining order of strength by library website interactivity, library website satisfaction, and library website quality. However, prototypicality though correlated with first impression, its contribution is not significant. Notable limitation of this study is that, data was collected from undergraduate students in only two universities focusing only Library and Information Science students. The results call for formidable efforts to improve the users experience on the web, because the first impression counts. This study has implications for the users patronizing the university library websites. The results show that Aesthetic perception of library website contributed mostly to the prediction of LIS students’ first impression towards university library website, followed by library website interactivity. These findings may not be applicable to other university library websites but this depend on the experience of the users.

Being a controversial industry, oil companies turn to corporate social responsibility (CSR) as a means to obtain legitimacy. Adopting a case study methodology, this research examines the characteristics of CSR strategies and CSR communication tactics of six oil companies by analyzing their 2011-2012 web site content. We found that all six companies engaged in CSR activities addressing the needs of various stakeholders and had cross-sector partnerships. CSR information on these companies' web sites was easily accessible, often involving the use of multimedia technologies and sometimes social media platforms. Furthermore, to boost the credibility of their CSR messages, these companies utilized a variety of tactics, such as factual arguments and two-sided messages. In sum, this research unveils the interconnectedness among business strategy, CSR practices, and CSR communication in oil companies' attempt to gain legitimacy in an environment of controversy. The article ends with a discussion of the theoretical and practical implications of the research findings.

Phishing is described as the art of echoing a website of a creditable firm intending to grab user's private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as the browser-based security indicators provided along with the website. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be employed in predicting phishing attacks is based on data mining, particularly the ‘induction of classification rules’ since anti-phishing solutions aim to predict the website class accurately and that exactly matches the data mining classification technique goals. In this study, the authors shed light on the important features that distinguish phishing websites from legitimate ones and assess how good rule-based data mining classification techniques are in predicting phishing websites and which classification technique is proven to be more reliable.

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

Website morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset number of clicks and then selects the best \"morph\" using expected Gittins indices. Switching costs, potential website exit, and all clicks prior to morphing are ignored. We model switching costs, potential website exit, and the (potentially differential) impact of all clicks to determine when to morph for each customer. Morphing earlier means more customer clicks are based on the optimal morph; morphing later reveals more about the customer's latent segment. We couple this within-customer optimization to between-customer expected Gittins index optimization to determine which website \"look and feel\" to give to each customer at each click. We evaluate the improved algorithm with synthetic data and with a proof-of-feasibility application to Japanese bank card loans. The proposed algorithm generalizes the established algorithm, is feasible in real time, performs substantially better when tuning parameters are identified from calibration data, and is reasonably robust to misspecification. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1961 . This paper was accepted by Eric Bradlow, special issue on business analytics .

Creating effective online customer experiences through well-designed product web pages is critical to success in online retailing. How such web pages should look specifically, however, remains unclear. Previous work has only addressed a few online design elements in isolation, without accounting for the potential need to adjust experiences to reflect the characteristics of the products or brands being sold. Across 16 experiments, this research investigates how 13 unique design elements shape four dimensions of the online customer experience (informativeness, entertainment, social presence, and sensory appeal) and thus influence purchase. Product (search vs. experience) and brand (trustworthiness) characteristics exacerbate or mitigate the uncertainty inherent in online shopping, such that they moderate the influence of each experience dimension on purchases. A field experiment that manipulates real product pages on Amazon.com affirms these findings. The results thus provide managers with clear strategic guidance on how to build effective web pages.

It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware of the digital safety of websites, we have tried to identify and learn the pattern on a dataset consisting of features of malicious and benign websites. We treated the problem of differentiation between malicious and benign websites as a classification problem and applied several machine learning techniques, for example, random forest, decision tree, logistic regression, and support vector machines to this data. Several evaluation metrics such as accuracy, precision, recall, F1 score, and false positive rate, were used to evaluate the performance of each classification technique. Since the dataset was imbalanced, the machine learning models developed a bias during training toward a specific class of websites. Multiple data balancing techniques, for example, undersampling, oversampling, and SMOTE, were applied for balancing the dataset and removing the bias. Our experiments showed that after balancing the data, the random forest algorithm using the oversampling technique showed the best results in all evaluation metrics for the benign and malicious website feature dataset.

Purpose – Online retailing has attracted a lot of attention in recent years due to its great potential and significant implications for buyers and sellers. This study adopts the stimulus-organism-response (S-O-R) framework to illustrate how store layout design and atmosphere influence consumers' shopping intention on the website. Design/methodology/approach – The sample for this study comprised 626 respondents from the internet users. A structural equation model was employed to identify the interrelationships of store layout design, atmosphere, emotional arousal, attitude toward the website, and purchase intention. Findings – The analytical results of this study indicate that store layout design has significant impacts on emotional arousal and attitude toward the website, and thus has a positive influence on purchase intention. In addition, atmosphere has a more influential effect on emotional arousal than store layout design. Originality/value – This study provides new insights into the influences of store layout design and atmosphere on consumer online shopping intentions.

Consumers engage in costly searches to evaluate the increasing number of product options available from online retailers. Presenting the best alternatives at the beginning reduces search costs associated with a consumer finding the right product. We use rich data on consumer click-stream behavior from a major web-based hotel comparison platform to estimate a model of search and click. We propose a method of determining the ranking of search results that maximizes consumers’ click-through rates (CTRs) based on partial information available to the platform at the time of the consumer request, its assessment of consumers’ preferences, and the expected consumer type based on request parameters from the current visit. Our method has two distinct advantages. First, we endogenize a consumer response to the ranking using search refinement tools, such as sorting and filtering of product options. Accounting for these search refinement actions is important since the ranking and consumer search actions together shape the consideration set from which clicks are made. Second, rankings are targeted to anonymous consumers by relating price sensitivity to request parameters, such as the length of stay, number of guests, and day of the week of the stay. We find that predicted CTRs under our proposed ranking are almost double those of the platform’s default ranking. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1036 .