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Advances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).

First, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.

The results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.

The evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.

User behavior in online information evaluation is the result of a multitude of factors related to social, cultural, personal and psychological issues. The present study aimed to examine the effects of three important psychological variables including personality, self-efficacy and attitude on online information evaluation.

Four validated measures were administrated in person and online among 355 postgraduate students at Shahed University, Tehran, Iran. For testing the possible relationships among the variables, the reliability, normality and Pearson correlation tests were performed by using SPSS 24.0. Moreover, to test the ten hypotheses of the research, the structural equation modeling was considered using AMOS 26.0.

The findings confirmed the first five research hypotheses indicating the direct positive relationships among the four variables except for the impact of self-efficacy on attitude. The mediated effects of the variables were not supported except for the mediating role of attitude in the impact of personality on online evaluation behavior. The variable personality was found to be fundamental among the tested paths because it influenced the information evaluation behavior, both directly and indirectly.

The study showed the impacts of the three variables, which demonstrates that online information evaluation is greatly affected by psychological factors.

This paper aims to provide an introductory overview and selected annotated bibliography of recent resources on library instruction and information literacy across all library types.

It introduces and annotates English-language periodical articles, monographs, dissertations and other materials on library instruction and information literacy published in 2014.

It provides information about each source, discusses the characteristics of current scholarship and highlights sources that contain unique or significant scholarly contributions.

The information may be used by librarians and interested parties as a quick reference to literature on library instruction and information literacy.

The purpose of this paper is to provide a selected bibliography of recent resources on library instruction and information literacy.

The paper introduces and annotates periodical articles, monographs, and audiovisual material examining library instruction and information literacy.

Information about each source is provided. The paper discusses the characteristics of current scholarship, and describes sources that contain unique scholarly contributions and quality reproductions.

The information in the paper may be used by librarians and interested parties as a quick reference to literature on library instruction and information literacy.

Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

Online product ratings play an important role in the decision-making process of consumers, which are not only sources of information used by consumers to understand the function and quality of a product or service but also sources of information used to find desirable products. The purpose of this paper is to develop a decision-based method for supporting the purchase decisions of consumers based on not only the online product ratings but also the actual product attributes.

First, two types of utility values are designed to measure the preference of the consumer based on either online ratings or actual product attributes. Then, the traditional TOPSIS method is adopted to achieve a comprehensive value by integrating the two types of utility values so that all of the alternative products can be ranked. Further, a product selection support system prototype is designed and developed to support the purchase decisions of consumers.

To help consumers select desirable products efficiently, it is necessary to develop a product selection method based on the online ratings of alternative products and consumer expectations.

The research shows that the proposed method can not only support consumers’ purchase decisions based on a large number of online product ratings but also help manufacturers to find out consumers’ demands or requirements on products so as to facilitate the design of new products or the improvement of products. On the basis of the proposed method, the developed system prototype is helpful for consumers to select desirable products.

To support the purchase decisions of consumers, a new decision-based method for selecting desirable online products is proposed.

This study aims to evaluate the vitality of source, receiver and message factors on credibility assessment of online hotel reviews. Further, the current study determines the effects of perceived credibility of online reviews (PCOR) on hotel booking intentions (HBI).

The current study performs reliability analysis to determine the internal consistency of the measurement scales and to ensure the uni-dimensionality of the measurement scales the present study performs exploratory factor analysis. Further, the present study performs structural equation modeling to identify the relationships between the variables.

Two-sided online reviews have a more significant positive impact on PCOR as compared to the effects of receiver, review quality, review consistency and negative-sided online reviews.

This is one of the first study that analyses the impact of source, receiver and message on PCOR. Moreover, the present study offered theoretical justification behind the factors that affect the authenticity of online hotel reviews and its effects on HBI.

Brand image is considered as a signaling phenomenon because high brand image ensures quality product that can reduce consumer’s uncertainty. A strong brand image induces consumers to pay higher prices, which in turn provides competitive advantage and market success to a company. Online reviews, blogs and texts on brand usage experiences are more effective than oral communication to build a strong brand image. Online reviews on products create distinct places for brands in the consumer’s mind, and thus ultimately affect images of the brands. Therefore, the purpose of this study is to investigate the effects of online reviews on functional and hedonic brand images in the context of consumer electronic products in India.

The present study adopts a novel approach to collect data. The data have been collected from select e-commerce sites’ brand pages on Facebook through Google form application. A number of respondents are 1,038. Structural equation modeling technique has been used to examine the effects of online reviews on functional and hedonic brand images.

The data analysis reveals that source and review quality have more significant effect on credibility evaluation of online reviews as compared to the effects of review consistency and receiver. Moreover, credible online reviews have more impact on hedonic brand image rather than functional brand image in the context of consumer electronics product in India.

The present study combines Yale attitude change model and attribution theory to examine the effects of online reviews on brand image.

This study aims to develop and test a browser extension as a scaffolding tool to guide learners about evaluating online sources.

In total, 129 undergraduate students participated in this experimental study. Both groups completed two Web searching tasks, but the experimental group used a browser extension.

The results indicated that there are significant differences between groups in terms of the number of accurate sources and visited sites. There were no differences neither in the success status nor the access time. The browser extension guidance affected certain search parameters, but this effect seemed to be diminished in accordance with students’ cognitive abilities as well as their digital literacy levels.

The participants were from a vocational school, so any other study with different participants might reveal different findings.

The browser extension is convenient to be used with regards to interface and instructions. It can serve as a self-training tool with small changes in the code. The intervals and types of messages can be customized in line with the users’ needs.

The approach used in this study can contribute to the dissemination of misleading information on the Web. People of any age can use and benefit from this approach via a simple extension.

The extension can serve as a fundamental framework for the construction of adaptive or smart extensions. As this study revealed the importance of both cognitive abilities and digital literacy levels, the extension can be enriched with the inclusion of cognitive scaffolding.

This study aims to adopt a cognitive heuristic approach to investigate the interaction effect of a message source characteristic (reviewer expertise [RE]) and two message structure characteristics (review rating consistency [RC] and review valence [RV]) on the perceived credibility of hotel online reviews.

Data were collected from 242 university students and were analyzed by three-way analysis of variance through a 2 × 2 factorial experiments using a simulated hotel review page on TripAdvisor.

Results show a three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. The main effects of the three factors are also determined. Higher perceived credibility scores are found for negative reviews, reviews written by experts and reviews with a consistent rating.

This study adopts an experimental approach and is the first to investigate the three-way interactions of message source and message structure characteristics of online hotel reviews. Data were collected from students in a university in Hong Kong. Results may not be generalizable to other markets.

Results suggest that reviews written by experts have higher perceived credibility. Hotels should pay attention to the content of online reviews and the expertise level of reviewers. Efforts should be exerted to create positive experiences for hotel guests that motivate expert reviewers to write positive reviews. Note that negative reviews have higher perceived credibility than positive ones. Hotels should promptly address negative reviews and provide professional responses to reviewers. Platform operators of user-generated content (UGC) should create well-defined reviewer profiles that can serve as cues that communicate the different expertise of reviewers.

This study is the first to test the three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. Results provide recommendations to hotels and UGC operators and enable them to benefit from emerging UGC usage.