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The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.

The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.

The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.

In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.

The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

Responses to minimum income and minimum spending questions are used to produce economic well-being thresholds. Thresholds are estimated using a regression framework. Regression coefficients are based on U.S. Survey of Income and Program Participation (SIPP) data and then applied to U.S. Consumer Expenditure Survey (CE) data. Three different resource measures are compared to the estimated thresholds. The first resource measure is total before-tax money income, and the other two are expenditure based. The first of these two refers to expenditure outlays and the second to outlays adjusted for the value of the service flow of owner-occupied housing (rental equivalence). The income comparison is based on SIPP data while the outlays comparisons are based on CE data. Results using official poverty thresholds are shown for comparison. This is among the earliest work in the U.S. in which expenditure outlays have been used for economic well-being determinations in combination with personal assessments, and the first time rental equivalence has been used in such an exercise. Comparisons of expenditures for various bundles of commodities are compared to the CE derived thresholds to provide insight concerning what might be considered minimum or basic.

Results reveal that CE and SIPP MIQ thresholds are higher than MSQ thresholds, and resulting poverty rates are also higher with the MIQ. CE-based MSQ thresholds are not statistically different from average expenditure outlays for food, apparel, and shelter and utilities for primary residences. When reported rental equivalences for primary residences that are owner occupied are substituted for out-of-pocket shelter expenditures, single elderly are less likely to be as badly off as they would be with a strict outlays approach in defining resources.

This paper takes a stakeholder analysis approach derived from a study in one domain and applies this to safety issues in an organization making food products.

This paper uses a stakeholder analysis approach.

This paper has demonstrates that the approach advocated by Heidrich et al. for stakeholder analysis in waste management can be used in other domains.

Takes a stakeholder analysis approach derived from a study in one domain and applies this to safety issues in an organization making food products.

To show how an ratings‐based approach to stakeholder analysis may be applied using an example in the food production industry

An approach developed in an earlier paper, applying various dimensions and an “affect criterion” in the waste management industry is tested out in the food production domain using expert judgment to see how well it can work.

The approach is shown to work but is not static and would need re‐consideration at least biennially.

Future research might be conducted on comparable companies in the same sector to ascertain the extent to which stakeholders have common influences

This is a useful tool for senior managers and strategists but is likely to require an “outside” expert in order to generate unbiased assessments

The originality lies in the use of new dimensions to rate stakeholders

This paper aims to introduce the current-state of the energy grid, to reviews new and enabling peer-to-peer and blockchain-based solutions and to propose a strategic go-to-market framework, populated with energy companies strategically positioned to capture the unique opportunities present across the energy industry.

Hundreds of use-cases worldwide have been reviewed, and 50 worldwide blockchain energy companies and initiatives were included in this study based on the publicly accessible information that indicates their go-to-market strategies. As a result, these initiatives were classified into three main types of go-to-market strategies. Each company and its portfolio of blockchain-based solutions was described under these three categories.

Based on the research conducted in this review paper, it is evident that the adoption of blockchain-based technologies, solutions and services is accelerating at a rapid pace within the global energy industry to meet the needs and challenges that exist within it. Given the companies outlined in this paper, the opportunity to leverage blockchain technology while aligning to a social driver like green energy is perceived to be the most promising go-to-market strategy within this sector.

This study explores the apparent business plans of different blockchain initiatives around the world. Although there are a few other review papers recently published, to the best of the authors’ knowledge, this approach has not been taken in other studies in terms of the categorization of available use-cases.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

Social networking sites (SNSs) are an indispensable part of people’s daily lives. However, scant literature describes how SNSs affect users’ behaviors, especially consumer behavior in emerging markets. This research aims to fill this literature gap.

Two empirical studies were conducted using different methods. Study 1, a survey, provided correlational evidence. Study 2, a lab experiment, further verified the causal relationship.

From Chinese consumer data, SNS consumption exposure enhances luxury brand consumption, mediated by social comparison motivation and moderated by legitimacy perceptions of SNSs as information outlets.

This research bridges SNSs and luxury brand consumption, two islands among different streams of literature. In addition, the paper illuminates the psychological mechanism through which SNSs affect luxury brand consumption and the boundary condition in which this effect diminishes. Practically, this paper is also instructive for SNSs and luxury brands.

The purpose of the paper is to clarify the mixed empirical results concerning the association between educational level and job satisfaction. It seeks to test whether the positive relationship between educational level and job satisfaction is caused by indicators of job quality.

Three models are estimated. In the first model, the impact of the educational level on job satisfaction is examined using an ordinal regression analysis. The second model estimates the impact of the educational level on indicators of job quality, using the appropriate technique (OLS or binary logit). The third model reveals the “true” impact of the educational level on job satisfaction, when the job quality indicators are added as independent variables. Survey data on Flemish youth in their first job are used.

The results show that higher educated workers are more satisfied than their lower educated counterparts, because they have a job of better quality. When one controls for all job characteristics, a negative relationship appears, with higher educated workers reporting less job satisfaction.

The hypothesis is only tested for a sample of Flemish youth in their first job (cross‐sectional data).

Future empirical studies on job satisfaction should include indicators for job quality, in order to reveal the true effect of educational level on job satisfaction. Investing in the job quality of lower educated young workers might boost their job satisfaction and as a consequence also their productivity.

Suggests that the diverging results concerning the relationship between educational level and job satisfaction could be due to insufficient control for indicators of job quality.