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In this paper the authors aim to review the state-of-the-art literature on online review systems and their impacts on consumer behavior and retailers' performance with the aim of identifying research gaps related to different design features of review systems and developing future research agenda.

The authors conducted a systematic review based on PRISMA 2020 protocol, focusing on studies published in the domains of retailing and marketing. This procedure resulted in 48 selected papers investigating the design features of retailer online review systems.

The authors identify eight design features that are controllable by retailers in an online review system. The design features have been researched independently in previous literature, with some features receiving more attention. Most selected studies focus on the design features adapted metrics and review presentations, while other features are generally neglected (e.g. rating dimensions). Previous literature argues that design features affect consumer behaviors and retailers' performance. However, the interactions among the features are still neglected in the literature, creating a relevant gap for future research.

This paper distinguishes between different types of retailer online review systems based on how they are implemented. The authors summarize the state-of-the-art of relevant literature on design features of online review systems and their effects on consumer- and retailer-related outcome variables. This systematic literature review distinguishes between online reviews provided on websites controlled by retailers (internal systems) and third-party websites (external systems).

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

As internet banking service marketing platforms continue to advance, customers exhibit distinct behaviors. Given the extensive array of options and minimal barriers to switching to competitors, the concept of customer churn behavior has emerged as a subject of considerable debate. This study aims to delineate the scope of feature optimization methods for elucidating customer churn behavior within the context of internet banking service marketing. To achieve this goal, the author aims to predict the attrition and migration of customers who use internet banking services using tree-based classifiers.

The author used various feature optimization methods in tree-based classifiers to predict customer churn behavior using transaction data from customers who use internet banking services. First, the authors conducted feature reduction to eliminate ineffective features and project the data set onto a lower-dimensional space. Next, the author used Recursive Feature Elimination with Cross-Validation (RFECV) to extract the most practical features. Then, the author applied feature importance to assign a score to each input feature. Following this, the author selected C5.0 Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost and LightGBM as the six tree-based classifier structures.

This study acclaimed that transaction data is a reliable resource for elucidating customer churn behavior within the context of internet banking service marketing. Experimental findings highlight the operational benefits and enhanced customer retention afforded by implementing feature optimization and leveraging a variety of tree-based classifiers. The results indicate the significance of feature reduction, feature selection and feature importance as the three feature optimization methods in comprehending customer churn prediction. This study demonstrated that feature optimization can improve this prediction by increasing the accuracy and precision of tree-based classifiers and decreasing their error rates.

This research aims to enhance the understanding of customer behavior on internet banking service platforms by predicting churn intentions. This study demonstrates how feature optimization methods influence customer churn prediction performance. This approach included feature reduction, feature selection and assessing feature importance to optimize transaction data analysis. Additionally, the author performed feature optimization within tree-based classifiers to improve performance. The novelty of this approach lies in combining feature optimization methods with tree-based classifiers to effectively capture and articulate customer churn experience in internet banking service marketing.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

The authors' work aims to identify the employer-specific drivers of the college (or university) wage gap, which has been identified as one of the major determinants of the dynamics of overall wage and income inequality in the past decades. The authors focus on three employer-level features that can be associated with asymmetries in the employment relation orientation adopted for college and non-college-educated employees: (1) size, (2) the share of standard employment and (3) the pervasiveness of incentive pay schemes.

The authors' establishment-level analysis (data from the Basic Survey on Wage Structure (BSWS), 2005–2018) focusses on Japan, an economy characterised by many unique economic and institutional features relevant to the aims of the authors' analysis. The authors use an adjusted measure of firm-specific college wage premium, which is not biased by confounding individual and establishment-level factors and reflects unobservable characteristics of employees that determine the payment of a premium. The authors' empirical methods account for the complexity of the relationships they investigate, and the authors test their baseline outcomes with econometric approaches (propensity score methods) able to address crucial identification issues related to endogeneity and reverse causality.

The authors' findings indicate that larger establishment size, a larger share of regular workers and more pervasive implementation of IPSs for college workers tend to increase the college wage gap once all observable workers, job and establishment characteristics are controlled for. This evidence corroborates the authors' hypotheses that a larger establishment size, a higher share of regular workers and a more developed set-up of performance pay schemes for college workers are associated with a better capacity of employers to attract and keep highly educated employees with unobservable characteristics that justify a wage premium above average market levels. The authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

The authors' contribution to the existing knowledge is threefold. First, the authors combine the economics and management/organisation literature to develop new insights that underpin the authors' testable empirical hypotheses. This enables the authors to shed light on employer-level drivers of wage differentials (size, workforce composition, implementation of performance-pay schemes) related to many structural, institutional and strategic dimensions. The second contribution lies in the authors' measure of the “adjusted” college wage gap, which is calculated on the component of individual wages that differs between observationally identical workers in the same establishment. As such, the metric captures unobservable workers' characteristics that can generate a wage premium/penalty. Third, the authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.

A systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.

Results show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.

With societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.

A core challenge for leaders for deeper learning is scaling promising practices to provide students with systematic access to deeper learning experiences. This case illuminates how a group of researchers organized professional learning activities around conferring, a promising deeper learning practice.

The author examines how the leaders of a Networked Improvement Community (NIC) created the conditions for teachers to share their deeper learning practices through a case study. The case study centers on one school team’s learning through their participation in the NIC activities, as evidenced by the artifacts they created and their exchanges with their team, participants from other schools and researchers.

The trajectory of one team through three NIC activities – a video club, a pitch and user testing – shows how they examined their own conferring practice, got ideas for change and shifted their thinking and practice toward a more student-centered approach. Insights from the case suggest three design principles – a common problem of practice, shared representations of practice and intentional network configurations – for deeper professional learning, or learning experiences that engage educators in purposeful and collaborative inquiry into deeper learning practices.

Two limitations of the case are a lack of data on the perceived experience of participants, which could speak to the depth of Irving’s shift toward student-centered conferring, and the narrow time scope of the NIC, which limits exploration of the sustainability of the changes to conferring.

The design principles represent important features for researchers and leaders to consider in ongoing efforts to scale deeper learning. Leaders might use the principles to examine existing or future professional learning efforts.

This case study extends an understanding of one facet of leadership for deeper learning: fostering professional community. Future research is needed to examine the educator experience of participating in deeper professional learning and its sustained impact on practices.