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Challenges and opportunities arise from the significantly different perspectives of context-specific versus context-free researchers and the literatures they contribute to. Reviews of one type or the other or both types of literatures may provide different understandings of the state of the art in a particular area of health care management. Suggestions for writing quality reviews are also included along with suggested topics for future reviews.

Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.

First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.

Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.

To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

大多数先前预测评论有用性的研究忽视了嵌入在评论文本中的深层特征的重要性, 而主要依赖手工制作的特征。手工制作和深层特征具有高解释性和预测准确性的优势。本研究提出了一种新颖的评论有用性预测模型, 利用深度学习技术来考虑手工制作特征和深层特征之间的互补性。

首先, 采用先进的卷积神经网络从非结构化的评论文本中提取深层特征。其次, 本研究利用先前研究中提取的手工制作特征, 这些特征影响了评论的有用性并增强了其解释性。第三, 本研究将深层特征和手工制作特征结合到一个评论有用性预测模型中, 并使用Yelp.com数据集对其性能进行评估。为了衡量所提出模型的性能, 本研究使用了2,417,796条餐厅评论。

广泛的实验验证了所提出的方法优于传统的机器学习方法。此外, 通过实证分析, 本研究证实了结合手工制作和深层特征可以展现出更好的预测性能。

据我们所知, 这是首个在餐厅评论预测中应用深度学习技术, 并结合了结构化和非结构化数据来预测评论有用性的研究之一。此外, 本研究采用了先进的特征融合方法, 更好地利用了提取的特征信息, 并识别了特征之间的互补性。

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

This paper aims to examine existing literature on halal food consumption from the perspective of consumer behaviour research. It identifies progress, current state and gaps and provides direction for future research to advance the field.

This paper reviewed papers from 2007 to 2023 using Scopus and WOS databases. The review used the Theories–Context–Characteristics–Methods (TCCM) framework and followed the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) protocol.

The review summarises the methods, theory and approaches used in the topic and presents key findings and a proposed framework. It can guide future researchers in developing their models, making it a valuable starting point for future research. Despite the existence of SRL in halal food to the best of the authors’ knowledge, this work is the first review that proposes a model for halal food in consumer behaviour.

This review provides a twofold contribution. First, this review's theoretical contribution is advancing consumer behaviour literature on halal food. Second, this work's practical contribution is to enrich practitioners' understanding of the antecedents of consumer behaviour regarding halal food. This is the first review proposing a consumer behaviour model for Halal food. It makes a theoretical contribution to understanding consumer behaviour on Halal food and provides practical insights for practitioners.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

This study aims to evaluate and summarize the effectiveness of cognitive behavioral therapy (CBT) and internet-based CBT (ICBT) interventions on relapse prevention and severity of symptoms among individuals with major depressive disorder (MDD). CBT is one of the most used and suggested interventions to manage MDD, whereas ICBT is a novel effective proposed approach.

The review was conducted following the preferred reporting items for systematic review and meta-analysis protocol. A comprehensive and extensive search was performed to identify and evaluate the relevant studies about the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD.

A total of eight research studies met the inclusion criteria and were included in this systematic review. RCT studies were conducted to assess and evaluate the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD. It has been found that CBT is a well-supported and evidently based effective psychotherapy for managing depressive symptoms and reducing the relapse and readmission rate among patients diagnosed with MDD. The ICBT demonstrated greater improvements in depressive symptoms during major depressive episodes among patients with MDDS. The ICBT program had good acceptability and satisfaction among participants in different countries.

Despite the significant findings from this systematic review, certain limitations should be acknowledged. First, it is important to note that all the studies included in this review were exclusively conducted in the English language, potentially limiting the generalizability of the findings to non-English speaking populations. Second, the number of research studies incorporated in this systematic review was relatively limited, which may have resulted in a narrower scope of analysis. Finally, a few studies within the selected research had small sample sizes, which could potentially impact the precision and reliability of the overall conclusions drawn from this review. The authors recommend that nurses working in psychiatric units should use CBT interventions with patients with MDD.

This paper, a review of the literature gives an overview of CBT and ICBT interventions to reduce the severity of depressive symptoms and prevent patients’ relapse and rehospitalization and shows that CBT interventions are effective on relapse prevention among patients with MDD. In addition, there is still no standardized protocol to apply the CBT intervention in the scope of reducing the severity of depressive symptoms and preventing depression relapse among patients with major depressive disorder. Further research is needed to confirm the findings of this review. Future research is also needed to find out the most effective form and contents of CBT and ICBT interventions for MDD.

CBT is a psychological intervention that has been recommended by the literature for the treatment of major depressive disorder (MDD). It is a widely recognized and accepted approach that combines cognitive and behavioral techniques to assist individuals overcome their depressive symptoms and improve their overall mental well-being. This would speculate that effectiveness associated with several aspects and combinations of different approaches in CBT interventions and the impact of different delivery models are essential for clinical practice and appropriate selection of the interventional combinations.

This systematic review focuses on the various studies that explore the effectiveness of face-to-face CBT and ICBT in reducing depressive symptoms among patients with major depressive disorder. These studies were conducted in different countries such as Iran, Australia, Pennsylvania and the USA.

This study provides a meta-review of global virtual team (GVT)–related reviews, creating a resource that highlights dominant themes, research trends and shifts in topics over time culminating in a summary of opportunities for future research. By analyzing and grouping the evidence presented in previous research, this meta-review provides key insights toward future research and managerial implications.

This meta-review identifies 35 existing GVT-related reviews across 32 publication outlets, providing a longitudinal and cross-disciplinary view of GVT research to date.

Results of the analysis reveal over time that there has been a largely adopted reconceptualization of the GVT paradigm toward a continuum of virtuality. There has been a shift in the view of the cross-cultural and global components of GVTs toward a recognition that a greater variance of dimensionality exists. Additionally, popular themes across the literature emerge, notably, virtuality, concepts of culture, trust, leadership and communication technology.

As a multidisciplinary GVT-focused meta-review, this study complements previous efforts by taking a tour across this wide topic and is dedicated to those who are researching, teaching, working and managing GVT-related strategies. The reviews selected represent work published across multiple literature streams, providing a comprehensive and forward thinking perspective.

The study aims to identify the key advantages/enablers and disadvantages/barriers of augmented reality (AR) implementation in education through existing reviews. It also examines whether these factors differ across educational domains.

This study conducted a systematic review of reviews to synthesize evidence on the barriers and enablers influencing AR adoption in education. Searches were performed across five databases, with 27 reviews meeting the inclusion criteria. Data extraction and quality assessment were completed. Content analysis was conducted using the AR adoption factor model and consolidated framework for implementation research.

The findings reveal several enablers such as pedagogical benefits, skill development and engagement. Equally, multiple barriers were identified, including high costs, technical issues, curriculum design challenges and negative attitudes. Interestingly, duality emerged, whereby some factors served as both barriers and enablers depending on the educational context.

This review contributes a novel synthesis of the complex individual, organizational and technological factors influencing AR adoption in education across diverse domains. The identification of duality factors provides nuanced understanding of the multifaceted dynamics shaping AR integration over time. The findings can assist educators in tailoring context-sensitive AR implementation strategies to maximize benefits and minimize drawbacks. Further research should explore duality factors and their interrelationships in AR adoption.

Consumer-perceived review attitude determines consumer overall information adoption and is a core part of consumer’s online-shopping. This study aims to focus on factors that could influence consumer review attitude and can be used by marketers to shape individual information perception.

The study used the questionnaire method to collect data from online shoppers and the modelling of structural equations as an empirical approach to analyse the data.

The findings demonstrate that both systematic and heuristic cues impact the reviewer’s credibility and perceived website attitude differently, which, in turn, influence review attitude. Review characteristics, such as factuality, consistency and relevancy, have a positive relationship with reviewer credibility, while only review consistency and relevancy appears to have a relationship with review attitude. Website characteristics such as reputation, familiarity and social interactivity positively influence the website attitude, which positively influences review attitude. Apart from this, review skepticism has a significant negative relationship with review attitude.

This study could help to foster a positive attitude towards online reviews. Digital marketers need to motivate trusted reviewers to post consistent, fact-based reviews. Further improving the overall website reputation and interactivity could bring a positive attitude towards the reviews. Also, digital marketers must filter and avoid contradictory reviews or reviews that have a bipolar message and reviews expressing numerous emotions to enhance review relevance and consistency.

The current study addresses the need to understand the formation of consumer review attitude through both review and website characteristics using heuristic – systematic model. The paper captures the complex process undergone by the consumer to decipher review attitude and thereby extend the understanding of consumer information processing.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.