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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.

The continued relevance of technologies in halal industries requires managers to understand the factors contributing to such technologies’ acceptance. The technology acceptance model (TAM) is dominant in the literature that predicts user acceptance and behaviour towards technology. Despite the model’s significance, there has yet to be a systematic review of studies featuring halal sectors that use TAM. The purpose of this study is to systematically review the existing literature on TAM in halal industries to understand the research trends as well as TAM modifications and research opportunities in halal industries.

Guided by the preferred reporting items for systematic review and meta-analysis protocol, a framework-based review using the theories, contexts, characteristics and methods (TCCM) framework was conducted. The Scopus and Web of Science databases were used to retrieve English journal articles that investigated TAM in the context of halal markets. In total, 44 eligible articles were reviewed in terms of the developments and extensions of TAM in their studies across the halal industries.

The first study related to the use of TAM in the context of halal industries was published in 2014. The most prominent halal industry in the review, which used TAM, was Islamic finance. Indonesia was the leading economy in halal studies using TAM. Perceived usefulness was found to be a more significant factor than perceived ease of use for technology acceptance in TAM studies on halal industries. The significance of religiosity on TAM was inconsistent. Most research was done using quantitative surveys with consumers as the target sample.

The studies in this review are based on the Scopus and Web of Science databases, which may be perceived as a study limitation. This study also only considered English journal articles and research in which the focus was on the use of TAM in halal industries rather than general industries with Muslim consumers.

Halal industries will continue to rely on technology for the provision of goods and services. With the rise of emerging technological innovations, this review will provide managers with an appreciation of technology acceptance across different contexts. Researchers can use the results of this review to guide future studies and contribute toward the development of this research area.

This review contributes to the Islamic marketing literature by being the first to comprehensively review the TAM model in the context of halal industries using the TCCM framework-based review approach. A research agenda is proposed to advance research on technology acceptance and TAM in halal industries.

This study aims to examine the impact of consumer risk appetite, biases (specifically negative recency bias), and the importance of reviews in enhancing information quality. By analyzing these variables, the authors gain insights into their role in enriching the overall information spectrum available to consumers. The findings contribute to a better understanding of how risk appetite, biases and consumer reviews shape the quality of information.

The questionnaire assessed the relationship between dependent and independent variables by asking participants to rate their experiences in relevant scenarios. Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

The efficiency of reviews and ratings is greatly influenced by consumer risk appetite. Businesses should focus on clients who are willing to take risks and balance positive and negative feedback. It is essential to comprehend how customers understand reviews. Credibility is increased by taking biases into account and encouraging unbiased criticism. Promoting thorough reviews strengthens influence. Monitoring and making use of these elements improve online reputation and commercial success.

The research has limitations due to the simplicity of the attributes taken into account and the requirement for a larger sample size. Overcoming barriers to promote consistent client feedback is essential, and tailored emails can help with assessment generation. Increased customer participation in writing evaluations can be achieved by removing obstacles and highlighting the advantages of participation.

Businesses and buyers rely on this “organically” generated content as the basis of their promotional strategy and buying decisions. Most of the research is related to consumer reviews, their behavior and the importance of social validation. However, some critical aspects related to this need further investigation.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

This paper’s objective is to provide a systematic literature review of the contextual factors affecting downward communication from supervisors to subordinates in the audit environment. In addition, this review identifies emerging research themes and directions for future research.

I accomplish this review’s objectives by leveraging communication literature to establish a framework to identify and synthesize contextual factors affecting downward communication in the audit environment. The review identifies 50 published articles in the last 20 years from leading accounting and auditing journals.

This study consolidates research findings on downward communication under two primary contextual factors: (1) message and (2) channel. Findings indicate that empirical research examining communication in audit is fragmented and limited. Studies examining the message focus heavily on its content and treatment in the areas of feedback, nonverbal cues, and fraud brainstorming, and a handful of additional studies examine the effectiveness of the channel in these areas. Additional research is needed to understand a broader set of supervisor–subordinate communication practices, including those that are computer-mediated, and their effect on subordinate auditors’ judgments and behaviors in the contemporary audit environment.

Much of the audit literature examining communication to date is topic-versus construct-based, making it difficult to see how the research findings relate to one another. This review is the first to synthesize the literature to provide academics recommendations for a way forward, and inform practitioners of communication practices whereby supervisors can be trained to improve audit quality.

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.

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.

The purpose of this viewpoint is to spotlight the role of reviewers within the collaborative triad of academic publishing. It argues that the significance of reviewers is often disregarded, leading to a gap in our understanding of the peer review process. This perspective emphasizes reviewers as unsung heroes in the publishing ecosystem, providing intentional and thought-provoking insights into the less-discussed yet impactful developments in the evolving peer review landscape.

Leveraging the author’s distinguished background as a recipient of the Journal of Service Marketing Outstanding Reviewer Award, this paper offers a personal reflection and synthesised viewpoints on the peer review process. Serving as a representative voice for reviewers, it provides insightful perspectives from the vantage point of a peer reviewer, diverging from conventional editorials and commentaries authored by editors.

Acknowledging the shrinking reviewer pool, this viewpoint suggests a mandatory “review for review” system alongside incentives like editorial positions, while considering financial rewards for reviewers. The rise of generative artificial intelligence (AI) in review prompts ethical concerns but offers solutions for handling diverse submissions and dealing with “Reviewer 2.” While embracing open review for its transparency, potential pitfalls surrounding article confidence and copyright require attention. Ultimately, this viewpoint advocates for a collaborative approach, valuing reviewers, exploring innovative solutions, navigating ethical dilemmas in the technological age and implementing transparent practices responsibly for the betterment of scholarly discourse.

This viewpoint highlights the invaluable contributions of reviewers, enriching the scholarly community and promoting intellectual growth.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.