Search results

Repeat customers play an important role in the restaurant sector. Previous studies have confirmed the positive effect of managerial responses on customer relationship management. However, the practice of managerial response strategies toward repeat customers in the restaurant sector remains unclear. This study aims to explore how social influence and the revisit intention of customers affect the responding behavior of restaurant managers.

This study collects information of 251,944 customer reviews and managerial responses from 1,272 restaurants on Yelp (a leading restaurant review website around the world) and builds four econometric models (with restaurant and month fixed effects) to test the hypotheses empirically.

The empirical results show that restaurant managers are less likely to respond to reviews posted by repeat customers (10% lower than that of new customers). This effect is moderated by customer social influence, which entails that repeat customers with great social influence are more likely to receive managerial responses. Moreover, reviews from repeat customers who have had a longer time since their last consumption are also more likely to receive managerial responses.

The results present implications for restaurant managers in business practice regarding managerial response. Managers should take advantage of platform designs and tools (i.e. customer relationship management programs to keep track of repeat customers) to locate repeat customers and avoid the potential negative effects caused by their selected response strategies.

To the best of the authors’ knowledge, this study is among the first attempts to examine empirically how restaurant managers respond to reviews generated by repeat customers in real business practice and reveals what drives such activities from the perspectives of social influence and revisit intention.

Food fraud as intentional deception for economic gain relies on a low-tech food value chain, that applies a ‘paper-and-pencil approach’, unable to provide reliable and trusted data about food products, accompanied processes/activities and actors involved. Such approach has created the information asymmetry that leads to erosion of stakeholders and consumers trust, which in turn discourages cooperation within the food chain by damaging its ability to decrease uncertainty and capability to provide authentic, nutritional, accessible and affordable food for all. Lack of holistic approach, focus on stand-alone measures, lack of proactive measures and undermined role of customers have been major factors behind weaknesses of current anti-fraud measures system. Thus, the process of strong and fast digitalisation enabled by the new emerging technology called Industry 4.0 is a way to provide a shift from food fraud detection to efficient prevention. Therefore, the objective of this chapter is to shed light on current challenges and opportunities associated with Industry 4.0 technology enablers' guardian role in food fraud prevention with the hope to inform future researchers, experts and decision-makers about opportunities opened up by transforming to new cyber-physical-social ecosystem, or better to say ‘self-thinking’ food value chain whose foundations are already under development. The systematic literature network analysis is applied to fulfil the stated objective. Digitalisation and Industry 4.0 can be used to develop a system that is cost effective and ensures data integrity and prevents tampering and single point failure through offering fault tolerance, immutability, trust, transparency and full traceability of the stored transaction records to all agri-food value chain partners. In addition, such approach lays a foundation for adopting new business models, strengthening food chain resilience, sustainability and innovation capacity.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

This paper aims to extract the comment targets in Chinese online shopping platform.

The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment.

The extracting comment target method the authors proposed in this paper is effective.

First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information.

Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients.

The extracting comment target method the authors proposed in this paper is effective.

As intelligent technology advances, practical applications often involve data with multiple labels. Therefore, multi-label feature selection methods have attracted much attention to extract valuable information. However, current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal relationship.

To address the above problems, we propose an ensemble causal feature selection method based on mutual information and group fusion strategy (CMIFS) for multi-label data. First, the causal relationship between labels and features is analyzed by local causal structure learning, respectively, to obtain a causal feature set. Second, we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset reliability. Eventually, we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the results.

Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different metrics. Furthermore, the statistical analyses further validate the effectiveness of our approach.

The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multi-label data. Additionally, our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

Agriculture is an activity that plays an important role in human life. Similarly, the agricultural sector plays an important role in the national economy. One of the biggest problems of the agricultural sector is the carbon gas it produces during production. Fertilizing activities and tools used in plowing the fields cause this gas to be produced. The release of the said gas into nature causes serious damage to the environment. Therefore, carbon emissions in the agricultural sector are of vital importance. In line with this purpose, it is aimed to determine the most appropriate strategy for carbon emission in this study. As a result of the DEMATEL analysis, it was seen that the most appropriate strategy was effective regulations and auditing.

The most enduring measure of how individuals make personal decisions affecting their health and safety is the compensating wage differential for job safety risk revealed in the labor market via hedonic equilibrium outcomes. The decisions in turn reveal the value of a statistical life (VSL), the value of a statistical injury (VSI), and the value of a statistical life year (VSLY), which have both mortality and morbidity aspects that we describe and apply here. All such tradeoff rates play important roles in policy decisions concerning improving individual welfare. Specifically, we explicate the recent empirical research on VSL and its related concepts and link the empirical results to the ongoing examinations of many government policies intended to improve individuals' health and longevity. We pay special attention to recent issues such as the COVID pandemic and newly emerging foci on distributional consequences concerning which demographic groups may benefit most from certain regulations.