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Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

To come up with a prudent decision on the installation of an appropriate green wall (GW) on buildings, this study presents a novel decision-making algorithm. The proposed algorithm considers the importance of barriers hampering GW adoption, as well as their relationships with regard to different types of GWs existing in a contextual setting.

The proposed methodological approach is based on the integration of qualitative and quantitative techniques by employing focus group discussion, fuzzy-based best-worst method and fuzzy TOPSIS.

Based on the experiences of qualified experts involved in related projects in Hong Kong, the following conclusions are drawn: (1) cost, installation and maintenance-related barriers are perceived to have the highest importance, (2) modular living wall system is the most suitable GW system for the context of Hong Kong and (3) existing barriers are found to have a pivotal role in the ranking of the most suitable GW systems.

The findings provide valuable insight not only for policymakers and stakeholders, but also for establishing a methodological approach that can assist decision-makers in identifying the most beneficial GW system rather than the most applicable one. This could have significant implications and introduce potential changes to the common way of practice within the industry and lay the foundation for wider adoption of GW.

While previous studies have investigated the sustainability-related issues of GW façade applications, the current body of knowledge is deprived of a comprehensive methodological approach for the selection of the most suitable GW systems.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

The abolishment of the wine tax in Hong Kong has led to increased wine consumption and increased demand for wine-related professionals, such as sommeliers. Yet the importance of sommeliers’ value-adding performance in the context of upscale Chinese restaurants has not been examined. To address this gap, the SERVQUAL framework is adopted to examine the influence of sommeliers’ service quality (SQ) on customer satisfaction (CS) and loyalty in the context of upscale Chinese restaurants in Hong Kong.

The survey method is used to collect data from 302 units of the population of interest, partial least square-structural equation modelling (PLS-SEM) is used to test the links between constructs.

Four of the seven dimensions of sommeliers’ service quality, namely, empathy, tangibles, credibility and assurance, have a significant positive impact on customer satisfaction and customer loyalty, whereas the impact of perceived value and responsiveness on customer satisfaction and customer loyalty is positive but only marginally significant. Reliability has a weak and non-significant impact on customer satisfaction and customer loyalty.

Examining a small number of upscale Chinese restaurants in Hong Kong limits generalisation of the findings to other contexts. Replication of the research in different contexts will enhance generalizability. In terms of implications, the discussion highlights the importance of sommeliers’ service performance on customers’ SQ perceptions SQ, CS and loyalty, all of which are important variables for restaurateurs.

To the best of the authors’ knowledge, this is the first study of the influence of the quality of sommelier’s SQ on CS and loyalty in upscale Chinese restaurants in Hong Kong. Given the lack of attention to this service role in the literature, the study contributes theory from which further understanding can develop.

This paper investigates the usage and trust of Chinese social media in the travel planning process (pre-trip, during-trip and post-trip) of Chinese tourists.

Through a combination of structured online survey (n = 406) and follow-up interviews, the research identifies the diversification of the demand-and-supply patterns of social media users in China, as well as the allocation of functions of social media as tools before, during and after travel.

Social media users are diverse in terms of their adoption of social media, use behaviour and scope; the levels of trust and influence; and their ultimate travel decisions and actions. Correlations between the level of trust, influence of social media and the intended changes in travel decisions are observed. Destination marketers and tourism industries should observe and adapt to the needs of social media users and potential tourist markets by understanding more about user segmentation between platforms or apps and conducting marketing campaigns on social media platforms to attract a higher number of visitors.

This paper demonstrated the case of social media usage in mainland China, which has been regarded as one of the fastest growing and influential tourist-generating markets and social media expansions in the world. This study further addressed the knowledge gap by correlating social media usage and travel planning process of Chinese tourists. The research findings suggested diversification of the demand-and-supply pattern of social media users in China, as well as the use of social media as tools before, during and after travel. Users were diversified in terms of their adoption of social media, use behaviour, scope, the levels of trust, influence and the ultimate travel decisions.

Destination marketing organizations should note that some overseas social media platforms that are not accessible in China like TripAdvisor, Yelp, Facebook and Instagram are still valued by some Chinese tourists, especially during-trip period in journeys to Western countries. Some tactics for specific user segments should be carefully observed. When promoting specific tourism products to Chinese tourists, it is necessary to understand the user segmentation between platforms or apps.

Social media is a powerful tool for tourism development and sustainability in creating smart tourists and destinations worldwide. In China, the use of social media has stimulated the development of both information and communication technology and tourism.

The purpose of this paper is to present empirical evidence of risk balancing behavior by European farmers. More specifically, the authors investigate strategic adjustments in the level of financial risk (FR) in response to changes in the level of business risk (BR).

The authors conducted a correlation relationship analysis and run several linear fixed effects regression models using the European Union (EU)-15 FADN panel data set for the period 1995-2008.

Overall, the paper finds EU evidence of risk balancing. The correlation relationship analysis suggests that just over half of the farm observations are risk balancers whereas the other (smaller) half are not. The coefficient in our fixed effects regression suggests that a 1 percent increase in BR reduces FR by 0.043 percent and has a standard error so low that the existence of non-risk balancers is doubtful. The results reject evidence of strong-form risk balancing – inverse trade-offs between FR and BR keeping total risk (TR) constant – but cannot reject weak-form risk balancing – inverse trade-offs between FR and BR with some observed changes in TR. Furthermore, the extent of risk balancing behavior is found to differ between different European countries and across farm typologies.

This study provides European policy makers a first insight into risk balancing behavior of EU farmers. When risk balancing occurs, BR-reducing agricultural policies induce strategic upwards leverage adjustments that unintentionally reestablish or even increase total farm-level risk.

Making use of the large and unique FADN database, to the best of the authors knowledge, this study is the first that provides European (EU-15) evidence on risk balancing behavior, is conducted at an unprecedented large scale, and presents the first risk balancing evidence across countries and farming systems.

The purpose of this paper is to gain an understanding of the quality cues that consumers look for in purchasing fresh meat and fresh fruit and vegetables in Malaysia. Through a perceived quality model, this paper identifies the implicit, intrinsic, extrinsic and credence quality cues consumers’ use in their decision to purchase fresh meat and fresh fruit and vegetables.

This study utilised the shopping mall intercept survey method. Data were collected from traditional markets and modern retail outlets in the Klang Valley region in Malaysia. A structured questionnaire was designed to measure consumer’s perceptions and experiences of food quality when purchasing fresh meat and fresh fruit and vegetables from retail outlets. In this study, univariate data analysis (descriptive analysis, one-way analysis of variance) and exploratory factor analysis were performed to analyse the data sets.

Freshness (intrinsic cue), was the most frequently cited variable when respondents thought about the quality of both product categories. Other variables included price and cleanliness (extrinsic cues) and Halal (credence cue), which was associated with the quality of fresh meat. Quality was associated with freshness, food safety, nutrition and value. Exploratory factor analysis identified food safety (implicit cue) as the most important construct in the respondents’ evaluation of quality for both fresh meat and fresh fruit and vegetables.

There is a paucity of research focusing on consumer’s perceptions and experiences of food quality in the purchase of fresh meat and fresh produce in Malaysia. The findings of this research may assist the Malaysian food industry by providing new insights into the consumers’ perceptions of food quality.