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A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

Interest in the Electronic Word-of-Mouth (eWOM) in connection with tourism is constantly growing not only among consumers but also among theoreticians. Therefore, the objective of this chapter is to provide an overview of studies that focus on eWOM in the tourism sector using the snowball method. The article is based on a review of the literature of 60 studies that focus not only on consumer behavior in tourism and the impact of eWOM on tourism supply but also on the impact of hotel managers' responses to other consumer behavior and tourism companies. The results of the studies show that eWOM has a significant impact not only on consumer behavior but also on tourism supply. Manager responses can also strongly affect other consumer behavior in decision-making. When eWOM is distributed, consumers are influenced by their emotions, motives, and also by the websites to which they have decided to contribute. The chapter proposes further research areas for different authors.

Accredited Social Health Activists (ASHAs) are community health workers under the National Rural Health Mission of Government of India (NRHM). They have played a pivotal role during the COVID-19 pandemic in providing information and healthcare services to and from the remotest part of a village in India, working round the clock tracing patients and providing other COVID-19 related services along with fulfilling their basic duties of anti-natal care, immunization, sanitization, etc. The chapter seeks to understand the causative factors of invisibility and marginalization of ASHA workers. As most of them come from low-income and low-literacy background, they face discrimination and marginalization with long working hours, very low wages, and being treated as social pariah by the community they work in and work for. The study is particularly relevant because ASHA workers have worked as a communicating link between doctors, hospitals, and communities, and also through door-to-door survey, they have collected massive data during the pandemic, which has helped the governments to frame policies and take decisions. Both qualitative and quantitative methods have been used. I have interviewed some 55 ASHA workers (some of being my former students). I have used news clippings and government reports, regulations, directives and guidelines, survey reports of Thomas Reuters foundation, Amnesty International, Aziz Premji Foundation, as source material for information. There are certain gaps in policy making, social behavior, and attitude toward the ASHA workers, which need to be addressed.

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel type (premium versus economy) and employment status (current versus former).

A total of 78,535 online reviews by employees of 29 hotel companies for the period of 2011-2019 were scraped from Indeed.com. Structural topic modeling (STM) and sentiment analysis were used to extract topics influencing employee satisfaction and examine differences in sentiments in each topic.

Results showed that employees of premium hotels expressed more positive sentiments in their reviews than employees of economy hotels. The STM results demonstrated that 20 topics influenced employee satisfaction, the top three of which were workplace bullying and dirty work (18.01%), organizational support (16.29%) and career advancement (8.88%). The results indicated that the sentiments in each topic differed by employment status and hotel type.

Rather than relying on survey data to explore employee satisfaction, hotel industry practitioners can analyze employees’ online reviews to design action plans.

This study is one of only a few to use online reviews from an employment search engine to explore hotel employee satisfaction. This study found that workplace bullying and dirty work heavily influenced employee satisfaction. Moreover, analysis of the comments from previous employees identified antecedents of employees’ actual turnover behavior but not their turnover intention.

Due to the increased pollution and global warming, the banking sector is also implementing green practices in their operations to improve business ethics. However, there are few studies that have looked at how green practices affect performance outcomes. Considering this, the study aims to examine the impact of green banking adoption practices (GRBP) on consumer-related performance outcomes (i.e. consumer green satisfaction, consumer green perceived quality, consumer green trust, environmental friendliness and continuing relations with bank). The study used resource-based view theory and triple bottom line in connecting GBRP and consumer-related performance outcomes.

The data was gathered via a Likert scale questionnaire from banking personnel and consumers using a non-probability purposive sampling technique. The data of GRBP was collected from the banking employees, whereas the data for consumer-related performance outcomes were gathered from the banking consumers, and “Partial least square-structural equation modeling” (PLS-SEM) was used to examine research hypotheses.

The results of PLS-SEM reveal that GRBP positively affects consumer green trust, green perceived quality and green satisfaction. However, GRBP does not have any impact on environmental friendliness. The results further reveal that GRBP largely affects consumer green trust followed by green perceived quality and green satisfaction, respectively. Moreover, consumer green perceived quality, green trust and environmental friendliness positively affect the continuing relationship with the bank.

To the best of the authors’ knowledge, this is the first study in the context of green banking, i.e. two-dimensional, as it examines the impact of GRBP on consumer-related performance outcomes and confirms that GRBP enhances consumer-related performance outcomes. The findings of the study provide numerous insights to bank managers, environmentalists and policymakers.

This study aims to explore the strengths, weaknesses, opportunities and threats (SWOT) of the Muslim-friendly homestay business in Malaysia to help identify and recommend practical strategies to capitalize on the strengths and potentials while overcoming the current shortcomings and threats.

The Muslim-friendly business owner and operators in Sabah, Malaysia, were the subject of a series of focus groups and expert opinion interviews. The data was transcribed, and then the variables were categorized into the four SWOT categories using content and thematic analysis. Meanwhile, threats, opportunities, weaknesses and strengths (TOWS) analysis is used to identify the best strategy alternatives.

The SWOT analysis identifies several strengths (e.g. diverse and unique Islamic culture and heritage [S1], iconic Islamic landmarks [S2], rich natural beauty [S3], well-established halal tourism industry [S4]); weaknesses (e.g. limited awareness [W1], limited infrastructure and facilities [W2], limited human resources and trained personnel [W3], lack of Islamic tourism products and experiences [W4]); opportunities (e.g. growing demand for Islamic tourism [O1], increasing disposable income [O2], potential for collaborations [O3], potential for partnerships [O4], potential for expanding Sabah’s halal tourism offerings [O5]); and threats (e.g. competition [T1], political instability [T2], economic downturns [T3] and environmental and social challenges [T4]).

This paper could serve as a guideline and supplementary information for stakeholders in the homestay industry to grasp their business environment better.

To the best of the authors’ knowledge, this is the first study of its type to blend SWOT and TOWS analysis with the sector of Muslim-friendly homestays. Hence, the findings might expand understanding of the Muslim-friendly homestays industry and aid businesses in penetrating this growing market.

The purpose of this paper is to provide a detailed analysis of the usage pattern of non‐revenue earning customers (NREC) who cause revenue churn in the company and are susceptible to churn in the near future. These NREC customers were analyzed to discern a pattern in their usage and to serve as proactive measure to prevent customer churn.

Data from a leading telecom service provider were analyzed. The company has around seven lakh consumer mobile users. Within the seven lakhs consumer mobile users around two lakh customers are active users, i.e. revenue earning customers. This group of active customers also consists of around 37,388 customers who move to dormant state (from revenue earning to non‐revenue earning) every month. These customers were analyzed to understand their susceptibility to churn.

Analysis of revenue dump data indicates consumers with overall usage revenue per minute greater than 75 paise (USD 0.01) and those with greater usage of value added services are susceptible to churn. Also based on the nature of calls, churn occurs with the subscribers making more calls to other networks rather than to the same network.

In a fiercely competitive market, service providers constantly focus on customer retention. The study has high importance as it helps to find out the customers who are likely to churn. This would help telecom companies create proactive rather than reactive strategies toward customer churn.

Earlier studies identified the reasons for customer churn and attributed the same to it. The authors propose that prior to customer churn there is a distinct shift in his/her usage pattern with the current service provider and this behavior is termed revenue churn. This revenue churn ultimately leads to customer churn from the network. This revenue churn is not explored much in detail in the literature.