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1 – 10 of 241A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Abstract
Purpose
This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Design/methodology/approach
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.
Findings
The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.
Originality/value
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.
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Xiaofan Lai, Fan Wang and Xinrui Wang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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Xiaolin (Crystal) Shi and Zixi Chen
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Practical implications
Rather than relying on survey data to explore employee satisfaction, hotel industry practitioners can analyze employees’ online reviews to design action plans.
Originality/value
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.
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M. Geetha and Jensolin Abitha Kumari
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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Alekh Gour, Shikha Aggarwal and Mehmet Erdem
The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities…
Abstract
Purpose
The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities with limited marketing budgets. The past decade has witnessed an increased use of user-generated content (UGC) analysis as a marketing tool to make better informed decisions. Likewise, textual data analysis of UGC has gained much attention among tourism and hospitality scholars. Nonetheless, most of the scholarly works have focused on the singular application of an existing method or technique rather than using a multi-method approach. The purpose of this study is to propose a novel Web analytics methodology to examine online reviews posted by tourists in real time and assist decision-makers tasked with marketing strategy and intelligence.
Design/methodology/approach
For illustration, the case of tourism campaign in India was undertaken. A total of 305,298 reviews were collected, and after filtering, 276,154 reviews were qualified for analysis using a string of models. Descriptive charts, sentiment analysis, clustering, topic modeling and machine learning algorithms for real-time classification were applied.
Findings
Using big data from TripAdvisor, a total of 145 tourist destinations were clustered based on tourists’ perceptions. Further exploration of each cluster through topic modeling was conducted, which revealed interesting insights into satisfiers and dissatisfiers of different clusters of destinations. The results supported the use of the proposed multi-method Web-analytics approach.
Practical implications
The proposed machine learning model demonstrated that it could provide real-time information on the sentiments in each incoming review about a destination. This information might be useful for taking timely action for improvisation or controlling a service situation.
Originality/value
In terms of Web-analytics and UGC, a comprehensive analytical model to perform an end-to-end understanding of tourist behavior patterns and offer the potential for real-time interpretation is rarely proposed. The current study not only proposes such a model but also offers empirical evidence for a successful application. It contributes to the literature by providing scholars interested in textual analytics a step-by-step guide to implement a multi-method approach.
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Chunfeng Chen and Depeng Zhang
Negative word-of-mouth has a variety of negative effects on companies. Thus, how consumers process and evaluate negative word-of-mouth is an important issue for companies. This…
Abstract
Purpose
Negative word-of-mouth has a variety of negative effects on companies. Thus, how consumers process and evaluate negative word-of-mouth is an important issue for companies. This research aims to investigate the effect of emotional intensity of negative word-of-mouth on consumers' perceived helpfulness.
Design/methodology/approach
The research model was developed based on attribution theory. A four-study approach involving two field experiments and two online experiments was employed to examine the proposed hypotheses.
Findings
The results show that the emotional intensity of negative word-of-mouth negatively affects altruistic motive attributions, while altruistic motive attributions positively affect perceived helpfulness and plays a mediating role in the relationship between the emotional intensity of negative word-of-mouth and perceived helpfulness. Consumers' self-construal moderates the effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness, with the negative effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness being weaker for consumers with high interdependent self-construal than for those with high independent self-construal.
Originality/value
The findings not only have a significant theoretical contribution, deepening the understanding of the effects of negative word-of-mouth but also have useful implications for practitioners to improve the management of negative word-of-mouth.
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The purpose of this paper is to analyze the attribute preferences of buyers of branded pulses and to study the differences in preferences between consumers who purchase from…
Abstract
Purpose
The purpose of this paper is to analyze the attribute preferences of buyers of branded pulses and to study the differences in preferences between consumers who purchase from traditional retail stores and those who purchase from modern retail stores.
Design/methodology/approach
A total of 300 respondents (150 respondents from traditional and 150 respondents from modern retail outlet) participated in the study. Conjoint analysis was used to assess the consumers’ attribute preferences for branded pulses.
Findings
For both traditional and modern retail outlets, profile with highest utility was the profile with established brand, low price, high quality and normal packaging.
Research limitations/implications
Shoppers of traditional and modern retail outlets have similar attribute preferences for branded pulses. Hence, it can be concluded that the purchase point makes no difference in consumer attribute preferences.
Practical implications
Results indicate that in both traditional and modern retail outlet customers prefer the same profile of attributes. Two important attributes determining their purchase are also the same. Hence a company entering into the sale of branded pulses will have to focus on these two important attributes irrespective of the purchase point.
Originality/value
The topic is relatively less researched in emerging markets especially where both branded pulses and organized retail are in their nascent stages.
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Ree Chan Ho, Madusha Sandamali Withanage and Kok Wei Khong
With the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share…
Abstract
Purpose
With the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share their experiences, as well as reading online review before making a hotel reservation. Hotel customers considered it as reliable source and it influences customers' hotel selection. Most of these reviews reside in unstructured format, scattered across in the Internet and inherently unorganized. The purpose of this study was to use predictive text analytics to identify sentiment drivers from unstructured online reviews.
Design/methodology/approach
The research used sentiment classifications to analyze customers' reviews on hotels from TripAdvisor. In total, 9,286 written reviews by hotel customers were scrapped from 442 hotels in Malaysia. A detailed text analytic was conducted and was followed by a development of a theoretical framework based on the hybrid approach. AMOS was used to analyze the relationship between customer sentiments and overall review rating.
Findings
With the use of Structural Equation Modeling (SEM) and clustering technique, a list of sentiment drivers was detected, i.e. location, room, service, sleep, value for money and cleanliness. Among these variables, service quality and room facilities emerged as the most influential factors. Sentiment drivers obtained in this study provided the insights to hotel operators to improve the hotel conditions.
Research limitations/implications
Although this study extended the existing literature on sentiment analysis by providing valuable insights to hoteliers, it is not without its limitations. For instance, online hotel reviews collected for this study were limited to one specific online review platform. Despite the large sample size to support and justify the findings, the generalizability power was restricted. Thus, future research should also consider and expand to other type of online review channels. Therefore, a need to examine these data reside various social media applications, i.e. Facebook, Instagram and YouTube.
Practical implications
This study highlights the significance of hybrid predictive model in analyzing the unstructured hotel reviews. Based on the hybrid predictive model we developed, six sentiment drivers emerged from the data analysis, i.e. location, service quality, value for money, sleep quality, room design and cleanliness. This consideration is critical due to the ever-increasing unstructured data resides in the online space. This explores the possibility of applying data analytic technique in a more efficient manner to obtain customer insights for hotel managerial consideration.
Originality/value
This study analyzed customer sentiments toward the hotel in Malaysia with the use of predictive text analytics technique. The main contribution was the list of sentiment drivers and the insights needed to improve the hotel conditions in Malaysia. In addition, the findings demonstrated motivating findings from different methodological perspective and provided hoteliers with the recommendation for improved review ratings.
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Rishi Dwesar and Debajani Sahoo
Increased global air travel and competition in the airline industry entail better service delivery and failure management. This study examines how airline type, failure…
Abstract
Purpose
Increased global air travel and competition in the airline industry entail better service delivery and failure management. This study examines how airline type, failure criticality and the traveller's culture influence travellers' airline evaluations of service failure.
Design/methodology/approach
The study uses a large data set of customers' online reviews and incorporates quantitative and qualitative feedback from 20 major airlines across the world. Semantic tagging, sentiment and multivariate analyses have been used to analyse the data.
Findings
Failure criticality and travellers' cultural backgrounds significantly affect airline evaluations after service failures. Moreover, failure criticality influences evaluations of travellers from individualistic cultures more severely. Contrary to expectations, full-service airlines were evaluated positively after less critical service failures.
Practical implications
The findings support that customers undergo different emotional states when they experience service failure. Understanding these internal emotional sensitivities and how services would be judged by travellers across cultures can help airlines to better manage their service recovery efforts and to strategise prioritisation of scarce resources.
Originality/value
Though airline service failure has been well researched, this study examines the role of culture in service failure evaluations. The study uses a novel method to analyse a large data set of both quantitative and qualitative traveller feedback useful in service recovery management.
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