Exploring customer satisfaction in cold chain logistics using a text mining approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ming K. Lim ◽  
Yan Li ◽  
Xinyu Song
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Latent Dirichlet Allocation ◽  
Cold Chain ◽  
Error Handling ◽  
Content Type ◽  
Service Staff ◽  
Logistics Enterprises ◽  
Negative Sentiment ◽  
Significant Factors

PurposeWith the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.Design/methodology/approachThis research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.FindingsThe results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.Research limitations/implicationsThe data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.Originality/valuePrior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Platform ecological circle for cold chain logistics enterprises: the value co-creation analysis

2020 ◽  
Vol 120 (4) ◽  
pp. 675-691 ◽  
Author(s):  
Benhong Peng ◽  
Yuanyuan Wang ◽  
Sardar Zahid ◽  
Guo Wei ◽  
Ehsan Elahi
Keyword(s):  
Influencing Factors ◽  
Resource Sharing ◽  
Structural Equation ◽  
Dynamic Capabilities ◽  
Positive Influence ◽  
Cold Chain ◽  
Enterprise Performance ◽  
Logistics Industry ◽  
Content Type ◽  
Logistics Enterprises

Purpose The purpose of this paper is to propose a framework of value co-creation in platform ecological circle for cold chain logistics enterprises to guide the transformation and development of cold chain logistics industry. Design/methodology/approach This paper establishes a conceptual framework for the research on the platform ecological circle in cold chain logistics, utilizes a structural equation model to investigate the influencing factors of the value co-creation of the platform ecological circle in the cold chain logistics enterprises and elaborates the internal relations between different influencing factors regarding the value co-creation and enterprises’ performance. Findings Results show that resource sharing in logistics platform ecological circle can stimulate the interaction among enterprises and this produces a positive influence on their dynamic capabilities, which, in turn, affects the they to work together to plan, implement and solve problems, so as to achieve the goal of improving enterprise performance. Practical implications The shared resources and value co-creation activities in the platform ecological circle are very important for the transformation and development of cold chain logistics enterprises. Therefore, enterprises should promote value co-creation through realizing resource sharing and creating a win-win cooperation mechanism. Originality/value This paper targets at incorporating the resource sharing in platform ecological circle for cold chain logistics enterprises, explores from an empirical perspective the role of the resource sharing in cold chain logistics enterprises in enhancing the dynamic capabilities of enterprises, thereby encouraging the value co-creation behavior, and ultimately boosts enterprise performance and stimulates business development.

Determining banking service attributes from online reviews: text mining and sentiment analysis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Divya Mittal ◽  
Shiv Ratan Agrawal
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Online Reviews ◽  
Socioeconomic Background ◽  
Content Type ◽  
Customer Reviews ◽  
Banking Service ◽  
The Interest Rate

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

2019 ◽  
Vol 62 (2) ◽  
pp. 195-215
Author(s):  
Frederik Situmeang ◽  
Nelleke de Boer ◽  
Austin Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Research Methodology ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Reviews ◽  
Marketing Literature ◽  
Mining Technique ◽  
Online Product Reviews ◽  
The Relationship

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Identification of operational demand in law enforcement agencies

2019 ◽  
Vol 53 (3) ◽  
pp. 333-372 ◽  
Author(s):  
Marcio Pereira Basilio ◽  
Valdecy Pereira ◽  
Gabrielle Brum
Keyword(s):  
Law Enforcement ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Emergency Response ◽  
Latent Dirichlet Allocation ◽  
Social Dynamics ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
The Government ◽  
The City

Purpose The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities. Design/methodology/approach The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime. Findings The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5. Research limitations/implications The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics. Practical implications The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality. Social implications The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information. Originality/value The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

A model of policing strategy choice

2020 ◽  
Vol 15 (3) ◽  
pp. 849-891 ◽  
Author(s):  
Marcio Pereira Basilio ◽  
Gabrielle Souza Brum ◽  
Valdecy Pereira
Keyword(s):  
Text Mining ◽  
Emergency Response ◽  
Latent Dirichlet Allocation ◽  
Social Dynamics ◽  
Content Type ◽  
Area 5 ◽  
Proposed Model ◽  
Policing Strategy ◽  
Electre I ◽  
Different Characteristics

Purpose The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem. Design/methodology/approach The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method. Findings The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied. Research limitations/implications The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics. Practical implications The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices. Social implications It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime. Originality/value The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

Analysis of operating effectiveness of a cold chain model using Bayesian networks

2015 ◽  
Vol 21 (4) ◽  
pp. 722-742 ◽  
Author(s):  
Sanjay Sharma ◽  
Sushanth Satheesh Pai
Keyword(s):  
Causal Model ◽  
Investment Decision ◽  
Cold Chain ◽  
Chain Model ◽  
Extensive Literature ◽  
Content Type ◽  
Proposed Model ◽  
Integrated Study ◽  
Practical Implications ◽  
Significant Factors

Purpose – Cold chain has become an integral part of the supply chain domain. The purpose of this paper is to consider all the significant factors in a single study. This will result into a better model to study the effectiveness of a cold chain because there has been absence of such an integrated study. Design/methodology/approach – The basis of the factors is justified by performing extensive literature review. Inter relations are drawn based on critical analysis of each factor and its implications on cold chain. Bayesian Network is used to develop the model. Findings – The end result is an established model, depicting the interdependencies of the factors. The model ultimately provides effectiveness of a given cold chain when the corresponding values of factors are put in. Practical implications – The findings will be helpful for government and non-government bodies to analyse the effectiveness of a cold chain. This can be used to increase the performance of different stages in the cold chain. From a business perspective, an investor can analyse the cold chains of various geographies in order to make an investment decision. Originality/value – The value lies in developing and introducing new factors which were not considered in the related literature previously. To identify the inter relations among the factors in order to build a causal model is another contribution of the present paper. This would assist in decision-making process with respect to any given cold chain. It can be applied to any cold chain as proposed model is not specific to a particular country or material.

Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Semra Aktas-Polat ◽  
Serkan Polat
Keyword(s):  
Text Mining ◽  
Food Quality ◽  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Content Type ◽  
Factors Affecting ◽  
Customer Delight ◽  
Dirichlet Allocation

PurposeThe purpose of this study is to discover the factors affecting customer delight, satisfaction and dissatisfaction in fine dining experiences (FDEs).Design/methodology/approachOnline user generated 2,585 reviews on TripAdvisor for 46 five-star hotel restaurants operating in Istanbul were analyzed with the latent Dirichlet allocation (LDA) algorithm.FindingsLDA created nine, eight and seven topics for delight, satisfaction and dissatisfaction, respectively. The most salient topics for customer delight, satisfaction and dissatisfaction in FDEs are staff (17.3%), view (19%), and food quality (23%), respectively.Originality/valueThis study is one of the few studies investigating customer delight and satisfaction together. The study shows that FDEs can be analyzed with text mining techniques. Moreover, the study contributes to the literature on customer delight by adding staff topic as an antecedent.

The impact of COVID-19 on hotel customer satisfaction: evidence from Beijing and Shanghai in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shaolong Sun ◽  
Fuxin Jiang ◽  
Gengzhong Feng ◽  
Shouyang Wang ◽  
Chengyuan Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Consumer Satisfaction ◽  
Topic Model ◽  
Hotel Industry ◽  
Online Reviews ◽  
Control Measures ◽  
Content Type ◽  
Before And After ◽  
The Impact

Purpose The purpose of this study is to provide better service to hotel customers during the COVID-19 era. Specifically, this study focuses on understanding the changes in hotel customer satisfaction during the epidemic and formulating effective marketing strategies to satisfy and attract guests. Design/methodology/approach As the first victim of the COVID-19 virus, China’s hotel industry has been profoundly affected and customer satisfaction and needs have also changed. Taking 105,635 hotel reviews obtained from Tripadvisor.com in Beijing and Shanghai as samples, this study explores the changes in consumer satisfaction by using text-mining methods. Findings The results suggest that there are significant differences in overall ratings, spatial distribution and ratings of different traveller types before and after the epidemic. Generally, customers have higher “tolerance” and are more inclined to give higher ratings and pay more attention to hotel prevention and control measures to reduce health risks after the COVID-19. Research limitations/implications This paper proves the changes in customer satisfaction before and after the COVID-19 at the theoretical level and reveals the changes in customer attention through the topic model and provides a basis for guiding hotel managers to reduce the impact of the COVID-19 crisis. Practical implications Empirical findings would provide useful insights into tourism management and improve hotel service quality during the COVID-19 epidemic era. Originality/value This research explores the hotel customer satisfaction in the field of hotel management before COVID-19 and after COVID-19, by using text mining to analyse mandarin online reviews. The results of this study will suggest that the hotel industry should continuously adjust its products and services based on the effective information obtained from customer reviews, so as to realize the activation and revitalization of the hotel industry in the epidemic era.

Unveiling the success factors of BTS: a mixed-methods approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
So-Hyun Lee ◽  
Soobin Choi ◽  
Hee-Woong Kim
Keyword(s):  
Mixed Methods ◽  
Text Mining ◽  
Success Factors ◽  
Latent Dirichlet Allocation ◽  
Early Stage ◽  
World View ◽  
Key Factors ◽  
Content Type ◽  
Mixed Methods Approach ◽  
Key Success Factors

PurposeThe purpose of this paper is to explore the key success factors behind Bangtan Boys’ (BTS) popularity, and how they can contribute to sustaining it, along with detailed strategies for the success of global pop.Design/methodology/approachThis study adopts a mixed-methods approach that uses text mining and interviews and uses the success of BTS to find the key factors accounting for its sustained popularity. For use in text mining, we collected data related to BTS from social network sites (SNS) and analyzed this data using latent Dirichlet allocation (LDA) topic modeling, term frequency analysis and keyword extraction. In addition, we conducted interviews to explore the key factors accounting for the sustained popularity of BTS.FindingsWe found ten key success factors—active global fandom, SNS communication, fans' loyalty, empathy through music, storytelling and world view, performance quality, music video quality, overseas expansion at an early stage, efforts for self-development and teamwork among members— for a global pop group's success and sustained popularity.Research limitations/implicationsThis study contributes to the literature by finding key factors for success and sustained popularity of a global group through using a mixed-methods approach.Practical implicationsOur results suggest strategies to sustain the popularity of global groups and its potential to benefit across the entertainment industry.Originality/valueThis study is among the first to comprehensively examine the key factors for Korean pop’s (K-pop) sustained popularity by using a mixed-methods approach of text mining and interviews.

Influence of service-entry waiting on customer’s first impression and satisfaction

2019 ◽  
Vol 29 (5/6) ◽  
pp. 565-591 ◽  
Author(s):  
Yu Zhang ◽  
Bing-Jia Shao
Keyword(s):  
Customer Satisfaction ◽  
Waiting Time ◽  
Time Management ◽  
Negative Impact ◽  
Moderating Effect ◽  
Online Service ◽  
Content Type ◽  
Service Staff ◽  
First Impression ◽  
The Relationship

Purpose The purpose of this paper is to examine the influence mechanism of waiting time on customer satisfaction based on first impression bias, which explains how customers’ perceived service-entry waiting time (PSWT) influences their first impression of service staff and satisfaction in the context of online service. Furthermore, the moderating effect of three information formats (formal, informal and hybrid) of opening remark on the relationship between PSWT and first impression, and the moderating effect of perceived in-service waiting time (PIWT) on the relationship between first impression and customer satisfaction are investigated. Design/methodology/approach Two studies were used to verify the research model. First, an experiment on prepurchase consulting services for cruise tourism products was designed, and 810 Chinese individuals have participated. Second, 20 interviews with e-commerce practitioners in China were conducted. Findings The results show that, first, PSWT negatively influences customers’ first impression of service staff. Second, customers prefer the hybrid format to present opening remarks, which not only conveys the respect of the staff but also fosters a relationship. Third, in-service waits are equally as important as service-entry waits in online service. When PIWT is longer, the positive influence of first impression on customer satisfaction is weakening, resulting in lower customer satisfaction. Practical implications This study provides suggestions for online service enterprises to minimize the negative impact of waiting time and improve customer satisfaction through waiting time management. Originality/value This study provides a new perspective for exploring the mechanism of waiting time on customer satisfaction in online service context, and extends previous research related to waiting time by exploring the influence of waiting time in multiple service stages and expression modes of service staff.

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