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This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

The study examines the responsibilisation of an ethnocentric consumer in commercial, meta-organisational discourses. In addition to nationalistic and patriotic discourses, the focus is on wider conceptualisations of consumer responsibility.

The paper uses critical discourse analysis as a methodological approach to conduct an empirical case study on the texts of two producer-driven labelling campaigns.

The campaign texts create possibilities for ethnocentric consumption with positioning, argumentative and classificatory discourses. Patriotic responsibilisation is emphasised, together with rationales to take action on environmental concerns.

The study highlights the responsibility of marketers over their corporate responsibility communication, suggesting that ethnocentric promotions may have the power to alter how consumers take action on various responsibility concerns.

The study surfaces the tensions that responsible consumption can entail for consumers. Indeed, nationalistic and patriotic discourses may alter our understanding of responsibility issues that may seem completely separate from the concepts of nationalism and patriotism.

The paper shows how different organisational texts are deployed to bring about the idea of ethnocentric consumption and how this relates to responsibility discourses, nationalism and patriotism.

The purpose of this study is to apply a corpus-assisted analysis of keywords and their collocations in the US presidential discourse from Clinton to Trump to discover the meanings of these words and the collocates they have. Keywords are salient words in a corpus whose frequency is unusually high (positive keywords) or low (negative keywords) in comparison with a reference corpus. Collocation is the co-occurrence of words.

To achieve this purpose, the investigation of keywords and collocations is generated by AntConc, a corpus processing software.

This analysis leads to shed light on the similarities and/or differences amongst the past four American presidents concerning their key topics. Keyword analysis through keyness makes it evident that Clinton and Obama, being Democrats, demonstrate a clear tendency to improve Americans’ life inside their social sphere. Obama surpasses Clinton as regard foreign affairs. Clinton and Obama’s infrequent subjects have to do with terrorism and immigration. This complies with their condensed focus on social and economic improvements. Bush, a republican, concentrates only on external issues. This is proven by his keywords signifying war against terrorism. Bush’s negative use of words marking cooperative actions conforms to his positive use of words indicating external war. Trump’s positive keywords are about exaggerated descriptions without a defined target. He also shows an unusual frequency in referring to his name and position. His words used with negative keyness refer to reforming programs and external issues. Collocations around each top content keyword clarify the word and harmonize with the presidential orientation negotiated by the keywords.

Limitations have to do with the issue of the accurate representation of the samples.

This research is original in its methodology of applying corpus linguistics tools in the analysis of presidential discourses.

This study explores the levels of Facebook engagement of the two largest Europe-based shipping lines, Maersk and Mediterranean Shipping Company (MSC), to discover the marketing orientation of the topics advertised and to ascertain whether they tend to be about brand recognition, new transport services, or value propositions for stakeholders.

The Facebook posts of Maersk and MSC were analysed using social media text mining and social network analysis (SNA); in- and out-degree centrality analysis was performed to determine the key terms in their posts. NetMiner software was used to collect the respective data on Maersk and MSC. The inquiry period was set between May 2020 and February 2021.

The results indicated a divergence in their post contents, with higher engagement and a wider, more active follower base for MSC than for Maersk. Maersk primarily posts about logistics services and supply chain solutions. MSC communicates about new and large container vessels. Both companies seek greater brand recognition and information sharing through social media.

These results can be used by the stakeholders to evaluate whether Maersk and MSC truly deliver on their respective value propositions communicated online through their social media engagement. It can also help Maersk and MSC gauge the level of effectiveness of their communication with stakeholders and modify their digital engagement strategy accordingly.

Due to the important role of orthography in society, the project called IDeRBlog presented in this paper created a web-based tool to motivate pupils to write text as well as to read and to comment on texts written by fellow students. In addition, IDeRBlog aims to improve student’s German orthography skills and supports teachers and parents with training materials for their students. The paper aims to discuss these issues.

With the aid of learning analytics, the submitted text is analyzed and special feedback is given to the students so that they can try to correct the misspelled words themselves. The teachers as well as the parents are benefiting from the analysis and exercises suggested by the system.

A recent study showed the efficiency of the system in form of an improvement of the students’ orthographic skills. Over a period of four months 70 percent of the students achieved a significant reduction of their spelling mistakes.

IDeRBlog is an innovative approach to improving orthography skills combining blogging and new media with writing and practice.

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and China on the issues of trade were collected and analytically examined for US-China gaps in major foreign policies, such as bilateral trade and the Belt and Road Initiative. In this paper, a term frequency-inverse document frequency word cloud, a network similarities index, machine learning-processed latent Dirichlet allocation (LDA), and structural equivalence are applied to examine the meanings of the speeches. The main arguments in this paper are as follows. First, the document similarity between the speeches of Chinese and US leaders appears to be completely different. Also, while the documents from Chinese leaders are considerably similar, the documents from US leaders differ by far. Secondly, LDA topic analysis indicates that China concentrates more on international and collaborative relationships, while the U.S. has more focus on domestic and economic interests. Third, from a word hierarchy analysis, the basic words used by American and Chinese leaders are also completely different. Agriculture, farmers, automobiles, and negotiations are the basic words for American leaders, but for Chinese leaders, the basic words are planning, markets, and education.

Although there has been a significant amount of research on Smart Tourism, the articles have not yet been combined into a thorough literature review that can examine research streams and the scope of future research. The purpose of this study is to examine the literature on the impact of deploying the Internet of Things (IoT) in tourism sector development to attract more visitors using a text mining technique and citation based bibliometric analysis for the first time.

This study uses R programming to do a full-text analysis of 36 publications on IoT in tourism and visualization of similarities viewer software to conduct a bibliometric citation analysis of 469 papers from the Scopus database. Aside from that, the documents were subjected to a longitudinal study using Excel and word frequency using a trending topic using the R-tool.

Results from the bibliometric study revealed the networks that exist in the literature of Tourism Management. With the use of log-likelihood, the findings from text mining identified nine theme models on the basis of relevancy, which is presented alongside an overview of the existing papers and a list of the primary authors with posterior probability using latent Dirichlet allocation.

This study examines tourism literature in which IoT plays a significant role. To the best of the authors’ knowledge, this study is the first to combine text mining with a bibliometric review. It significantly analyzes and discusses the impact of technology in the tourism sector development on attracting tourists while presenting the most important and frequently discussed topics and research in these writings. These findings provide researchers, tourism managers and technology professionals with a complete understanding of e-tourism and to provide smart devices to attract tourists.

Aunque ha habido un número importante de estudios sobre el turismo inteligente, todavía no se dispone de una revisión bibliográfica exhaustiva que permita examinar las corrientes de investigación y las sugerencias de investigación futuras. Este estudio examina la literatura sobre el impacto del Internet de las cosas en el desarrollo del sector turístico para atraer más visitantes utilizando una técnica de minería de textos y un análisis bibliométrico basado en citas.

Este estudio utiliza la programación R para hacer un análisis de texto completo de 36 publicaciones sobre IoT en el turismo y el software de visualización de similitudes (VOS) para realizar un análisis bibliométrico de citas de 469 documentos de la base de datos Scopus. Además, los documentos fueron sometidos a un estudio longitudinal mediante Excel y a la frecuencia de palabras mediante un tema de tendencia utilizando la herramienta R.

Los resultados del estudio bibliométrico revelaron las redes existentes en la literatura de la Gestión Turística. Con el uso de la log-verosimilitud, los resultados de la minería de textos identificaron nueve modelos temáticos sobre la base de la relevancia, que se presentan junto con una visión general de los documentos existentes y una lista de los autores principales con probabilidad posterior utilizando la asignación latente de dirichlets.

Este estudio examina la literatura sobre turismo en la que la IoT desempeña un papel importante. Este estudio es el primero que combina la minería de textos con una revisión bibliométrica. Analiza y discute de forma significativa el impacto de la tecnología en el desarrollo del sector turístico para atraer a los turistas, a la vez que presenta los temas e investigaciones más importantes y más frecuentemente discutidos en estos escritos. Estos resultados proporcionan a los investigadores, gestores turísticos y profesionales de la tecnología una comprensión integral del turismo electrónico y los dispositivos inteligentes para atraer a los turistas.

虽然已经有大量关于智慧旅游的研究, 但这些文章尚未整合成一个全面的文献综述, 可以检阅目前的研究流和未来研究的范畴。本研究首次使用文本挖掘技术和基于引文的文献计量分析, 来研究有关在旅游业发展中部署物联网对吸引更多游客的影响的文献。

本研究使用R编程对36篇关于旅游业物联网的文章进行全文分析, 并使用相似性可视化（VOS）查看器软件对Scopus数据库中的469篇论文进行文献计量引文分析。除此之外, 还利用Excel对这些文献进行了纵向研究, 并使用R工具对趋势主题进行了词频分析。

文献计量研究的结果揭示了旅游管理文献中现有的网络。通过使用对数似然, 文本挖掘的结果根据相关性确定了9个主题模型, 这些模型与现有论文的概述和主要作者名单在使用潜在狄里奇分配（LDA）的后验概率一起呈现。

本研究对旅游物联网相关文献进行了分析研究, 它首次将文本挖掘与文献计量学审查相结合。这项研究着重分析和讨论了技术在旅游行业发展中对吸引游客的影响, 同时介绍了这些文章中最重要和经常讨论的主题和研究。这些发现为研究人员、旅游管理者和技术专家提供了对科技与旅游的全面了解, 并提供关于智能设备来吸引游客的建议。

This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature.

It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature.

The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability.

The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain.

This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers.

To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.