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The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The purpose of this paper is to propose a research approach to investigate brand alignment by exploiting textual data from online brand communities in the coffee industry. Specifically, consumer brand associations from user-generated content (UGC) and company brand associations from firm-generated content (FGC) are explored to measure the alignment between brand identity and brand image. The selected context of research is the beverage industry wherein companies are called on to develop appropriate digital websites and brand communication strategies to enhance the consumers' brand experience.

The authors introduce a research approach that integrates netnography with text mining analysis. Since brand associations were the basis of the study’s analysis, the authors focused on text mining procedures, providing data (co-occurrences) corresponding to brand associations that consumers perceive and that the company communicates. Data were used to develop the measurements of brand alignment.

The main findings of this research highlight the importance for both scholars and practitioners of determining brand alignment of beverage products in online communities. Knowing the alignment between the way a company communicates its brand identity and how this is perceived by consumers allows for effectively reviewing brand communication.

Although the combined analysis of the alignment between brand image and brand identification has received attention in marketing literature, most scholars have neglected how to measure brand alignment. This is a need for many marketing managers in the coffee industry who are now moving in digital environments where the role of consumers is not that of receivers of brand communication but rather that of cocreators of brand value.

The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm.

To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP.

The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises.

The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.

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 research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

Despite the interest in leaders' identity work as a framework for leadership development, coaching psychology has yet to expose its active ingredients and outcomes.

To do so, the authors reconcile published systematic literature reviews (SLRs) in the field to arrive at a more thorough understanding of the role of identity work in coaching. A total of 60 eligible SLRs on identity work and coaching were identified between 2010 and 2022. Four were included in the data extraction after selecting and screening, and the full texts of 196 primary studies reported therein were analysed.

Amongst the coachee-related factors of effective coaching, the coachee’s motivation, general self-efficacy beliefs, personality traits and goal orientation were the most frequently reported active ingredients, and performance improvement, self-awareness and goal specificity were the most frequently supported outcomes. The analysis indicates that leaders' identity work, as an active ingredient, can be a moderator variable for transformative coaching interventions, while strengthening leadership role identity could be one of the lasting outcomes because coaching interventions facilitate, deconstruct and enhance leaders' identity work. Further research is needed to explore the characteristics of these individual, relational and collective processes.

This study adds value by synthesising SLRs that report coachee-related active ingredients and outcomes of executive coaching research. It demonstrates that the role of leaders' identity work is a neglected factor affecting coaching results and encourages coaching psychologists to apply identity framework in their executive coaching practice.

Between 2006 and 2016, local communities in semi-arid Bobirwa sub-district in the Limpopo Basin part of Botswana had endured notable fluctuations in the delivery of critical ecosystem services. These changes have been coupled with adverse effects on local people’s livelihood options and well-being. However, a few such studies have focussed on the semi-arid to arid landscapes. This study therefore aims to provide recent knowledge and evidence of consequences of environmental change on semi-arid arid landscapes and communities.

To examine these recent changes in key ecosystem services, the authors conducted six participatory mapping processes, eight key informant interviews and several rapid scoping appraisals in three study villages. The analyses were centred on changes in seasonal quantities, seasonality, condition of ecosystem service sites, distance to ecosystem service sites and total area providing these services. Drivers of change in the delivery of key ecosystem services and the associated adverse impacts on human well-being of these recent changes in bundles of ecosystem services delivered were also analyzed.

Results show that adverse weather conditions, drought frequency, changes in land-use and/or land-cover together with unsustainable harvesting because of human influx on local resources have intensified in the past decade. There was circumstantial evidence that these drivers have resulted in adverse changes in quantities and seasonality of key ecosystem services such as edible Mopane caterpillars, natural pastures, wild fruits and cultivated crops. Similarly, distance to, condition and total area of sites providing some of the key ecosystem services such as firewood and natural pastures changed adversely. These adverse changes in the key ecosystem services were shown to increasingly threaten local livelihoods and human well-being.

This paper discusses the importance of engaging rural communities in semi-arid areas in a participatory manner and how such information can provide baseline information for further research. The paper also shows the utility of such processes and information toward integrating community values and knowledge into decisions regarding the management and utilization of local ecosystem services under a changing climate in data-poor regions such as the Bobirwa sub-district of Botswana. However, the extent to which this is possible depends on the decision makers’ willingness to support local initiatives through existing government structures and programmes.

This study shows the importance of engaging communities in a participatory manner to understand changes in local ecosystem services considering their unique connection with the natural environment. This is a critical step for decision makers toward integrating community values in the management and utilization of ecosystem services under a changing climate as well as informing more sustainable adaptive responses in semi-arid areas. However, the extent to which decision makers can integrate such findings to inform more sustainable responses to declining capacity of local ecosystems in semi-arid areas depends on how they value the bottom-up approach of gaining local knowledge as well as their willingness to support local initiatives through existing government structures and programmes.