Search results

Online web searches have played crucial roles in influencing consumers’ purchasing decisions. Web search traffic information enables researchers and practitioners to better understand consumers in terms of their preferences and interests, among other things. The purpose of this paper is to use web search traffic information provided by Google Trends to derive relationships among product brands as well as those between product brands and product attributes to propose a method to enhance the visibility of consumer brand positioning.

This study builds upon the interesting observation that consumers’ behavior in performing simultaneous searches, or searches including two or more keywords, can be converted into data indicating relationships among brands as well as those between brands and their attributes. The study focuses on the cases of hybrid cars and tablet PCs, and applies a social network analysis method to identify these relationships. Time series information on web search traffic is used because it can track these two product groups from the early stages to the present. This step is completed to verify the changes in the status of each brand and in their relationships that occurred in consumers’ minds over time.

Results show that consumers’ web search behaviors reveal the brand positioning and brand-attribute associations in their minds. Specifically, using consumers’ simultaneous search data, the authors derived relationships among brands (brand-brand network) from consumers’ behaviors of searching simultaneously for two brands and the relationships between brands and attributes (brand-product attributes network) from consumers’ behavior of searching simultaneously for a specific brand and certain product attributes.

Theoretically, this study verifies that consumers’ web search traffic information can be used to microscopically identify the positions of individual brands and their relationships in the minds of consumers. Regarding practical applications, this study proposes a method that can be used by companies to track how consumers perceive their brands by performing a simple and cost-effective analysis using the free search traffic information provided by Google.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Purpose — The chapter presents the practical applications of web search statistics analysis. The process description highlights the potential use of search queries and statistical data and how they could be used in various forecasting situations. The presented case is an example of applied computational intelligence and the main focus is oriented towards the decision support offered by the software mechanism and its capabilities to automatically gather, process and analyse data.

Methodology/approach — The statistics of the search queries as a source of prognostic information are analysed in a step-by-step process, starting from their content and scope, their processing and applications, and concluding with usage in a software-based intelligent framework.

Research implications — The analysis of search engine trends offers a great opportunity for many areas of research. Into the future, deploying this information in the prognosis will further develop intelligent data processing.

Practical implications — This functionality offers a unique possibility, impossible until now, to observe, estimate and predict various processes using wide, precise and accurate behaviour observations. The scope and quality of data allow practitioners to successfully use it in various prognostic problems (i.e. political, medical, or economic).

Originality/value of paper — The chapter presents practical implications of technology. The chapter then highlights potential areas that would benefit from the analysis of queries statistics. Moreover, it introduces ‘WebPerceiver’, an intelligent platform, built to make the analysis and usage of search trends easier and more generally available to a wide audience, including non-skilled users.

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.

The purpose of this paper is to aim at modelling the trails, which are search patterns with several search systems across the heterogeneous information environment. In addition, the author seeks to examine what kinds of trails occur in routine, semi-complex and complex tasks, and what barrier types occur during the trail-blazing.

The author used qualitative task-based approach with shadowing of six molecular medicine researchers during six months, and collected their web interaction logs. Data triangulation made this kind of detailed search system integration analysis possible.

Five trail patterns emerged: branches, chains, lists, singles and berrypicking trails. The berrypicking was typical to complex work tasks, whereas the branches were common in routine work tasks. Singles and lists were employed typically in semi-complex tasks. In all kinds of trails, the barriers occurred often during the interaction with a single system, but there was a considerable number of barriers with the malfunctioning system integration, and lacking integration features. The findings propose that the trails could be used to reduce the amount of laborious manual system integration, and that there is a need for support to explorative search process in berrypicking trails.

Research of information behaviour yielding to different types of search patters with several search systems during real-world work task performance in molecular medicine have not been published previously. The author presents a task-based approach how to model search behaviour patterns. The author discusses the issue of system integration, which is a great challenge in biomedical domain, from the viewpoints of information studies and search behaviour.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The purpose of this case study is to illustrate how a university library collaborated with a specific college to preserve scholarship with a sustainable approach. The practical process described is recommended for increasing content submissions in a newly established institutional repository. Of the eight colleges at Middle Tennessee State University (MTSU), the Honors College was selected as a case study for a library–college collaboration on content curation for the institutional repository that is maintained by MTSU’s Walker Library.

Concept of shared and divided responsibilities for the upload, maintenance and sustainability of institutional repository submissions based on a particular case study and aided with literature on data management, digital publishing, library publishing and preservation research.

The partner approach, the sharing and division of responsibilities, is instrumental to the growth and sustainability of a library publishing program and for the preservation of university scholarship.

The (college) partner approach not only educates campus units about a new resource (e.g. institutional repository), but also encourages campus units to rethink other current and outdated practices that need to adapt to technological changes that support the unit and its students. This approach will help the library with campus outreach after an institutional repository is implemented and offers guidance on a collaborative approach to repository submission growth.

This paper suggests a (college) partner approach that mutually benefits the College and its students, departments and the library that maintains the institutional repository on behalf of the university. During the implementation process of this case study, an Americans with Disabilities Act (ADA)/accessibility compliance issue of repository items surfaced and allowed for a new course of action to be taken campus wide which adds to the originality of this case study.