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The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research.

The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map.

The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.”

This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

El objetivo de esta investigación fue descubrir nichos representativos de áreas emergentes y examinar el área de Marketing, Turismo y Big Data, evaluando cómo han evolucionado estas áreas temáticas durante un período de 27 años desde 1996–2022. Analizamos 1.152 investigaciones para identificar las principales áreas temáticas y temas emergentes, las principales teorías utilizadas, las formas de análisis predominantes y los autores más productivos en términos de investigación.

Todos los artículos para esta investigación fueron seleccionados de la base de datos Web of Science. Realizamos una revisión sistemática y cuantitativa de la literatura. Utilizamos el software SciMAT para extraer indicadores. Específicamente, analizamos la productividad y elaboramos un mapeo científico.

Los hallazgos sugieren que el interés en esta área ha aumentado gradualmente. Los resultados también revelan el esfuerzo innovador de la industria en nuevas tecnologías para desarrollar modelos de marketing turístico. Se identificaron diez áreas de investigación (“marketing de destinos”, “patrones de movilidad”, “co-creación”, “gastronomía”, “sostenibilidad”, “comportamiento turístico”, “segmentación de mercado”, “redes neuronales artificiales”, “precios”, y “satisfacción del turista”).

Este trabajo es único al proponer una agenda para futuras investigaciones en investigación de Marketing Turístico con nuevas tecnologías como Big Data y técnicas de Inteligencia Artificial. Además, los resultados presentados aquí llenan el vacío actual en la investigación ya que si bien se han realizado revisiones de literatura que cubren Turismo con Big Data o Marketing, estas áreas no se han estudiado como un conjunto.

这一特定研究领域的目标是发现具有代表性的新兴领域, 并考察市场营销、旅游和大数据研究领域, 以评估这些主题领域在1996年至2022年的27年间是如何发展的。我们分析了1152项研究, 以确定主要专题领域和新兴主题、使用的主要理论、主要的分析形式以及在研究方面最有成效的作者。

本研究的文章都是从Web of Science数据库中选出的。我们进行了系统化的定量文献审查, 并使用SciMAT软件来提取指标。具体来说, 我们分析了生产力并制作了一个科学研究地图。

研究结果表明, 人们对这一领域的兴趣已经逐渐增加。本文也揭示了工业界在开发旅游营销模式的新技术方面的创新努力。研究确定了十个研究领域：“目的地营销”、“流动模式”、“共同创造”、“美食”、“可持续性”、“游客行为”、“市场细分”、“人工神经网络”、“定价 “和游客满意度”。

这项研究的独特之处在于提出了未来利用大数据和人工智能技术等新技术进行旅游营销研究的议程。此外, 本文的结果填补了目前的研究空白, 因为虽然有文献综述涉及旅游与大数据或市场营销, 但这些领域还没有被作为一个整体来研究。

The article introduces selected theoretical approaches to time and temporality relevant to the field of library and information science, and it briefly introduces the papers gathered in this special issue. A number of issues that could potentially be followed in future research are presented.

The authors review a selection of theoretical and empirical approaches to the study of time that originate in or are of particular relevance to library and information science. Four main themes are identified: (1) information as object in temporal perspectives; (2) time and information as tools of power and control; (3) time in society; and (4) experiencing and practicing time.

The paper advocates a thorough engagement with how time and temporality shape notions of information more broadly. This includes, for example, paying attention to how various dimensions of the late-modern time regime of acceleration feed into the ways in which information is operationalised, how information work is commodified, and how hierarchies of information are established; paying attention to the changing temporal dynamics that networked information systems imply for our understanding of documents or of memory institutions; or how external events such as social and natural crises quickly alter modes, speed, and forms of data production and use, in areas as diverse as information practices, policy, management, representation, and organisation, amongst others.

By foregrounding temporal perspectives in library and information science, the authors advocate dialogue with important perspectives on time that come from other fields. Rather than just including such perspectives in library and information science, however, the authors find that the focus on information and documents that the library and information science field contributes has great potential to advance the understanding of how notions and experiences of time shape late-modern societies and individuals.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

People around the world are not sufficiently capable or willing to engage in retirement planning. New technological tools have been proposed as a promising solution to foster involvement and consequently encourage retirement planning. This paper aims to test whether an interactive online pension planner can improve participants’ behaviour, behavioural intentions, attitude, knowledge and perceived ease of use, usefulness and enjoyment.

In collaboration with a company specialised in technologically advanced pension planners, three different versions of an online pension planner were created. The control condition only allowed participants to check their pension situation and the composition of future retirement income. In the medium interactivity level, participants could choose to modify certain variables affecting their pension income, on top of the features from the control. The highly interactive planner additionally included an interactive budget tool and showed whether the accumulated pension income was sufficient to cover the desired spending. Data were collected with the help of an online panel (N = 285).

This paper finds a positive effect of interactivity on behaviour within the planner, that is, the number of clicked options, as well as on participants’ intention to check their personal pension situation in the upcoming three to six months. Moreover, this paper finds gender differences: male participants prefer a high level of interactivity, while women prefer a medium level.

An interesting modification to the current research design would be to use personal, self-relevant data in the online pension planner. Moreover, conducting the study in a computer laboratory could increase concentration on the task, and hence involvement. Next to gender, there might be other factors that possibly influence the results. It would be interesting to investigate other measures of behaviour such as the time spent on the pension planner. Further research should also study the effects of other features that shape user’s perception of interactivity, which include human-to-human interactivity.

The results show that technological services, such as advanced online pension planners, can positively affect engagement with retirement planning. Thus, pension providers are encouraged to use interactive online pension planners. The results with respect to gender suggest tailoring pension planners to match specific preferences of recipients. New service technologies provide novel opportunities to cater to individual differences by, for example, integrating less interactive features for women than for men in a pension planner. Moreover, cognitive involvement should be stimulated by integrating relevant, interesting and valuable information.

Lack of engagement with retirement planning is an important challenge to Western societies. People who do not sufficiently search for information about their expected pension benefits may encounter significant pension gaps resulting in detrimental welfare effects at retirement. This problem is enhanced by the fact that increasingly, the risks and responsibility for retirement planning are being shifted towards pension plan participants themselves. Thus, finding ways to increase engagement with retirement planning by making use of advances in service technologies brings benefits to society.

First, this paper contributes to the customer engagement literature by studying how new technological interfaces improve user experiences, knowledge and engagement within the low involvement context of retirement planning. Second, this paper advances service research by zooming in on customer heterogeneity in using the technology-based online pension planner and studying the moderating effect of involvement and gender more closely. Third, this paper contributes to the financial services literature by studying how new service technologies can help to increase attitudes, knowledge and engagement with retirement planning.

This paper aims to present the findings from a European study on the digital skills gaps in tourism and hospitality companies.

Mixed methods research was adopted. The sample includes 1,668 respondents (1,404 survey respondents and 264 interviewees) in 5 tourism sectors (accommodation establishments, tour operators and travel agents, food and beverage, visitor attractions and destination management organisations) in 8 European countries (UK, Italy, Ireland, Spain, Hungary, Germany, the Netherlands and Bulgaria).

The most important future digital skills include online marketing and communication skills, social media skills, MS Office skills, operating systems use skills and skills to monitor online reviews. The largest gaps between the current and the future skill levels were identified for artificial intelligence and robotics skills and augmented reality and virtual reality skills, but these skills, together with computer programming skills, were considered also as the least important digital skills. Three clusters were identified on the basis of their reported gaps between the current level and the future needs of digital skills. The country of registration, sector and size shape respondents’ answers regarding the current and future skills levels and the skills gap between them.

The paper discusses the digital skills gap of tourism and hospitality employees and identifies the most important digital skills they would need in the future.

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The purpose of this paper is to look into the mechanism in which customers involve themselves in omni-channel retail setting and use its advantages.

Via an empirical analysis through surveying customers, this paper assesses and confirms the drivers of omni-channel shopping intention within the context of fashion retailing sector in Danang.

The findings highlight the significance of customer perception of research shopping (including showrooming and webrooming) behaviours, compatibility and risk to their intention towards omni-channel shopping, implying profound understanding of designing effective omni-channel retailing strategy.

From a theoretical perspective, comprehending customer perception of the omni-channel concept has emerged as an important theme in recent literature as well as in practitioners' reports. Hence, the meaningful contribution of this study is the involvement in the attractive steam of study. From a managerial perspective, this study could offer guidance to retailers or managers about developing a successful omni-channel strategy from a customer point of view.

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

The purpose of this paper is to adopt the uses and gratifications theory to tourism.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.