Integrating big data and marketing concepts into tourism, hospitality operations and strategy development

Big data (BD) research articles are on new issues, this study sought to fill the knowledge gap of linkage the relationships between big data and marketing strategy with comprehensive viewpoints across different research fields in tourism and hospitality literatures. Content analysis was conducted to gather materials from the particular studies. For each study, the content analysis included the title, abstract, journal, type of sample, exploration design, statistical and analytical techniques, data collection process and keywords was also conducted to confirm the main results of the criteria. The research shows that big data adds value to marketing strategies by using social media to collect information from consumers, which is complemented with appropriate evidence relevant to predicting their needs and behaviors.

Identification of Mobility Patterns of Clusters of City Visitors: An Application of Artificial Intelligence Techniques to Social Media Data

In order to enhance tourists’ experiences, Destination Management Organizations need to know who their tourists are, their travel preferences, and their flows around the destination. The study develops a methodology that, through the application of Artificial Intelligence techniques to social media data, creates clusters of tourists according to their mobility and visiting preferences at the destination. The applied method improves the knowledge about the different mobility patterns of tourists (the most visited points and the main flows between them within a destination) depending on who they are and what their preferences are. Clustering tourists by their travel mobility permits uncovering much more information about them and their preferences than previous studies. This knowledge will allow DMOs and tourism service providers to offer personalized services and information, to attract specific types of tourists to certain points of interest, to create new routes, or to enhance public transport services.

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.

The effect of big data in transforming to learning organization a single-case study in IT sector

Purpose

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

Design/methodology/approach

The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

Findings

As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

Research limitations/implications

The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

Originality/value

In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.

Do the DMO and the Tourists Deliver the Similar Image? Research on Representation of the Health Destination Image Based on UGC and the Theory of Discourse Power: A Case Study of Bama, China

Even though destination image is an important expression of discovering the local landscapes and place significance, the construction and measurement of destination image neglect the place component. This research explores the image of health destinations, as well as its representation mechanism, combining the triadic structure of tourism image proposed by Marine-Roig et al. with the theory of discourse power put forward by Michel Foucault, taking Bama, Guangxi as a case. In addition, this paper uses the IPA matrix to visually unveil the pronounced gap between the projected image by Destination Management Organizations (DMOs) and the perceived image of tourists and suggests strategies that DMOs should adopt in the different dimensions.

Two Decades of Research on Consumer Behaviour and Analytics

The present study is a systematic literature review that identifies the context of consumer behaviour and analytics in business to forecast the future of consumer behaviour with changing business trends through TCCM (theory, context, characteristics, method) guidelines. The authors identified that prior research used theories in different disciplines to explain the phenomenon in customer behaviour and analytics literature. When considering the theory, these phenomena often can be segregated based on the industry (e.g., marketing, advertising, sales, healthcare, human resource management, tourism), focusing on status-based mechanisms (e.g., cross-gaming predictive models), inertia-based mechanisms (e.g., theory of rational expectations and adaptive learning), or relationship-based mechanisms (e.g., theory of consumer engagement behaviour).

Study on the Evolution of the Source-Flow-Sink Pattern of China’s Chunyun Population Migration Network: Evidence from Tencent Big Data

We construct a comprehensive analysis framework of population flow in China. To do so, we take prefecture-level administrative regions as the basic research unit of population flow and use source-sink theory and flow space theory. Additionally, we reveal the dynamic differentiation of population flow patterns and the evolution of population source-flow-sink systems. We try to provide a theoretical basis for the formulation of population development policies and regional spatial governance. The results show the following: (1) The Hu Huanyong Line has a strong spatial lock-in effect on population flow. Additionally, provincial capital cities, headed by Hangzhou, Nanjing, and Hefei, have played an increasingly prominent role in population flow. (2) The developed eastern coastal areas have undertaken China’s main population outflow. The net population flow is spatially high in the middle of the region and low on the two sides, exhibiting an “inverted U-shaped” pattern. Furthermore, the borders of the central provinces form a continuous population inflow area. (3) The hierarchical characteristics of the population flow network are obvious. Strong connections occur between developed cities, and the effect of distance attenuation is weakened. The medium connection network is consistent with the traffic skeleton, and population flow exhibits a strong “bypass effect”. (4) The source and sink areas are divided into four regions similar to China’s three major economic belts. The 10 regions can be refined to identify the main population source and sink regions, and the 18 regions can basically reflect China’s level of urbanization. The network of the population flow source-flow-sink system exhibits notable nesting characteristics. As a result, it creates a situation in which the source areas on both sides of the east and the west are convective to the middle. The hierarchical differentiation of the source-flow sink system is related to the differences between the east and the west and between the north and the south, as well as local differences in China.

The Asymmetric Pattern of Population Mobility during the Spring Festival in the Yangtze River Delta Based on Complex Network Analysis: An Empirical Analysis of “Tencent Migration” Big Data

Population mobility patterns are an important reflection of the future distribution of migrant populations and the evolution trends of urbanization patterns. However, although research based on statistical data can reveal the pattern of population flow, it also shows a time lag. Most of the population flow network research based on location services data has failed to fully discuss the symmetry of directional outflows and inflows in the same place and the two-way symmetrical connections between places. This paper creatively proposes and constructs the concept and analysis framework of population flow asymmetry. We used the Yangtze River Delta (YRD) as a typical case and the results of our analysis reveal the temporal and spatial asymmetry of the population flow using complex network analysis methods based on the Spring Festival (SF) population migration big data. We found that the timing asymmetry manifested in such a way that the closer it was to the festival, the greater the scale and intensity of the population movement. This is a feature of the lack of scale and regional differences within China. The spatial asymmetry was manifested in three aspects, network, node, and link, and the core cities with administrative and economic hierarchical advantages dominated the asymmetric pattern of regional population mobility. In addition, distance and administrative boundaries are factors that cannot be ignored in population movements, and they were implicated in the degree of asymmetry by distance enhancement and administrative boundary blocking. The conclusions of this study can not only provide policy decision-making guidelines for population management and resource allocation in the YRD, but they can also provide a reference value for achieving the goal of regional, high-quality, integrated development. Future research will further the discussion and management of socio-economic attributes in order to develop a more detailed and microscopic understanding of the mechanisms of population mobility patterns.

The Value of Big Data Analytics Pillars in Telecommunication Industry

In the Big Data age, businesses in every industry must deal with vast volumes of data. Several experts and practitioners have lately emphasized the need of understanding how, why, and when Big Data Analytics (BDA) applications may be a valuable resource for businesses seeking a competitive edge. However, BDA pays off for some firms while failing to pay off for others due to the fact that investment in Big Data continues to present significant challenges due to the missing link between analytics capabilities and firm performance. According to a recent survey, many businesses spend the bulk of their time analyzing data, with only a tiny fraction employing Big Data Analytics to forecast outcomes and even fewer utilizing analytics apps to enhance processes and strategies. As a result, BDA is not widely used, and only a few companies have seen any benefit from it. To address this issue in the telecommunications domain and in light of the paucity of research on the subject, this study focused on the BDA Pillars (BDAP) in order to achieve benefits through increased revenues and cost savings. For the purpose of this research we have adopted qualitative approach with case study method, and technique of data collection includes semi-structure interview and document analysis. The Delphi technique and in-depth interviews conducted confirmed the existence of five critical elements that contribute to the sustainability of BDAPs and their impact on firm performance.

Spatial Diversity of Tourism in the Countries of the European Union

The aim of the article is to present the spatial diversity of tourism in the countries of the European Union (EU). The main objective of the article can be divided into three immediate goals, each of which is to determine countries that are similar by means of: (1) accommodation base; (2) tourism traffic; and (3) tourism-related expenditures and revenues. In order to group countries, Ward’s cluster analysis method is used. The aim is verified with the use of 2017 United Nations World Tourism Organization (UNWTO) and Eurostat data. The analysis covers all EU member states. The research conducted confirms, inter alia, the key role of the accommodation base in the development of tourism in those countries.

Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis

This study evaluates bibliometric analysis of sustainable tourism in the open innovation realm, depicts emerging themes, and offers critical discussion for theory development and further research. Through the use of bibliometrix, this paper investigates the amount of studies conducted in this area and verifies if such studies have represented a contribution to the evolving research in the field of sustainable tourism. Specifically, the paper identifies whether and to what extent scholars have explored these interconnections and maps to get to a conceptual structure of the field under investigation. The results identify the development status and the leading trends in terms of impact, main journals, papers, topics, authors, and countries. The analysis and the graphical presentations are crucial, as they can help both researchers and practitioners to better understand the state of the art of sustainable tourism in the experiential and digital era.

Value creation through big data application process management: the case of the oil and gas industry

Purpose

The purpose of this study is twofold: to investigate the role of big data in firms’ co-knowledge and value creation and to understand the underlying drivers behind value creation through big data in the oil and gas industry by underscoring the role of firms’ capabilities, trends and challenges.

Design/methodology/approach

Following an inductive approach, semi-structured interviews were conducted with senior managers and analysts working in oil and gas companies across eight countries. The data collected from these key informants were then analysed using the qualitative data analysis software ATLAS.ti.

Findings

Value creation through big data is an important factor for enhancing performance. It has a positive impact on both tangible (organisational performance) and intangible (societal) aspects depending on the context. Oil and gas companies understand the importance of big data to creating value in their operations. However, implementing and using big data has been problematic. In this study, a framework was developed to show that factors such as the shortage of data experts, poor data quality, the risk of cyber-attacks and unsupportive organisational cultures impede its implementation and utilisation.

Research limitations/implications

The findings from this study have implications for managers and executives implementing big data and creating value across various data-intensive industries. The research findings, are contextual, however, and should be applied cautiously.

Originality/value

This study contributes to the value creation literature in the big data context. The findings identify the key areas to be considered for the effective implementation and utilisation of big data in the oil and gas sector. This study addresses a broad but under-explored issue (i.e. knowledge creation from big data and its implementation) and strengthens the academic debate within this research stream.

Using Big Data to Measure Tourist Sustainability: Myth or Reality?

The concern about the production of international standards to measure the sustainability of tourism is present today, especially the discourse on the introduction of new sources. This article aims to survey and describe the main approaches and methodologies to use big data to measure tourism sustainability. Successful cases are addressed by explaining the main opportunities and challenges for the creation of official tourist statistics. A comprehensive review of publications regarding this field was carried out by applying the systematic literature review technique. This contributes a knowledge base to destination management organisations to encourage the implementation of official tourism statistics systems using big data.

An Innovative Management Perspective for Organizations through a Reputation Intelligence Management Model

Banking companies aiming to maintain their sustainability in financial markets need to develop an integrated management based on the most important intangibles assets of relational capital. Decision- makers need to analyze and understand a huge volume of opinions continuously generated in digital ecosystems about emotions and feelings that their stakeholders associate with the performance and communication of the brand. Current tools of management fail to consider transversal and holistic models, which study the frequency and value of existing relationships between the relational capital and intangible assets. In this research, an innovative management model based on reputation intelligence is proposed. This model incorporates methodology from business intelligence models, through OLAP and data mining techniques, to analyses the complex relationships among intangible assets experience, emotion and attitude. The proposed model was applied to companies in the banking sector and the results obtained permit a conclusion about the kinds of relationships for these intangibles in each bank.

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

Open Innovation and Social Big Data for Sustainability: Evidence from the Tourism Industry

This paper aims to contribute to the debate on Open Innovation in the age of Big Data by shedding new light on the role that social networks can play as enabling platforms for tourists’ involvement and sources for the creation and management of valuable knowledge assets. The huge amount of data generated on social media by tourists related to their travel experiences can be a valid source of open innovation. To achieve this aim, this paper presents evidence of a digital tourism experience, through a longitudinal case study of a destination in Apulia, a Southern European region. The findings of the study demonstrate how social Big Data could open up innovation processes that could be of support in defining sustainable tourism experiences in a destination.

Gender bias in sentiment analysis

Purpose

The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.

Design/methodology/approach

This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females.

Findings

Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis.

Research limitations/implications

Only one lexical sentiment analysis algorithm was used.

Practical implications

Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results.

Originality/value

This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

Public libraries: roles in Big Data

Purpose

The purpose of this paper is to explore the roles of public libraries in the context of Big Data.

Design/methodology/approach

A mixed method approach was used and had two main data collection phases. A survey of public libraries was used to generate an overview of which professional roles connect public libraries with Big Data. Eight roles were identified, namely, educator, marketer, data organiser, data container, advocator, advisor, developer and organisation server. Semi-structured interviews with library directors and managers were then conducted to gain a deeper understanding of these roles and how they connect to the library’s overall functions.

Findings

Results of the survey indicated that librarians lack a proper comprehension of and a pragmatic application of Big Data. Their opinions on the eight roles are slightly stronger than neutral. However, they do not demonstrate any strong agreement on these eight roles. In the interviews, the eight roles attained more clear support and are classified into two groups: service-oriented and system-oriented roles.

Originality/value

As an emerging research field, Big Data is not widely discussed in the library context, especially in public libraries. Therefore, this study fills a research gap between public libraries and Big Data. In addition, Big Data in public libraries could be well managed and readily approached by citizens in undertaking such roles, which entails that public libraries will eventually benefit from the Big Data era.

Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector

Purpose

The purpose of this paper is to explore the relationship between big data and knowledge management (KM).

Design/methodology/approach

The study adopts a qualitative research methodology and a case study approach was followed by conducting nine semi-structured interviews with open-ended and probing questions.

Findings

Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision-making ability.

Research limitations/implications

The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability.

Originality/value

This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.