Search results

This paper aims to examine project management segments based on customer satisfaction drivers and loyalty rather than traditional demographic or behavioural variables.

Data were gathered over 18 consecutive months, and 3,129 surveys were completed using a questionnaire. The statistical methods included partial least squares (PLS) structural equation modelling, finite mixture segmentation, prediction-oriented segmentation (PLS-POS) and multi-group analysis (PLS-MGA).

The findings indicate the existence of three segments among system delivery project customers based on the differences in the strengths of the path coefficients in the customer-centric structural model. In Segment 1, satisfaction based on the proposal was crucial for loyalty, with the value-for-money construct negatively impacting the repurchase intent construct. Segment 2 had a solid value-for-money orientation. In Segment 3, the critical path indicated that satisfaction drove repurchase intention, with satisfaction based mainly on the installation.

The research contributes to the segmentation theory by introducing a new way to segment the systems delivery projects customers based on the perceived strength of the relationships in a customer-centric structural model, which aligns with traditional segmentation theory in a way that most segmentation analyses do not. A new segmentation approach to the domain of project management theory is presented. Based on the results, treating the system delivery project customer base as a single homogenous group can lead to managerially misleading conclusions.

This study attempts to identify and explicate the unique segmentation of the increasingly growing virtual reality (VR) user market based on the user experience. Consequently, it collects five hundred forty-five online survey questionnaires through the Prolific platform and deploys cluster analysis to identify mutually exclusive groups of VR users. The research variable, user experience, contains 16 indicators explained by four dimensions. As a result, this study is able to unveil three mutually exclusive markets which are labeled as (1) beginner, (2) aficionado, and (3) utilitarian. The unique features of these three groups are further compared based on their VR tour behaviors. In the conclusion section, it offers managerial implications for devising novel marketing strategies.

This study aims to explore the heterogeneity of the tourist market for people with a physical disability (PwPD) based on travel barriers, to serve them better, from a tourism marketing perspective.

A market segmentation analysis was conducted on a sample of 480 PwPD in Sichuan Province, China, based on their perceived travel barriers. Data were obtained through three on-site and four online surveys. A four-step factor-item mixed segmentation, including factor analysis, cluster analysis, discriminant analysis and chi-square tests, was applied to examine the differences among PwPD tourist market segments in terms of various demographic characteristics, disability conditions (e.g. duration of disabilities and causes of impairment) and travel features (e.g. travel frequency and tourist destinations).

This study revealed that the PwPD tourist market is heterogeneous due to individual perceived travel barriers. Three market segments were identified, namely, the Explorer Moderates group, the Explorer Minimals group and the Explorer Intensives group. Additionally, the three market segments were found to have significant differences in terms of travel barriers, demographic characteristics, travel features and disability conditions.

This research provides suggestions for authorities and private entities to optimize the layout of accessible facilities in public areas for the benefit of all. It also offers crucial implications for tourism marketers to determine the key facets of marketing, for travel organizers to evolve the organization of travel groups for PwPD, and for practitioners to provide personalized tourism services.

To the best of the authors’ knowledge, this study is the first to apply perceived travel barriers as a market segmentation criterion in understanding PwPD as a heterogeneous travel market. The findings of this study initially expand the scope of application of the travel barrier model and deepen understanding of the Chinese PwPD tourist market from a marketing perspective. The study results elucidated the heterogeneity and characteristics of this market through a four-step factor-item mixed segmentation approach, offering new insights into the behaviors and experiences of travelers with disabilities.

本研究旨在探索肢体残障人士旅游市场的异质性, 以便从旅游营销的角度更好地为他们服务。

基于对中国四川480名肢残人士出游障碍感知的问卷调查, 探索了肢残人士的旅游市场细分。数据是通过七次现场和在线调查获得; 采用四步因子-项目混合细分法, 根据残障状况、人口统计特征和旅游特征, 识别出肢残群体旅游细分市场之间的差异。

研究发现, 基于个体感知的出游障碍, 肢残群体旅游市场是异质的, 研究确定了三个细分市场, 即低度、中度和高度受限群体。三个细分市场在出行障碍、人口特征、出游特征和残障状况方面存在显著差异。

这项研究有助于政府管理部门优化公共区域无障碍设施布局; 旅游营销者确定营销的重点, 并为旅游组织者设计肢残旅游团体成员构成, 以及从业者提供个性化旅游服务提供重要的启示。

论文首次将感知出游障碍作为市场细分标准, 用以理解肢残群体作为异质游客市场。本研究的发现拓展了出游障碍模型的应用范围, 并从市场营销的角度加深了对中国肢残游客市场的理解。研究结果通过四步因子-项目混合细分方法阐明了该市场的异质性和特点, 为肢残游客的行为和体验研究提供了新见解。

Este estudio explora la heterogeneidad del mercado turístico de las personas con discapacidad física (PcDF) en función de las barreras percibidas para viajar, con el fin de prestarles un mejor servicio desde una perspectiva de marketing turístico.

Se realizó un análisis de segmentación de mercado en una muestra de 480 PcDF en Sichuan, China, en función de las barreras que percibían para viajar. Los datos se obtuvieron a través de tres encuestas in situ y cuatro encuestas en línea. Se aplicó una segmentación mixta factor-ítem de cuatro pasos que incluye análisis factorial, análisis de conglomerados, análisis discriminante y pruebas de chi-cuadrado para examinar las diferencias entre los segmentos del mercado turístico de PcDF, en términos de diversas características demográficas, condiciones de discapacidad (por ejemplo, duración de la discapacidad, causas de la discapacidad) y características de los viajes (por ejemplo, frecuencia de viaje, destinos turísticos).

Este estudio reveló que el mercado turístico de las PcDF es heterogéneo debido a las barreras de viaje percibidas por cada individuo. Se identificaron tres segmentos de mercado, a saber, el grupo de Exploradores Moderados, el grupo de Exploradores Mínimos y el grupo de Exploradores Intensivos. Los tres segmentos de mercado presentaban diferencias significativas en cuanto a las barreras para viajar, las características demográficas, las características del viaje y las condiciones de discapacidad.

Este estudio es el primero en aplicar las barreras percibidas para viajar como criterio de segmentación de mercado para comprender a las PcDF como un mercado turístico heterogéneo. Los hallazgos de este estudio amplían inicialmente el ámbito de aplicación del modelo de barreras para viajar y profundizan en la comprensión del mercado turístico chino de PcDF desde una perspectiva de marketing. Los resultados de nuestro estudio explicaron la heterogeneidad y las características de este mercado a través de un enfoque de segmentación mixta factor-ítem de cuatro pasos, contribuyendo a la literatura sobre el comportamiento y las experiencias de los viajeros con discapacidad.

Esta investigación proporciona sugerencias para que las autoridades y las entidades privadas puedan optimizar la disposición de instalaciones accesibles en zonas públicas en beneficio de todos. También ofrece implicaciones importantes a los comercializadores turísticos para que determinen aspectos clave del marketing, a los organizadores de viajes para que evolucionen en la organización de grupos de viaje para PcDF y a los profesionales para que presten servicios turísticos personalizados.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.

This research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.

The study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.

Empirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.

The findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.

This research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.

The objective of this study is to analyze the experiences in wine tourism according to narratives shared by the visitors themselves. Furthermore, this study aims to examine the levels of intensity associated with these experiences within a wine destination, considering the segmentation of visitors.

This study uses a mixed-methods approach to analyze data, incorporating a deductive process followed by content analysis. Data collection procedures include a bibliographic review and a data survey conducted through netnography research to analyze 954 visitor reviews on TripAdvisor shared by visitors to Vale dos Vinhedos. Also, statistical analysis is performed to assess whether there are any significant variations in attribute citations among different market segmentation profiles.

The study’s discoveries indicate that there are no significant differences in intensity among profile segments for the dimensions of entertainment, aesthetics, educational and interactions, unlike escapism. The findings reveal that attributes such as “winery,” “wine,” “products and services” and “landscape” are essential for all visitors. In addition, the study shows that social interactions in the wine tourism destination are not as significant as previously assumed.

This research study constitutes a methodological advancement in the field of market segmentation using electronic word-of-mouth data. It provides crucial insights into the experiential nuances of the research locus and the varying degrees of these experiences in relation to visitor segmentation. Additionally, the contributions of this study are not only of theoretical importance but also hold practical implications for market segmentation strategies.

With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

The purpose of the paper is to present an empirical study that examines the impact of digitalization on informative, strategic and operational marketing activities in manufacturing companies from the entrepreneurial perspective.

A research project was carried out in 205 Italian manufacturing companies by using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis.

The analysis shows the existence of seven clusters of manufacturing companies that differ by the impact of digitalization on marketing activities from the entrepreneurial perspective. Two clusters have a high positive impact of digitalization, primarily on informative and strategic marketing activities. Two clusters are characterized by a low positive impact of digitalization and three clusters perform an intermediate level of digitalization. Furthermore, these groups of clusters differ in terms of the influence of digitalization on customer value.

The small size of the sample and the geographic origin of the companies imply limited generalizability; further research on the topic is thus recommended.

The study suggests that companies should digitalize many key marketing activities to increase marketing effectiveness and customer value. To achieve high levels of digitalization and thus increase their competitiveness, manufacturing companies should consider the importance of relevant technologies and skills.

By focussing on the impact of digitalization on informative, strategic and operational marketing, which has not yet been empirically investigated, the present study reveals many new elements concerning the marketing process in the digital era from the entrepreneur's point of view.