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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 study aims to analyse how multinational corporations (MNCs) organise value chain activities to penetrate new market segments. It contributes by expanding traditional decisions regarding the vertical fine-slicing of value chain activities (whether performed internally or externally) and the consideration of resource-sharing decisions (integration or separation) for each value chain function.

The authors draw on primary data collected from two case study firms operating in the large emerging Chinese market: Volvo Construction Equipment AB and Epiroc AB. In-depth cases illustrate how foreign MNCs expand into new market segments and simultaneously target both the lower-priced mid-market and the premium segments in the Chinese mining and construction industry.

The results reveal that product diversification creates challenges for managers who must oversee new (vertical) value chains, often simultaneously. Beyond geography and modes of governance, managers must decide whether to integrate or separate value chain activities for the new product lines. The study identifies four main strategic choices for firms to address this complexity, focusing on the decision to internalise or externalise (i.e. within or across organisational boundaries) and integrate or separate value chain activities between different product lines.

This study builds upon the internalisation theory and recent international business contributions that focus on value chain configurations to explain MNCs’ product diversification as a growth strategy in a host emerging market. It also sheds light on the choice of conducting new activities in-house or externally and elucidates firms’ managerial decisions to operationally integrate or separate individual value chain activities. The study provides insights into the drivers explaining managerial decisions to configure value chain activities across product lines and contributes to the growing body of literature on MNC activities in emerging economies by highlighting that product diversification impacts entry mode diversity and resource sharing across units.

This article contributes to the debate on how social policies and labour market regulation have been used to limit the socio-economic consequences of the pandemic by focusing on one specific economic segment of European labour markets: private consumption services, such as trade, tourism, catering and other support services.

The analysis combines mixed methods and a variety of sources. First, we built a set of indicators from the EU-LFS microdata for 2019 and the 2018 Eurostat “Structure of earnings survey” and performed a cluster analysis (k-means) on the dimensions and indicators considered. Second, we elaborated EU-LFS data covering 2019 and 2020 (by quarter) and OECD 2020 data, and finally we traced Covid-related policy reforms for the period March 2020–December 2021 and analysed documents and information collected in different policy repositories.

The paper shows the relevance and characteristics of private consumption services in different countries, demonstrating that so-called labour market “outsiders” are highly represented in this sector and illustrates the policies adopted to respond to the pandemic in different European countries. The paper asks whether this emergency has been a window of opportunity to redefine regulation in this sector, making it more inclusive. It demonstrates, however, that the common approach in Europe has been dominated by temporary, short-term and one-off measures, which do not represent major changes to the social security schemes that were in place before the pandemic.

This article builds on the literature on labour market dualization, but approaches the concept from a different perspective – one not centred on the nature of employment relations (stable/unstable) but on economic sectors/branches. This article does not, therefore, discuss in general terms what happened to labour market outsiders during the pandemic, but rather focus attention on a specific group of workers who are highly exposed to risks stemming from dualization: those employed in the private consumption services. The economic sector perspective is an integrative way of framing dualization which is still under-researched.

This paper presents a decision-making framework for e-banking operations, based on the Quality Function Deployment and the Analytic Hierarchy Process. The main research question is the development of a framework and its potential contributions in supporting decision makers in implementing quality strategies that will encourage the usage of e-banking services (EBS).

The introduced framework comprises four distinct stages which include criteria determination, field surveys and EFA, QFD application including AHP and finally sensitivity analysis investigating the dynamic nature of the environment. AHP determines the intensity of the relationship between e-banking quality criteria and customer banking activities. A novel House of Quality (HoQ) is proposed, based on a market mix founded on key e-banking activities, and sensitivity analysis is used to investigate alternative scenarios. To illustrate the steps of applying this framework, the authors use a convenience sample from the Greek e-banking sector.

Through the illustrated example is supported that the proposed approach can reveal valuable information when contemplating strategies to improve e-banking usage and expand its acceptance. In addition, sensitivity analysis leads to purposeful insights regarding the effects of market segmentation and/or target settings on the ranking of e-banking quality/selection criteria provided by the HoQ. In the specific numerical example, the most critical quality factors were “Security and Reliability”, “Convenience”, “Design”, “Pricing” and “Skills,” although different rankings night well appear in different contexts or geographical regions. Moreover, sensitivity analysis showed that these results depend on the specific market mix and targets. As mentioned above, the implementation of the framework in different geographical regions or e-service sectors might certainly reveal different critical factors.

Discussed in the paper body.

Discussed in the paper body.

The paper presents a well-defined four stage framework for improving EBS penetration. It utilizes a structured qualitative and quantitative approach and outlines and ranks e-banking quality factors stemming from the market mix and allows assessment of alternative scenarios through sensitivity analysis.

The purpose of this study is to identify segments in the Gen Z population (born between 1996 and 2010) in Europe, the USA and Australia, based on brand- and lifestyle-related variables and perceptions about their online activities. This study explores how these segments differ and provide insights into cross-country similarities and differences.

An online survey was conducted with 4,304 participants, and cluster analysis and analysis of variance were used to identify and profile Gen Z segments in each of three geographical areas.

Five segments in Europe and four segments in the USA and in Australia were identified. Segments differ in terms of the importance they attach to exclusivity, inclusivity and sustainability of brands, how Gen Z members perceive money issues and stand in life and how they perceive their online activities. Similar segments are found in the three geographical areas.

This study proposes a conceptual and analytical approach for exploring intra-cohort diversity. Future research can apply this approach to different generational cohorts and use it to study intra-cohort diversity in other parts of the world.

This study provides input for marketing practitioners to create better focused and more effective campaigns.

Cross-country generational cohort research is scarce, and especially intra-cohort diversity is under-researched. This study offers a deep and fine-grained insight into the diversity of the Gen Z cohort across three geographical areas, based on representative samples in these areas.

Given the increased significance and rapid growth of an ageing population, this review paper aims to define the mature consumer segment chronologically to resolve definitional inconsistencies found in prior marketing communications literature, identify the current state of the marketing communications field in terms of mature consumer research and highlight future research directions on mature consumers for marketing communications academics and practitioners.

A synthesis of existing marketing communication research on mature consumers (those aged 50+), published in top-tier journals since 1972, is provided. In total, 106 papers were identified in 21 marketing journals.

Three existing research themes were identified: market segmentation of mature consumers (we ground this theme in three inter-related facets: chronological age, health [physical and neurological] and self-perception of age [also referred to as cognitive age]); attitudes and behaviours of mature consumers; and marketing to mature consumers. This paper also proposes several future research themes: further definition of mature consumers and widening the scope of examination; segmenting mature consumers to account for heterogeneity; information processing of mature consumers cannot use a one-size-fits-all approach; the influence of marketing mix elements on mature consumers; and alternative methodologies to better understand mature consumers.

Recognising the heterogeneity within the chronologically based mature consumer segment, this paper proposes an extended mature consumer definition which includes biological, psychological and social dimensions, as well as life events and life circumstances, rather than biological age alone.

In practical terms, understanding information processing of mature consumers cannot use a one-size-fits-all approach and marketing mix elements may affect behaviour differently within this segment. This will require alternative methodologies to understand these processes fully.

This synthesis of mature consumers research within the marketing communications field provides key research questions for future research to better understand this market segment and its implications for marketing communications, theory development and practice.

This paper aims to address how the status consumption tendency of consumers in emerging markets is negatively influenced by five individual traits: self-control, self-actualization, religiosity, future orientation and self-efficacy.

A conjoint experiment measured the importance of certain smartphone product attributes. A latent class regression analysis was then employed to estimate segment-level part-worths using conjoint data collected from 500 Bangladeshi consumers.

The results revealed three segments with members that differ in how they evaluate smartphone product attributes. Those susceptible to a product's brand name (i.e. status seekers) appear to have low self-control, are less religious and are more myopic.

An issue may exist with generalizability, as the analysis was conducted based on data collected in one country and for one product category. However, this study's framework provides direction for future researchers to better understand status consumption in emerging countries.

The findings are useful for marketers selling status products to improve market segmentation and target their offerings more efficiently.

The originality of this paper is twofold. First, it investigates the influencing factors of status consumption that have not been addressed in the extant literature. Second, it is the first to use experimental data to measure segment-level status consumption accurately.

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.

Calls for empirical and theory-based outcome measures in the place marketing literature are made more pressing as policymakers manage post-COVID high street recovery. This study aims to evaluate how knowledge of repeat buying established in the consumer marketing domain might be adapted to benchmark place marketing effectiveness, applying the Law of Double Jeopardy to capture the predictable relationship between footfall and visit frequency on competing high streets.

The authors match footfall and survey data collected simultaneously on nine local high streets in one London borough to ask if a predictable Double Jeopardy relationship exists. The authors then test the theoretical assumptions of independence that underpin the Law in patterns of switching; the predictable distribution of regular, infrequent and new visitors; and the absence of user segmentation.

The authors observe that Double Jeopardy constrains behavioural outcomes, that a simple model fits high street footfall data well and that its theoretical assumptions are supported.

This paper makes several practical and theoretical contributions. The authors demonstrate a method to model expected repeat visit frequency from footfall density and elaborate footfall data into its frequency classes. The authors also locate the effects of loyalty over time within existing knowledge of spatial competition for high street patronage and demonstrate how place marketing insights can be derived from applications of this useful law.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.