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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.

A multi‐segmenting methodology is proposed for comparing the segmenting capabilities of segmentation variables and providing complete market segmentation information. Demographic and psychographic variables based on the differentiation of consumer brand preference were used to elicit the characteristics of market segments. In a comparative evaluation, the multi‐combination variables of demographic segmentation exhibited market‐segmenting capabilities equivalent to those of psychographic segmentation. The purpose of this research is utilizing multiple segmentation variables to identify smaller, better‐defined target sub‐markets for enhancing business competitive advantages.

It is important to remain creative when conducting segmentation research, as many different ways to segment a market can exist. Five main bases are discussed: geographic, demographic, psychographic, behaviouristic and image. This is followed by an overview of the main techniques used to establish and verify segments, including automatic interaction detector, conjoint analysis, multidimensional scaling and canonical analysis.

Although it is well established in the academic literature that entrepreneurs share common traits, there has been limited research dedicated to evaluating psychographic profiles of the self-employed. Using the Nominal Group Technique, the authors gleaned insight from a panel of experts in an effort to segment the self-employed based on personality traits and the benefits they receive from an entrepreneurial career. The findings show that self-employed individuals can be classified into four distinct segments: Exemplars, Generals, Moms and Dads, and Altruists. Each group derives different benefits from self-employment. Understanding these benefits can greatly assist entrepreneurship educators and marketers of small business oriented products and services.

Demographic segmentation variables are cheap and easy to measure, while psychographic variables are more expensive and harder to measure, but can provide more insight into consumers’ psychology. Suggests that a prima facie case exists for the suitability of astrology as a segmentation variable with the potential to combine the measurement advantages of demographics with the psychological insights of psychographics and to create segments which are measurable, substantial, exhaustive, stable over time, and relatively accessible. Tests the premise empirically using results from a Government data set, the British General Household Survey. The analyses show that astrology does have a significant, and sometimes predictable, effect on behavior in the leisure, tobacco, and drinks markets. Discusses managerial implications of the results in terms of market segmentation and promotion.

The purpose of this paper is to illustrate the use of a domain-specific research instrument (the wine-related lifestyle (WRL)) to determine the different lifestyle-related wine market segments in a country and compare the findings cross-culturally.

The research instrument included 48 psychographic activities, interests and opinions (AIO) statements, plus socio-demographic, product consumption and purchasing questions. A final sample of 376 South African wine consumers was utilised, whereon exploratory and confirmatory factor analysis, combined with Hough’s Euclidean test, were applied to determine the existence of clusters (segments), assign descriptions to them, and link these to product consumption data.

Five wine segments recurred when comparing wine market segmentation studies cross-culturally. This means that cross-culturally, similar types of people drink wine with similar AIOs towards wine. In the South African market, four of these segments were identified.

This study achieved a robust lifestyle-based market segmentation algorithm which can be used by researchers in different country environments for wine as a domain-specific product.

The contribution of this research is threefold in that it tested the WRL instrument in a cross-cultural context which is the first time this has been done; in the process it interpreted the wine consumer segments in South Africa for the first time; using a WRL-based (AIO) approach. The study demonstrated that market segmentation based on psychographic (lifestyle) behaviour is strengthened when supported by two additional segmentation methods, namely, socio-demographics and product involvement (purchasing and consumption).

In the present era of green consumerism, consumers are shifting towards sustainable consumption with the rising demand of green products. Despite consumers' demand of such products, their attitudes towards eco-friendly practices can neither be the same for different consumer groups nor can be treated as they all are equally green. The purpose of this study is to operationalize the green market segmentation based on demographic, psychographic and behavioural characterization in the Indian context of green consumerism.

The deductive approach of questionnaire survey method has been adapted to collect the responses from convenience sampling of Indian consumers using the measured constructs concerning to green consumer psychology. The data were analyzed by applying multivariate techniques of confirmatory factor analysis (CFA), cluster and discriminant analysis.

The results revealed that the three distinctive set of consumer groups are evolved as “keen greens”, “moderate greens” and “reluctant greens” based on the eight cognitive variables used in this study, namely environmental concern (EC), perceived environmental knowledge (PEK), perceived consumer effectiveness (PCE), perception of eco-label, perception of eco-brand and environmental advertisements, green purchase intention (GPI) and green purchasing behaviour (GPB) in the Indian context.

The research findings may lack its generalizability in the Indian context, as the survey strategy is confined with the most populated territory of India. To provide its robustness, the future studies need more heterogeneous sample across the country. The research findings could provide the key insights into policymakers and enterprises in the framing of marketing strategies to promote green consumerism in the setting of emerging economies.

There is dearth of literature concerning to green consumer segmentation based on the “attitude–intention–behaviour” approach in the Indian context. Therefore, the present study endeavours to manifest a holistic description of green consumer classification based on demographic, psychographic and behavioural characterization. At Prima facie, the study is the first that elucidates consumers' segments profile by incorporating environmental cognitive factors from both the perspectives; “consumers” inner stimuli' and “external green marketing cues”, especially in the indigenous Indian setting.

Since the publication of Van Raaij and Verhallen’s seminal work in European Journal of Marketing in 1994, identifying the domain-specific market segmentation approach as one of the most feasible for segmenting markets, there has been surprisingly limited development in this field, with the food domain as the only exception. This study aims to develop a methodological approach using latent class mixture modelling as contribution in the domain-specific market segmentation field.

This study captures the AIO lifestyle perspective using a domain-specific 80-item algorithm which has the wine (product) domain as its focus. A sample size of 811 consumers is used from data collected by means of the CATI approach.

The authors use four criteria for model selection: comparison of the Bayesian information criterion (BIC) statistic, comparison of classification error, verification of the interpretation of the derived segments and, finally, use of the conditional bootstrap procedure to test whether the selected model provides a significant improvement over the previous model. The five-segment model option yields a minimum BIC, the classification error measure is minimal and is easier to interpret than the other models. Segment descriptions for the five identified lifestyle-based segments are developed.

Segmentation by traditional k-means clustering has proven to be less useful than the more innovative alternative of mixture regression modelling; therefore, the authors identify segments in the market on the basis of individuals’ domain-specific lifestyle characteristics using a latent class mixture modelling approach.

Following the attainment of a clear and robust market segmentation structure, the simultaneous analysis of the lifestyles, demographics and behaviours of consumers as nexus of the domain-specific segmentation approach, provides rich and valid information accurately informing the market segment descriptions.

The authors make a substantive contribution by developing a methodological approach using latent class mixture modelling; the first of its kind in the area of domain-specific segmentation. Next, they use the discriminant and/or predictive validity of the 80-scale items to predict cluster membership using the WRL algorithm. Finally, the authors describe the identified market segments in detail and outline the practical implications.

The purpose of this paper is to investigate the lender-borrower relationship as it relates to Sannong loans for agricultural and rural financial markets by Rural Credit Cooperatives (RCCs) and other rural lenders. This paper is motivated by recent reforms to the rural credit market designed to encourage increased lending, particularly to farmers. Little is understood about the lender-borrower relationship in rural China. This paper fills that gap.

The paper investigates relational attitudes between 120 loan officers at RCCs in China's costal Shandong province, paired with a field survey using matched questions to 394 farm households in the same region. Pairing lenders’ perception toward borrowers regarding RCC microcredit lending mechanism, against borrowers’ perception toward lenders and how themselves were perceived by lenders in the same regards, the paper investigates the degree of disconnect between lenders and with distinct cluster groupings based on their perceptions, the paper analyzes the influence of demographics on the borrower and lender cluster memberships.

The paper identifies four borrower clusters and two lender clusters. Borrower clusters are segmented on credit access and satisfaction with their rural lender. The paper also identifies two lender clusters, segmented principally on financial incentives and lending activities. While all lenders view farming with higher regard than farmers believe they do, one cluster is clearly pro-farmer while the second is somewhat indifferent. Indifference is more related to current portfolio activities. The paper draws conclusions that policy initiatives should be put in place at RCCs that close the gap between lender and borrower in their credit relationship. Rural lenders should concentrate on advocating RCCs’ care and trust toward agriculture and farm households. At the institutional level, effort should be extended to train a dedicated team of loan officers that specialize in servicing farm households with standardized lending practices. This research provides financial institutions with outreach mechanisms to borrowers, while also training lenders to borrowers’ sensitivities.

Management studies of RCCs are few. This is the first paper that the authors are aware of that studies farmer and lender attitudes on the same scale.

For segmentation purposes, demographic variables are cheap and easy to measure, while psychographic variables are harder to measure, but can provide a more accurate insight into consumer psychology. The use of astrology, potentially, allows us to combine the measurement of demographics, with the psychological insights of psychographics. Suggests that a prima facie case exists for the suitability of astrology as a segmentation variable since it creates segments which are measurable, substantial, accessible, exhaustive, relatively stable over time, likely to respond differently towards different marketing programmes and have clear boundaries. Although it would be unrealistic to claim that astrology can be universally applied to all markets in all industries, it may have some use when applied to certain populations or sub‐groups within certain lifestyle and personality‐related markets.